WorldWideScience

Sample records for global crop estimation

  1. Effect of Estimated Daily Global Solar Radiation Data on the Results of Crop Growth Models

    Herbert Formayer

    2007-10-01

    Full Text Available The results of previous studies have suggested that estimated daily globalradiation (RG values contain an error that could compromise the precision of subsequentcrop model applications. The following study presents a detailed site and spatial analysis ofthe RG error propagation in CERES and WOFOST crop growth models in Central Europeanclimate conditions. The research was conducted i at the eight individual sites in Austria andthe Czech Republic where measured daily RG values were available as a reference, withseven methods for RG estimation being tested, and ii for the agricultural areas of the CzechRepublic using daily data from 52 weather stations, with five RG estimation methods. In thelatter case the RG values estimated from the hours of sunshine using the ångström-Prescottformula were used as the standard method because of the lack of measured RG data. At thesite level we found that even the use of methods based on hours of sunshine, which showedthe lowest bias in RG estimates, led to a significant distortion of the key crop model outputs.When the ångström-Prescott method was used to estimate RG, for example, deviationsgreater than ±10 per cent in winter wheat and spring barley yields were noted in 5 to 6 percent of cases. The precision of the yield estimates and other crop model outputs was lowerwhen RG estimates based on the diurnal temperature range and cloud cover were used (mean bias error 2.0 to 4.1 per cent. The methods for estimating RG from the diurnal temperature range produced a wheat yield bias of more than 25 per cent in 12 to 16 per cent of the seasons. Such uncertainty in the crop model outputs makes the reliability of any seasonal yield forecasts or climate change impact assessments questionable if they are based on this type of data. The spatial assessment of the RG data uncertainty propagation over the winter wheat yields also revealed significant differences within the study area. We

  2. SACRA - global data sets of satellite-derived crop calendars for agricultural simulations: an estimation of a high-resolution crop calendar using satellite-sensed NDVI

    Kotsuki, S.; Tanaka, K.

    2015-01-01

    To date, many studies have performed numerical estimations of food production and agricultural water demand to understand the present and future supply-demand relationship. A crop calendar (CC) is an essential input datum to estimate food production and agricultural water demand accurately with the numerical estimations. CC defines the date or month when farmers plant and harvest in cropland. This study aims to develop a new global data set of a satellite-derived crop calendar for agricultural simulations (SACRA) and reveal advantages and disadvantages of the satellite-derived CC compared to other global products. We estimate global CC at a spatial resolution of 5 min (≈10 km) using the satellite-sensed NDVI data, which corresponds well to vegetation growth and death on the land surface. We first demonstrate that SACRA shows similar spatial pattern in planting date compared to a census-based product. Moreover, SACRA reflects a variety of CC in the same administrative unit, since it uses high-resolution satellite data. However, a disadvantage is that the mixture of several crops in a grid is not considered in SACRA. We also address that the cultivation period of SACRA clearly corresponds to the time series of NDVI. Therefore, accuracy of SACRA depends on the accuracy of NDVI used for the CC estimation. Although SACRA shows different CC from a census-based product in some regions, multiple usages of the two products are useful to take into consideration the uncertainty of the CC. An advantage of SACRA compared to the census-based products is that SACRA provides not only planting/harvesting dates but also a peak date from the time series of NDVI data.

  3. Effect of Estimated Daily Global Solar Radiation Data on the Results of Crop Growth Models

    Trnka, M.; Eitzinger, J.; Kapler, P.; Dubrovský, Martin; Semerádová, Daniela; Žalud, Z.; Formayer, H.

    2007-01-01

    Roč. 7, č. 10 (2007), s. 2330-2362 ISSN 1424-8220 R&D Projects: GA ČR GA205/05/2265 Grant - others:ADAGIO(XE) SSPE-CT-2006-044210 Institutional research plan: CEZ:AV0Z30420517 Keywords : crop yields * spring barley * winter wheat * CERES-Barley * CERES-Wheat * WOFOST Subject RIV: DG - Athmosphere Sciences, Meteorology Impact factor: 1.573, year: 2007 http://www.mdpi.org/sensors/papers/s7102330.pdf

  4. Decomposing global crop yield variability

    Ben-Ari, Tamara; Makowski, David

    2014-11-01

    Recent food crises have highlighted the need to better understand the between-year variability of agricultural production. Although increasing future production seems necessary, the globalization of commodity markets suggests that the food system would also benefit from enhanced supplies stability through a reduction in the year-to-year variability. Here, we develop an analytical expression decomposing global crop yield interannual variability into three informative components that quantify how evenly are croplands distributed in the world, the proportion of cultivated areas allocated to regions of above or below average variability and the covariation between yields in distinct world regions. This decomposition is used to identify drivers of interannual yield variations for four major crops (i.e., maize, rice, soybean and wheat) over the period 1961-2012. We show that maize production is fairly spread but marked by one prominent region with high levels of crop yield interannual variability (which encompasses the North American corn belt in the USA, and Canada). In contrast, global rice yields have a small variability because, although spatially concentrated, much of the production is located in regions of below-average variability (i.e., South, Eastern and South Eastern Asia). Because of these contrasted land use allocations, an even cultivated land distribution across regions would reduce global maize yield variance, but increase the variance of global yield rice. Intermediate results are obtained for soybean and wheat for which croplands are mainly located in regions with close-to-average variability. At the scale of large world regions, we find that covariances of regional yields have a negligible contribution to global yield variance. The proposed decomposition could be applied at any spatial and time scales, including the yearly time step. By addressing global crop production stability (or lack thereof) our results contribute to the understanding of a key

  5. Field-based estimates of global warming potential in bioenergy systems of Hawaii: Crop choice and deficit irrigation

    Replacing fossil fuel with biofuel is environmentally viable only if the net greenhouse gas (GHG) footprint of the system is reduced. The effects of replacing annual arable crops with perennial bioenergy feedstocks on net GHG production and soil carbon (C) stock are critical to the system-level bal...

  6. Global Crop Monitoring: A Satellite-Based Hierarchical Approach

    Bingfang Wu

    2015-04-01

    Full Text Available Taking advantage of multiple new remote sensing data sources, especially from Chinese satellites, the CropWatch system has expanded the scope of its international analyses through the development of new indicators and an upgraded operational methodology. The approach adopts a hierarchical system covering four spatial levels of detail: global, regional, national (thirty-one key countries including China and “sub-countries” (for the nine largest countries. The thirty-one countries encompass more that 80% of both production and exports of maize, rice, soybean and wheat. The methodology resorts to climatic and remote sensing indicators at different scales. The global patterns of crop environmental growing conditions are first analyzed with indicators for rainfall, temperature, photosynthetically active radiation (PAR as well as potential biomass. At the regional scale, the indicators pay more attention to crops and include Vegetation Health Index (VHI, Vegetation Condition Index (VCI, Cropped Arable Land Fraction (CALF as well as Cropping Intensity (CI. Together, they characterize crop situation, farming intensity and stress. CropWatch carries out detailed crop condition analyses at the national scale with a comprehensive array of variables and indicators. The Normalized Difference Vegetation Index (NDVI, cropped areas and crop conditions are integrated to derive food production estimates. For the nine largest countries, CropWatch zooms into the sub-national units to acquire detailed information on crop condition and production by including new indicators (e.g., Crop type proportion. Based on trend analysis, CropWatch also issues crop production supply outlooks, covering both long-term variations and short-term dynamic changes in key food exporters and importers. The hierarchical approach adopted by CropWatch is the basis of the analyses of climatic and crop conditions assessments published in the quarterly “CropWatch bulletin” which

  7. Estimating yield gaps at the cropping system level.

    Guilpart, Nicolas; Grassini, Patricio; Sadras, Victor O; Timsina, Jagadish; Cassman, Kenneth G

    2017-05-01

    Yield gap analyses of individual crops have been used to estimate opportunities for increasing crop production at local to global scales, thus providing information crucial to food security. However, increases in crop production can also be achieved by improving cropping system yield through modification of spatial and temporal arrangement of individual crops. In this paper we define the cropping system yield potential as the output from the combination of crops that gives the highest energy yield per unit of land and time, and the cropping system yield gap as the difference between actual energy yield of an existing cropping system and the cropping system yield potential. Then, we provide a framework to identify alternative cropping systems which can be evaluated against the current ones. A proof-of-concept is provided with irrigated rice-maize systems at four locations in Bangladesh that represent a range of climatic conditions in that country. The proposed framework identified (i) realistic alternative cropping systems at each location, and (ii) two locations where expected improvements in crop production from changes in cropping intensity (number of crops per year) were 43% to 64% higher than from improving the management of individual crops within the current cropping systems. The proposed framework provides a tool to help assess food production capacity of new systems ( e.g. with increased cropping intensity) arising from climate change, and assess resource requirements (water and N) and associated environmental footprint per unit of land and production of these new systems. By expanding yield gap analysis from individual crops to the cropping system level and applying it to new systems, this framework could also be helpful to bridge the gap between yield gap analysis and cropping/farming system design.

  8. Field-Based Estimates of Global Warming Potential in Bioenergy Systems of Hawaii: Crop Choice and Deficit Irrigation.

    Meghan N Pawlowski

    Full Text Available Replacing fossil fuel with biofuel is environmentally viable from a climate change perspective only if the net greenhouse gas (GHG footprint of the system is reduced. The effects of replacing annual arable crops with perennial bioenergy feedstocks on net GHG production and soil carbon (C stock are critical to the system-level balance. Here, we compared GHG flux, crop yield, root biomass, and soil C stock under two potential tropical, perennial grass biofuel feedstocks: conventional sugarcane and ratoon-harvested, zero-tillage napiergrass. Evaluations were conducted at two irrigation levels, 100% of plantation application and at a 50% deficit. Peaks and troughs of GHG emission followed agronomic events such as ratoon harvest of napiergrass and fertilization. Yet, net GHG flux was dominated by carbon dioxide (CO2, as methane was oxidized and nitrous oxide (N2O emission was very low even following fertilization. High N2O fluxes that frequently negate other greenhouse gas benefits that come from replacing fossil fuels with agronomic forms of bioenergy were mitigated by efficient water and fertilizer management, including direct injection of fertilizer into buried irrigation lines. From soil intensively cultivated for a century in sugarcane, soil C stock and root biomass increased rapidly following cultivation in grasses selected for robust root systems and drought tolerance. The net soil C increase over the two-year crop cycle was three-fold greater than the annualized soil surface CO2 flux. Deficit irrigation reduced yield, but increased soil C accumulation as proportionately more photosynthetic resources were allocated belowground. In the first two years of cultivation napiergrass did not increase net greenhouse warming potential (GWP compared to sugarcane, and has the advantage of multiple ratoon harvests per year and less negative effects of deficit irrigation to yield.

  9. Field-Based Estimates of Global Warming Potential in Bioenergy Systems of Hawaii: Crop Choice and Deficit Irrigation.

    Pawlowski, Meghan N; Crow, Susan E; Meki, Manyowa N; Kiniry, James R; Taylor, Andrew D; Ogoshi, Richard; Youkhana, Adel; Nakahata, Mae

    2017-01-01

    Replacing fossil fuel with biofuel is environmentally viable from a climate change perspective only if the net greenhouse gas (GHG) footprint of the system is reduced. The effects of replacing annual arable crops with perennial bioenergy feedstocks on net GHG production and soil carbon (C) stock are critical to the system-level balance. Here, we compared GHG flux, crop yield, root biomass, and soil C stock under two potential tropical, perennial grass biofuel feedstocks: conventional sugarcane and ratoon-harvested, zero-tillage napiergrass. Evaluations were conducted at two irrigation levels, 100% of plantation application and at a 50% deficit. Peaks and troughs of GHG emission followed agronomic events such as ratoon harvest of napiergrass and fertilization. Yet, net GHG flux was dominated by carbon dioxide (CO2), as methane was oxidized and nitrous oxide (N2O) emission was very low even following fertilization. High N2O fluxes that frequently negate other greenhouse gas benefits that come from replacing fossil fuels with agronomic forms of bioenergy were mitigated by efficient water and fertilizer management, including direct injection of fertilizer into buried irrigation lines. From soil intensively cultivated for a century in sugarcane, soil C stock and root biomass increased rapidly following cultivation in grasses selected for robust root systems and drought tolerance. The net soil C increase over the two-year crop cycle was three-fold greater than the annualized soil surface CO2 flux. Deficit irrigation reduced yield, but increased soil C accumulation as proportionately more photosynthetic resources were allocated belowground. In the first two years of cultivation napiergrass did not increase net greenhouse warming potential (GWP) compared to sugarcane, and has the advantage of multiple ratoon harvests per year and less negative effects of deficit irrigation to yield.

  10. Estimating Canopy Dark Respiration for Crop Models

    Monje Mejia, Oscar Alberto

    2014-01-01

    Crop production is obtained from accurate estimates of daily carbon gain.Canopy gross photosynthesis (Pgross) can be estimated from biochemical models of photosynthesis using sun and shaded leaf portions and the amount of intercepted photosyntheticallyactive radiation (PAR).In turn, canopy daily net carbon gain can be estimated from canopy daily gross photosynthesis when canopy dark respiration (Rd) is known.

  11. Resistance Genes in Global Crop Breeding Networks.

    Garrett, K A; Andersen, K F; Asche, F; Bowden, R L; Forbes, G A; Kulakow, P A; Zhou, B

    2017-10-01

    Resistance genes are a major tool for managing crop diseases. The networks of crop breeders who exchange resistance genes and deploy them in varieties help to determine the global landscape of resistance and epidemics, an important system for maintaining food security. These networks function as a complex adaptive system, with associated strengths and vulnerabilities, and implications for policies to support resistance gene deployment strategies. Extensions of epidemic network analysis can be used to evaluate the multilayer agricultural networks that support and influence crop breeding networks. Here, we evaluate the general structure of crop breeding networks for cassava, potato, rice, and wheat. All four are clustered due to phytosanitary and intellectual property regulations, and linked through CGIAR hubs. Cassava networks primarily include public breeding groups, whereas others are more mixed. These systems must adapt to global change in climate and land use, the emergence of new diseases, and disruptive breeding technologies. Research priorities to support policy include how best to maintain both diversity and redundancy in the roles played by individual crop breeding groups (public versus private and global versus local), and how best to manage connectivity to optimize resistance gene deployment while avoiding risks to the useful life of resistance genes. [Formula: see text] Copyright © 2017 The Author(s). This is an open access article distributed under the CC BY 4.0 International license .

  12. Principal component regression for crop yield estimation

    Suryanarayana, T M V

    2016-01-01

    This book highlights the estimation of crop yield in Central Gujarat, especially with regard to the development of Multiple Regression Models and Principal Component Regression (PCR) models using climatological parameters as independent variables and crop yield as a dependent variable. It subsequently compares the multiple linear regression (MLR) and PCR results, and discusses the significance of PCR for crop yield estimation. In this context, the book also covers Principal Component Analysis (PCA), a statistical procedure used to reduce a number of correlated variables into a smaller number of uncorrelated variables called principal components (PC). This book will be helpful to the students and researchers, starting their works on climate and agriculture, mainly focussing on estimation models. The flow of chapters takes the readers in a smooth path, in understanding climate and weather and impact of climate change, and gradually proceeds towards downscaling techniques and then finally towards development of ...

  13. Estimating crop yields and crop evapotranspiration distributions from remote sensing and geospatial agricultural data

    Smith, T.; McLaughlin, D.

    2017-12-01

    Growing more crops to provide a secure food supply to an increasing global population will further stress land and water resources that have already been significantly altered by agriculture. The connection between production and resource use depends on crop yields and unit evapotranspiration (UET) rates that vary greatly, over both time and space. For regional and global analyses of food security it is appropriate to treat yield and UET as uncertain variables conditioned on climatic and soil properties. This study describes how probability distributions of these variables can be estimated by combining remotely sensed land use and evapotranspiration data with in situ agronomic and soils data, all available at different resolutions and coverages. The results reveal the influence of water and temperature stress on crop yield at large spatial scales. They also provide a basis for stochastic modeling and optimization procedures that explicitly account for uncertainty in the environmental factors that affect food production.

  14. Air Pollution Impacts on Global Crop Productivity and Nitrogen Depositio

    Heald, C. L.; Tai, A. P. K.; Val Martin, M.

    2014-12-01

    The biosphere is undeniably transformed by air pollution. Emissions, climate change, and land use change are all expected to substantially alter future air quality. In this presentation, we discuss near-term projections (2050) of air quality impacts on both crop productivity and nitrogen deposition. First, we contrast the relative impacts of ozone air pollution and a warming climate on global crop yields. To do so, we define statistical crop yield functions to a warming climate based on the historical record. We combine these relationships with ozone-damage estimates and apply these to future air quality and climate projections from a global coupled chemistry-climate model (CESM). We find substantial variability in the response, with certain regions or crops more sensitive to ozone pollution and others more sensitive to warming. This work demonstrates that air quality management is a key element to ensuring global food security. Second, we examine the relative impacts of anthropogenic emissions, climate change, and land use change on global nitrogen deposition. Nitrogen deposition has rapidly increased over the Anthropocene. Excess deposition of nitrogen to ecosystems can lead to eutrophication of waters, and a decrease in biodiversity. We use the CESM to investigate two scenarios (RCP 4.5 and RCP8.5) and focus our analysis on the impacts on diverse ecoregions in North America, Europe, and Asia.

  15. Estimation of Rice Crop Yields Using Random Forests in Taiwan

    Chen, C. F.; Lin, H. S.; Nguyen, S. T.; Chen, C. R.

    2017-12-01

    Rice is globally one of the most important food crops, directly feeding more people than any other crops. Rice is not only the most important commodity, but also plays a critical role in the economy of Taiwan because it provides employment and income for large rural populations. The rice harvested area and production are thus monitored yearly due to the government's initiatives. Agronomic planners need such information for more precise assessment of food production to tackle issues of national food security and policymaking. This study aimed to develop a machine-learning approach using physical parameters to estimate rice crop yields in Taiwan. We processed the data for 2014 cropping seasons, following three main steps: (1) data pre-processing to construct input layers, including soil types and weather parameters (e.g., maxima and minima air temperature, precipitation, and solar radiation) obtained from meteorological stations across the country; (2) crop yield estimation using the random forests owing to its merits as it can process thousands of variables, estimate missing data, maintain the accuracy level when a large proportion of the data is missing, overcome most of over-fitting problems, and run fast and efficiently when handling large datasets; and (3) error verification. To execute the model, we separated the datasets into two groups of pixels: group-1 (70% of pixels) for training the model and group-2 (30% of pixels) for testing the model. Once the model is trained to produce small and stable out-of-bag error (i.e., the mean squared error between predicted and actual values), it can be used for estimating rice yields of cropping seasons. The results obtained from the random forests-based regression were compared with the actual yield statistics indicated the values of root mean square error (RMSE) and mean absolute error (MAE) achieved for the first rice crop were respectively 6.2% and 2.7%, while those for the second rice crop were 5.3% and 2

  16. A global sensitivity analysis of crop virtual water content

    Tamea, S.; Tuninetti, M.; D'Odorico, P.; Laio, F.; Ridolfi, L.

    2015-12-01

    The concepts of virtual water and water footprint are becoming widely used in the scientific literature and they are proving their usefulness in a number of multidisciplinary contexts. With such growing interest a measure of data reliability (and uncertainty) is becoming pressing but, as of today, assessments of data sensitivity to model parameters, performed at the global scale, are not known. This contribution aims at filling this gap. Starting point of this study is the evaluation of the green and blue virtual water content (VWC) of four staple crops (i.e. wheat, rice, maize, and soybean) at a global high resolution scale. In each grid cell, the crop VWC is given by the ratio between the total crop evapotranspiration over the growing season and the crop actual yield, where evapotranspiration is determined with a detailed daily soil water balance and actual yield is estimated using country-based data, adjusted to account for spatial variability. The model provides estimates of the VWC at a 5x5 arc minutes and it improves on previous works by using the newest available data and including multi-cropping practices in the evaluation. The model is then used as the basis for a sensitivity analysis, in order to evaluate the role of model parameters in affecting the VWC and to understand how uncertainties in input data propagate and impact the VWC accounting. In each cell, small changes are exerted to one parameter at a time, and a sensitivity index is determined as the ratio between the relative change of VWC and the relative change of the input parameter with respect to its reference value. At the global scale, VWC is found to be most sensitive to the planting date, with a positive (direct) or negative (inverse) sensitivity index depending on the typical season of crop planting date. VWC is also markedly dependent on the length of the growing period, with an increase in length always producing an increase of VWC, but with higher spatial variability for rice than for

  17. Drought impacts and resilience on crops via evapotranspiration estimations

    Timmermans, Joris; Asadollahi Dolatabad, Saeid

    2015-04-01

    Currently, the global needs for food and water is at a critical level. It has been estimated that 12.5 % of the global population suffers from malnutrition and 768 million people still do not have access to clean drinking water. This need is increasing because of population growth but also by climate change. Changes in precipitation patterns will result either in flooding or droughts. Consequently availability, usability and affordability of water is becoming challenge and efficient use of water and water management is becoming more important, particularly during severe drought events. Drought monitoring for agricultural purposes is very hard. While meteorological drought can accurately be monitored using precipitation only, estimating agricultural drought is more difficult. This is because agricultural drought is dependent on the meteorological drought, the impacts on the vegetation, and the resilience of the crops. As such not only precipitation estimates are required but also evapotranspiration at plant/plot scale. Evapotranspiration (ET) describes the amount of water evaporated from soil and vegetation. As 65% of precipitation is lost by ET, drought severity is highly linked with this variable. In drought research, the precise quantification of ET and its spatio-temporal variability is therefore essential. In this view, remote sensing based models to estimate ET, such as SEBAL and SEBS, are of high value. However the resolution of current evapotranspiration products are not good enough for monitoring the impact of the droughts on the specific crops. This limitation originates because plot scales are in general smaller than the resolution of the available satellite ET products. As such remote sensing estimates of evapotranspiration are always a combination of different land surface types and cannot be used for plant health and drought resilience studies. The goal of this research is therefore to enable adequate resolutions of daily evapotranspiration estimates

  18. Globally Increased Crop Growth and Cropping Intensity from the Long-Term Satellite-Based Observations

    Chen, Bin

    2018-04-01

    Understanding the spatiotemporal change trend of global crop growth and multiple cropping system under climate change scenarios is a critical requirement for supporting the food security issue that maintains the function of human society. Many studies have predicted the effects of climate changes on crop production using a combination of filed studies and models, but there has been limited evidence relating decadal-scale climate change to global crop growth and the spatiotemporal distribution of multiple cropping system. Using long-term satellite-derived Normalized Difference Vegetation Index (NDVI) and observed climate data from 1982 to 2012, we investigated the crop growth trend, spatiotemporal pattern trend of agricultural cropping intensity, and their potential correlations with respect to the climate change drivers at a global scale. Results show that 82.97 % of global cropland maximum NDVI witnesses an increased trend while 17.03 % of that shows a decreased trend over the past three decades. The spatial distribution of multiple cropping system is observed to expand from lower latitude to higher latitude, and the increased cropping intensity is also witnessed globally. In terms of regional major crop zones, results show that all nine selected zones have an obvious upward trend of crop maximum NDVI (p impact on the crop growth trend.

  19. GLOBALLY INCREASED CROP GROWTH AND CROPPING INTENSITY FROM THE LONG-TERM SATELLITE-BASED OBSERVATIONS

    B. Chen

    2018-04-01

    Full Text Available Understanding the spatiotemporal change trend of global crop growth and multiple cropping system under climate change scenarios is a critical requirement for supporting the food security issue that maintains the function of human society. Many studies have predicted the effects of climate changes on crop production using a combination of filed studies and models, but there has been limited evidence relating decadal-scale climate change to global crop growth and the spatiotemporal distribution of multiple cropping system. Using long-term satellite-derived Normalized Difference Vegetation Index (NDVI and observed climate data from 1982 to 2012, we investigated the crop growth trend, spatiotemporal pattern trend of agricultural cropping intensity, and their potential correlations with respect to the climate change drivers at a global scale. Results show that 82.97 % of global cropland maximum NDVI witnesses an increased trend while 17.03 % of that shows a decreased trend over the past three decades. The spatial distribution of multiple cropping system is observed to expand from lower latitude to higher latitude, and the increased cropping intensity is also witnessed globally. In terms of regional major crop zones, results show that all nine selected zones have an obvious upward trend of crop maximum NDVI (p < 0.001, and as for climatic drivers, the gradual temperature and precipitation changes have had a measurable impact on the crop growth trend.

  20. Global Polynomial Kernel Hazard Estimation

    Hiabu, Munir; Miranda, Maria Dolores Martínez; Nielsen, Jens Perch

    2015-01-01

    This paper introduces a new bias reducing method for kernel hazard estimation. The method is called global polynomial adjustment (GPA). It is a global correction which is applicable to any kernel hazard estimator. The estimator works well from a theoretical point of view as it asymptotically redu...

  1. Estimating Major Crop Water Productivity at Neyshabour Basin and Optimize Crop Area

    Yavar Pourmohamad

    2017-06-01

    Full Text Available Introductionin current situation when world is facing massive population, producing enough food and adequate income for people is a big challenge specifically for governors. This challenge gets even harder in recent decades, due to global population growth which was projected to increase to 7.8 billion in 2025. Agriculture as the only industry that has ability to produce food is consuming 90 percent of fresh water globally. Despite of increasing for food demand, appropriate agricultural land and fresh water resources are restricted. To solve this problem, one is to increase water productivity which can be obtain by irrigation. Iran is not only exempted from this situation but also has more critical situation due to its dry climate and inappropriate precipitation distribution spatially and temporally, also uneven distribution of population which is concentrate in small area. The only reasonable solution by considering water resources limitation and also restricted crop area is changing crop pattern to reach maximum or at least same amount of income by using same or less amount of water. The purpose of this study is to assess financial water productivity and optimize farmer’s income by changing in each crop acreage at basin and sub-basin level with no extra groundwater withdrawals, also in order to repair the damages which has enforce to groundwater resources during last decades a scenario of using only 80percent of renewable water were applied and crop area were optimize to provide maximum or same income for farmers. Materials and methodsThe Neyshabour basin is located in northeast of Iran, the total geographical area of basin is 73,000 km2 consisting of 41,000 km2 plain and the rest of basin is mountains. This Basin is a part of Kalshoor catchment that is located in southern part of Binaloud heights and northeast of KavirMarkazi. In this study whole Neyshabour basin were divided into 199 sub-basins based on pervious study.Based on official

  2. Hierarchical Satellite-based Approach to Global Monitoring of Crop Condition and Food Production

    Zheng, Y.; Wu, B.; Gommes, R.; Zhang, M.; Zhang, N.; Zeng, H.; Zou, W.; Yan, N.

    2014-12-01

    The assessment of global food security goes beyond the mere estimate of crop production: It needs to take into account the spatial and temporal patterns of food availability, as well as physical and economic access. Accurate and timely information is essential to both food producers and consumers. Taking advantage of multiple new remote sensing data sources, especially from Chinese satellites, such as FY-2/3A, HJ-1 CCD, CropWatch has expanded the scope of its international analyses through the development of new indicators and an upgraded operational methodology. The new monitoring approach adopts a hierarchical system covering four spatial levels of detail: global (sixty-five Monitoring and Reporting Units, MRU), seven major production zones (MPZ), thirty-one key countries (including China) and "sub- countries." The thirty-one countries encompass more that 80% of both global exports and production of four major crops (maize, rice, soybean and wheat). The methodology resorts to climatic and remote sensing indicators at different scales, using the integrated information to assess global, regional, and national (as well as sub-national) crop environmental condition, crop condition, drought, production, and agricultural trends. The climatic indicators for rainfall, temperature, photosynthetically active radiation (PAR) as well as potential biomass are first analysed at global scale to describe overall crop growing conditions. At MPZ scale, the key indicators pay more attention to crops and include Vegetation health index (VHI), Vegetation condition index (VCI), Cropped arable land fraction (CALF) as well as Cropping intensity (CI). Together, they characterise agricultural patterns, farming intensity and stress. CropWatch carries out detailed crop condition analyses for thirty one individual countries at the national scale with a comprehensive array of variables and indicators. The Normalized difference vegetation index (NDVI), cropped areas and crop condition are

  3. New indicators for global crop monitoring in CropWatch -case study in North China Plain

    Bingfang, Wu; Miao, Zhang; Hongwei, Zeng; Guoshui, Liu; Sheng, Chang; Gommes, René

    2014-01-01

    CropWatch is a monitoring system developed and operated by the Institute of Remote Sensing and Digital Earth (Chinese Academy of Sciences) to provide global-scale crop information. Now in its 15th year of operation, CropWatch was modified several times to be a timely, comprehensive and independent global agricultural monitoring system using advanced remote sensing technology. Currently CropWatch is being upgraded with new indicators based on new sensors, especially those on board of China Environmental Satellite (HJ-1 CCD), the Medium Resolution Spectral Imager (MERSI) on Chinese meteorological satellite (FY-3A) and cloud classification products of FY-2. With new satellite data, CropWatch will generate new indicators such as fallow land ratio (FLR), crop condition for irrigated (CCI) and non-irrigated (CCNI) areas separately, photosynthetically active radiation (PAR), radiation use efficiency for the photosynthetically active radiation (RUE PAR ) and cropping index (CI) with crop rotation information (CRI). In this paper, the methods for monitoring the new indicators are applied to the North China Plain which is one of the major grain producing areas in China. This paper shows the preliminary results of the new indicators and methods; they still need to be thoroughly validated before being incorporated into the operational CropWatch system. In the future, the new and improved indicators will help us to better understand the global situation of food security

  4. Changes in crop yields and their variability at different levels of global warming

    Ostberg, Sebastian; Schewe, Jacob; Childers, Katelin; Frieler, Katja

    2018-05-01

    An assessment of climate change impacts at different levels of global warming is crucial to inform the policy discussion about mitigation targets, as well as for the economic evaluation of climate change impacts. Integrated assessment models often use global mean temperature change (ΔGMT) as a sole measure of climate change and, therefore, need to describe impacts as a function of ΔGMT. There is already a well-established framework for the scalability of regional temperature and precipitation changes with ΔGMT. It is less clear to what extent more complex biological or physiological impacts such as crop yield changes can also be described in terms of ΔGMT, even though such impacts may often be more directly relevant for human livelihoods than changes in the physical climate. Here we show that crop yield projections can indeed be described in terms of ΔGMT to a large extent, allowing for a fast estimation of crop yield changes for emissions scenarios not originally covered by climate and crop model projections. We use an ensemble of global gridded crop model simulations for the four major staple crops to show that the scenario dependence is a minor component of the overall variance of projected yield changes at different levels of ΔGMT. In contrast, the variance is dominated by the spread across crop models. Varying CO2 concentrations are shown to explain only a minor component of crop yield variability at different levels of global warming. In addition, we find that the variability in crop yields is expected to increase with increasing warming in many world regions. We provide, for each crop model, geographical patterns of mean yield changes that allow for a simplified description of yield changes under arbitrary pathways of global mean temperature and CO2 changes, without the need for additional climate and crop model simulations.

  5. Estimation of paddy water temperature during crop development

    Centeno, H.G.S.; Horie, T.

    1996-01-01

    The crop meristem is in direct contact with paddy water during crop's vegetative stage. Ambient air temperature becomes an important factor in crop development only when internodes elongate sufficiently for the meristem to rise above the water surface. This does not occur until after panicle initiation. Crop growth at vegetative stage is affected more by water temperature than the most commonly measured air temperature. During transplanting in 1992 dry season, the maximum paddy water temperature was 10 deg C higher than the maximum air temperature. For rice crop models, the development of a submodel to estimate water temperature is important to account the effect of paddy water temperature on plant growth. Paddy water temperature is estimated from mean air temperature, solar radiation, and crop canopy. The parameters of the model were derived using the simplex method on data from the 1993 wet- and dry-season field experiments at IRRI

  6. Global scale climate-crop yield relationships and the impacts of recent warming

    Lobell, David B; Field, Christopher B

    2007-01-01

    Changes in the global production of major crops are important drivers of food prices, food security and land use decisions. Average global yields for these commodities are determined by the performance of crops in millions of fields distributed across a range of management, soil and climate regimes. Despite the complexity of global food supply, here we show that simple measures of growing season temperatures and precipitation-spatial averages based on the locations of each crop-explain ∼30% or more of year-to-year variations in global average yields for the world's six most widely grown crops. For wheat, maize and barley, there is a clearly negative response of global yields to increased temperatures. Based on these sensitivities and observed climate trends, we estimate that warming since 1981 has resulted in annual combined losses of these three crops representing roughly 40 Mt or $5 billion per year, as of 2002. While these impacts are small relative to the technological yield gains over the same period, the results demonstrate already occurring negative impacts of climate trends on crop yields at the global scale

  7. Global Rice Atlas: Disaggregated seasonal crop calendar and production

    Balanza, Jane Girly; Gutierrez, Mary Anne; Villano, Lorena; Nelson, A.D.; Zwart, S.J.; Boschetti, Mirco; Koo, Jawoo; Reinke, Russell; Murty, M. V.R.; Laborte, Alice G.

    2014-01-01

    Purpose: Rice is an important staple crop cultivated in more than 163 million ha globally. Although information on the distribution of global rice production is available by country and, at times, at subnational level, information on its distribution within a year is often lacking in different rice

  8. Methods to estimate irrigated reference crop evapotranspiration - a review.

    Kumar, R; Jat, M K; Shankar, V

    2012-01-01

    Efficient water management of crops requires accurate irrigation scheduling which, in turn, requires the accurate measurement of crop water requirement. Irrigation is applied to replenish depleted moisture for optimum plant growth. Reference evapotranspiration plays an important role for the determination of water requirements for crops and irrigation scheduling. Various models/approaches varying from empirical to physically base distributed are available for the estimation of reference evapotranspiration. Mathematical models are useful tools to estimate the evapotranspiration and water requirement of crops, which is essential information required to design or choose best water management practices. In this paper the most commonly used models/approaches, which are suitable for the estimation of daily water requirement for agricultural crops grown in different agro-climatic regions, are reviewed. Further, an effort has been made to compare the accuracy of various widely used methods under different climatic conditions.

  9. Global Food Security Support Analysis Data (GFSAD) Crop Mask 2010 Global 1 km V001

    National Aeronautics and Space Administration — The NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security Support Analysis Data (GFSAD) Crop Mask Global 1 kilometer...

  10. Understanding the Changes in Global Crop Yields Through Changes in Climate and Technology

    Najafi, Ehsan; Devineni, Naresh; Khanbilvardi, Reza M.; Kogan, Felix

    2018-03-01

    During the last few decades, the global agricultural production has risen and technology enhancement is still contributing to yield growth. However, population growth, water crisis, deforestation, and climate change threaten the global food security. An understanding of the variables that caused past changes in crop yields can help improve future crop prediction models. In this article, we present a comprehensive global analysis of the changes in the crop yields and how they relate to different large-scale and regional climate variables, climate change variables and technology in a unified framework. A new multilevel model for yield prediction at the country level is developed and demonstrated. The structural relationships between average yield and climate attributes as well as trends are estimated simultaneously. All countries are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. El Niño-southern oscillation (ENSO), Palmer drought severity index (PDSI), geopotential height anomalies (GPH), historical carbon dioxide (CO2) concentration and country-based time series of GDP per capita as an approximation of technology measurement are used as predictors to estimate annual agricultural crop yields for each country from 1961 to 2013. Results indicate that these variables can explain the variability in historical crop yields for most of the countries and the model performs well under out-of-sample verifications. While some countries were not generally affected by climatic factors, PDSI and GPH acted both positively and negatively in different regions for crop yields in many countries.

  11. The Crop Journal: A new scientific journal for the global crop science community

    Jianmin Wan

    2013-10-01

    Full Text Available As global population increases and demands for food supplies become greater, we face great challenges in providing more products and in larger quantities from less arable land. Crop science has gained increasing importance in meeting these challenges and results of scientific research must be communicated worldwide on a regular basis. In many countries, however, crop scientists have to publish the results of their investigations in national journals with heterogeneous contents and in their native languages. As a consequence, valuable work often remains unknown to scientists elsewhere. As a big country with a large number of crop scientists, China has a wide range of climatic and ecological environments, diverse plant species and cropping systems, and different regional needs for food supplies, which justify the recent decision by the Crop Science Society of China and the Institute of Crop Science within the Chinese Academy of Agricultural Sciences, to launch a new communication channel, The Crop Journal. The goal of The Crop Journal is to meet an urgent need for a major Asia-based journal that covers the diverse fields of crop science. Our aim is to create a vital and thought-provoking journal that will highlight state-of-the-art original work and reviews by high-profile crop scientists and investigative groups throughout the world — a journal that will respond to the needs of specialists in strategic crop research. We will work with scientific and publishing colleagues worldwide, using The Plant Journal and Crop Science as models, to establish The Crop Journal as a broadly based high quality journal and a premier forum for issues in crop science. The Crop Journal will cover a wide range of topics, including crop genetics, breeding, agronomy, crop physiology, germplasm resources, grain chemistry, grain storage and processing, crop management practices, crop biotechnology, and biomathematics. The journal also encourages the submission of review

  12. Climate change and global crop yield: impacts, uncertainties and adaptation

    Deryng, Delphine

    2014-01-01

    As global mean temperature continues to rise steadily, agricultural systems are projected to face unprecedented challenges to cope with climate change. However, understanding of climate change impacts on global crop yield, and of farmers’ adaptive capacity, remains incomplete as previous global assessments: (1) inadequately evaluated the role of extreme weather events; (2) focused on a small subset of the full range of climate change predictions; (3) overlooked uncertainties related to the ch...

  13. Global ex-situ crop diversity conservation and the Svalbard Global Seed Vault: assessing the current status.

    Ola T Westengen

    Full Text Available Ex-situ conservation of crop diversity is a global concern, and the development of an efficient and sustainable conservation system is a historic priority recognized in international law and policy. We assess the completeness of the safety duplication collection in the Svalbard Global Seed Vault with respect to data on the world's ex-situ collections as reported by the Food and Agriculture Organization of the United Nations. Currently, 774,601 samples are deposited at Svalbard by 53 genebanks. We estimate that more than one third of the globally distinct accessions of 156 crop genera stored in genebanks as orthodox seeds are conserved in the Seed Vault. The numbers of safety duplicates of Triticum (wheat, Sorghum (sorghum, Pennisetum (pearl millet, Eleusine (finger millet, Cicer (chickpea and Lens (lentil exceed 50% of the estimated numbers of distinct accessions in global ex-situ collections. The number of accessions conserved globally generally reflects importance for food production, but there are significant gaps in the safety collection at Svalbard in some genera of high importance for food security in tropical countries, such as Amaranthus (amaranth, Chenopodium (quinoa, Eragrostis (teff and Abelmoschus (okra. In the 29 food-crop genera with the largest number of accessions stored globally, an average of 5.5 out of the ten largest collections is already represented in the Seed Vault collection or is covered by existing deposit agreements. The high coverage of ITPGRFA Annex 1 crops and of those crops for which there is a CGIAR mandate in the current Seed Vault collection indicates that existence of international policies and institutions are important determinants for accessions to be safety duplicated at Svalbard. As a back-up site for the global conservation system, the Seed Vault plays not only a practical but also a symbolic role for enhanced integration and cooperation for conservation of crop diversity.

  14. Global ex-situ crop diversity conservation and the Svalbard Global Seed Vault: assessing the current status.

    Westengen, Ola T; Jeppson, Simon; Guarino, Luigi

    2013-01-01

    Ex-situ conservation of crop diversity is a global concern, and the development of an efficient and sustainable conservation system is a historic priority recognized in international law and policy. We assess the completeness of the safety duplication collection in the Svalbard Global Seed Vault with respect to data on the world's ex-situ collections as reported by the Food and Agriculture Organization of the United Nations. Currently, 774,601 samples are deposited at Svalbard by 53 genebanks. We estimate that more than one third of the globally distinct accessions of 156 crop genera stored in genebanks as orthodox seeds are conserved in the Seed Vault. The numbers of safety duplicates of Triticum (wheat), Sorghum (sorghum), Pennisetum (pearl millet), Eleusine (finger millet), Cicer (chickpea) and Lens (lentil) exceed 50% of the estimated numbers of distinct accessions in global ex-situ collections. The number of accessions conserved globally generally reflects importance for food production, but there are significant gaps in the safety collection at Svalbard in some genera of high importance for food security in tropical countries, such as Amaranthus (amaranth), Chenopodium (quinoa), Eragrostis (teff) and Abelmoschus (okra). In the 29 food-crop genera with the largest number of accessions stored globally, an average of 5.5 out of the ten largest collections is already represented in the Seed Vault collection or is covered by existing deposit agreements. The high coverage of ITPGRFA Annex 1 crops and of those crops for which there is a CGIAR mandate in the current Seed Vault collection indicates that existence of international policies and institutions are important determinants for accessions to be safety duplicated at Svalbard. As a back-up site for the global conservation system, the Seed Vault plays not only a practical but also a symbolic role for enhanced integration and cooperation for conservation of crop diversity.

  15. Global warming impact assessment of a crop residue gasification project—A dynamic LCA perspective

    Yang, Jin; Chen, Bin

    2014-01-01

    Highlights: • A dynamic LCA is proposed considering time-varying factors. • Dynamic LCA is used to highlight GHG emission hotspots of gasification projects. • Indicators are proposed to reflect GHG emission performance. • Dynamic LCA alters the static LCA results. • Crop residue gasification project has high GHG abatement potential. - Abstract: Bioenergy from crop residues is one of the prevailing sustainable energy sources owing to the abundant reserves worldwide. Amongst a wide variety of energy conversion technologies, crop residue gasification has been regarded as promising owing to its higher energy efficiency than that of direct combustion. However, prior to large-scale application of crop residue gasification, the lifetime environmental performance should be investigated to shed light on sustainable strategies. As traditional static life cycle assessment (LCA) does not include temporal information for dynamic processes, we proposed a dynamic life cycle assessment approach, which improves the static LCA approach by considering time-varying factors, e.g., greenhouse gas characterization factors and energy intensity. As the gasification project can reduce greenhouse gas (GHG) discharge compared with traditional direct fuel combustion, trade-offs between the benefits of global warming mitigation and the impact on global warming of crop residue gasification should be considered. Therefore, indicators of net global warming mitigation benefit and global warming impact mitigation period are put forward to justify the feasibility of the crop residue gasification project. The proposed dynamic LCA and indicators were then applied to estimate the life cycle global warming impact of a crop residue gasification system in China. Results show that the crop residue gasification project has high net global warming mitigation benefit and a short global warming impact mitigation period, indicating its prominent potential in alleviating global warming impact. During

  16. Increasing global crop harvest frequency: recent trends and future directions

    Ray, Deepak K; Foley, Jonathan A

    2013-01-01

    The world’s agricultural systems face the challenge of meeting the rising demands from population growth, changing dietary preferences, and expanding biofuel use. Previous studies have put forward strategies for meeting this growing demand by increasing global crop production, either expanding the area under cultivation or intensifying the crop yields of our existing agricultural lands. However, another possible means for increasing global crop production has received less attention: increasing the frequency of global cropland harvested each year. Historically, many of the world’s croplands were left fallow, or had failed harvests, each year, foregoing opportunities for delivering crop production. Furthermore, many regions, particularly in the tropics, may be capable of multiple harvests per year, often more than are harvested today. Here we analyze a global compilation of agricultural statistics to show how the world’s harvested cropland has changed. Between 2000 and 2011, harvested land area grew roughly 4 times faster than total standing cropland area. Using a metric of cropland harvest frequency (CHF)—the ratio of land harvested each year to the total standing cropland—and its recent trends, we identify countries that harvest their croplands more frequently, and those that have the potential to increase their cropland harvest frequency. We suggest that a possible ‘harvest gap’ may exist in many countries that represents an opportunity to increase crop production on existing agricultural lands. However, increasing the harvest frequency of existing croplands could have significant environmental and social impacts, which need careful evaluation. (letter)

  17. Global Simulation of Bioenergy Crop Productivity: Analytical Framework and Case Study for Switchgrass

    Kang, Shujiang [ORNL; Kline, Keith L [ORNL; Nair, S. Surendran [University of Tennessee, Knoxville (UTK); Nichols, Dr Jeff A [ORNL; Post, Wilfred M [ORNL; Brandt, Craig C [ORNL; Wullschleger, Stan D [ORNL; Wei, Yaxing [ORNL; Singh, Nagendra [ORNL

    2013-01-01

    A global energy crop productivity model that provides geospatially explicit quantitative details on biomass potential and factors affecting sustainability would be useful, but does not exist now. This study describes a modeling platform capable of meeting many challenges associated with global-scale agro-ecosystem modeling. We designed an analytical framework for bioenergy crops consisting of six major components: (i) standardized natural resources datasets, (ii) global field-trial data and crop management practices, (iii) simulation units and management scenarios, (iv) model calibration and validation, (v) high-performance computing (HPC) simulation, and (vi) simulation output processing and analysis. The HPC-Environmental Policy Integrated Climate (HPC-EPIC) model simulated a perennial bioenergy crop, switchgrass (Panicum virgatum L.), estimating feedstock production potentials and effects across the globe. This modeling platform can assess soil C sequestration, net greenhouse gas (GHG) emissions, nonpoint source pollution (e.g., nutrient and pesticide loss), and energy exchange with the atmosphere. It can be expanded to include additional bioenergy crops (e.g., miscanthus, energy cane, and agave) and food crops under different management scenarios. The platform and switchgrass field-trial dataset are available to support global analysis of biomass feedstock production potential and corresponding metrics of sustainability.

  18. Regional crop gross primary production and yield estimation using fused Landsat-MODIS data

    He, M.; Kimball, J. S.; Maneta, M. P.; Maxwell, B. D.; Moreno, A.

    2017-12-01

    Accurate crop yield assessments using satellite-based remote sensing are of interest for the design of regional policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations are generally too coarse to capture cropland heterogeneity. Merging information from sensors with reciprocal spatial and temporal resolution can improve the accuracy of these retrievals. In this study, we estimate annual crop yields for seven important crop types -alfalfa, barley, corn, durum wheat, peas, spring wheat and winter wheat over Montana, United States (U.S.) from 2008 to 2015. Yields are estimated as the product of gross primary production (GPP) and a crop-specific harvest index (HI) at 30 m spatial resolution. To calculate GPP we used a modified form of the MOD17 LUE algorithm driven by a 30 m 8-day fused NDVI dataset constructed by blending Landsat (5 or 7) and MODIS Terra reflectance data. The fused 30-m NDVI record shows good consistency with the original Landsat and MODIS data, but provides better spatiotemporal information on cropland vegetation growth. The resulting GPP estimates capture characteristic cropland patterns and seasonal variations, while the estimated annual 30 m crop yield results correspond favorably with county-level crop yield data (r=0.96, pcrop yield performance was generally lower, but still favorable in relation to field-scale crop yield surveys (r=0.42, p<0.01). Our methods and results are suitable for operational applications at regional scales.

  19. The imprint of crop choice on global nutrient needs

    Jobbágy, Esteban G; Sala, Osvaldo E

    2014-01-01

    Solutions to meet growing food requirements in a world of limited suitable land and degrading environment focus mainly on increasing crop yields, particularly in poorly performing regions, and reducing animal product consumption. Increasing yields could alleviate land requirements, but imposing higher soil nutrient withdrawals and in most cases larger fertilizer inputs. Lowering animal product consumption favors a more efficient use of land as well as soil and fertilizer nutrients; yet actual saving may largely depend on which crops and how much fertilizer are used to feed livestock versus people. We show, with a global analysis, how the choice of cultivated plant species used to feed people and livestock influences global food production as well as soil nutrient withdrawals and fertilizer additions. The 3 to 15-fold differences in soil nutrient withdrawals per unit of energy or protein produced that we report across major crops explain how composition shifts over the last 20 years have reduced N, maintained P and increased K harvest withdrawals from soils while contributing to increasing dietary energy, protein and, particularly, vegetable fat outputs. Being highly variable across crops, global fertilization rates do not relate to actual soil nutrient withdrawals, but to monetary values of harvested products. Future changes in crop composition could contribute to achieve more sustainable food systems, optimizing land and fertilizer use. (letter)

  20. Trends in global approvals of biotech crops (1992–2014)

    Aldemita, Rhodora R; Reaño, Ian Mari E; Solis, Renando O; Hautea, Randy A

    2015-01-01

    ABSTRACT With the increasing number of genetically modified (GM) events, traits, and crops that are developed to benefit the global population, approval of these technologies for food, feed, cultivation and import in each country may vary depending on needs, demand and trade interest. ISAAA established a GMO Approval Database to document global approvals of biotech crops. GM event name, crops, traits, developer, year of approval for cultivation, food/feed, import, and relevant dossiers were sourced from credible government regulatory websites and biosafety clearinghouses. This paper investigates the trends in GM approvals for food, feed and cultivation based on the number of approving countries, GM crops, events, and traits in the last 23 y (1992–2014), rationale for approval, factors influencing approvals, and their implications in GM crop adoption. Results show that in 2014, there was an accumulative increase in the number of countries granting approvals at 29 (79% developing countries) for commercial cultivation and 31 (70% developing countries) for food and 19 (80% developing developing) for feed; 2012 had the highest number of approving countries and cultivation approvals; 2011 had the highest number of country approvals for feed, and 2014 for food approvals. Herbicide tolerance trait had the highest events approved, followed by insect tolerance traits. Approvals for food product quality increased in the second decade. Maize had the highest number of events approved (single and stacked traits), and stacked traits product gradually increased which is already 30% of the total trait approvals. These results may indicate understanding and acceptance of countries to enhance regulatory capability to be able to benefit from GM crop commercialization. Hence, the paper provided information on the trends on the growth of the GM crop industry in the last 23 y which may be vital in predicting future GM crops and traits. PMID:26039675

  1. Trends in global approvals of biotech crops (1992-2014).

    Aldemita, Rhodora R; Reaño, Ian Mari E; Solis, Renando O; Hautea, Randy A

    2015-01-01

    With the increasing number of genetically modified (GM) events, traits, and crops that are developed to benefit the global population, approval of these technologies for food, feed, cultivation and import in each country may vary depending on needs, demand and trade interest. ISAAA established a GMO Approval Database to document global approvals of biotech crops. GM event name, crops, traits, developer, year of approval for cultivation, food/feed, import, and relevant dossiers were sourced from credible government regulatory websites and biosafety clearinghouses. This paper investigates the trends in GM approvals for food, feed and cultivation based on the number of approving countries, GM crops, events, and traits in the last 23 y (1992-2014), rationale for approval, factors influencing approvals, and their implications in GM crop adoption. Results show that in 2014, there was an accumulative increase in the number of countries granting approvals at 29 (79% developing countries) for commercial cultivation and 31 (70% developing countries) for food and 19 (80% developing developing) for feed; 2012 had the highest number of approving countries and cultivation approvals; 2011 had the highest number of country approvals for feed, and 2014 for food approvals. Herbicide tolerance trait had the highest events approved, followed by insect tolerance traits. Approvals for food product quality increased in the second decade. Maize had the highest number of events approved (single and stacked traits), and stacked traits product gradually increased which is already 30% of the total trait approvals. These results may indicate understanding and acceptance of countries to enhance regulatory capability to be able to benefit from GM crop commercialization. Hence, the paper provided information on the trends on the growth of the GM crop industry in the last 23 y which may be vital in predicting future GM crops and traits.

  2. Estimation of N2O emission factors for soils depending on environmental conditions and crop management

    Lesschen, J.P.; Velthof, G.L.

    2009-01-01

    Nitrous oxide (N2O) contributes 8% to anthropogenic global warming, of which about one third are direct emissions of agricultural soils. These N2O emissions are often estimated using the default IPCC 2006 emission factor of 1% of the amount of N applied for mineral fertilizer, manure and crop

  3. An integrated model to simulate sown area changes for major crops at a global scale

    SHIBASAKI; Ryosuke

    2008-01-01

    Dynamics of land use systems have attracted much attention from scientists around the world due to their ecological and socio-economic implications. An integrated model to dynamically simulate future changes in sown areas of four major crops (rice, maize, wheat and soybean) on a global scale is pre- sented. To do so, a crop choice model was developed on the basis of Multinomial Logit (Logit) model to model land users’ decisions on crop choices among a set of available alternatives with using a crop utility function. A GIS-based Environmental Policy Integrated Climate (EPIC) model was adopted to simulate the crop yields under a given geophysical environment and farming management conditions, while the International Food Policy and Agricultural Simulation (IFPSIM) model was utilized to estimate crop price in the international market. The crop choice model was linked with the GIS-based EPIC model and the IFPSIM model through data exchange. This integrated model was then validated against the FAO statistical data in 2001-2003 and the Moderate Resolution Imaging Spectroradiometer (MODIS) global land cover product in 2001. Both validation approaches indicated reliability of the model for ad- dressing the dynamics in agricultural land use and its capability for long-term scenario analysis. Finally, the model application was designed to run over a time period of 30 a, taking the year 2000 as baseline. The model outcomes can help understand and explain the causes, locations and consequences of land use changes, and provide support for land use planning and policy making.

  4. International Global Crop Condition Assessments in the framework of GEOGLAM

    Becker-Reshef, I.; Justice, C. O.; Vermote, E.; Whitcraft, A. K.; Claverie, M.

    2013-12-01

    The Group on Earth Observations (partnership of governments and international organizations) developed the Global Agricultural Monitoring (GEOGLAM) initiative in response to the growing calls for improved agricultural information. The goal of GEOGLAM is to strengthen the international community's capacity to produce and disseminate relevant, timely and accurate forecasts of agricultural production at national, regional and global scales through the use of Earth observations. This initiative is designed to build on existing agricultural monitoring initiatives at national, regional and global levels and to enhance and strengthen them through international networking, operationally focused research, and data/method sharing. GEOGLAM was adopted by the G20 as part of the action plan on food price volatility and agriculture and is being implemented through building on the extensive GEO Agricultural Community of Practice (CoP) that was initiated in 2007 and includes key national and international agencies, organizations, and universities involved in agricultural monitoring. One of the early GEOGLAM activities is to provide harmonized global crop outlooks that offer timely qualitative consensus information on crop status and prospects. This activity is being developed in response to a request from the G-20 Agricultural Market Information System (AMIS) and is implemented within the global monitoring systems component of GEOGLAM. The goal is to develop a transparent, international, multi-source, consensus assessment of crop growing conditions, status, and agro-climatic conditions, likely to impact global production. These assessments are focused on the four primary crop types (corn, wheat, soy and rice) within the main agricultural producing regions of the world. The GEOGLAM approach is to bring together international experts from global, regional and national monitoring systems that can share and discuss information from a variety of independent complementary sources in

  5. Estimating the impact of mineral aerosols on crop yields in food insecure regions using statistical crop models

    Hoffman, A.; Forest, C. E.; Kemanian, A.

    2016-12-01

    A significant number of food-insecure nations exist in regions of the world where dust plays a large role in the climate system. While the impacts of common climate variables (e.g. temperature, precipitation, ozone, and carbon dioxide) on crop yields are relatively well understood, the impact of mineral aerosols on yields have not yet been thoroughly investigated. This research aims to develop the data and tools to progress our understanding of mineral aerosol impacts on crop yields. Suspended dust affects crop yields by altering the amount and type of radiation reaching the plant, modifying local temperature and precipitation. While dust events (i.e. dust storms) affect crop yields by depleting the soil of nutrients or by defoliation via particle abrasion. The impact of dust on yields is modeled statistically because we are uncertain which impacts will dominate the response on national and regional scales considered in this study. Multiple linear regression is used in a number of large-scale statistical crop modeling studies to estimate yield responses to various climate variables. In alignment with previous work, we develop linear crop models, but build upon this simple method of regression with machine-learning techniques (e.g. random forests) to identify important statistical predictors and isolate how dust affects yields on the scales of interest. To perform this analysis, we develop a crop-climate dataset for maize, soybean, groundnut, sorghum, rice, and wheat for the regions of West Africa, East Africa, South Africa, and the Sahel. Random forest regression models consistently model historic crop yields better than the linear models. In several instances, the random forest models accurately capture the temperature and precipitation threshold behavior in crops. Additionally, improving agricultural technology has caused a well-documented positive trend that dominates time series of global and regional yields. This trend is often removed before regression with

  6. Satellite Estimation of Fractional Cover in Several California Specialty Crops

    Johnson, L.; Cahn, M.; Rosevelt, C.; Guzman, A.; Lockhart, T.; Farrara, B.; Melton, F. S.

    2016-12-01

    Past research in California and elsewhere has revealed strong relationships between satellite NDVI, photosynthetically active vegetation fraction (Fc), and crop evapotranspiration (ETc). Estimation of ETc can support efficiency of irrigation practice, which enhances water security and may mitigate nitrate leaching. The U.C. Cooperative Extension previously developed the CropManage (CM) web application for evaluation of crop water requirement and irrigation scheduling for several high-value specialty crops. CM currently uses empirical equations to predict daily Fc as a function of crop type, planting date and expected harvest date. The Fc prediction is transformed to fraction of reference ET and combined with reference data from the California Irrigation Management Information System to estimate daily ETc. In the current study, atmospherically-corrected Landsat NDVI data were compared with in-situ Fc estimates on several crops in the Salinas Valley during 2011-2014. The satellite data were observed on day of ground collection or were linearly interpolated across no more than an 8-day revisit period. Results will be presented for lettuce, spinach, celery, broccoli, cauliflower, cabbage, peppers, and strawberry. An application programming interface (API) allows CM and other clients to automatically retrieve NDVI and associated data from NASA's Satellite Irrigation Management Support (SIMS) web service. The SIMS API allows for queries both by individual points or user-defined polygons, and provides data for individual days or annual timeseries. Updates to the CM web app will convert these NDVI data to Fc on a crop-specific basis. The satellite observations are expected to play a support role in Salinas Valley, and may eventually serve as a primary data source as CM is extended to crop systems or regions where Fc is less predictable.

  7. The global impact of ozone on agricultural crop yields under current and future air quality legislation

    Van Dingenen, Rita; Dentener, Frank J.; Raes, Frank; Krol, Maarten C.; Emberson, Lisa; Cofala, Janusz

    In this paper we evaluate the global impact of surface ozone on four types of agricultural crop. The study is based on modelled global hourly ozone fields for the year 2000 and 2030, using the global 1°×1° 2-way nested atmospheric chemical transport model (TM5). Projections for the year 2030 are based on the relatively optimistic "current legislation (CLE) scenario", i.e. assuming that currently approved air quality legislation will be fully implemented by the year 2030, without a further development of new abatement policies. For both runs, the relative yield loss due to ozone damage is evaluated based on two different indices (accumulated concentration above a 40 ppbV threshold and seasonal mean daytime ozone concentration respectively) on a global, regional and national scale. The cumulative metric appears to be far less robust than the seasonal mean, while the seasonal mean shows satisfactory agreement with measurements in Europe, the US, China and Southern India and South-East Asia. Present day global relative yield losses are estimated to range between 7% and 12% for wheat, between 6% and 16% for soybean, between 3% and 4% for rice, and between 3% and 5% for maize (range resulting from different metrics used). Taking into account possible biases in our assessment, introduced through the global application of "western" crop exposure-response functions, and through model performance in reproducing ozone-exposure metrics, our estimates may be considered as being conservative. Under the 2030 CLE scenario, the global situation is expected to deteriorate mainly for wheat (additional 2-6% loss globally) and rice (additional 1-2% loss globally). India, for which no mitigation measures have been assumed by 2030, accounts for 50% of these global increase in crop yield loss. On a regional-scale, significant reductions in crop losses by CLE-2030 are only predicted in Europe (soybean) and China (wheat). Translating these assumed yield losses into total global economic

  8. Global Warming Estimation from MSU

    Prabhakara, C.; Iacovazzi, Robert, Jr.

    1999-01-01

    In this study, we have developed time series of global temperature from 1980-97 based on the Microwave Sounding Unit (MSU) Ch 2 (53.74 GHz) observations taken from polar-orbiting NOAA operational satellites. In order to create these time series, systematic errors (approx. 0.1 K) in the Ch 2 data arising from inter-satellite differences are removed objectively. On the other hand, smaller systematic errors (approx. 0.03 K) in the data due to orbital drift of each satellite cannot be removed objectively. Such errors are expected to remain in the time series and leave an uncertainty in the inferred global temperature trend. With the help of a statistical method, the error in the MSU inferred global temperature trend resulting from orbital drifts and residual inter-satellite differences of all satellites is estimated to be 0.06 K decade. Incorporating this error, our analysis shows that the global temperature increased at a rate of 0.13 +/- 0.06 K decade during 1980-97.

  9. Winter Crop Mapping for Improving Crop Production Estimates in Argentina Using Moderation Resolution Satellite Imagery

    Humber, M. L.; Copati, E.; Sanchez, A.; Sahajpal, R.; Puricelli, E.; Becker-Reshef, I.

    2017-12-01

    Accurate crop production data is fundamental for reducing uncertainly and volatility in the domestic and international agricultural markets. The Agricultural Estimates Department of the Buenos Aires Grain Exchange has worked since 2000 on the estimation of different crop production data. With this information, the Grain Exchange helps different actors of the agricultural chain, such as producers, traders, seed companies, market analyst, policy makers, into their day to day decision making. Since 2015/16 season, the Grain Exchange has worked on the development of a new earth observations-based method to identify winter crop planted area at a regional scale with the aim of improving crop production estimates. The objective of this new methodology is to create a reliable winter crop mask at moderate spatial resolution using Landsat-8 imagery by exploiting bi-temporal differences in the phenological stages of winter crops as compared to other landcover types. In collaboration with the University of Maryland, the map has been validated by photointerpretation of a stratified statistically random sample of independent ground truth data in the four largest producing provinces of Argentina: Buenos Aires, Cordoba, La Pampa, and Santa Fe. In situ measurements were also used to further investigate conditions in the Buenos Aires province. Preliminary results indicate that while there are some avenues for improvement, overall the classification accuracy of the cropland and non-cropland classes are sufficient to improve downstream production estimates. Continuing research will focus on improving the methodology for winter crop mapping exercises on a yearly basis as well as improving the sampling methodology to optimize collection of validation data in the future.

  10. Practical global oceanic state estimation

    Wunsch, Carl; Heimbach, Patrick

    2007-06-01

    The problem of oceanographic state estimation, by means of an ocean general circulation model (GCM) and a multitude of observations, is described and contrasted with the meteorological process of data assimilation. In practice, all such methods reduce, on the computer, to forms of least-squares. The global oceanographic problem is at the present time focussed primarily on smoothing, rather than forecasting, and the data types are unlike meteorological ones. As formulated in the consortium Estimating the Circulation and Climate of the Ocean (ECCO), an automatic differentiation tool is used to calculate the so-called adjoint code of the GCM, and the method of Lagrange multipliers used to render the problem one of unconstrained least-squares minimization. Major problems today lie less with the numerical algorithms (least-squares problems can be solved by many means) than with the issues of data and model error. Results of ongoing calculations covering the period of the World Ocean Circulation Experiment, and including among other data, satellite altimetry from TOPEX/POSEIDON, Jason-1, ERS- 1/2, ENVISAT, and GFO, a global array of profiling floats from the Argo program, and satellite gravity data from the GRACE mission, suggest that the solutions are now useful for scientific purposes. Both methodology and applications are developing in a number of different directions.

  11. Potential Air Quality Impacts of Global Bioenergy Crop Cultivation

    Porter, W. C.; Rosenstiel, T. N.; Barsanti, K. C.

    2012-12-01

    The use of bioenergy crops as a replacement for traditional coal-powered electricity generation will require large-scale land-use change, and the resulting changes in emissions of biogenic volatile organic compounds (BVOCs) may have negative impacts on local to regional air quality. BVOCs contribute to the formation of both ozone (O3) and fine particulate matter (PM2.5), with magnitudes of specific compound emissions governed largely by plant speciation and land coverage. For this reason, large-scale land-use change has the potential to markedly alter regional O3 and PM2.5 levels, especially if there are large differences between the emission profiles of the replacement bioenergy crops (many of which are high BVOC emitters) and the previous crops or land cover. In this work, replacement areas suitable for the cultivation of the bioenergy crops switchgrass (Panicum virgatum) and giant reed (Arundo donax) were selected based on existing global inventories of under-utilized cropland and local climatological conditions. These two crops are among the most popular current candidates for bioenergy production, and provide contrasting examples of energy densities and emissions profiles. While giant reed has been selected in an ongoing large-scale coal-to-biocharcoal conversion in the Northwestern United States due to its high crop yields and energy density, it is also among the highest biogenic emitters of isoprene. On the other hand, switchgrass produces less biomass per acre, but also emits essentially no isoprene and low total BVOCs. The effects of large-scale conversion to these crops on O3 and PM2.5 were simulated using version 1.1 of the Community Earth System Model (CESM) coupled with version 2.1 of the Model of Emissions of Gases and Aerosols from Nature (MEGAN). By comparing crop replacement scenarios involving A. donax and P. virgatum, the sensitivities of O3 and PM2.5 levels to worldwide increases in bioenergy production were examined, providing an initial

  12. Increasing crop production in Russia and Ukraine—regional and global impacts from intensification and recultivation

    Deppermann, Andre; Balkovič, Juraj; Bundle, Sophie-Charlotte; Di Fulvio, Fulvio; Havlik, Petr; Leclère, David; Lesiv, Myroslava; Prishchepov, Alexander V.; Schepaschenko, Dmitry

    2018-02-01

    Russia and Ukraine are countries with relatively large untapped agricultural potentials, both in terms of abandoned agricultural land and substantial yield gaps. Here we present a comprehensive assessment of Russian and Ukrainian crop production potentials and we analyze possible impacts of their future utilization, on a regional as well as global scale. To this end, the total amount of available abandoned land and potential yields in Russia and Ukraine are estimated and explicitly implemented in an economic agricultural sector model. We find that cereal (barley, corn, and wheat) production in Russia and Ukraine could increase by up to 64% in 2030 to 267 million tons, compared to a baseline scenario. Oilseeds (rapeseed, soybean, and sunflower) production could increase by 84% to 50 million tons, respectively. In comparison to the baseline, common net exports of Ukraine and Russia could increase by up to 86.3 million tons of cereals and 18.9 million tons of oilseeds in 2030, representing 4% and 3.6% of the global production of these crops, respectively. Furthermore, we find that production potentials due to intensification are ten times larger than potentials due to recultivation of abandoned land. Consequently, we also find stronger impacts from intensification at the global scale. A utilization of crop production potentials in Russia and Ukraine could globally save up to 21 million hectares of cropland and reduce average global crop prices by more than 3%.

  13. Added-values of high spatiotemporal remote sensing data in crop yield estimation

    Gao, F.; Anderson, M. C.

    2017-12-01

    Timely and accurate estimation of crop yield before harvest is critical for food market and administrative planning. Remote sensing derived parameters have been used for estimating crop yield by using either empirical or crop growth models. The uses of remote sensing vegetation index (VI) in crop yield modeling have been typically evaluated at regional and country scales using coarse spatial resolution (a few hundred to kilo-meters) data or assessed over a small region at field level using moderate resolution spatial resolution data (10-100m). Both data sources have shown great potential in capturing spatial and temporal variability in crop yield. However, the added value of data with both high spatial and temporal resolution data has not been evaluated due to the lack of such data source with routine, global coverage. In recent years, more moderate resolution data have become freely available and data fusion approaches that combine data acquired from different spatial and temporal resolutions have been developed. These make the monitoring crop condition and estimating crop yield at field scale become possible. Here we investigate the added value of the high spatial and temporal VI for describing variability of crop yield. The explanatory ability of crop yield based on high spatial and temporal resolution remote sensing data was evaluated in a rain-fed agricultural area in the U.S. Corn Belt. Results show that the fused Landsat-MODIS (high spatial and temporal) VI explains yield variability better than single data source (Landsat or MODIS alone), with EVI2 performing slightly better than NDVI. The maximum VI describes yield variability better than cumulative VI. Even though VI is effective in explaining yield variability within season, the inter-annual variability is more complex and need additional information (e.g. weather, water use and management). Our findings augment the importance of high spatiotemporal remote sensing data and supports new moderate

  14. Impact of management strategies on the global warming potential at the cropping system level

    Goglio, Pietro; Grant, Brian B.; Smith, Ward N.; Desjardins, Raymond L.; Worth, Devon E.; Zentner, Robert; Malhi, Sukhdev S.

    2014-01-01

    Estimating the greenhouse gas (GHG) emissions from agricultural systems is important in order to assess the impact of agriculture on climate change. In this study experimental data supplemented with results from a biophysical model (DNDC) were combined with life cycle assessment (LCA) to investigate the impact of management strategies on global warming potential of long-term cropping systems at two locations (Breton and Ellerslie) in Alberta, Canada. The aim was to estimate the difference in global warming potential (GWP) of cropping systems due to N fertilizer reduction and residue removal. Reducing the nitrogen fertilizer rate from 75 to 50 kg N ha −1 decreased on average the emissions of N 2 O by 39%, NO by 59% and ammonia volatilisation by 57%. No clear trend for soil CO 2 emissions was determined among cropping systems. When evaluated on a per hectare basis, cropping systems with residue removal required 6% more energy and had a little change in GWP. Conversely, when evaluated on the basis of gigajoules of harvestable biomass, residue removal resulted in 28% less energy requirement and 33% lower GWP. Reducing nitrogen fertilizer rate resulted in 18% less GWP on average for both functional units at Breton and 39% less GWP at Ellerslie. Nitrous oxide emissions contributed on average 67% to the overall GWP per ha. This study demonstrated that small changes in N fertilizer have a minimal impact on the productivity of the cropping systems but can still have a substantial environmental impact. - Highlights: • LCA was combined with DNDC model to estimate the GWP of a cropping system. • N 2 O, NO and NH 3 flux increased by 39% under the higher fertilizer rate. • A change from 75 to 50 kg N ha −1 reduced the GWP per ha and GJ basis by 18%. • N 2 O emissions contributed 67% to the overall GWP of the cropping system. • Small changes in N fertilizer can have a substantial environmental impact

  15. A generic model for estimating biomass accumulation and greenhouse gas emissions from perennial crops

    Ledo, Alicia; Heathcote, Richard; Hastings, Astley; Smith, Pete; Hillier, Jonathan

    2017-04-01

    Agriculture is essential to maintain humankind but is, at the same time, a substantial emitter of greenhouse gas (GHG) emissions. With a rising global population, the need for agriculture to provide secure food and energy supply is one of the main human challenges. At the same time, it is the only sector which has significant potential for negative emissions through the sequestration of carbon and offsetting via supply of feedstock for energy production. Perennial crops accumulate carbon during their lifetime and enhance organic soil carbon increase via root senescence and decomposition. However, inconsistency in accounting for this stored biomass undermines efforts to assess the benefits of such cropping systems when applied at scale. A consequence of this exclusion is that efforts to manage this important carbon stock are neglected. Detailed information on carbon balance is crucial to identify the main processes responsible for greenhouse gas emissions in order to develop strategic mitigation programs. Perennial crops systems represent 30% in area of total global crop systems, a considerable amount to be ignored. Furthermore, they have a major standing both in the bioenergy and global food industries. In this study, we first present a generic model to calculate the carbon balance and GHGs emissions from perennial crops, covering both food and bioenergy crops. The model is composed of two simple process-based sub-models, to cover perennial grasses and other perennial woody plants. The first is a generic individual based sub-model (IBM) covering crops in which the yield is the fruit and the plant biomass is an unharvested residue. Trees, shrubs and climbers fall into this category. The second model is a generic area based sub-model (ABM) covering perennial grasses, in which the harvested part includes some of the plant parts in which the carbon storage is accounted. Most second generation perennial bioenergy crops fall into this category. Both generic sub

  16. Trading carbon for food: global comparison of carbon stocks vs. crop yields on agricultural land.

    West, Paul C; Gibbs, Holly K; Monfreda, Chad; Wagner, John; Barford, Carol C; Carpenter, Stephen R; Foley, Jonathan A

    2010-11-16

    Expanding croplands to meet the needs of a growing population, changing diets, and biofuel production comes at the cost of reduced carbon stocks in natural vegetation and soils. Here, we present a spatially explicit global analysis of tradeoffs between carbon stocks and current crop yields. The difference among regions is striking. For example, for each unit of land cleared, the tropics lose nearly two times as much carbon (∼120 tons·ha(-1) vs. ∼63 tons·ha(-1)) and produce less than one-half the annual crop yield compared with temperate regions (1.71 tons·ha(-1)·y(-1) vs. 3.84 tons·ha(-1)·y(-1)). Therefore, newly cleared land in the tropics releases nearly 3 tons of carbon for every 1 ton of annual crop yield compared with a similar area cleared in the temperate zone. By factoring crop yield into the analysis, we specify the tradeoff between carbon stocks and crops for all areas where crops are currently grown and thereby, substantially enhance the spatial resolution relative to previous regional estimates. Particularly in the tropics, emphasis should be placed on increasing yields on existing croplands rather than clearing new lands. Our high-resolution approach can be used to determine the net effect of local land use decisions.

  17. An integrated model to simulate sown area changes for major crops at a global scale

    WU WenBin; YANG Peng; MENG ChaoYing; SHIBASAKI Ryosuke; ZHOU QingBo; TANG HuaJun; SHI Yun

    2008-01-01

    Dynamics of land use systems have attracted much attention from scientists around the world due to their ecological and socio-economic implications. An integrated model to dynamically simulate future changes in sown areas of four major crops (rice, maize, wheat and soybean) on a global scale is presented. To do so, a crop choice model was developed on the basis of Multinomial Logit (Logit) model to model land users' decisions on crop choices among a set of available alternatives with using a crop utility function. A GIS-based Environmental Policy Integrated Climate (EPIC) model was adopted to simulate the crop yields under a given geophysical environment and farming management conditions,while the International Food Policy and Agricultural Simulation (IFPSIM) model was utilized to estimate crop price in the international market. The crop choice model was linked with the GIS-based EPIC model and the IFPSIM model through data exchange. This integrated model was then validated against the FAO statistical data in 2001-2003 and the Moderate Resolution Imaging Spectroradiometer (MODIS)global land cover product in 2001. Both validation approaches indicated reliability of the model for addressing the dynamics in agricultural land use and its capability for long-term scenario analysis. Finally,the model application was designed to run over a time period of 30 a, taking the year 2000 as baseline.The model outcomes can help understand and explain the causes, locations and consequences of land use changes, and provide support for land use planning and policy making.

  18. Global crop exposure to critical high temperatures in the reproductive period: historical trends and future projections

    Gourdji, Sharon M; Sibley, Adam M; Lobell, David B

    2013-01-01

    Long-term warming trends across the globe have shifted the distribution of temperature variability, such that what was once classified as extreme heat relative to local mean conditions has become more common. This is also true for agricultural regions, where exposure to extreme heat, particularly during key growth phases such as the reproductive period, can severely damage crop production in ways that are not captured by most crop models. Here, we analyze exposure of crops to physiologically critical temperatures in the reproductive stage (T crit ), across the global harvested areas of maize, rice, soybean and wheat. Trends for the 1980–2011 period show a relatively weak correspondence (r = 0.19) between mean growing season temperature and T crit exposure trends, emphasizing the importance of separate analyses for T crit . Increasing T crit exposure in the past few decades is apparent for wheat in Central and South Asia and South America, and for maize in many diverse locations across the globe. Maize had the highest percentage (15%) of global harvested area exposed to at least five reproductive days over T crit in the 2000s, although this value is somewhat sensitive to the exact temperature used for the threshold. While there was relatively little sustained exposure to reproductive days over T crit for the other crops in the past few decades, all show increases with future warming. Using projections from climate models we estimate that by the 2030s, 31, 16, and 11% respectively of maize, rice, and wheat global harvested area will be exposed to at least five reproductive days over T crit in a typical year, with soybean much less affected. Both maize and rice exhibit non-linear increases with time, with total area exposed for rice projected to grow from 8% in the 2000s to 27% by the 2050s, and maize from 15 to 44% over the same period. While faster development should lead to earlier flowering, which would reduce reproductive extreme heat exposure for wheat on a

  19. Assessment of global grey water footprint of major food crops

    Yang, Hong; Liu, Wenfeng; Antonelli, Marta

    2016-04-01

    Agricultural production is one of the major sources of water pollution in the world. This is closely related to the excess application of fertilizers. Leaching of N and P to water bodies has caused serious degradation of water quality in many places. With the persistent increase in the demand for agricultural products, agricultural intensification evident during the past decades will continue in the future. This will lead to further increase in fertilizer application and consequently water pollution. Grey water footprint is a measure of the intensity of water pollution caused by water use for human activities. It is defined as the volume of water that is required to assimilate a load of pollutants to a freshwater body, based on natural background concentrations and water quality standards. This study conducts a global assessment of grey water footprint for major cereal crops, wheat, maize and rice. A crop model, Python-based EPIC (PEPIT), is applied to quantify the leaching of N and P from the fertilizer application in the three crops on a global scale with 0.5 degree spatial resolution. The hotspots of leaching are identified. The results suggest that, based on the definition and method of grey water footprint proposed by the World Water Footprint Network, the grey water footprint in many parts of the world has exceeded their total water resources availability. This indicates the seriousness of water pollution caused by agricultural production. However, the situation may also call for the development of a realistic measurement of grey water footprint which is more pertinent to water resources management. This paper proposes some alternatives in measuring grey water footprint and also discusses incorporation of grey water footprint assessment into water policy formulation and river basins plan development.

  20. Climate resilient crops for improving global food security and safety.

    Dhankher, Om Parkash; Foyer, Christine H

    2018-05-01

    Food security and the protection of the environment are urgent issues for global society, particularly with the uncertainties of climate change. Changing climate is predicted to have a wide range of negative impacts on plant physiology metabolism, soil fertility and carbon sequestration, microbial activity and diversity that will limit plant growth and productivity, and ultimately food production. Ensuring global food security and food safety will require an intensive research effort across the food chain, starting with crop production and the nutritional quality of the food products. Much uncertainty remains concerning the resilience of plants, soils, and associated microbes to climate change. Intensive efforts are currently underway to improve crop yields with lower input requirements and enhance the sustainability of yield through improved biotic and abiotic stress tolerance traits. In addition, significant efforts are focused on gaining a better understanding of the root/soil interface and associated microbiomes, as well as enhancing soil properties. © 2018 The Authors Plant, Cell & Environment Published by John Wiley & Sons Ltd.

  1. Targeting the right input data to improve crop modeling at global level

    Adam, M.; Robertson, R.; Gbegbelegbe, S.; Jones, J. W.; Boote, K. J.; Asseng, S.

    2012-12-01

    practices, initial soil conditions, and soil characteristics information. Management practices were represented by planting date and the amount of fertilizer, initial conditions estimates for initial nitrogen, soil water, and stable soil carbon, and soil information is based on a simplified version of the WISE database, characterized by soil organic matter, texture and soil depth. We considered these factors as the most important determinants of nutrient supply to crops during their growing season. Our first global results demonstrate that the model is most sensitive to the initial conditions in terms of soil carbon and nitrogen (CN): wheat yields decreased by 45% when soil CN is null and increase by 15% when twice the soil CN content of the reference run is used. The yields did not appear to be very sensitive to initial soil water conditions, varying from 0% yield increase when initial soil water is set to wilting point to 6% yield increase when it was set to field capacity. They are slightly sensitive to nitrogen application: 8% yield decrease when no N is applied to 9% yield increase when 150 kg.ha-1 is applied. However, with closer examination of results, the model is more sensitive to nitrogen application than to initial soil CN content in Vietnam, Thailand and Japan compared to the rest of the world. More analyses per region and results on the planting dates and soil properties will be presented.

  2. Impact of management strategies on the global warming potential at the cropping system level.

    Goglio, Pietro; Grant, Brian B; Smith, Ward N; Desjardins, Raymond L; Worth, Devon E; Zentner, Robert; Malhi, Sukhdev S

    2014-08-15

    Estimating the greenhouse gas (GHG) emissions from agricultural systems is important in order to assess the impact of agriculture on climate change. In this study experimental data supplemented with results from a biophysical model (DNDC) were combined with life cycle assessment (LCA) to investigate the impact of management strategies on global warming potential of long-term cropping systems at two locations (Breton and Ellerslie) in Alberta, Canada. The aim was to estimate the difference in global warming potential (GWP) of cropping systems due to N fertilizer reduction and residue removal. Reducing the nitrogen fertilizer rate from 75 to 50 kg N ha(-1) decreased on average the emissions of N2O by 39%, NO by 59% and ammonia volatilisation by 57%. No clear trend for soil CO2 emissions was determined among cropping systems. When evaluated on a per hectare basis, cropping systems with residue removal required 6% more energy and had a little change in GWP. Conversely, when evaluated on the basis of gigajoules of harvestable biomass, residue removal resulted in 28% less energy requirement and 33% lower GWP. Reducing nitrogen fertilizer rate resulted in 18% less GWP on average for both functional units at Breton and 39% less GWP at Ellerslie. Nitrous oxide emissions contributed on average 67% to the overall GWP per ha. This study demonstrated that small changes in N fertilizer have a minimal impact on the productivity of the cropping systems but can still have a substantial environmental impact. Crown Copyright © 2014. Published by Elsevier B.V. All rights reserved.

  3. Impact of management strategies on the global warming potential at the cropping system level

    Goglio, Pietro; Grant, Brian B.; Smith, Ward N. [Eastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food Canada, K.W. Neatby Building, Ottawa, Ontario K1A 0C6 (Canada); Desjardins, Raymond L., E-mail: ray.desjardins@agr.gc.ca [Eastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food Canada, K.W. Neatby Building, Ottawa, Ontario K1A 0C6 (Canada); Worth, Devon E. [Eastern Cereal and Oilseed Research Centre, Agriculture and Agri-Food Canada, K.W. Neatby Building, Ottawa, Ontario K1A 0C6 (Canada); Zentner, Robert [Swift Current Research Station, Swift Current, Saskatchewan S0E 1A0 (Canada); Malhi, Sukhdev S. [Melfort Research Farm, PO Box 1240, Melfort, Saskatchewan S0E 1A0 (Canada)

    2014-08-15

    Estimating the greenhouse gas (GHG) emissions from agricultural systems is important in order to assess the impact of agriculture on climate change. In this study experimental data supplemented with results from a biophysical model (DNDC) were combined with life cycle assessment (LCA) to investigate the impact of management strategies on global warming potential of long-term cropping systems at two locations (Breton and Ellerslie) in Alberta, Canada. The aim was to estimate the difference in global warming potential (GWP) of cropping systems due to N fertilizer reduction and residue removal. Reducing the nitrogen fertilizer rate from 75 to 50 kg N ha{sup −1} decreased on average the emissions of N{sub 2}O by 39%, NO by 59% and ammonia volatilisation by 57%. No clear trend for soil CO{sub 2} emissions was determined among cropping systems. When evaluated on a per hectare basis, cropping systems with residue removal required 6% more energy and had a little change in GWP. Conversely, when evaluated on the basis of gigajoules of harvestable biomass, residue removal resulted in 28% less energy requirement and 33% lower GWP. Reducing nitrogen fertilizer rate resulted in 18% less GWP on average for both functional units at Breton and 39% less GWP at Ellerslie. Nitrous oxide emissions contributed on average 67% to the overall GWP per ha. This study demonstrated that small changes in N fertilizer have a minimal impact on the productivity of the cropping systems but can still have a substantial environmental impact. - Highlights: • LCA was combined with DNDC model to estimate the GWP of a cropping system. • N{sub 2}O, NO and NH{sub 3} flux increased by 39% under the higher fertilizer rate. • A change from 75 to 50 kg N ha{sup −1} reduced the GWP per ha and GJ basis by 18%. • N{sub 2}O emissions contributed 67% to the overall GWP of the cropping system. • Small changes in N fertilizer can have a substantial environmental impact.

  4. Global and Time-Resolved Monitoring of Crop Photosynthesis with Chlorophyll Fluorescence

    Guanter, Luis; Zhang, Yongguang; Jung, Martin; Joiner, Joanna; Voigt, Maximilian; Berry, Joseph A.; Frankenberg, Christian; Huete, Alfredo R.; Zarco-Tejada, Pablo; Lee, Jung-Eun; hide

    2014-01-01

    Photosynthesis is the process by which plants harvest sunlight to produce sugars from carbon dioxide and water. It is the primary source of energy for all life on Earth; hence it is important to understand how this process responds to climate change and human impact. However, model-based estimates of gross primary production (GPP, output from photosynthesis) are highly uncertain, in particular over heavily managed agricultural areas. Recent advances in spectroscopy enable the space-based monitoring of sun-induced chlorophyll fluorescence (SIF) from terrestrial plants. Here we demonstrate that spaceborne SIF retrievals provide a direct measure of the GPP of cropland and grassland ecosystems. Such a strong link with crop photosynthesis is not evident for traditional remotely sensed vegetation indices, nor for more complex carbon cycle models. We use SIF observations to provide a global perspective on agricultural productivity. Our SIF-based crop GPP estimates are 50-75% higher than results from state-of-the-art carbon cycle models over, for example, the US Corn Belt and the Indo-Gangetic Plain, implying that current models severely underestimate the role of management. Our results indicate that SIF data can help us improve our global models for more accurate projections of agricultural productivity and climate impact on crop yields. Extension of our approach to other ecosystems, along with increased observational capabilities for SIF in the near future, holds the prospect of reducing uncertainties in the modeling of the current and future carbon cycle.

  5. GPP estimates in a biodiesel crop using MERIS products

    Sánchez, M. L.; Pardo, N.; Pérez, I.; García, M. A.; Paredes, V.

    2012-04-01

    Greenhouse gas emissions in Spain in 2008-2009 were 34.3 % higher than the base-year level, significantly above the burden-sharing target of 15 % for the period 2008-2012. Based on this result, our country will need to make a major effort to meet the committed target on time using domestic measures as well as others foreseen in the Kyoto Protocol, such as LULUFC activities. In this framework, agrofuels, in other words biofuels produced by crops that contain high amounts of vegetable oil such as sorghum, sunflower, rape seed and jatropha, appear to be an interesting mitigation alternative. Bearing in mind the meteorological conditions in Spain, sunflower and rape seed in particular are considered the most viable crops. Sunflower cultivated surface in Spain has remained fairly constant in recent years, in contrast to rapeseed crop surface which, although still scarce, has followed an increasing trend. In order to assess rape seed ability as a CO2 sink as well as to describe GPP dynamic evolution, we installed an eddy correlation station in an agricultural plot of the Spanish plateau. Measurements at the plot consisted of 30-min NEE flux measurements (using a LI-7500 and a METEK USA-1 sonic anemometer) as well as other common meteorological variables. Measurements were performed from March to October. This paper presents the results of the GPP 8-d estimated values using a Light Use Efficiency Model, LUE. Input data for the LUE model were the FPAR 8-d products supplied by MERIS, the PAR in situ measurements, and a scalar f varying, between 0 and 1, to take into account the reduction of the maximum PAR conversion efficiency, ɛ0, under limiting environmental conditions. The f values were assumed to be dependent on air temperature and the evaporative fraction, EF, which was considered as a proxy of soil moisture. ɛ0, a key parameter, which depends on biome types, was derived through the results of a linear regression fit between the GPP 8-d eddy covariance composites

  6. The shared and unique values of optical, fluorescence, thermal and microwave satellite data for estimating large-scale crop yields

    Large-scale crop monitoring and yield estimation are important for both scientific research and practical applications. Satellite remote sensing provides an effective means for regional and global cropland monitoring, particularly in data-sparse regions that lack reliable ground observations and rep...

  7. Global Population Density Grid Time Series Estimates

    National Aeronautics and Space Administration — Global Population Density Grid Time Series Estimates provide a back-cast time series of population density grids based on the year 2000 population grid from SEDAC's...

  8. A future scenario of the global regulatory landscape regarding genome-edited crops

    Araki, Motoko

    2017-01-01

    ABSTRACT The global agricultural landscape regarding the commercial cultivation of genetically modified (GM) crops is mosaic. Meanwhile, a new plant breeding technique, genome editing is expected to make genetic engineering-mediated crop breeding more socially acceptable because it can be used to develop crop varieties without introducing transgenes, which have hampered the regulatory review and public acceptance of GM crops. The present study revealed that product- and process-based concepts have been implemented to regulate GM crops in 30 countries. Moreover, this study analyzed the regulatory responses to genome-edited crops in the USA, Argentina, Sweden and New Zealand. The findings suggested that countries will likely be divided in their policies on genome-edited crops: Some will deregulate transgene-free crops, while others will regulate all types of crops that have been modified by genome editing. These implications are discussed from the viewpoint of public acceptance. PMID:27960622

  9. Improvements in crop water productivity increase water sustainability and food security—a global analysis

    Brauman, Kate A; Foley, Jonathan A; Siebert, Stefan

    2013-01-01

    Irrigation consumes more water than any other human activity, and thus the challenges of water sustainability and food security are closely linked. To evaluate how water resources are used for food production, we examined global patterns of water productivity—food produced (kcal) per unit of water (l) consumed. We document considerable variability in crop water productivity globally, not only across different climatic zones but also within climatic zones. The least water productive systems are disproportionate freshwater consumers. On precipitation-limited croplands, we found that ∼40% of water consumption goes to production of just 20% of food calories. Because in many cases crop water productivity is well below optimal levels, in many cases farmers have substantial opportunities to improve water productivity. To demonstrate the potential impact of management interventions, we calculated that raising crop water productivity in precipitation-limited regions to the 20th percentile of productivity would increase annual production on rainfed cropland by enough to provide food for an estimated 110 million people, and water consumption on irrigated cropland would be reduced enough to meet the annual domestic water demands of nearly 1.4 billion people. (letter)

  10. Heterogeneous global crop yield response to biochar: a meta-regression analysis

    Crane-Droesch, Andrew; Torn, Margaret S; Abiven, Samuel; Jeffery, Simon

    2013-01-01

    Biochar may contribute to climate change mitigation at negative cost by sequestering photosynthetically fixed carbon in soil while increasing crop yields. The magnitude of biochar’s potential in this regard will depend on crop yield benefits, which have not been well-characterized across different soils and biochars. Using data from 84 studies, we employ meta-analytical, missing data, and semiparametric statistical methods to explain heterogeneity in crop yield responses across different soils, biochars, and agricultural management factors, and then estimate potential changes in yield across different soil environments globally. We find that soil cation exchange capacity and organic carbon were strong predictors of yield response, with low cation exchange and low carbon associated with positive response. We also find that yield response increases over time since initial application, compared to non-biochar controls. High reported soil clay content and low soil pH were weaker predictors of higher yield response. No biochar parameters in our dataset—biochar pH, percentage carbon content, or temperature of pyrolysis—were significant predictors of yield impacts. Projecting our fitted model onto a global soil database, we find the largest potential increases in areas with highly weathered soils, such as those characterizing much of the humid tropics. Richer soils characterizing much of the world’s important agricultural areas appear to be less likely to benefit from biochar. (letter)

  11. Vegetation index-based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems

    Glenn, E.P.; Neale, C. M. U.; Hunsaker, D.J.; Nagler, P.L.

    2011-01-01

    Crop coefficients were developed to determine crop water needs based on the evapotranspiration (ET) of a reference crop under a given set of meteorological conditions. Starting in the 1980s, crop coefficients developed through lysimeter studies or set by expert opinion began to be supplemented by remotely sensed vegetation indices (VI) that measured the actual status of the crop on a field-by-field basis. VIs measure the density of green foliage based on the reflectance of visible and near infrared (NIR) light from the canopy, and are highly correlated with plant physiological processes that depend on light absorption by a canopy such as ET and photosynthesis. Reflectance-based crop coefficients have now been developed for numerous individual crops, including corn, wheat, alfalfa, cotton, potato, sugar beet, vegetables, grapes and orchard crops. Other research has shown that VIs can be used to predict ET over fields of mixed crops, allowing them to be used to monitor ET over entire irrigation districts. VI-based crop coefficients can help reduce agricultural water use by matching irrigation rates to the actual water needs of a crop as it grows instead of to a modeled crop growing under optimal conditions. Recently, the concept has been applied to natural ecosystems at the local, regional and continental scales of measurement, using time-series satellite data from the MODIS sensors on the Terra satellite. VIs or other visible-NIR band algorithms are combined with meteorological data to predict ET in numerous biome types, from deserts, to arctic tundra, to tropical rainforests. These methods often closely match ET measured on the ground at the global FluxNet array of eddy covariance moisture and carbon flux towers. The primary advantage of VI methods for estimating ET is that transpiration is closely related to radiation absorbed by the plant canopy, which is closely related to VIs. The primary disadvantage is that they cannot capture stress effects or soil

  12. The estimation of soil parameters using observations on crop biophysical variables and the crop model STICS improve the predictions of agro environmental variables.

    Varella, H.-V.

    2009-04-01

    Dynamic crop models are very useful to predict the behavior of crops in their environment and are widely used in a lot of agro-environmental work. These models have many parameters and their spatial application require a good knowledge of these parameters, especially of the soil parameters. These parameters can be estimated from soil analysis at different points but this is very costly and requires a lot of experimental work. Nevertheless, observations on crops provided by new techniques like remote sensing or yield monitoring, is a possibility for estimating soil parameters through the inversion of crop models. In this work, the STICS crop model is studied for the wheat and the sugar beet and it includes more than 200 parameters. After a previous work based on a large experimental database for calibrate parameters related to the characteristics of the crop, a global sensitivity analysis of the observed variables (leaf area index LAI and absorbed nitrogen QN provided by remote sensing data, and yield at harvest provided by yield monitoring) to the soil parameters is made, in order to determine which of them have to be estimated. This study was made in different climatic and agronomic conditions and it reveals that 7 soil parameters (4 related to the water and 3 related to the nitrogen) have a clearly influence on the variance of the observed variables and have to be therefore estimated. For estimating these 7 soil parameters, a Bayesian data assimilation method is chosen (because of available prior information on these parameters) named Importance Sampling by using observations, on wheat and sugar beet crop, of LAI and QN at various dates and yield at harvest acquired on different climatic and agronomic conditions. The quality of parameter estimation is then determined by comparing the result of parameter estimation with only prior information and the result with the posterior information provided by the Bayesian data assimilation method. The result of the

  13. Global impacts of surface ozone changes on crop yields and land use

    Chuwah, Clifford; van Noije, Twan; van Vuuren, Detlef P.; Stehfest, Elke; Hazeleger, Wilco

    2015-01-01

    Exposure to surface ozone has detrimental impacts on vegetation and crop yields. In this study, we estimate ozone impacts on crop production and subsequent impacts on land use in the 2005-2050 period using results of the TM5 atmospheric chemistry and IMAGE integrated assessment model. For the crops

  14. GEOGLAM Crop Assessment Tool: Adapting from global agricultural monitoring to food security monitoring

    Humber, M. L.; Becker-Reshef, I.; Nordling, J.; Barker, B.; McGaughey, K.

    2014-12-01

    The GEOGLAM Crop Monitor's Crop Assessment Tool was released in August 2013 in support of the GEOGLAM Crop Monitor's objective to develop transparent, timely crop condition assessments in primary agricultural production areas, highlighting potential hotspots of stress/bumper crops. The Crop Assessment Tool allows users to view satellite derived products, best available crop masks, and crop calendars (created in collaboration with GEOGLAM Crop Monitor partners), then in turn submit crop assessment entries detailing the crop's condition, drivers, impacts, trends, and other information. Although the Crop Assessment Tool was originally intended to collect data on major crop production at the global scale, the types of data collected are also relevant to the food security and rangelands monitoring communities. In line with the GEOGLAM Countries at Risk philosophy of "foster[ing] the coordination of product delivery and capacity building efforts for national and regional organizations, and the development of harmonized methods and tools", a modified version of the Crop Assessment Tool is being developed for the USAID Famine Early Warning Systems Network (FEWS NET). As a member of the Countries at Risk component of GEOGLAM, FEWS NET provides agricultural monitoring, timely food security assessments, and early warnings of potential significant food shortages focusing specifically on countries at risk of food security emergencies. While the FEWS NET adaptation of the Crop Assessment Tool focuses on crop production in the context of food security rather than large scale production, the data collected is nearly identical to the data collected by the Crop Monitor. If combined, the countries monitored by FEWS NET and GEOGLAM Crop Monitor would encompass over 90 countries representing the most important regions for crop production and food security.

  15. Estimating national crop yield potential and the relevance of weather data sources

    Van Wart, Justin

    2011-12-01

    To determine where, when, and how to increase yields, researchers often analyze the yield gap (Yg), the difference between actual current farm yields and crop yield potential. Crop yield potential (Yp) is the yield of a crop cultivar grown under specific management limited only by temperature and solar radiation and also by precipitation for water limited yield potential (Yw). Yp and Yw are critical components of Yg estimations, but are very difficult to quantify, especially at larger scales because management data and especially daily weather data are scarce. A protocol was developed to estimate Yp and Yw at national scales using site-specific weather, soils and management data. Protocol procedures and inputs were evaluated to determine how to improve accuracy of Yp, Yw and Yg estimates. The protocol was also used to evaluate raw, site-specific and gridded weather database sources for use in simulations of Yp or Yw. The protocol was applied to estimate crop Yp in US irrigated maize and Chinese irrigated rice and Yw in US rainfed maize and German rainfed wheat. These crops and countries account for >20% of global cereal production. The results have significant implications for past and future studies of Yp, Yw and Yg. Accuracy of national long-term average Yp and Yw estimates was significantly improved if (i) > 7 years of simulations were performed for irrigated and > 15 years for rainfed sites, (ii) > 40% of nationally harvested area was within 100 km of all simulation sites, (iii) observed weather data coupled with satellite derived solar radiation data were used in simulations, and (iv) planting and harvesting dates were specified within +/- 7 days of farmers actual practices. These are much higher standards than have been applied in national estimates of Yp and Yw and this protocol is a substantial step in making such estimates more transparent, robust, and straightforward. Finally, this protocol may be a useful tool for understanding yield trends and directing

  16. Global Status of Genetically Modified Crops: Current Trends and Prospects

    Hautea, Randy A.

    2002-01-01

    Modern biotechnology-facilitated crop improvement is undoubtedly one of the most significant technological developments in agriculture. The first wave of genetically-modified (GM) or transgenic crops include cultivars with important input traits such as herbicide tolerance and insect resistance. Future products are expected to provide benefits that could include tolerance to environmental stresses and enhanced nutritional content, which can be particularly valuable in crops that are important...

  17. The Global Pipeline of GM crops: an outlook for 2020

    PARISI CLAUDIA; TILLIE PASCAL; RODRIGUEZ CEREZO Emilio

    2015-01-01

    This study presents the worldwide pipeline of genetically modified (GM) crops that are likely to be commercialized and cultivated by farmers in the short to medium term. The database presented has been built by collecting information about the status of GM crops both in the regulatory pipeline of national biotechnology agencies and in the advanced phase of development by technology providers. Particular attention will be given to the 2020 outlook of new crops and traits, with a special fo...

  18. Estimating Hydrologic Fluxes, Crop Water Use, and Agricultural Land Area in China using Data Assimilation

    Smith, Tiziana; McLaughlin, Dennis B.; Hoisungwan, Piyatida

    2016-04-01

    Crop production has significantly altered the terrestrial environment by changing land use and by altering the water cycle through both co-opted rainfall and surface water withdrawals. As the world's population continues to grow and individual diets become more resource-intensive, the demand for food - and the land and water necessary to produce it - will continue to increase. High-resolution quantitative data about water availability, water use, and agricultural land use are needed to develop sustainable water and agricultural planning and policies. However, existing data covering large areas with high resolution are susceptible to errors and can be physically inconsistent. China is an example of a large area where food demand is expected to increase and a lack of data clouds the resource management dialogue. Some assert that China will have insufficient land and water resources to feed itself, posing a threat to global food security if they seek to increase food imports. Others believe resources are plentiful. Without quantitative data, it is difficult to discern if these concerns are realistic or overly dramatized. This research presents a quantitative approach using data assimilation techniques to characterize hydrologic fluxes, crop water use (defined as crop evapotranspiration), and agricultural land use at 0.5 by 0.5 degree resolution and applies the methodology in China using data from around the year 2000. The approach uses the principles of water balance and of crop water requirements to assimilate existing data with a least-squares estimation technique, producing new estimates of water and land use variables that are physically consistent while minimizing differences from measured data. We argue that this technique for estimating water fluxes and agricultural land use can provide a useful basis for resource management modeling and policy, both in China and around the world.

  19. A Global Geospatial Ecosystem Services Estimate of Urban Agriculture

    Clinton, Nicholas; Stuhlmacher, Michelle; Miles, Albie; Uludere Aragon, Nazli; Wagner, Melissa; Georgescu, Matei; Herwig, Chris; Gong, Peng

    2018-01-01

    Though urban agriculture (UA), defined here as growing of crops in cities, is increasing in popularity and importance globally, little is known about the aggregate benefits of such natural capital in built-up areas. Here, we introduce a quantitative framework to assess global aggregate ecosystem services from existing vegetation in cities and an intensive UA adoption scenario based on data-driven estimates of urban morphology and vacant land. We analyzed global population, urban, meteorological, terrain, and Food and Agriculture Organization (FAO) datasets in Google Earth Engine to derive global scale estimates, aggregated by country, of services provided by UA. We estimate the value of four ecosystem services provided by existing vegetation in urban areas to be on the order of 33 billion annually. We project potential annual food production of 100-180 million tonnes, energy savings ranging from 14 to 15 billion kilowatt hours, nitrogen sequestration between 100,000 and 170,000 tonnes, and avoided storm water runoff between 45 and 57 billion cubic meters annually. In addition, we estimate that food production, nitrogen fixation, energy savings, pollination, climate regulation, soil formation and biological control of pests could be worth as much as 80-160 billion annually in a scenario of intense UA implementation. Our results demonstrate significant country-to-country variability in UA-derived ecosystem services and reduction of food insecurity. These estimates represent the first effort to consistently quantify these incentives globally, and highlight the relative spatial importance of built environments to act as change agents that alleviate mounting concerns associated with global environmental change and unsustainable development.

  20. Simulating the effects of climate and agricultural management practices on global crop yield

    Deryng, D.; Sacks, W. J.; Barford, C. C.; Ramankutty, N.

    2011-06-01

    Climate change is expected to significantly impact global food production, and it is important to understand the potential geographic distribution of yield losses and the means to alleviate them. This study presents a new global crop model, PEGASUS 1.0 (Predicting Ecosystem Goods And Services Using Scenarios) that integrates, in addition to climate, the effect of planting dates and cultivar choices, irrigation, and fertilizer application on crop yield for maize, soybean, and spring wheat. PEGASUS combines carbon dynamics for crops with a surface energy and soil water balance model. It also benefits from the recent development of a suite of global data sets and analyses that serve as model inputs or as calibration data. These include data on crop planting and harvesting dates, crop-specific irrigated areas, a global analysis of yield gaps, and harvested area and yield of major crops. Model results for present-day climate and farm management compare reasonably well with global data. Simulated planting and harvesting dates are within the range of crop calendar observations in more than 75% of the total crop-harvested areas. Correlation of simulated and observed crop yields indicates a weighted coefficient of determination, with the weighting based on crop-harvested area, of 0.81 for maize, 0.66 for soybean, and 0.45 for spring wheat. We found that changes in temperature and precipitation as predicted by global climate models for the 2050s lead to a global yield reduction if planting and harvesting dates remain unchanged. However, adapting planting dates and cultivar choices increases yield in temperate regions and avoids 7-18% of global losses.

  1. Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images

    Thomas Moeckel

    2018-05-01

    Full Text Available 3D point cloud analysis of imagery collected by unmanned aerial vehicles (UAV has been shown to be a valuable tool for estimation of crop phenotypic traits, such as plant height, in several species. Spatial information about these phenotypic traits can be used to derive information about other important crop characteristics, like fresh biomass yield, which could not be derived directly from the point clouds. Previous approaches have often only considered single date measurements using a single point cloud derived metric for the respective trait. Furthermore, most of the studies focused on plant species with a homogenous canopy surface. The aim of this study was to assess the applicability of UAV imagery for capturing crop height information of three vegetables (crops eggplant, tomato, and cabbage with a complex vegetation canopy surface during a complete crop growth cycle to infer biomass. Additionally, the effect of crop development stage on the relationship between estimated crop height and field measured crop height was examined. Our study was conducted in an experimental layout at the University of Agricultural Science in Bengaluru, India. For all the crops, the crop height and the biomass was measured at five dates during one crop growth cycle between February and May 2017 (average crop height was 42.5, 35.5, and 16.0 cm for eggplant, tomato, and cabbage. Using a structure from motion approach, a 3D point cloud was created for each crop and sampling date. In total, 14 crop height metrics were extracted from the point clouds. Machine learning methods were used to create prediction models for vegetable crop height. The study demonstrates that the monitoring of crop height using an UAV during an entire growing period results in detailed and precise estimates of crop height and biomass for all three crops (R2 ranging from 0.87 to 0.97, bias ranging from −0.66 to 0.45 cm. The effect of crop development stage on the predicted crop height was

  2. Estimating riparian and agricultural evapotranspiration by reference crop evapotranspiration and MODIS Enhanced Vegetation Index

    Nagler, Pamela L.; Glenn, Edward P.; Nguyen, Uyen; Scott, Russell; Doody, Tania

    2013-01-01

    Dryland river basins frequently support both irrigated agriculture and riparian vegetation and remote sensing methods are needed to monitor water use by both crops and natural vegetation in irrigation districts. We developed an algorithm for estimating actual evapotranspiration (ETa) based on the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectrometer (MODIS) sensor on the EOS-1 Terra satellite and locally-derived measurements of reference crop ET (ETo). The algorithm was calibrated with five years of ETa data from three eddy covariance flux towers set in riparian plant associations on the upper San Pedro River, Arizona, supplemented with ETa data for alfalfa and cotton from the literature. The algorithm was based on an equation of the form ETa = ETo [a(1 − e−bEVI) − c], where the term (1 − e−bEVI) is derived from the Beer-Lambert Law to express light absorption by a canopy, with EVI replacing leaf area index as an estimate of the density of light-absorbing units. The resulting algorithm capably predicted ETa across riparian plants and crops (r2 = 0.73). It was then tested against water balance data for five irrigation districts and flux tower data for two riparian zones for which season-long or multi-year ETa data were available. Predictions were within 10% of measured results in each case, with a non-significant (P = 0.89) difference between mean measured and modeled ETa of 5.4% over all validation sites. Validation and calibration data sets were combined to present a final predictive equation for application across crops and riparian plant associations for monitoring individual irrigation districts or for conducting global water use assessments of mixed agricultural and riparian biomes.

  3. Combining Remote Sensing imagery of both fine and coarse spatial resolution to Estimate Crop Evapotranspiration and quantifying its Influence on Crop Growth Monitoring.

    Sepulcre-Cantó, Guadalupe; Gellens-Meulenberghs, Françoise; Arboleda, Alirio; Duveiller, Gregory; Piccard, Isabelle; de Wit, Allard; Tychon, Bernard; Bakary, Djaby; Defourny, Pierre

    2010-05-01

    This study has been carried out in the framework of the GLOBAM -Global Agricultural Monitoring system by integration of earth observation and modeling techniques- project whose objective is to fill the methodological gap between the state of the art of local crop monitoring and the operational requirements of the global monitoring system programs. To achieve this goal, the research aims to develop an integrated approach using remote sensing and crop growth modeling. Evapotranspiration (ET) is a valuable parameter in the crop monitoring context since it provides information on the plant water stress status, which strongly influences crop development and, by extension, crop yield. To assess crop evapotranspiration over the GLOBAM study areas (300x300 km sites in Northern Europe and Central Ethiopia), a Soil-Vegetation-Atmosphere Transfer (SVAT) model forced with remote sensing and numerical weather prediction data has been used. This model runs at pre-operational level in the framework of the EUMETSAT LSA-SAF (Land Surface Analysis Satellite Application Facility) using SEVIRI and ECMWF data, as well as the ECOCLIMAP database to characterize the vegetation. The model generates ET images at the Meteosat Second Generation (MSG) spatial resolution (3 km at subsatellite point),with a temporal resolution of 30 min and monitors the entire MSG disk which covers Europe, Africa and part of Sud America . The SVAT model was run for 2007 using two approaches. The first approach is at the standard pre-operational mode. The second incorporates remote sensing information at various spatial resolutions going from LANDSAT (30m) to SEVIRI (3-5 km) passing by AWIFS (56m) and MODIS (250m). Fine spatial resolution data consists of crop type classification which enable to identify areas where pure crop specific MODIS time series can be compiled and used to derive Leaf Area Index estimations for the most important crops (wheat and maize). The use of this information allowed to characterize

  4. Crop residue inventory estimates for Texas High Plains cotton

    Interest in the use of cotton crop by-products for the production of bio-fuels and value-added products is increasing. Research documenting the availability of cotton crop by-products after machine harvest is needed. The objectives of this work were to document the total biomass production for moder...

  5. Anisotropic Density Estimation in Global Illumination

    Schjøth, Lars

    2009-01-01

    Density estimation employed in multi-pass global illumination algorithms gives cause to a trade-off problem between bias and noise. The problem is seen most evident as blurring of strong illumination features. This thesis addresses the problem, presenting four methods that reduce both noise...

  6. Non-bee insects are important contributors to global crop pollination

    Rader, Romina; Bartomeus, Ignasi; Garibaldi, Lucas A.; Kleijn, David; Scheper, Jeroen

    2016-01-01

    Wild andmanaged bees arewell documented as effective pollinators of global crops of economic importance. However, the contributions by pollinators other than bees have been little explored despite their potential to contribute to crop production and stability in the face of environmental change.

  7. Root biomass in cereals, catch crops and weeds can be reliably estimated without considering aboveground biomass

    Hu, Teng; Sørensen, Peter; Wahlström, Ellen Margrethe

    2018-01-01

    and management factors may affect this allometric relationship making such estimates uncertain and biased. Therefore, we aimed to explore how root biomass for typical cereal crops, catch crops and weeds could most reliably be estimated. Published and unpublished data on aboveground and root biomass (corrected...

  8. Erratum to: Estimating the crop response to fertilizer nitrogen residues in long-continued field experiments

    Petersen, Jens; Thomsen, Ingrid Kaag; Mattson, L

    2012-01-01

    Knowledge of the cumulated effect of long-continued nitrogen (N) inputs is important for both agronomic and environmental reasons. However, only little attention has been paid to estimate the crop response to mineral fertilizer N residues. Before interpreting estimates for the crop response...

  9. Evaluation of small area crop estimation techniques using LANDSAT- and ground-derived data. [South Dakota

    Amis, M. L.; Martin, M. V.; Mcguire, W. G.; Shen, S. S. (Principal Investigator)

    1982-01-01

    Studies completed in fiscal year 1981 in support of the clustering/classification and preprocessing activities of the Domestic Crops and Land Cover project. The theme throughout the study was the improvement of subanalysis district (usually county level) crop hectarage estimates, as reflected in the following three objectives: (1) to evaluate the current U.S. Department of Agriculture Statistical Reporting Service regression approach to crop area estimation as applied to the problem of obtaining subanalysis district estimates; (2) to develop and test alternative approaches to subanalysis district estimation; and (3) to develop and test preprocessing techniques for use in improving subanalysis district estimates.

  10. The California Biomass Crop Adoption Model estimates biofuel feedstock crop production across diverse agro-ecological zones within the state, under different future climates

    Kaffka, S.; Jenner, M.; Bucaram, S.; George, N.

    2012-12-01

    Both regulators and businesses need realistic estimates for the potential production of biomass feedstocks for biofuels and bioproducts. This includes the need to understand how climate change will affect mid-tem and longer-term crop performance and relative advantage. The California Biomass Crop Adoption Model is a partial mathematical programming optimization model that estimates the profit level needed for new crop adoption, and the crop(s) displaced when a biomass feedstock crop is added to the state's diverse set of cropping systems, in diverse regions of the state. Both yield and crop price, as elements of profit, can be varied. Crop adoption is tested against current farmer preferences derived from analysis of 10 years crop production data for all crops produced in California, collected by the California Department of Pesticide Regulation. Analysis of this extensive data set resulted in 45 distinctive, representative farming systems distributed across the state's diverse agro-ecological regions. Estimated yields and water use are derived from field trials combined with crop simulation, reported elsewhere. Crop simulation is carried out under different weather and climate assumptions. Besides crop adoption and displacement, crop resource use is also accounted, derived from partial budgets used for each crop's cost of production. Systematically increasing biofuel crop price identified areas of the state where different types of crops were most likely to be adopted. Oilseed crops like canola that can be used for biodiesel production had the greatest potential to be grown in the Sacramento Valley and other northern regions, while sugar beets (for ethanol) had the greatest potential in the northern San Joaquin Valley region, and sweet sorghum in the southern San Joaquin Valley. Up to approximately 10% of existing annual cropland in California was available for new crop adoption. New crops are adopted if the entire cropping system becomes more profitable. In

  11. Estimating seed crops of conifer and hardwood species

    Philip M. McDonald

    1992-01-01

    Cone, acorn, and berry crops of ponderosa pine (Pinus ponderosa Dougl. ex Laws. var. ponderosa), sugar pine (Pinus lambertiana Dougl.), Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco), California white fir (Abies concolor var. lowiana (Gord...

  12. Global Simulation of Bioenergy Crop Productivity: Analytical framework and Case Study for Switchgrass

    Nair, S. Surendran [University of Tennessee, Knoxville (UTK); Nichols, Jeff A. {Cyber Sciences} [ORNL; Post, Wilfred M [ORNL; Wang, Dali [ORNL; Wullschleger, Stan D [ORNL; Kline, Keith L [ORNL; Wei, Yaxing [ORNL; Singh, Nagendra [ORNL; Kang, Shujiang [ORNL

    2014-01-01

    Contemporary global assessments of the deployment potential and sustainability aspects of biofuel crops lack quantitative details. This paper describes an analytical framework capable of meeting the challenges associated with global scale agro-ecosystem modeling. We designed a modeling platform for bioenergy crops, consisting of five major components: (i) standardized global natural resources and management data sets, (ii) global simulation unit and management scenarios, (iii) model calibration and validation, (iv) high-performance computing (HPC) modeling, and (v) simulation output processing and analysis. A case study with the HPC- Environmental Policy Integrated Climate model (HPC-EPIC) to simulate a perennial bioenergy crop, switchgrass (Panicum virgatum L.) and global biomass feedstock analysis on grassland demonstrates the application of this platform. The results illustrate biomass feedstock variability of switchgrass and provide insights on how the modeling platform can be expanded to better assess sustainable production criteria and other biomass crops. Feedstock potentials on global grasslands and within different countries are also shown. Future efforts involve developing databases of productivity, implementing global simulations for other bioenergy crops (e.g. miscanthus, energycane and agave), and assessing environmental impacts under various management regimes. We anticipated this platform will provide an exemplary tool and assessment data for international communities to conduct global analysis of biofuel biomass feedstocks and sustainability.

  13. The AgMIP GRIDded Crop Modeling Initiative (AgGRID) and the Global Gridded Crop Model Intercomparison (GGCMI)

    Elliott, Joshua; Muller, Christoff

    2015-01-01

    Climate change is a significant risk for agricultural production. Even under optimistic scenarios for climate mitigation action, present-day agricultural areas are likely to face significant increases in temperatures in the coming decades, in addition to changes in precipitation, cloud cover, and the frequency and duration of extreme heat, drought, and flood events (IPCC, 2013). These factors will affect the agricultural system at the global scale by impacting cultivation regimes, prices, trade, and food security (Nelson et al., 2014a). Global-scale evaluation of crop productivity is a major challenge for climate impact and adaptation assessment. Rigorous global assessments that are able to inform planning and policy will benefit from consistent use of models, input data, and assumptions across regions and time that use mutually agreed protocols designed by the modeling community. To ensure this consistency, large-scale assessments are typically performed on uniform spatial grids, with spatial resolution of typically 10 to 50 km, over specified time-periods. Many distinct crop models and model types have been applied on the global scale to assess productivity and climate impacts, often with very different results (Rosenzweig et al., 2014). These models are based to a large extent on field-scale crop process or ecosystems models and they typically require resolved data on weather, environmental, and farm management conditions that are lacking in many regions (Bondeau et al., 2007; Drewniak et al., 2013; Elliott et al., 2014b; Gueneau et al., 2012; Jones et al., 2003; Liu et al., 2007; M¨uller and Robertson, 2014; Van den Hoof et al., 2011;Waha et al., 2012; Xiong et al., 2014). Due to data limitations, the requirements of consistency, and the computational and practical limitations of running models on a large scale, a variety of simplifying assumptions must generally be made regarding prevailing management strategies on the grid scale in both the baseline and

  14. A global overview of biotech (GM) crops: adoption, impact and future prospects.

    James, Clive

    2010-01-01

    In the early 1990s, some were skeptical that genetically modified (GM) crops, now referred to as biotech crops, could deliver improved products and make an impact at the farm level. There was even more skepticism that developing countries would adopt biotech crops. The adoption of and commercialization of biotech crops in 2008 is reviewed. The impact of biotech crops are summarized including their contribution to: global food, feed and fiber security; a safer environment; a more sustainable agriculture; and the alleviation of poverty, and hunger in the developing countries of the world. Future prospects are discussed. Notably, Egypt planted Bt maize for the first time in 2008 thereby becoming the first country in the Arab world to commercialize biotech crops.

  15. Global impacts of surface ozone changes on crop yields and land use

    Chuwah, C.D.; Noije, van Twan; Vuuren, van Detlef P.; Stehfest, Elke; Hazeleger, Wilco

    2015-01-01

    Exposure to surface ozone has detrimental impacts on vegetation and crop yields. In this study, we estimate ozone impacts on crop production and subsequent impacts on land use in the 2005-2050 period using results of the TM5 atmospheric chemistry and IMAGE integrated assessment model. For the

  16. A review of global potentially available cropland estimates and their consequences for model-based assessments

    Eitelberg, D.A.; van Vliet, J.; Verburg, P.H.

    2015-01-01

    The world's population is growing and demand for food, feed, fiber, and fuel is increasing, placing greater demand on land and its resources for crop production. We review previously published estimates of global scale cropland availability, discuss the underlying assumptions that lead to

  17. Particulate matter air pollution may offset ozone damage to global crop production

    Schiferl, Luke D.; Heald, Colette L.

    2018-04-01

    Ensuring global food security requires a comprehensive understanding of environmental pressures on food production, including the impacts of air quality. Surface ozone damages plants and decreases crop production; this effect has been extensively studied. In contrast, the presence of particulate matter (PM) in the atmosphere can be beneficial to crops given that enhanced light scattering leads to a more even and efficient distribution of photons which can outweigh total incoming radiation loss. This study quantifies the impacts of ozone and PM on the global production of maize, rice, and wheat in 2010 and 2050. We show that accounting for the growing season of these crops is an important factor in determining their air pollution exposure. We find that the effect of PM can offset much, if not all, of the reduction in yield associated with ozone damage. Assuming maximum sensitivity to PM, the current (2010) global net impact of air quality on crop production varies by crop (+5.6, -3.7, and +4.5 % for maize, wheat, and rice, respectively). Future emissions scenarios indicate that attempts to improve air quality can result in a net negative effect on crop production in areas dominated by the PM effect. However, we caution that the uncertainty in this assessment is large, due to the uncertainty associated with crop response to changes in diffuse radiation; this highlights that a more detailed physiological study of this response for common cultivars is crucial.

  18. Particulate matter air pollution may offset ozone damage to global crop production

    L. D. Schiferl

    2018-04-01

    Full Text Available Ensuring global food security requires a comprehensive understanding of environmental pressures on food production, including the impacts of air quality. Surface ozone damages plants and decreases crop production; this effect has been extensively studied. In contrast, the presence of particulate matter (PM in the atmosphere can be beneficial to crops given that enhanced light scattering leads to a more even and efficient distribution of photons which can outweigh total incoming radiation loss. This study quantifies the impacts of ozone and PM on the global production of maize, rice, and wheat in 2010 and 2050. We show that accounting for the growing season of these crops is an important factor in determining their air pollution exposure. We find that the effect of PM can offset much, if not all, of the reduction in yield associated with ozone damage. Assuming maximum sensitivity to PM, the current (2010 global net impact of air quality on crop production varies by crop (+5.6, −3.7, and +4.5 % for maize, wheat, and rice, respectively. Future emissions scenarios indicate that attempts to improve air quality can result in a net negative effect on crop production in areas dominated by the PM effect. However, we caution that the uncertainty in this assessment is large, due to the uncertainty associated with crop response to changes in diffuse radiation; this highlights that a more detailed physiological study of this response for common cultivars is crucial.

  19. An integrated crop and hydrologic modeling system to estimate hydrologic impacts of crop irrigation demands

    R.T. McNider; C. Handyside; K. Doty; W.L. Ellenburg; J.F. Cruise; J.R. Christy; D. Moss; V. Sharda; G. Hoogenboom; Peter Caldwell

    2015-01-01

    The present paper discusses a coupled gridded crop modeling and hydrologic modeling system that can examine the benefits of irrigation and costs of irrigation and the coincident impact of the irrigation water withdrawals on surface water hydrology. The system is applied to the Southeastern U.S. The system tools to be discussed include a gridded version (GriDSSAT) of...

  20. Global economic effects of changes in crops, pasture, and forests due to changing climate, carbon dioxide, and ozone

    Reilly, J.; Paltsev, S.; Felzer, B.; Wang, X.; Kicklighter, D.; Melillo, J.; Prinn, R.; Sarofim, M.; Sokolov, A.; Wang, C.

    2007-01-01

    Multiple environmental changes will have consequences for global vegetation. To the extent that crop yields and pasture and forest productivity are affected, there can be important economic consequences. We examine the combined effects of changes in climate, increases in carbon dioxide (CO 2 ), and changes in tropospheric ozone on crop, pasture, and forest lands and the consequences for the global and regional economies. We examine scenarios where there is limited or little effort to control these substances, and policy scenarios that limit emissions of CO 2 and ozone precursors. We find the effects of climate and CO 2 to be generally positive, and the effects of ozone to be very detrimental. Unless ozone is strongly controlled, damage could offset CO 2 and climate benefits. We find that resource allocation among sectors in the economy, and trade among countries, can strongly affect the estimate of economic effect in a country

  1. Estimation of leaf area index in cereal crops using red-green images

    Kirk, Kristian; Andersen, Hans Jørgen; Thomsen, Anton G

    2009-01-01

    A new method for estimating the leaf area index (LAI) in cereal crops based on red-green images taken from above the crop canopy is introduced. The proposed method labels pixels into vegetation and soil classes using a combination of greenness and intensity derived from the red and green colour b...

  2. Estimating water use of mature pecan orchards: A six stage crop growth curve approach

    Ibraimo, NA

    2016-11-01

    Full Text Available previous study in New Mexico, revealed that a six stage crop coefficient curve should be considered for pecans, together with higher mid-season crop coefficient (K(subc)) values for mature orchards. More accurate estimates of monthly ET for mature pecan...

  3. Geophysical Global Modeling for Extreme Crop Production Using Photosynthesis Models Coupled to Ocean SST Dipoles

    Kaneko, D.

    2016-12-01

    Climate change appears to have manifested itself along with abnormal meteorological disasters. Instability caused by drought and flood disasters is producing poor harvests because of poor photosynthesis and pollination. Fluctuations of extreme phenomena are increasing rapidly because amplitudes of change are much greater than average trends. A fundamental cause of these phenomena derives from increased stored energy inside ocean waters. Geophysical and biochemical modeling of crop production can elucidate complex mechanisms under seasonal climate anomalies. The models have progressed through their combination with global climate reanalysis, environmental satellite data, and harvest data on the ground. This study examined adaptation of crop production to advancing abnormal phenomena related to global climate change. Global environmental surface conditions, i.e., vegetation, surface air temperature, and sea surface temperature observed by satellites, enable global modeling of crop production and monitoring. Basic streams of the concepts of modeling rely upon continental energy flow and carbon circulation among crop vegetation, land surface atmosphere combining energy advection from ocean surface anomalies. Global environmental surface conditions, e.g., vegetation, surface air temperature, and sea surface temperature observed by satellites, enable global modeling of crop production and monitoring. The method of validating the modeling relies upon carbon partitioning in biomass and grains through carbon flow by photosynthesis using carbon dioxide unit in photosynthesis. Results of computations done for this study show global distributions of actual evaporation, stomata opening, and photosynthesis, presenting mechanisms related to advection effects from SST anomalies in the Pacific, Atlantic, and Indian oceans on global and continental croplands. For North America, climate effects appear clearly in severe atmospheric phenomena, which have caused drought and forest fires

  4. Modern Estimates of Global Water Cycle Fluxes

    Rodell, M.; Beaudoing, H. K.; L'Ecuyer, T. S.; Olson, W. S.

    2014-12-01

    The goal of the first phase of the NASA Energy and Water Cycle Study (NEWS) Water and Energy Cycle Climatology project was to develop "state of the global water cycle" and "state of the global energy cycle" assessments based on data from modern ground and space based observing systems and data integrating models. Here we describe results of the water cycle assessment, including mean annual and monthly fluxes over continents and ocean basins during the first decade of the millennium. To the extent possible, the water flux estimates are based on (1) satellite measurements and (2) data-integrating models. A careful accounting of uncertainty in each flux was applied within a routine that enforced multiple water and energy budget constraints simultaneously in a variational framework, in order to produce objectively-determined, optimized estimates. Simultaneous closure of the water and energy budgets caused the ocean evaporation and precipitation terms to increase by about 10% and 5% relative to the original estimates, mainly because the energy budget required turbulent heat fluxes to be substantially larger in order to balance net radiation. In the majority of cases, the observed annual, surface and atmospheric water budgets over the continents and oceans close with much less than 10% residual. Observed residuals and optimized uncertainty estimates are considerably larger for monthly surface and atmospheric water budget closure, often nearing or exceeding 20% in North America, Eurasia, Australia and neighboring islands, and the Arctic and South Atlantic Oceans. The residuals in South America and Africa tend to be smaller, possibly because cold land processes are a non-issue. Fluxes are poorly observed over the Arctic Ocean, certain seas, Antarctica, and the Australasian and Indonesian Islands, leading to reliance on atmospheric analysis estimates. Other details of the study and future directions will be discussed.

  5. Global warming: Temperature estimation in annealers

    Jack Raymond

    2016-11-01

    Full Text Available Sampling from a Boltzmann distribution is NP-hard and so requires heuristic approaches. Quantum annealing is one promising candidate. The failure of annealing dynamics to equilibrate on practical time scales is a well understood limitation, but does not always prevent a heuristically useful distribution from being generated. In this paper we evaluate several methods for determining a useful operational temperature range for annealers. We show that, even where distributions deviate from the Boltzmann distribution due to ergodicity breaking, these estimates can be useful. We introduce the concepts of local and global temperatures that are captured by different estimation methods. We argue that for practical application it often makes sense to analyze annealers that are subject to post-processing in order to isolate the macroscopic distribution deviations that are a practical barrier to their application.

  6. Exploring the direct impacts of particulate matter and surface ozone on global crop production

    Schiferl, L. D.; Heald, C. L.

    2016-12-01

    The current era of rising food demand to feed an increasing population along with expansion of industrialization throughout the globe has been accompanied by deteriorating air quality and an enhancement in agricultural activity. Both air quality and the food supply are vitally important to sustaining human enterprise, and understanding the effects air quality may have on agricultural production is critical. Particulate matter (PM) in the atmosphere decreases the total photosynthetically available radiation (PAR) available to crops through the scattering and absorption of radiation while also increasing the diffuse fraction (DF) of this PAR. Since plants respond positively to a higher DF through the more even distribution of photons to all leaves, the net effect of PM on crop production depends on the magnitudes of these values and the response mechanisms of a specific crop. In contrast, atmospheric ozone always acts to decrease crop production through its phytotoxic properties. While the relationships between ozone and crop production have been readily studied, the effects of PM on crop production and their relative importance compared to ozone is much more uncertain. This study uses the GEOS-Chem chemical transport model linked to the RRTMG radiative transfer model and the DSSAT crop model to explore the impacts of PM and ozone on the globally distributed production of maize, rice, wheat and soybeans. First, we examine how air quality differentially affects total seasonal production by crop and region. Second, we investigate the dependence of simulated production on air quality over different timescales and under varying cloud conditions.

  7. Response and potential of agroforestry crops under global change

    Calfapietra, C.; Gielen, B.; Karnosky, D.; Ceulemans, R.; Scarascia Mugnozza, G.

    2010-01-01

    The use of agroforestry crops is a promising tool for reducing atmospheric carbon dioxide concentration through fossil fuel substitution. In particular, plantations characterised by high yields such as short rotation forestry (SRF) are becoming popular worldwide for biomass production and their role acknowledged in the Kyoto Protocol. While their contribution to climate change mitigation is being investigated, the impact of climate change itself on growth and productivity of these plantations needs particular attention, since their management might need to be modified accordingly. Besides the benefits deriving from the establishment of millions of hectares of these plantations, there is a risk of increased release into the atmosphere of volatile organic compounds (VOC) emitted in large amounts by most of the species commonly used. These hydrocarbons are known to play a crucial role in tropospheric ozone formation. This might represent a negative feedback, especially in regions already characterized by elevated ozone level. - Growth and management of agroforestry plantations will be influenced by climate change.

  8. Response and potential of agroforestry crops under global change

    Calfapietra, C., E-mail: carlo.calfapietra@ibaf.cnr.i [Institute of Agro-Environmental and Forest Biology (IBAF), National Research Council (CNR), Via Salaria km 29300, 00015 Monterotondo Scalo, Roma (Italy); Gielen, B. [University of Antwerpen, Campus Drie Eiken, Department of Biology, Research Group of Plant and Vegetation Ecology, Universiteitsplein 1, B-2610 Wilrijk (Belgium); Karnosky, D. [Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931 (United States); Ceulemans, R. [University of Antwerpen, Campus Drie Eiken, Department of Biology, Research Group of Plant and Vegetation Ecology, Universiteitsplein 1, B-2610 Wilrijk (Belgium); Scarascia Mugnozza, G. [Department of Agronomy, Forestry and Land Use (DAF), Agricultural Research Council of Italy (CRA), Via del Caravita 7/a 00186 Roma (Italy)

    2010-04-15

    The use of agroforestry crops is a promising tool for reducing atmospheric carbon dioxide concentration through fossil fuel substitution. In particular, plantations characterised by high yields such as short rotation forestry (SRF) are becoming popular worldwide for biomass production and their role acknowledged in the Kyoto Protocol. While their contribution to climate change mitigation is being investigated, the impact of climate change itself on growth and productivity of these plantations needs particular attention, since their management might need to be modified accordingly. Besides the benefits deriving from the establishment of millions of hectares of these plantations, there is a risk of increased release into the atmosphere of volatile organic compounds (VOC) emitted in large amounts by most of the species commonly used. These hydrocarbons are known to play a crucial role in tropospheric ozone formation. This might represent a negative feedback, especially in regions already characterized by elevated ozone level. - Growth and management of agroforestry plantations will be influenced by climate change.

  9. Response and potential of agroforestry crops under global change.

    Calfapietra, C; Gielen, B; Karnosky, D; Ceulemans, R; Scarascia Mugnozza, G

    2010-04-01

    The use of agroforestry crops is a promising tool for reducing atmospheric carbon dioxide concentration through fossil fuel substitution. In particular, plantations characterised by high yields such as short rotation forestry (SRF) are becoming popular worldwide for biomass production and their role acknowledged in the Kyoto Protocol. While their contribution to climate change mitigation is being investigated, the impact of climate change itself on growth and productivity of these plantations needs particular attention, since their management might need to be modified accordingly. Besides the benefits deriving from the establishment of millions of hectares of these plantations, there is a risk of increased release into the atmosphere of volatile organic compounds (VOC) emitted in large amounts by most of the species commonly used. These hydrocarbons are known to play a crucial role in tropospheric ozone formation. This might represent a negative feedback, especially in regions already characterized by elevated ozone level. 2009 Elsevier Ltd. All rights reserved.

  10. Estimating Global Cropland Extent with Multi-year MODIS Data

    Christopher O. Justice

    2010-07-01

    Full Text Available This study examines the suitability of 250 m MODIS (MODerate Resolution Imaging Spectroradiometer data for mapping global cropland extent. A set of 39 multi-year MODIS metrics incorporating four MODIS land bands, NDVI (Normalized Difference Vegetation Index and thermal data was employed to depict cropland phenology over the study period. Sub-pixel training datasets were used to generate a set of global classification tree models using a bagging methodology, resulting in a global per-pixel cropland probability layer. This product was subsequently thresholded to create a discrete cropland/non-cropland indicator map using data from the USDA-FAS (Foreign Agricultural Service Production, Supply and Distribution (PSD database describing per-country acreage of production field crops. Five global land cover products, four of which attempted to map croplands in the context of multiclass land cover classifications, were subsequently used to perform regional evaluations of the global MODIS cropland extent map. The global probability layer was further examined with reference to four principle global food crops: corn, soybeans, wheat and rice. Overall results indicate that the MODIS layer best depicts regions of intensive broadleaf crop production (corn and soybean, both in correspondence with existing maps and in associated high probability matching thresholds. Probability thresholds for wheat-growing regions were lower, while areas of rice production had the lowest associated confidence. Regions absent of agricultural intensification, such as Africa, are poorly characterized regardless of crop type. The results reflect the value of MODIS as a generic global cropland indicator for intensive agriculture production regions, but with little sensitivity in areas of low agricultural intensification. Variability in mapping accuracies between areas dominated by different crop types also points to the desirability of a crop-specific approach rather than attempting

  11. Estimating Soil and Root Parameters of Biofuel Crops using a Hydrogeophysical Inversion

    Kuhl, A.; Kendall, A. D.; Van Dam, R. L.; Hyndman, D. W.

    2017-12-01

    Transpiration is the dominant pathway for continental water exchange to the atmosphere, and therefore a crucial aspect of modeling water balances at many scales. The root water uptake dynamics that control transpiration are dependent on soil water availability, as well as the root distribution. However, the root distribution is determined by many factors beyond the plant species alone, including climate conditions and soil texture. Despite the significant contribution of transpiration to global water fluxes, modelling the complex critical zone processes that drive root water uptake remains a challenge. Geophysical tools such as electrical resistivity (ER), have been shown to be highly sensitive to water dynamics in the unsaturated zone. ER data can be temporally and spatially robust, covering large areas or long time periods non-invasively, which is an advantage over in-situ methods. Previous studies have shown the value of using hydrogeophysical inversions to estimate soil properties. Others have used hydrological inversions to estimate both soil properties and root distribution parameters. In this study, we combine these two approaches to create a coupled hydrogeophysical inversion that estimates root and retention curve parameters for a HYDRUS model. To test the feasibility of this new approach, we estimated daily water fluxes and root growth for several biofuel crops at a long-term ecological research site in Southwest Michigan, using monthly ER data from 2009 through 2011. Time domain reflectometry data at seven depths was used to validate modeled soil moisture estimates throughout the model period. This hydrogeophysical inversion method shows promise for improving root distribution and transpiration estimates across a wide variety of settings.

  12. Safeguarding fruit crops in the age of agricultural globalization

    The expansion of fruit production and markets into new geographic areas provides novel opportunities and challenges for the agricultural and marketing industries. In today’s competitive global market environment, growers need access to the best material available in terms of genetics and plant heal...

  13. A global algorithm for estimating Absolute Salinity

    McDougall, T. J.; Jackett, D. R.; Millero, F. J.; Pawlowicz, R.; Barker, P. M.

    2012-12-01

    The International Thermodynamic Equation of Seawater - 2010 has defined the thermodynamic properties of seawater in terms of a new salinity variable, Absolute Salinity, which takes into account the spatial variation of the composition of seawater. Absolute Salinity more accurately reflects the effects of the dissolved material in seawater on the thermodynamic properties (particularly density) than does Practical Salinity. When a seawater sample has standard composition (i.e. the ratios of the constituents of sea salt are the same as those of surface water of the North Atlantic), Practical Salinity can be used to accurately evaluate the thermodynamic properties of seawater. When seawater is not of standard composition, Practical Salinity alone is not sufficient and the Absolute Salinity Anomaly needs to be estimated; this anomaly is as large as 0.025 g kg-1 in the northernmost North Pacific. Here we provide an algorithm for estimating Absolute Salinity Anomaly for any location (x, y, p) in the world ocean. To develop this algorithm, we used the Absolute Salinity Anomaly that is found by comparing the density calculated from Practical Salinity to the density measured in the laboratory. These estimates of Absolute Salinity Anomaly however are limited to the number of available observations (namely 811). In order to provide a practical method that can be used at any location in the world ocean, we take advantage of approximate relationships between Absolute Salinity Anomaly and silicate concentrations (which are available globally).

  14. Estimating crop net primary production using inventory data and MODIS-derived parameters

    Bandaru, Varaprasad; West, Tristram O.; Ricciuto, Daniel M.; Izaurralde, Roberto C.

    2013-06-03

    National estimates of spatially-resolved cropland net primary production (NPP) are needed for diagnostic and prognostic modeling of carbon sources, sinks, and net carbon flux. Cropland NPP estimates that correspond with existing cropland cover maps are needed to drive biogeochemical models at the local scale and over national and continental extents. Existing satellite-based NPP products tend to underestimate NPP on croplands. A new Agricultural Inventory-based Light Use Efficiency (AgI-LUE) framework was developed to estimate individual crop biophysical parameters for use in estimating crop-specific NPP. The method is documented here and evaluated for corn and soybean crops in Iowa and Illinois in years 2006 and 2007. The method includes a crop-specific enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), shortwave radiation data estimated using Mountain Climate Simulator (MTCLIM) algorithm and crop-specific LUE per county. The combined aforementioned variables were used to generate spatially-resolved, crop-specific NPP that correspond to the Cropland Data Layer (CDL) land cover product. The modeling framework represented well the gradient of NPP across Iowa and Illinois, and also well represented the difference in NPP between years 2006 and 2007. Average corn and soybean NPP from AgI-LUE was 980 g C m-2 yr-1 and 420 g C m-2 yr-1, respectively. This was 2.4 and 1.1 times higher, respectively, for corn and soybean compared to the MOD17A3 NPP product. Estimated gross primary productivity (GPP) derived from AgI-LUE were in close agreement with eddy flux tower estimates. The combination of new inputs and improved datasets enabled the development of spatially explicit and reliable NPP estimates for individual crops over large regional extents.

  15. Global Adoption of Genetically Modified (GM) Crops: Challenges for the Public Sector.

    Huesing, Joseph E; Andres, David; Braverman, Michael P; Burns, Andrea; Felsot, Allan S; Harrigan, George G; Hellmich, Richard L; Reynolds, Alan; Shelton, Anthony M; Jansen van Rijssen, Wilna; Morris, E Jane; Eloff, Jacobus N

    2016-01-20

    Advances in biotechnology continue to drive the development of a wide range of insect-protected, herbicide-tolerant, stress-tolerant, and nutritionally enhanced genetically modified (GM) crops, yet societal and public policy considerations may slow their commercialization. Such restrictions may disproportionately affect developing countries, as well as smaller entrepreneurial and public sector initiatives. The 2014 IUPAC International Congress of Pesticide Chemistry (San Francisco, CA, USA; August 2014) included a symposium on "Challenges Associated with Global Adoption of Agricultural Biotechnology" to review current obstacles in promoting GM crops. Challenges identified by symposium presenters included (i) poor public understanding of GM technology and the need for enhanced communication strategies, (ii) nonharmonized and prescriptive regulatory requirements, and (iii) limited experience with regulations and product development within some public sector programs. The need for holistic resistance management programs to enable the most effective use of insect-protected crops was also a point of emphasis. This paper provides details on the symposium discussion and provides background information that can be used in support of further adoption of beneficial GM crops. Overall, it emphasizes that global adoption of modern agricultural biotechnology has not only provided benefits to growers and consumers but has great potential to provide solutions to an increasing global population and diminishing agricultural land. This potential will be realized by continued scientific innovation, harmonized regulatory systems, and broader communication of the benefits of the high-yielding, disease-resistant, and nutritionally enhanced crops attainable through modern biotechnology.

  16. A scalable satellite-based crop yield mapper: Integrating satellites and crop models for field-scale estimation in India

    Jain, M.; Singh, B.; Srivastava, A.; Lobell, D. B.

    2015-12-01

    Food security will be challenged over the upcoming decades due to increased food demand, natural resource degradation, and climate change. In order to identify potential solutions to increase food security in the face of these changes, tools that can rapidly and accurately assess farm productivity are needed. With this aim, we have developed generalizable methods to map crop yields at the field scale using a combination of satellite imagery and crop models, and implement this approach within Google Earth Engine. We use these methods to examine wheat yield trends in Northern India, which provides over 15% of the global wheat supply and where over 80% of farmers rely on wheat as a staple food source. In addition, we identify the extent to which farmers are shifting sow date in response to heat stress, and how well shifting sow date reduces the negative impacts of heat stress on yield. To identify local-level decision-making, we map wheat sow date and yield at a high spatial resolution (30 m) using Landsat satellite imagery from 1980 to the present. This unique dataset allows us to examine sow date decisions at the field scale over 30 years, and by relating these decisions to weather experienced over the same time period, we can identify how farmers learn and adapt cropping decisions based on weather through time.

  17. Physiological and Agronomic Performance of the Coffee Crop in the Context of Climate Change and Global Warming: A Review.

    DaMatta, Fábio M; Avila, Rodrigo T; Cardoso, Amanda A; Martins, Samuel C V; Ramalho, José C

    2018-05-30

    Coffee is one of the most important global crops and provides a livelihood to millions of people living in developing countries. Coffee species have been described as being highly sensitive to climate change, as largely deduced from modeling studies based on predictions of rising temperatures and changing rainfall patterns. Here, we discuss the physiological responses of the coffee tree in the context of present and ongoing climate changes, including drought, heat, and light stresses, and interactions between these factors. We also summarize recent insights on the physiological and agronomic performance of coffee at elevated atmospheric CO 2 concentrations and highlight the key role of CO 2 in mitigating the harmful effects of heat stress. Evidence is shown suggesting that warming, per se, may be less harmful to coffee suitability than previously estimated, at least under the conditions of an adequate water supply. Finally, we discuss several mitigation strategies to improve crop performance in a changing world.

  18. A global algorithm for estimating Absolute Salinity

    T. J. McDougall

    2012-12-01

    Full Text Available The International Thermodynamic Equation of Seawater – 2010 has defined the thermodynamic properties of seawater in terms of a new salinity variable, Absolute Salinity, which takes into account the spatial variation of the composition of seawater. Absolute Salinity more accurately reflects the effects of the dissolved material in seawater on the thermodynamic properties (particularly density than does Practical Salinity.

    When a seawater sample has standard composition (i.e. the ratios of the constituents of sea salt are the same as those of surface water of the North Atlantic, Practical Salinity can be used to accurately evaluate the thermodynamic properties of seawater. When seawater is not of standard composition, Practical Salinity alone is not sufficient and the Absolute Salinity Anomaly needs to be estimated; this anomaly is as large as 0.025 g kg−1 in the northernmost North Pacific. Here we provide an algorithm for estimating Absolute Salinity Anomaly for any location (x, y, p in the world ocean.

    To develop this algorithm, we used the Absolute Salinity Anomaly that is found by comparing the density calculated from Practical Salinity to the density measured in the laboratory. These estimates of Absolute Salinity Anomaly however are limited to the number of available observations (namely 811. In order to provide a practical method that can be used at any location in the world ocean, we take advantage of approximate relationships between Absolute Salinity Anomaly and silicate concentrations (which are available globally.

  19. Time-Varying Estimation of Crop Insurance Program in Altering North Dakota Farm Economic Structure

    Coleman, Jane A.; Shaik, Saleem

    2009-01-01

    This study examines how federal farm policies, specifically crop insurance, have affected the farm economic structure of North Dakota’s agriculture sector. The system of derived input demand equations is estimated to quantify the changes in North Dakota farmers’ input use when they purchase crop insurance. Further, the cumulative rolling regression technique is applied to capture the varying effects of the farm policies over time. Empirical results from the system of input demand functions in...

  20. Estimation of net greenhouse gas balance using crop- and soil-based approaches: Two case studies

    Huang, Jianxiong; Chen, Yuanquan; Sui, Peng; Gao, Wansheng

    2013-01-01

    The net greenhouse gas balance (NGHGB), estimated by combining direct and indirect greenhouse gas (GHG) emissions, can reveal whether an agricultural system is a sink or source of GHGs. Currently, two types of methods, referred to here as crop-based and soil-based approaches, are widely used to estimate the NGHGB of agricultural systems on annual and seasonal crop timescales. However, the two approaches may produce contradictory results, and few studies have tested which approach is more reliable. In this study, we examined the two approaches using experimental data from an intercropping trial with straw removal and a tillage trial with straw return. The results of the two approaches provided different views of the two trials. In the intercropping trial, NGHGB estimated by the crop-based approach indicated that monocultured maize (M) was a source of GHGs (− 1315 kg CO 2 −eq ha −1 ), whereas maize–soybean intercropping (MS) was a sink (107 kg CO 2 −eq ha −1 ). When estimated by the soil-based approach, both cropping systems were sources (− 3410 for M and − 2638 kg CO 2 −eq ha −1 for MS). In the tillage trial, mouldboard ploughing (MP) and rotary tillage (RT) mitigated GHG emissions by 22,451 and 21,500 kg CO 2 −eq ha −1 , respectively, as estimated by the crop-based approach. However, by the soil-based approach, both tillage methods were sources of GHGs: − 3533 for MP and − 2241 kg CO 2 −eq ha −1 for RT. The crop-based approach calculates a GHG sink on the basis of the returned crop biomass (and other organic matter input) and estimates considerably more GHG mitigation potential than that calculated from the variations in soil organic carbon storage by the soil-based approach. These results indicate that the crop-based approach estimates higher GHG mitigation benefits compared to the soil-based approach and may overestimate the potential of GHG mitigation in agricultural systems. - Highlights: • Net greenhouse gas balance (NGHGB) of

  1. Similar estimates of temperature impacts on global wheat yield by three independent methods

    Liu, Bing; Asseng, Senthold; Müller, Christoph

    2016-01-01

    The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produ......-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.......The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce...... similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries...

  2. Crop yield estimation in 2014 for Vojvodina using methods of remote sensing

    Jovanović Dušan

    2014-01-01

    Full Text Available Monitoring phenology of crops and yield estimate based on vegetation indices as well as other parameters such as temperature or amount of rainfall were largely reported in literature. In this research, MODIS Normalized Difference Vegetation Index (NDVI was used as an indicator of specific crop condition; the other parameter was Land Surface Temperature (LST which can indicate the amount of crop moisture. Trial years were 2011, 2012, and 2013. For those years sowing structure was acquired from agricultural organizations Nova Budućnost from Žarkovac and Sava Kovačević from Vrbas, both in Serbia. Also, satellite images with high and medium resolution for these areas and years were available. Multiple linear regression was used for crop yield estimate for Vojvodina Province, Serbia where the NDVI and LST were independent variables and the average yield for specific crop was the dependent variable. The results of crop yield estimate two months before harvest are presented (excluding wheat.

  3. Estimating the Global Agricultural Impact of Solar Radiation Management using Volcanic Eruptions as Natural Experiments

    Proctor, J.; Hsiang, S. M.; Burney, J. A.; Burke, M.; Schlenker, W.

    2017-12-01

    Solar radiation management (SRM) is increasingly considered an option for managing global temperatures, yet the economic impacts of ameliorating climatic changes by scattering sunlight back to space remain largely unknown. Though SRM may increase crop yields by reducing heat stress, its impacts from concomitant changes in available sunlight have never been empirically estimated. Here we use the volcanic eruptions that inspired modern SRM proposals as natural experiments to provide the first estimates of how the stratospheric sulfate aerosols (SS) created by the eruptions of El Chichon and Pinatubo altered the quantity and quality of global sunlight, how those changes in sunlight impacted global crop yields, and the total effect that SS may have on yields in an SRM scenario when the climatic and sunlight effects are jointly considered. We find that the sunlight-mediated impact of SS on yields is negative for both C4 (maize) and C3 (soy, rice, wheat) crops. Applying our yield model to a geoengineering scenario using SS-based SRM from 2050-2069, we find that SRM damages due to scattering sunlight are roughly equal in magnitude to SRM benefits from cooling. This suggests that SRM - if deployed using SS similar to those emitted by the volcanic eruptions it seeks to mimic - would attenuate little of the damages from climate change to global agriculture on net. Our approach could be extended to study SRM impacts on other global systems, such as human health or ecosystem function.

  4. A Spatial Allocation Procedure to Downscale Regional Crop Production Estimates from an Integrated Assessment Model

    Moulds, S.; Djordjevic, S.; Savic, D.

    2017-12-01

    The Global Change Assessment Model (GCAM), an integrated assessment model, provides insight into the interactions and feedbacks between physical and human systems. The land system component of GCAM, which simulates land use activities and the production of major crops, produces output at the subregional level which must be spatially downscaled in order to use with gridded impact assessment models. However, existing downscaling routines typically consider cropland as a homogeneous class and do not provide information about land use intensity or specific management practices such as irrigation and multiple cropping. This paper presents a spatial allocation procedure to downscale crop production data from GCAM to a spatial grid, producing a time series of maps which show the spatial distribution of specific crops (e.g. rice, wheat, maize) at four input levels (subsistence, low input rainfed, high input rainfed and high input irrigated). The model algorithm is constrained by available cropland at each time point and therefore implicitly balances extensification and intensification processes in order to meet global food demand. It utilises a stochastic approach such that an increase in production of a particular crop is more likely to occur in grid cells with a high biophysical suitability and neighbourhood influence, while a fall in production will occur more often in cells with lower suitability. User-supplied rules define the order in which specific crops are downscaled as well as allowable transitions. A regional case study demonstrates the ability of the model to reproduce historical trends in India by comparing the model output with district-level agricultural inventory data. Lastly, the model is used to predict the spatial distribution of crops globally under various GCAM scenarios.

  5. A new method to estimate genetic gain in annual crops

    Flávio Breseghello

    1998-12-01

    Full Text Available The genetic gain obtained by breeding programs to improve quantitative traits may be estimated by using data from regional trials. A new statistical method for this estimate is proposed and includes four steps: a joint analysis of regional trial data using a generalized linear model to obtain adjusted genotype means and covariance matrix of these means for the whole studied period; b calculation of the arithmetic mean of the adjusted genotype means, exclusively for the group of genotypes evaluated each year; c direct year comparison of the arithmetic means calculated, and d estimation of mean genetic gain by regression. Using the generalized least squares method, a weighted estimate of mean genetic gain during the period is calculated. This method permits a better cancellation of genotype x year and genotype x trial/year interactions, thus resulting in more precise estimates. This method can be applied to unbalanced data, allowing the estimation of genetic gain in series of multilocational trials.Os ganhos genéticos obtidos pelo melhoramento de caracteres quantitativos podem ser estimados utilizando resultados de ensaios regionais de avaliação de linhagens e cultivares. Um novo método estatístico para esta estimativa é proposto, o qual consiste em quatro passos: a análise conjunta da série de dados dos ensaios regionais através de um modelo linear generalizado de forma a obter as médias ajustadas dos genótipos e a matriz de covariâncias destas médias; b para o grupo de genótipos avaliados em cada ano, cálculo da média aritmética das médias ajustadas obtidas na análise conjunta; c comparação direta dos anos, conforme as médias aritméticas obtidas, e d estimativa de um ganho genético médio, por regressão. Aplicando-se o método de quadrados mínimos generalizado, é calculada uma estimativa ponderada do ganho genético médio no período. Este método permite um melhor cancelamento das interações genótipo x ano e gen

  6. The global income and production effects of genetically modified (GM) crops 1996-2011.

    Brookes, Graham; Barfoot, Peter

    2013-01-01

    A key part of any assessment of the global value of crop biotechnology in agriculture is an examination of its economic impact at the farm level. This paper follows earlier annual studies which examined economic impacts on yields, key costs of production, direct farm income and effects and impacts on the production base of the four main crops of soybeans, corn, cotton and canola. The commercialization of genetically modified (GM) crops has continued to occur at a rapid rate, with important changes in both the overall level of adoption and impact occurring in 2011. This annual updated analysis shows that there have been very significant net economic benefits at the farm level amounting to $19.8 billion in 2011 and $98.2 billion for the 16 year period (in nominal terms). The majority (51.2%) of these gains went to farmers in developing countries. GM technology have also made important contributions to increasing global production levels of the four main crops, having added 110 million tonnes and 195 million tonnes respectively, to the global production of soybeans and maize since the introduction of the technology in the mid-1990s.

  7. Economic impact of GM crops: the global income and production effects 1996-2012.

    Brookes, Graham; Barfoot, Peter

    2014-01-01

    A key part of any assessment of the global value of crop biotechnology in agriculture is an examination of its economic impact at the farm level. This paper follows earlier annual studies which examined economic impacts on yields, key costs of production, direct farm income and effects, and impacts on the production base of the four main crops of soybeans, corn, cotton and canola. The commercialization of genetically modified (GM) crops has continued to occur at a rapid rate, with important changes in both the overall level of adoption and impact occurring in 2012. This annual updated analysis shows that there have been very significant net economic benefits at the farm level amounting to $18.8 billion in 2012 and $116.6 billion for the 17-year period (in nominal terms). These economic gains have been divided roughly 50% each to farmers in developed and developing countries. GM technology have also made important contributions to increasing global production levels of the four main crops, having added 122 million tonnes and 230 million tonnes respectively, to the global production of soybeans and maize since the introduction of the technology in the mid-1990s.

  8. Global crop yield response to extreme heat stress under multiple climate change futures

    Deryng, D.; Conway, D.; Ramankutty, N.; Price, J.; Warren, R.

    2014-12-01

    Extreme heat stress during the crop reproductive period can be critical for crop productivity. Projected changes in the frequency and severity of extreme climatic events are expected to negatively impact crop yields and global food production. This study applies the global crop model PEGASUS to quantify, for the first time at the global scale, impacts of extreme heat stress on maize, spring wheat and soybean yields resulting from 72 climate change scenarios for the 21st century. Our results project maize to face progressively worse impacts under a range of RCPs but spring wheat and soybean to improve globally through to the 2080s due to CO2 fertilization effects, even though parts of the tropic and sub-tropic regions could face substantial yield declines. We find extreme heat stress at anthesis (HSA) by the 2080s (relative to the 1980s) under RCP 8.5, taking into account CO2 fertilization effects, could double global losses of maize yield (dY = -12.8 ± 6.7% versus -7.0 ± 5.3% without HSA), reduce projected gains in spring wheat yield by half (dY = 34.3 ± 13.5% versus 72.0 ± 10.9% without HSA) and in soybean yield by a quarter (dY = 15.3 ± 26.5% versus 20.4 ± 22.1% without HSA). The range reflects uncertainty due to differences between climate model scenarios; soybean exhibits both positive and negative impacts, maize is generally negative and spring wheat generally positive. Furthermore, when assuming CO2 fertilization effects to be negligible, we observe drastic climate mitigation policy as in RCP 2.6 could avoid more than 80% of the global average yield losses otherwise expected by the 2080s under RCP 8.5. We show large disparities in climate impacts across regions and find extreme heat stress adversely affects major producing regions and lower income countries.

  9. Remote sensing and gis based wheat crop acreage and yield estimation of district hyderabad, pakistan

    Siyal, A.

    2015-01-01

    Pre-harvest reliable and timely yield forecast and area estimates of cropped area is vital to planners and policy makers for making important and timely decisions with respect to food security in a country. The present study was conducted to estimate the wheat cropped area and crop yield in Hyderabad District, Pakistan from the Landsat 8 satellite imagery for Rabi 2013-14 and ground trothing. The required imagery of district Hyderabad was acquired from GLOVIS and was classified with maximum likelihood algorithm using ArcGIS 10.1. The classified image revealed that in district Hyderabad wheat covered 10,210 hectares (9.74% of total area) during Rabi season 2013-14 against 15,000 hectares (14.3% of total area) reported by Crop reporting Services (CRS), Sindh which is 30% less than that of reported by CRS. A positive linear relation between the wheat crop yield and the peak NDVI with coefficient of determination R2 = 0.91 was observed. Crop area and yield forecast through remote sensing is easy, cost effective, quick and reliable hence this technology needs to be introduced and propagated in the concerned government departments of Pakistan. (author)

  10. Estimating effectiveness of crop management for reduction of soil erosion and runoff

    Hlavcova, K.; Studvova, Z.; Kohnova, S.; Szolgay, J.

    2017-10-01

    The paper focuses on erosion processes in the Svacenický Creek catchment which is a small sub-catchment of the Myjava River basin. To simulate soil loss and sediment transport the USLE/SDR and WaTEM/SEDEM models were applied. The models were validated by comparing the simulated results with the actual bathymetry of a polder at the catchment outlet. Methods of crop management based on rotation and strip cropping were applied for the reduction of soil loss and sediment transport. The comparison shows that the greatest intensities of soil loss were achieved by the bare soil without vegetation and from the planting of maize for corn. The lowest values were achieved from the planting of winter wheat. At the end the effectiveness of row crops and strip cropping for decreasing design floods from the catchment was estimated.

  11. Applying a particle filtering technique for canola crop growth stage estimation in Canada

    Sinha, Abhijit; Tan, Weikai; Li, Yifeng; McNairn, Heather; Jiao, Xianfeng; Hosseini, Mehdi

    2017-10-01

    Accurate crop growth stage estimation is important in precision agriculture as it facilitates improved crop management, pest and disease mitigation and resource planning. Earth observation imagery, specifically Synthetic Aperture Radar (SAR) data, can provide field level growth estimates while covering regional scales. In this paper, RADARSAT-2 quad polarization and TerraSAR-X dual polarization SAR data and ground truth growth stage data are used to model the influence of canola growth stages on SAR imagery extracted parameters. The details of the growth stage modeling work are provided, including a) the development of a new crop growth stage indicator that is continuous and suitable as the state variable in the dynamic estimation procedure; b) a selection procedure for SAR polarimetric parameters that is sensitive to both linear and nonlinear dependency between variables; and c) procedures for compensation of SAR polarimetric parameters for different beam modes. The data was collected over three crop growth seasons in Manitoba, Canada, and the growth model provides the foundation of a novel dynamic filtering framework for real-time estimation of canola growth stages using the multi-sensor and multi-mode SAR data. A description of the dynamic filtering framework that uses particle filter as the estimator is also provided in this paper.

  12. Crop area estimation using high and medium resolution satellite imagery in areas with complex topography

    Husak, G. J.; Marshall, M. T.; Michaelsen, J.; Pedreros, D.; Funk, C.; Galu, G.

    2008-07-01

    Reliable estimates of cropped area (CA) in developing countries with chronic food shortages are essential for emergency relief and the design of appropriate market-based food security programs. Satellite interpretation of CA is an effective alternative to extensive and costly field surveys, which fail to represent the spatial heterogeneity at the country-level. Bias-corrected, texture based classifications show little deviation from actual crop inventories, when estimates derived from aerial photographs or field measurements are used to remove systematic errors in medium resolution estimates. In this paper, we demonstrate a hybrid high-medium resolution technique for Central Ethiopia that combines spatially limited unbiased estimates from IKONOS images, with spatially extensive Landsat ETM+ interpretations, land-cover, and SRTM-based topography. Logistic regression is used to derive the probability of a location being crop. These individual points are then aggregated to produce regional estimates of CA. District-level analysis of Landsat based estimates showed CA totals which supported the estimates of the Bureau of Agriculture and Rural Development. Continued work will evaluate the technique in other parts of Africa, while segmentation algorithms will be evaluated, in order to automate classification of medium resolution imagery for routine CA estimation in the future.

  13. Global income and production impacts of using GM crop technology 1996–2014

    Brookes, Graham; Barfoot, Peter

    2016-01-01

    ABSTRACT This paper provides an economic assessment of the value of using genetically modified (GM) crop technology in agriculture at the farm level. It follows and updates earlier annual studies which examined economic impacts on yields, key costs of production, direct farm income and effects, and impacts on the production base of the 4 main crops of soybeans, corn, cotton and canola. The commercialisation of GM crops has continued to occur at a rapid rate since the mid 1990s, with important changes in both the overall level of adoption and impact occurring in 2014. This annual updated analysis shows that there continues to be very significant net economic benefits at the farm level amounting to $17.7 billion in 2014 and $150.3 billion for the 19-year period 1996–2014 (in nominal terms). These economic gains have been divided roughly 50% each to farmers in developed and developing countries. About 65% of the gains have derived from yield and production gains with the remaining 35% coming from cost savings. The technology has also made important contributions to increasing global production levels of the 4 main crops, having, for example, added 158 million tonnes and 322 million tonnes respectively, to the global production of soybeans and maize since the introduction of the technology in the mid 1990s. PMID:27116697

  14. Global income and production impacts of using GM crop technology 1996–2013

    Brookes, Graham; Barfoot, Peter

    2015-01-01

    abstract This paper provides an economic assessment of the value of using genetically modified (GM) crop technology in agriculture at the farm level. It follows and updates earlier annual studies which examined economic impacts on yields, key costs of production, direct farm income and effects, and impacts on the production base of the 4 main crops of soybeans, corn, cotton and canola. The commercialisation of GM crops has continued to occur at a rapid rate since the mid 1990s, with important changes in both the overall level of adoption and impact occurring in 2013. This annual updated analysis shows that there continues to be very significant net economic benefits at the farm level amounting to $20.5 billion in 2013 and $133.4 billion for the 18 years period (in nominal terms). These economic gains have been divided roughly 50% each to farmers in developed and developing countries. About 70% of the gains have derived from yield and production gains with the remaining 30% coming from cost savings. The technology have also made important contributions to increasing global production levels of the 4 main crops, having added 138 million tonnes and 273 million tonnes respectively, to the global production of soybeans and maize since the introduction of the technology in the mid 1990s. PMID:25738324

  15. Historical effects of CO2 and climate trends on global crop water demand

    Urban, Daniel W.; Sheffield, Justin; Lobell, David B.

    2017-12-01

    A critical question for agricultural production and food security is how water demand for staple crops will respond to climate and carbon dioxide (CO2) changes1, especially in light of the expected increases in extreme heat exposure2. To quantify the trade-offs between the effects of climate and CO2 on water demand, we use a `sink-strength' model of demand3,4 which relies on the vapour-pressure deficit (VPD), incident radiation and the efficiencies of canopy-radiation use and canopy transpiration; the latter two are both dependent on CO2. This model is applied to a global data set of gridded monthly weather data over the cropping regions of maize, soybean, wheat and rice during the years 1948-2013. We find that this approach agrees well with Penman-Monteith potential evapotranspiration (PM) for the C3 crops of soybean, wheat and rice, where the competing CO2 effects largely cancel each other out, but that water demand in maize is significantly overstated by a demand measure that does not include CO2, such as the PM. We find the largest changes in wheat, for which water demand has increased since 1981 over 86% of the global cropping area and by 2.3-3.6 percentage points per decade in different regions.

  16. Estimation Of Effective Dose In Ingestion Of Food Crops For 137Cs

    Angeleska, A.; Dimitrieska-Stojkovic, E.; Uzunov, R.; Hajrulai-Musliu, Z.; Stojanovska-Dimzoska, B.; Jankuloski, D.; Crceva-Nikolovska, R.

    2015-01-01

    The interaction of the ionizing radiation with the human body leads to various biological effects which afterwards can be manifested as clinical symptoms. The nature and the seriousness of the symptoms depend on the absorbed dose, as well as the dose rate, and many diseases which were supposed to be effectively managed if information for the radiation level of an environment was available. The knowledge of the concentration of radioactivity of our environment is of essential relevance in the assessment of the dose that is accumulated in the population, as well as for the formation of the basis for estimation of the level of radioactive contamination or contamination in the environment in future. Taking into consideration the relevance of the distribution and the transfer of radionuclides from the soil to the crops, this work was aimed to estimate the effective dose in ingestion of separate crops for 137Cs. The effective dose was determined by means of already known transfer factors from the soil to the plants and measured concentrations of activities of soil from specific locations in the surrounding of the city of Skopje. The agricultural crops used for analysis are the most commonly applied crops (vegetables, legumes, root crops) in Republic of Macedonia. The radiometric analysis of these samples was conducted by applying a spectrometer for gamma-rays with Germanium with high purity (HPGe). The estimated effective dose would apply for adults who ingested the mentioned crops which were produced at the mentioned locations, that is, in the region of Skopje. These data can be the basis for estimation of risk for radioactive contamination of the population, received by ingestion of produced food. (author).

  17. Global Estimated Net Migration Grids by Decade: 1970-2000

    National Aeronautics and Space Administration — The Global Estimated Net Migration by Decade: 1970-2000 data set provides estimates of net migration over the three decades from 1970 to 2000. Because of the lack of...

  18. Towards a Quantitative Use of Satellite Remote Sensing in Crop Growth Models for Large Scale Agricultural Production Estimate (Invited)

    Defourny, P.

    2013-12-01

    The development of better agricultural monitoring capabilities is clearly considered as a critical step for strengthening food production information and market transparency thanks to timely information about crop status, crop area and yield forecasts. The documentation of global production will contribute to tackle price volatility by allowing local, national and international operators to make decisions and anticipate market trends with reduced uncertainty. Several operational agricultural monitoring systems are currently operating at national and international scales. Most are based on the methods derived from the pioneering experiences completed some decades ago, and use remote sensing to qualitatively compare one year to the others to estimate the risks of deviation from a normal year. The GEO Agricultural Monitoring Community of Practice described the current monitoring capabilities at the national and global levels. An overall diagram summarized the diverse relationships between satellite EO and agriculture information. There is now a large gap between the current operational large scale systems and the scientific state of the art in crop remote sensing, probably because the latter mainly focused on local studies. The poor availability of suitable in-situ and satellite data over extended areas hampers large scale demonstrations preventing the much needed up scaling research effort. For the cropland extent, this paper reports a recent research achievement using the full ENVISAT MERIS 300 m archive in the context of the ESA Climate Change Initiative. A flexible combination of classification methods depending to the region of the world allows mapping the land cover as well as the global croplands at 300 m for the period 2008 2012. This wall to wall product is then compared with regards to the FP 7-Geoland 2 results obtained using as Landsat-based sampling strategy over the IGADD countries. On the other hand, the vegetation indices and the biophysical variables

  19. Integrating remote sensing, geographic information system and modeling for estimating crop yield

    Salazar, Luis Alonso

    This thesis explores various aspects of the use of remote sensing, geographic information system and digital signal processing technologies for broad-scale estimation of crop yield in Kansas. Recent dry and drought years in the Great Plains have emphasized the need for new sources of timely, objective and quantitative information on crop conditions. Crop growth monitoring and yield estimation can provide important information for government agencies, commodity traders and producers in planning harvest, storage, transportation and marketing activities. The sooner this information is available the lower the economic risk translating into greater efficiency and increased return on investments. Weather data is normally used when crop yield is forecasted. Such information, to provide adequate detail for effective predictions, is typically feasible only on small research sites due to expensive and time-consuming collections. In order for crop assessment systems to be economical, more efficient methods for data collection and analysis are necessary. The purpose of this research is to use satellite data which provides 50 times more spatial information about the environment than the weather station network in a short amount of time at a relatively low cost. Specifically, we are going to use Advanced Very High Resolution Radiometer (AVHRR) based vegetation health (VH) indices as proxies for characterization of weather conditions.

  20. Estimating Water Footprints of Vegetable Crops: Influence of Growing Season, Solar Radiation Data and Functional Unit

    Betsie le Roux

    2016-10-01

    Full Text Available Water footprint (WF accounting as proposed by the Water Footprint Network (WFN can potentially provide important information for water resource management, especially in water scarce countries relying on irrigation to help meet their food requirements. However, calculating accurate WFs of short-season vegetable crops such as carrots, cabbage, beetroot, broccoli and lettuce presented some challenges. Planting dates and inter-annual weather conditions impact WF results. Joining weather datasets of just rainfall, minimum and maximum temperature with ones that include solar radiation and wind-speed affected crop model estimates and WF results. The functional unit selected can also have a major impact on results. For example, WFs according to the WFN approach do not account for crop residues used for other purposes, like composting and animal feed. Using yields in dry matter rather than fresh mass also impacts WF metrics, making comparisons difficult. To overcome this, using the nutritional value of crops as a functional unit can connect water use more directly to potential benefits derived from different crops and allow more straightforward comparisons. Grey WFs based on nitrogen only disregards water pollution caused by phosphates, pesticides and salinization. Poor understanding of the fate of nitrogen complicates estimation of nitrogen loads into the aquifer.

  1. Effects of LiDAR point density, sampling size and height threshold on estimation accuracy of crop biophysical parameters.

    Luo, Shezhou; Chen, Jing M; Wang, Cheng; Xi, Xiaohuan; Zeng, Hongcheng; Peng, Dailiang; Li, Dong

    2016-05-30

    Vegetation leaf area index (LAI), height, and aboveground biomass are key biophysical parameters. Corn is an important and globally distributed crop, and reliable estimations of these parameters are essential for corn yield forecasting, health monitoring and ecosystem modeling. Light Detection and Ranging (LiDAR) is considered an effective technology for estimating vegetation biophysical parameters. However, the estimation accuracies of these parameters are affected by multiple factors. In this study, we first estimated corn LAI, height and biomass (R2 = 0.80, 0.874 and 0.838, respectively) using the original LiDAR data (7.32 points/m2), and the results showed that LiDAR data could accurately estimate these biophysical parameters. Second, comprehensive research was conducted on the effects of LiDAR point density, sampling size and height threshold on the estimation accuracy of LAI, height and biomass. Our findings indicated that LiDAR point density had an important effect on the estimation accuracy for vegetation biophysical parameters, however, high point density did not always produce highly accurate estimates, and reduced point density could deliver reasonable estimation results. Furthermore, the results showed that sampling size and height threshold were additional key factors that affect the estimation accuracy of biophysical parameters. Therefore, the optimal sampling size and the height threshold should be determined to improve the estimation accuracy of biophysical parameters. Our results also implied that a higher LiDAR point density, larger sampling size and height threshold were required to obtain accurate corn LAI estimation when compared with height and biomass estimations. In general, our results provide valuable guidance for LiDAR data acquisition and estimation of vegetation biophysical parameters using LiDAR data.

  2. Towards systematic evaluation of crop model outputs for global land-use models

    Leclere, David; Azevedo, Ligia B.; Skalský, Rastislav; Balkovič, Juraj; Havlík, Petr

    2016-04-01

    Land provides vital socioeconomic resources to the society, however at the cost of large environmental degradations. Global integrated models combining high resolution global gridded crop models (GGCMs) and global economic models (GEMs) are increasingly being used to inform sustainable solution for agricultural land-use. However, little effort has yet been done to evaluate and compare the accuracy of GGCM outputs. In addition, GGCM datasets require a large amount of parameters whose values and their variability across space are weakly constrained: increasing the accuracy of such dataset has a very high computing cost. Innovative evaluation methods are required both to ground credibility to the global integrated models, and to allow efficient parameter specification of GGCMs. We propose an evaluation strategy for GGCM datasets in the perspective of use in GEMs, illustrated with preliminary results from a novel dataset (the Hypercube) generated by the EPIC GGCM and used in the GLOBIOM land use GEM to inform on present-day crop yield, water and nutrient input needs for 16 crops x 15 management intensities, at a spatial resolution of 5 arc-minutes. We adopt the following principle: evaluation should provide a transparent diagnosis of model adequacy for its intended use. We briefly describe how the Hypercube data is generated and how it articulates with GLOBIOM in order to transparently identify the performances to be evaluated, as well as the main assumptions and data processing involved. Expected performances include adequately representing the sub-national heterogeneity in crop yield and input needs: i) in space, ii) across crop species, and iii) across management intensities. We will present and discuss measures of these expected performances and weight the relative contribution of crop model, input data and data processing steps in performances. We will also compare obtained yield gaps and main yield-limiting factors against the M3 dataset. Next steps include

  3. Global market integration increases likelihood that a future African Green Revolution could increase crop land use and CO2 emissions.

    Hertel, Thomas W; Ramankutty, Navin; Baldos, Uris Lantz C

    2014-09-23

    There has been a resurgence of interest in the impacts of agricultural productivity on land use and the environment. At the center of this debate is the assertion that agricultural innovation is land sparing. However, numerous case studies and global empirical studies have found little evidence of higher yields being accompanied by reduced area. We find that these studies overlook two crucial factors: estimation of a true counterfactual scenario and a tendency to adopt a regional, rather than a global, perspective. This paper introduces a general framework for analyzing the impacts of regional and global innovation on long run crop output, prices, land rents, land use, and associated CO2 emissions. In so doing, it facilitates a reconciliation of the apparently conflicting views of the impacts of agricultural productivity growth on global land use and environmental quality. Our historical analysis demonstrates that the Green Revolution in Asia, Latin America, and the Middle East was unambiguously land and emissions sparing, compared with a counterfactual world without these innovations. In contrast, we find that the environmental impacts of a prospective African Green Revolution are potentially ambiguous. We trace these divergent outcomes to relative differences between the innovating region and the rest of the world in yields, emissions efficiencies, cropland supply response, and intensification potential. Globalization of agriculture raises the potential for adverse environmental consequences. However, if sustained for several decades, an African Green Revolution will eventually become land sparing.

  4. Global market integration increases likelihood that a future African Green Revolution could increase crop land use and CO2 emissions

    Hertel, Thomas W.; Ramankutty, Navin; Baldos, Uris Lantz C.

    2014-01-01

    There has been a resurgence of interest in the impacts of agricultural productivity on land use and the environment. At the center of this debate is the assertion that agricultural innovation is land sparing. However, numerous case studies and global empirical studies have found little evidence of higher yields being accompanied by reduced area. We find that these studies overlook two crucial factors: estimation of a true counterfactual scenario and a tendency to adopt a regional, rather than a global, perspective. This paper introduces a general framework for analyzing the impacts of regional and global innovation on long run crop output, prices, land rents, land use, and associated CO2 emissions. In so doing, it facilitates a reconciliation of the apparently conflicting views of the impacts of agricultural productivity growth on global land use and environmental quality. Our historical analysis demonstrates that the Green Revolution in Asia, Latin America, and the Middle East was unambiguously land and emissions sparing, compared with a counterfactual world without these innovations. In contrast, we find that the environmental impacts of a prospective African Green Revolution are potentially ambiguous. We trace these divergent outcomes to relative differences between the innovating region and the rest of the world in yields, emissions efficiencies, cropland supply response, and intensification potential. Globalization of agriculture raises the potential for adverse environmental consequences. However, if sustained for several decades, an African Green Revolution will eventually become land sparing. PMID:25201962

  5. Crop biomass and evapotranspiration estimation using SPOT and Formosat-2 Data

    Veloso, Amanda; Demarez, Valérie; Ceschia, Eric; Claverie, Martin

    2013-04-01

    The use of crop models allows simulating plant development, growth and yield under different environmental and management conditions. When combined with high spatial and temporal resolution remote sensing data, these models provide new perspectives for crop monitoring at regional scale. We propose here an approach to estimate time courses of dry aboveground biomass, yield and evapotranspiration (ETR) for summer (maize, sunflower) and winter crops (wheat) by assimilating Green Area Index (GAI) data, obtained from satellite observations, into a simple crop model. Only high spatial resolution and gap-free satellite time series can provide enough information for efficient crop monitoring applications. The potential of remote sensing data is often limited by cloud cover and/or gaps in observation. Data from different sensor systems need then to be combined. For this work, we employed a unique set of Formosat-2 and SPOT images (164 images) and in-situ measurements, acquired from 2006 to 2010 in southwest France. Among the several land surface biophysical variables accessible from satellite observations, the GAI is the one that has a key role in soil-plant-atmosphere interactions and in biomass accumulation process. Many methods have been developed to relate GAI to optical remote sensing signal. Here, seasonal dynamics of remotely sensed GAI were estimated by applying a method based on the inversion of a radiative transfer model using artificial neural networks. The modelling approach is based on the Simple Algorithm for Yield and Evapotranspiration estimate (SAFYE) model, which couples the FAO-56 model with an agro-meteorological model, based on Monteith's light-use efficiency theory. The SAFYE model is a daily time step crop model that simulates time series of GAI, dry aboveground biomass, grain yield and ETR. Crop and soil model parameters were determined using both in-situ measurements and values found in the literature. Phenological parameters were calibrated by the

  6. Non-bee insects are important contributors to global crop pollination.

    Rader, Romina; Bartomeus, Ignasi; Garibaldi, Lucas A; Garratt, Michael P D; Howlett, Brad G; Winfree, Rachael; Cunningham, Saul A; Mayfield, Margaret M; Arthur, Anthony D; Andersson, Georg K S; Bommarco, Riccardo; Brittain, Claire; Carvalheiro, Luísa G; Chacoff, Natacha P; Entling, Martin H; Foully, Benjamin; Freitas, Breno M; Gemmill-Herren, Barbara; Ghazoul, Jaboury; Griffin, Sean R; Gross, Caroline L; Herbertsson, Lina; Herzog, Felix; Hipólito, Juliana; Jaggar, Sue; Jauker, Frank; Klein, Alexandra-Maria; Kleijn, David; Krishnan, Smitha; Lemos, Camila Q; Lindström, Sandra A M; Mandelik, Yael; Monteiro, Victor M; Nelson, Warrick; Nilsson, Lovisa; Pattemore, David E; Pereira, Natália de O; Pisanty, Gideon; Potts, Simon G; Reemer, Menno; Rundlöf, Maj; Sheffield, Cory S; Scheper, Jeroen; Schüepp, Christof; Smith, Henrik G; Stanley, Dara A; Stout, Jane C; Szentgyörgyi, Hajnalka; Taki, Hisatomo; Vergara, Carlos H; Viana, Blandina F; Woyciechowski, Michal

    2016-01-05

    Wild and managed bees are well documented as effective pollinators of global crops of economic importance. However, the contributions by pollinators other than bees have been little explored despite their potential to contribute to crop production and stability in the face of environmental change. Non-bee pollinators include flies, beetles, moths, butterflies, wasps, ants, birds, and bats, among others. Here we focus on non-bee insects and synthesize 39 field studies from five continents that directly measured the crop pollination services provided by non-bees, honey bees, and other bees to compare the relative contributions of these taxa. Non-bees performed 25-50% of the total number of flower visits. Although non-bees were less effective pollinators than bees per flower visit, they made more visits; thus these two factors compensated for each other, resulting in pollination services rendered by non-bees that were similar to those provided by bees. In the subset of studies that measured fruit set, fruit set increased with non-bee insect visits independently of bee visitation rates, indicating that non-bee insects provide a unique benefit that is not provided by bees. We also show that non-bee insects are not as reliant as bees on the presence of remnant natural or seminatural habitat in the surrounding landscape. These results strongly suggest that non-bee insect pollinators play a significant role in global crop production and respond differently than bees to landscape structure, probably making their crop pollination services more robust to changes in land use. Non-bee insects provide a valuable service and provide potential insurance against bee population declines.

  7. Estimation of leaf area index in cereal crops using red–green images

    Nielsen, Kristian Kirk; Andersen, Hans Jørgen; Thomsen, Anton

    2009-01-01

    A new method for estimating the leaf area index (LAI) in cereal crops based on red–green images taken from above the crop canopy is introduced. The proposed method labels pixels into vegetation and soil classes using a combination of greenness and intensity derived from the red and green colour b....... Conclusions Acknowledgements Appendix. Modelling the correlation between greenness and brightness References   Fig. 1. Simulated image of a vegetation canopy (left), with distribution of pixel greenness and brightness (right). View Within Article...

  8. National-scale crop type mapping and area estimation using multi-resolution remote sensing and field survey

    Song, X. P.; Potapov, P.; Adusei, B.; King, L.; Khan, A.; Krylov, A.; Di Bella, C. M.; Pickens, A. H.; Stehman, S. V.; Hansen, M.

    2016-12-01

    Reliable and timely information on agricultural production is essential for ensuring world food security. Freely available medium-resolution satellite data (e.g. Landsat, Sentinel) offer the possibility of improved global agriculture monitoring. Here we develop and test a method for estimating in-season crop acreage using a probability sample of field visits and producing wall-to-wall crop type maps at national scales. The method is first illustrated for soybean cultivated area in the US for 2015. A stratified, two-stage cluster sampling design was used to collect field data to estimate national soybean area. The field-based estimate employed historical soybean extent maps from the U.S. Department of Agriculture (USDA) Cropland Data Layer to delineate and stratify U.S. soybean growing regions. The estimated 2015 U.S. soybean cultivated area based on the field sample was 341,000 km2 with a standard error of 23,000 km2. This result is 1.0% lower than USDA's 2015 June survey estimate and 1.9% higher than USDA's 2016 January estimate. Our area estimate was derived in early September, about 2 months ahead of harvest. To map soybean cover, the Landsat image archive for the year 2015 growing season was processed using an active learning approach. Overall accuracy of the soybean map was 84%. The field-based sample estimated area was then used to calibrate the map such that the soybean acreage of the map derived through pixel counting matched the sample-based area estimate. The strength of the sample-based area estimation lies in the stratified design that takes advantage of the spatially explicit cropland layers to construct the strata. The success of the mapping was built upon an automated system which transforms Landsat images into standardized time-series metrics. The developed method produces reliable and timely information on soybean area in a cost-effective way and could be implemented in an operational mode. The approach has also been applied for other crops in

  9. Global Warming Estimation From Microwave Sounding Unit

    Prabhakara, C.; Iacovazzi, R., Jr.; Yoo, J.-M.; Dalu, G.

    1998-01-01

    Microwave Sounding Unit (MSU) Ch 2 data sets, collected from sequential, polar-orbiting, Sun-synchronous National Oceanic and Atmospheric Administration operational satellites, contain systematic calibration errors that are coupled to the diurnal temperature cycle over the globe. Since these coupled errors in MSU data differ between successive satellites, it is necessary to make compensatory adjustments to these multisatellite data sets in order to determine long-term global temperature change. With the aid of the observations during overlapping periods of successive satellites, we can determine such adjustments and use them to account for the coupled errors in the long-term time series of MSU Ch 2 global temperature. In turn, these adjusted MSU Ch 2 data sets can be used to yield global temperature trend. In a pioneering study, Spencer and Christy (SC) (1990) developed a procedure to derive the global temperature trend from MSU Ch 2 data. Such a procedure can leave unaccounted residual errors in the time series of the temperature anomalies deduced by SC, which could lead to a spurious long-term temperature trend derived from their analysis. In the present study, we have developed a method that avoids the shortcomings of the SC procedure, the magnitude of the coupled errors is not determined explicitly. Furthermore, based on some assumptions, these coupled errors are eliminated in three separate steps. Such a procedure can leave unaccounted residual errors in the time series of the temperature anomalies deduced by SC, which could lead to a spurious long-term temperature trend derived from their analysis. In the present study, we have developed a method that avoids the shortcomings of the SC procedures. Based on our analysis, we find there is a global warming of 0.23+/-0.12 K between 1980 and 1991. Also, in this study, the time series of global temperature anomalies constructed by removing the global mean annual temperature cycle compares favorably with a similar

  10. Estimation of the global regularity of a multifractional Brownian motion

    Lebovits, Joachim; Podolskij, Mark

    This paper presents a new estimator of the global regularity index of a multifractional Brownian motion. Our estimation method is based upon a ratio statistic, which compares the realized global quadratic variation of a multifractional Brownian motion at two different frequencies. We show that a ...... that a logarithmic transformation of this statistic converges in probability to the minimum of the Hurst functional parameter, which is, under weak assumptions, identical to the global regularity index of the path....

  11. How agro-ecological research helps to address food security issues under new IPM and pesticide reduction policies for global crop production systems.

    E Birch, A Nicholas; Begg, Graham S; Squire, Geoffrey R

    2011-06-01

    Drivers behind food security and crop protection issues are discussed in relation to food losses caused by pests. Pests globally consume food estimated to feed an additional one billion people. Key drivers include rapid human population increase, climate change, loss of beneficial on-farm biodiversity, reduction in per capita cropped land, water shortages, and EU pesticide withdrawals under policies relating to 91/414 EEC. IPM (Integrated Pest Management) will be compulsory for all EU agriculture by 2014 and is also being widely adopted globally. IPM offers a 'toolbox' of complementary crop- and region-specific crop protection solutions to address these rising pressures. IPM aims for more sustainable solutions by using complementary technologies. The applied research challenge now is to reduce selection pressure on single solution strategies, by creating additive/synergistic interactions between IPM components. IPM is compatible with organic, conventional, and GM cropping systems and is flexible, allowing regional fine-tuning. It reduces pests below economic thresholds utilizing key 'ecological services', particularly biocontrol. A recent global review demonstrates that IPM can reduce pesticide use and increase yields of most of the major crops studied. Landscape scale 'ecological engineering', together with genetic improvement of new crop varieties, will enhance the durability of pest-resistant cultivars (conventional and GM). IPM will also promote compatibility with semiochemicals, biopesticides, precision pest monitoring tools, and rapid diagnostics. These combined strategies are urgently needed and are best achieved via multi-disciplinary research, including complex spatio-temporal modelling at farm and landscape scales. Integrative and synergistic use of existing and new IPM technologies will help meet future food production needs more sustainably in developed and developing countries, in an era of reduced pesticide availability. Current IPM research gaps are

  12. Estimating the Sensitivity of CLM-Crop to Plant Date and Growing Season Length

    Drewniak, B. A.; Kotamarthi, V. R.

    2012-12-01

    The Community Land Model (CLM), the land component of the Community Earth System Model (CESM), is designed to estimate the land surface response to climate through simulated vegetation phenology and soil carbon and nitrogen dynamics. Since human influences play a significant role shaping the land surface, the vegetation has been expanded to include agriculture (CLM-Crop) for three crop types: corn, soybean, and spring wheat. CLM-Crop parameters, which define crop phenology, are optimized against AmeriFlux observations of gross primary productivity, net ecosystem exchange, and stored biomass and carbon, for two sites in the U.S. growing corn and soybean. However, there is uncertainty in the measurements and using a small subset of data to determine model parameters makes validation difficult. In order to account for the differences in plant behavior across climate zones, an input dataset is used to define the planting dates and the length of the growing season. In order to improve model performance, and to understand the impacts of uncertainty from the input data, we evaluate the sensitivity of crop productivity and production against planting date and the length of the growing season. First, CLM-Crop is modified to establish plant date based on temperature trends for the previous 10-day period, constrained against the range of observed planting dates. This new climate-based model is compared with the standard fixed plant dates to determine how sensitive the model is to when seeding occurs, and how comparable the climate calculated plant dates are to the fixed dates. Next, the length of the growing season will be revised to account for an alternative climate. Finally, both the climate-based planting and new growth season will be simulated together. Results of the different model runs will be compared to the standard model and to observations to determine the importance of planting date and growing season length on crop productivity and yield.

  13. Economic and physical determinants of the global distributions of crop pests and pathogens.

    Bebber, Daniel P; Holmes, Timothy; Smith, David; Gurr, Sarah J

    2014-05-01

    Crop pests and pathogens pose a significant and growing threat to food security, but their geographical distributions are poorly understood. We present a global analysis of pest and pathogen distributions, to determine the roles of socioeconomic and biophysical factors in determining pest diversity, controlling for variation in observational capacity among countries. Known distributions of 1901 pests and pathogens were obtained from CABI. Linear models were used to partition the variation in pest species per country amongst predictors. Reported pest numbers increased with per capita gross domestic product (GDP), research expenditure and research capacity, and the influence of economics was greater in micro-organisms than in arthropods. Total crop production and crop diversity were the strongest physical predictors of pest numbers per country, but trade and tourism were insignificant once other factors were controlled. Islands reported more pests than mainland countries, but no latitudinal gradient in species richness was evident. Country wealth is likely to be a strong indicator of observational capacity, not just trade flow, as has been interpreted in invasive species studies. If every country had US levels of per capita GDP, then 205 ± 9 additional pests per country would be reported, suggesting that enhanced investment in pest observations will reveal the hidden threat of crop pests and pathogens. © 2014 The Authors. New Phytologist © 2014 New Phytologist Trust.

  14. REMOTE-SENSING-BASED BIOPHYSICAL MODELS FOR ESTIMATING LAI OF IRRIGATED CROPS IN MURRY DARLING BASIN

    I. Wittamperuma

    2012-07-01

    Full Text Available Remote sensing is a rapid and reliable method for estimating crop growth data from individual plant to crops in irrigated agriculture ecosystem. The LAI is one of the important biophysical parameter for determining vegetation health, biomass, photosynthesis and evapotranspiration (ET for the modelling of crop yield and water productivity. Ground measurement of this parameter is tedious and time-consuming due to heterogeneity across the landscape over time and space. This study deals with the development of remote-sensing based empirical relationships for the estimation of ground-based LAI (LAIG using NDVI, modelled with and without atmospheric correction models for three irrigated crops (corn, wheat and rice grown in irrigated farms within Coleambally Irrigation Area (CIA which is located in southern Murray Darling basin, NSW in Australia. Extensive ground truthing campaigns were carried out to measure crop growth and to collect field samples of LAI using LAI- 2000 Plant Canopy Analyser and reflectance using CROPSCAN Multi Spectral Radiometer at several farms within the CIA. A Set of 12 cloud free Landsat 5 TM satellite images for the period of 2010-11 were downloaded and regression analysis was carried out to analyse the co-relationships between satellite and ground measured reflectance and to check the reliability of data sets for the crops. Among all the developed regression relationships between LAI and NDVI, the atmospheric correction process has significantly improved the relationship between LAI and NDVI for Landsat 5 TM images. The regression analysis also shows strong correlations for corn and wheat but weak correlations for rice which is currently being investigated.

  15. Water consumption of the estevia (Stevia rebaudiana (Bert. Bertoni crop estimated through microlysimeter

    Fronza Diniz

    2003-01-01

    Full Text Available The knowledge of water requirement of crops in the different growing phases elicits higher crop yield and rational use of water resource. The aim of this work was to estimate the water consumption of stevia using two constant watertable microlysimeters. The research was conducted in San Piero a Grado, Pisa, Italy. The data were collected daily from June, 1st, to October, 22th, 2000. Reference evapotranspiration was determined by the Penman-Monteith-FAO method, in the same period. Microlysimeters watertables level were maintained at the 35 cm depth. Crop evapotranspiration for the total cicle (80 days was 464 mm. For the most water consuming phase, crop average evapotranspiration was 5.44 mm day-1. The crop coefficient values were 1.45 for the first 25 days, 1.14 for the next period (26 to 50 days, and 1.16 for the latest period (51 to 80 days. The stevia leaf yield of the microlysimeters was 4.369 kg ha-1 and their steviosideo content 6.49%.

  16. Global crop yield response to extreme heat stress under multiple climate change futures

    Deryng, Delphine; Warren, Rachel; Conway, Declan; Ramankutty, Navin; Price, Jeff

    2014-01-01

    Extreme heat stress during the crop reproductive period can be critical for crop productivity. Projected changes in the frequency and severity of extreme climatic events are expected to negatively impact crop yields and global food production. This study applies the global crop model PEGASUS to quantify, for the first time at the global scale, impacts of extreme heat stress on maize, spring wheat and soybean yields resulting from 72 climate change scenarios for the 21st century. Our results project maize to face progressively worse impacts under a range of RCPs but spring wheat and soybean to improve globally through to the 2080s due to CO 2 fertilization effects, even though parts of the tropic and sub-tropic regions could face substantial yield declines. We find extreme heat stress at anthesis (HSA) by the 2080s (relative to the 1980s) under RCP 8.5, taking into account CO 2 fertilization effects, could double global losses of maize yield (ΔY = −12.8 ± 6.7% versus − 7.0 ± 5.3% without HSA), reduce projected gains in spring wheat yield by half (ΔY = 34.3 ± 13.5% versus 72.0 ± 10.9% without HSA) and in soybean yield by a quarter (ΔY = 15.3 ± 26.5% versus 20.4 ± 22.1% without HSA). The range reflects uncertainty due to differences between climate model scenarios; soybean exhibits both positive and negative impacts, maize is generally negative and spring wheat generally positive. Furthermore, when assuming CO 2 fertilization effects to be negligible, we observe drastic climate mitigation policy as in RCP 2.6 could avoid more than 80% of the global average yield losses otherwise expected by the 2080s under RCP 8.5. We show large disparities in climate impacts across regions and find extreme heat stress adversely affects major producing regions and lower income countries. (paper)

  17. Estimating the potential intensification of global grazing systems based on climate adjusted yield gap analysis

    Sheehan, J. J.

    2016-12-01

    We report here a first-of-its-kind analysis of the potential for intensification of global grazing systems. Intensification is calculated using the statistical yield gap methodology developed previously by others (Mueller et al 2012 and Licker et al 2010) for global crop systems. Yield gaps are estimated by binning global pasture land area into 100 equal area sized bins of similar climate (defined by ranges of rainfall and growing degree days). Within each bin, grid cells of pastureland are ranked from lowest to highest productivity. The global intensification potential is defined as the sum of global production across all bins at a given percentile ranking (e.g. performance at the 90th percentile) divided by the total current global production. The previous yield gap studies focused on crop systems because productivity data on these systems is readily available. Nevertheless, global crop land represents only one-third of total global agricultural land, while pasture systems account for the remaining two-thirds. Thus, it is critical to conduct the same kind of analysis on what is the largest human use of land on the planet—pasture systems. In 2013, Herrero et al announced the completion of a geospatial data set that augmented the animal census data with data and modeling about production systems and overall food productivity (Herrero et al, PNAS 2013). With this data set, it is now possible to apply yield gap analysis to global pasture systems. We used the Herrero et al data set to evaluate yield gaps for meat and milk production from pasture based systems for cattle, sheep and goats. The figure included with this abstract shows the intensification potential for kcal per hectare per year of meat and milk from global cattle, sheep and goats as a function of increasing levels of performance. Performance is measured as the productivity achieved at a given ranked percentile within each bin.We find that if all pasture land were raised to their 90th percentile of

  18. Estimation of crop water requirements using remote sensing for operational water resources management

    Vasiliades, Lampros; Spiliotopoulos, Marios; Tzabiras, John; Loukas, Athanasios; Mylopoulos, Nikitas

    2015-06-01

    An integrated modeling system, developed in the framework of "Hydromentor" research project, is applied to evaluate crop water requirements for operational water resources management at Lake Karla watershed, Greece. The framework includes coupled components for operation of hydrotechnical projects (reservoir operation and irrigation works) and estimation of agricultural water demands at several spatial scales using remote sensing. The study area was sub-divided into irrigation zones based on land use maps derived from Landsat 5 TM images for the year 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) was used to derive actual evapotranspiration (ET) and crop coefficient (ETrF) values from Landsat TM imagery. Agricultural water needs were estimated using the FAO method for each zone and each control node of the system for a number of water resources management strategies. Two operational strategies of hydro-technical project development (present situation without operation of the reservoir and future situation with the operation of the reservoir) are coupled with three water demand strategies. In total, eight (8) water management strategies are evaluated and compared. The results show that, under the existing operational water resources management strategies, the crop water requirements are quite large. However, the operation of the proposed hydro-technical projects in Lake Karla watershed coupled with water demand management measures, like improvement of existing water distribution systems, change of irrigation methods, and changes of crop cultivation could alleviate the problem and lead to sustainable and ecological use of water resources in the study area.

  19. Estimation of effects of photosynthesis response functions on rice yields and seasonal variation of CO2 fixation using a photosynthesis-sterility type of crop yield model

    Kaneko, D.; Moriwaki, Y.

    2008-01-01

    This study presents a crop production model improvement: the previously adopted Michaelis-Menten (MM) type photosynthesis response function (fsub(rad-MM)) was replaced with a Prioul-Chartier (PC) type function (fsub(rad-PC)). The authors' analysis reflects concerns regarding the background effect of global warming, under simultaneous conditions of high air temperature and strong solar radiation. The MM type function fsub(rad-MM) can give excessive values leading to an overestimate of photosynthesis rate (PSN) and grain yield for paddy-rice. The MM model is applicable to many plants whose (PSN) increases concomitant with increased insolation: wheat, maize, soybean, etc. For paddy rice, the PSN apparently shows a maximum PSN. This paper proves that the MM model overestimated the PSN for paddy rice for sufficient solar radiation: the PSN using the PC model yields 10% lower values. However, the unit crop production index (CPIsub(U)) is almost independent of the MM and PC models because of respective standardization of both PSN and crop production index using average PSNsub(0) and CPIsub(0). The authors improved the estimation method using a photosynthesis-and-sterility based crop situation index (CSIsub(E)) to produce a crop yield index (CYIsub(E)), which is used to estimate rice yields in place of the crop situation index (CSI); the CSI gives a percentage of rice yields compared to normal annual production. The model calculates PSN including biomass effects, low-temperature sterility, and high-temperature injury by incorporating insolation, effective air temperature, the normalized difference vegetation index (NDVI), and effects of temperature on photosynthesis. Based on routine observation data, the method enables automated crop-production monitoring in remote regions without special observations. This method can quantify grain production early to raise an alarm in Southeast Asian countries, which must confront climate fluctuation through this era of global

  20. What is the potential of cropland albedo management in the fight against global warming? A case study based on the use of cover crops

    Carrer, Dominique; Pique, Gaétan; Ferlicoq, Morgan; Ceamanos, Xavier; Ceschia, Eric

    2018-04-01

    Land cover management in agricultural areas is a powerful tool that could play a role in the mitigation of climate change and the counterbalance of global warming. First, we attempted to quantify the radiative forcing that would increase the surface albedo of croplands in Europe following the inclusion of cover crops during the fallow period. This is possible since the albedo of bare soil in many areas of Europe is lower than the albedo of vegetation. By using satellite data, we demonstrated that the introduction of cover crops into the crop rotation during the fallow period would increase the albedo over 4.17% of Europe’s surface. According to our study, the effect resulting from this increase in the albedo of the croplands would be equivalent to a mitigation of 3.16 MtCO2-eq.year‑1 over a 100 year time horizon. This is equivalent to a mitigation potential per surface unit (m2) of introduced cover crop over Europe of 15.91 gCO2-eq.year‑1.m‑2. This value, obtained at the European scale, is consistent with previous estimates. We show that this mitigation potential could be increased by 27% if the cover crop is maintained for a longer period than 3 months and reduced by 28% in the case of no irrigation. In the second part of this work, based on recent studies estimating the impact of cover crops on soil carbon sequestration and the use of fertilizer, we added the albedo effect to those estimates, and we argued that, by considering areas favourable to their introduction, cover crops in Europe could mitigate human-induced agricultural greenhouse gas emissions by up to 7% per year, using 2011 as a reference. The impact of the albedo change per year would be between 10% and 13% of this total impact. The countries showing the greatest mitigation potentials are France, Bulgaria, Romania, and Germany.

  1. Light- and water-use efficiency model synergy: a revised look at crop yield estimation for agricultural decision-making

    Marshall, M.; Tu, K. P.

    2015-12-01

    Large-area crop yield models (LACMs) are commonly employed to address climate-driven changes in crop yield and inform policy makers concerned with climate change adaptation. Production efficiency models (PEMs), a class of LACMs that rely on the conservative response of carbon assimilation to incoming solar radiation absorbed by a crop contingent on environmental conditions, have increasingly been used over large areas with remote sensing spectral information to improve the spatial resolution of crop yield estimates and address important data gaps. Here, we present a new PEM that combines model principles from the remote sensing-based crop yield and evapotranspiration (ET) model literature. One of the major limitations of PEMs is that they are evaluated using data restricted in both space and time. To overcome this obstacle, we first validated the model using 2009-2014 eddy covariance flux tower Gross Primary Production data in a rice field in the Central Valley of California- a critical agro-ecosystem of the United States. This evaluation yielded a Willmot's D and mean absolute error of 0.81 and 5.24 g CO2/d, respectively, using CO2, leaf area, temperature, and moisture constraints from the MOD16 ET model, Priestley-Taylor ET model, and the Global Production Efficiency Model (GLOPEM). A Monte Carlo simulation revealed that the model was most sensitive to the Enhanced Vegetation Index (EVI) input, followed by Photosynthetically Active Radiation, vapor pressure deficit, and air temperature. The model will now be evaluated using 30 x 30m (Landsat resolution) biomass transects developed in 2011 and 2012 from spectroradiometric and other non-destructive in situ metrics for several cotton, maize, and rice fields across the Central Valley. Finally, the model will be driven by Daymet and MODIS data over the entire State of California and compared with county-level crop yield statistics. It is anticipated that the new model will facilitate agro-climatic decision-making in

  2. Crop improvement in the CGIAR as a global success story of open access and international collaboration

    Derek Byerlee

    2009-12-01

    Full Text Available International agricultural research has historically been an example par excellence of open source approach to biological research. Beginning in the 1950s and especially in the 1960s, a looming global food crisis led to the development of a group of international agricultural research centers with a specific mandate to foster international exchange and crop improvement relevant to many countries. This formalization of a global biological commons in genetic resources was implemented through an elaborate system of international nurseries with a breeding hub, free sharing of germplasm, collaboration in information collection, the development of human resources, and an international collaborative network. This paper traces the history of the international wheat program with particular attention to how this truly open source system operated in practice and the impacts that it had on world poverty and hunger. The paper also highlights the challenges of maintaining and evolving such a system over the long term, both in terms of financing, as well the changing ‘rules of the game’ resulting from international agreements on intellectual property rights and biodiversity. Yet the open source approach is just as relevant today, as witnessed by current crises in food prices and looming crop diseases problem of global significance.

  3. Estimating Field Scale Crop Evapotranspiration using Landsat and MODIS Satellite Observations

    Wong, A.; Jin, Y.; Snyder, R. L.; Daniele, Z.; Gao, F.

    2016-12-01

    Irrigation accounts for 80% of human freshwater consumption, and most of it return to the atmosphere through Evapotranspiration (ET). Given the challenges of already-stressed water resources and ground water regulation in California, a cost-effective, timely, and consistent spatial estimate of crop ET, from the farm to watershed level, is becoming increasingly important. The Priestley-Taylor (PT) approach, calibrated with field data and driven by satellite observations, shows great promise for accurate ET estimates across diverse ecosystems. We here aim to improve the robustness of the PT approach in agricultural lands, to enable growers and farm managers to tailor irrigation management based on in-field spatial variability and in-season variation. We optimized the PT coefficients for each crop type with available ET measurements from eddy covariance towers and/or surface renewal stations at six crop fields (Alfalfa, Almond, Citrus, Corn, Pistachio and Rice) in California. Good agreement was found between satellite-based estimates and field measurements of net radiation, with a RMSE of less than 36 W m-2. The crop type specific optimization performed well, with a RMSE of 30 W m-2 and a correlation of 0.81 for predicted daily latent heat flux. The calibrated algorithm was used to estimate ET at 30 m resolution over the Sacramento-San Joaquin Delta region for 2015 water year. It captures well the seasonal dynamics and spatial distribution of ET in Sacramento-San Joaquin Delta. A continuous monitoring of the dynamics and spatial heterogeneity of canopy and consumptive water use at a field scale, will help the growers to be well prepared and informed to adaptively manage water, canopy, and grove density to maximize the yield with the least amount of water.

  4. Use of thermal and visible imagery for estimating crop water status of irrigated grapevine.

    Möller, M; Alchanatis, V; Cohen, Y; Meron, M; Tsipris, J; Naor, A; Ostrovsky, V; Sprintsin, M; Cohen, S

    2007-01-01

    Achieving high quality wine grapes depends on the ability to maintain mild to moderate levels of water stress in the crop during the growing season. This study investigates the use of thermal imaging for monitoring water stress. Experiments were conducted on a wine-grape (Vitis vinifera cv. Merlot) vineyard in northern Israel. Irrigation treatments included mild, moderate, and severe stress. Thermal and visible (RGB) images of the crop were taken on four days at midday with a FLIR thermal imaging system and a digital camera, respectively, both mounted on a truck-crane 15 m above the canopy. Aluminium crosses were used to match visible and thermal images in post-processing and an artificial wet surface was used to estimate the reference wet temperature (T(wet)). Monitored crop parameters included stem water potential (Psi(stem)), leaf conductance (g(L)), and leaf area index (LAI). Meteorological parameters were measured at 2 m height. CWSI was highly correlated with g(L) and moderately correlated with Psi(stem). The CWSI-g(L) relationship was very stable throughout the season, but for that of CWSI-Psi(stem) both intercept and slope varied considerably. The latter presumably reflects the non-direct nature of the physiological relationship between CWSI and Psi(stem). The highest R(2) for the CWSI to g(L) relationship, 0.91 (n=12), was obtained when CWSI was computed using temperatures from the centre of the canopy, T(wet) from the artificial wet surface, and reference dry temperature from air temperature plus 5 degrees C. Using T(wet) calculated from the inverted Penman-Monteith equation and estimated from an artificially wetted part of the canopy also yielded crop water-stress estimates highly correlated with g(L) (R(2)=0.89 and 0.82, respectively), while a crop water-stress index using 'theoretical' reference temperatures computed from climate data showed significant deviations in the late season. Parameter variability and robustness of the different CWSI estimates

  5. Dependency of global primary bioenergy crop potentials in 2050 on food systems, yields, biodiversity conservation and political stability

    Erb, Karl-Heinz; Haberl, Helmut; Plutzar, Christoph

    2012-01-01

    The future bioenergy crop potential depends on (1) changes in the food system (food demand, agricultural technology), (2) political stability and investment security, (3) biodiversity conservation, (4) avoidance of long carbon payback times from deforestation, and (5) energy crop yields. Using a biophysical biomass-balance model, we analyze how these factors affect global primary bioenergy potentials in 2050. The model calculates biomass supply and demand balances for eleven world regions, eleven food categories, seven food crop types and two livestock categories, integrating agricultural forecasts and scenarios with a consistent global land use and NPP database. The TREND scenario results in a global primary bioenergy potential of 77 EJ/yr, alternative assumptions on food-system changes result in a range of 26–141 EJ/yr. Exclusion of areas for biodiversity conservation and inaccessible land in failed states reduces the bioenergy potential by up to 45%. Optimistic assumptions on future energy crop yields increase the potential by up to 48%, while pessimistic assumptions lower the potential by 26%. We conclude that the design of sustainable bioenergy crop production policies needs to resolve difficult trade-offs such as food vs. energy supply, renewable energy vs. biodiversity conservation or yield growth vs. reduction of environmental problems of intensive agriculture. - Highlights: ► Global energy crop potentials in 2050 are calculated with a biophysical biomass-balance model. ► The study is focused on dedicated energy crops, forestry and residues are excluded. ► Depending on food-system change, global energy crop potentials range from 26–141 EJ/yr. ► Exclusion of protected areas and failed states may reduce the potential up to 45%. ► The bioenergy potential may be 26% lower or 45% higher, depending on energy crop yields.

  6. A METHOD TO ESTIMATE TEMPORAL INTERACTION IN A CONDITIONAL RANDOM FIELD BASED APPROACH FOR CROP RECOGNITION

    P. M. A. Diaz

    2016-06-01

    Full Text Available This paper presents a method to estimate the temporal interaction in a Conditional Random Field (CRF based approach for crop recognition from multitemporal remote sensing image sequences. This approach models the phenology of different crop types as a CRF. Interaction potentials are assumed to depend only on the class labels of an image site at two consecutive epochs. In the proposed method, the estimation of temporal interaction parameters is considered as an optimization problem, whose goal is to find the transition matrix that maximizes the CRF performance, upon a set of labelled data. The objective functions underlying the optimization procedure can be formulated in terms of different accuracy metrics, such as overall and average class accuracy per crop or phenological stages. To validate the proposed approach, experiments were carried out upon a dataset consisting of 12 co-registered LANDSAT images of a region in southeast of Brazil. Pattern Search was used as the optimization algorithm. The experimental results demonstrated that the proposed method was able to substantially outperform estimates related to joint or conditional class transition probabilities, which rely on training samples.

  7. Estimating Biomass of Barley Using Crop Surface Models (CSMs Derived from UAV-Based RGB Imaging

    Juliane Bendig

    2014-10-01

    Full Text Available Crop monitoring is important in precision agriculture. Estimating above-ground biomass helps to monitor crop vitality and to predict yield. In this study, we estimated fresh and dry biomass on a summer barley test site with 18 cultivars and two nitrogen (N-treatments using the plant height (PH from crop surface models (CSMs. The super-high resolution, multi-temporal (1 cm/pixel CSMs were derived from red, green, blue (RGB images captured from a small unmanned aerial vehicle (UAV. Comparison with PH reference measurements yielded an R2 of 0.92. The test site with different cultivars and treatments was monitored during “Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie” (BBCH Stages 24–89. A high correlation was found between PH from CSMs and fresh biomass (R2 = 0.81 and dry biomass (R2 = 0.82. Five models for above-ground fresh and dry biomass estimation were tested by cross-validation. Modelling biomass between different N-treatments for fresh biomass produced the best results (R2 = 0.71. The main limitation was the influence of lodging cultivars in the later growth stages, producing irregular plant heights. The method has potential for future application by non-professionals, i.e., farmers.

  8. A Remote-Sensing Driven Tool for Estimating Crop Stress and Yields

    Martha C. Anderson

    2013-07-01

    Full Text Available Biophysical crop simulation models are normally forced with precipitation data recorded with either gauges or ground-based radar. However, ground-based recording networks are not available at spatial and temporal scales needed to drive the models at many critical places on earth. An alternative would be to employ satellite-based observations of either precipitation or soil moisture. Satellite observations of precipitation are currently not considered capable of forcing the models with sufficient accuracy for crop yield predictions. However, deduction of soil moisture from space-based platforms is in a more advanced state than are precipitation estimates so that these data may be capable of forcing the models with better accuracy. In this study, a mature two-source energy balance model, the Atmosphere Land Exchange Inverse (ALEXI model, was used to deduce root zone soil moisture for an area of North Alabama, USA. The soil moisture estimates were used in turn to force the state-of-the-art Decision Support System for Agrotechnology Transfer (DSSAT crop simulation model. The study area consisted of a mixture of rainfed and irrigated cornfields. The results indicate that the model forced with the ALEXI moisture estimates produced yield simulations that compared favorably with observed yields and with the rainfed model. The data appear to indicate that the ALEXI model did detect the soil moisture signal from the mixed rainfed/irrigation corn fields and this signal was of sufficient strength to produce adequate simulations of recorded yields over a 10 year period.

  9. Estimation of different data compositions for early-season crop type classification.

    Hao, Pengyu; Wu, Mingquan; Niu, Zheng; Wang, Li; Zhan, Yulin

    2018-01-01

    Timely and accurate crop type distribution maps are an important inputs for crop yield estimation and production forecasting as multi-temporal images can observe phenological differences among crops. Therefore, time series remote sensing data are essential for crop type mapping, and image composition has commonly been used to improve the quality of the image time series. However, the optimal composition period is unclear as long composition periods (such as compositions lasting half a year) are less informative and short composition periods lead to information redundancy and missing pixels. In this study, we initially acquired daily 30 m Normalized Difference Vegetation Index (NDVI) time series by fusing MODIS, Landsat, Gaofen and Huanjing (HJ) NDVI, and then composited the NDVI time series using four strategies (daily, 8-day, 16-day, and 32-day). We used Random Forest to identify crop types and evaluated the classification performances of the NDVI time series generated from four composition strategies in two studies regions from Xinjiang, China. Results indicated that crop classification performance improved as crop separabilities and classification accuracies increased, and classification uncertainties dropped in the green-up stage of the crops. When using daily NDVI time series, overall accuracies saturated at 113-day and 116-day in Bole and Luntai, and the saturated overall accuracies (OAs) were 86.13% and 91.89%, respectively. Cotton could be identified 40∼60 days and 35∼45 days earlier than the harvest in Bole and Luntai when using daily, 8-day and 16-day composition NDVI time series since both producer's accuracies (PAs) and user's accuracies (UAs) were higher than 85%. Among the four compositions, the daily NDVI time series generated the highest classification accuracies. Although the 8-day, 16-day and 32-day compositions had similar saturated overall accuracies (around 85% in Bole and 83% in Luntai), the 8-day and 16-day compositions achieved these

  10. Advances in regional crop yield estimation over the United States using satellite remote sensing data

    Johnson, D. M.; Dorn, M. F.; Crawford, C.

    2015-12-01

    Since the dawn of earth observation imagery, particularly from systems like Landsat and the Advanced Very High Resolution Radiometer, there has been an overarching desire to regionally estimate crop production remotely. Research efforts integrating space-based imagery into yield models to achieve this need have indeed paralleled these systems through the years, yet development of a truly useful crop production monitoring system has been arguably mediocre in coming. As a result, relatively few organizations have yet to operationalize the concept, and this is most acute in regions of the globe where there are not even alternative sources of crop production data being collected. However, the National Agricultural Statistics Service (NASS) has continued to push for this type of data source as a means to complement its long-standing, traditional crop production survey efforts which are financially costly to the government and create undue respondent burden on farmers. Corn and soybeans, the two largest field crops in the United States, have been the focus of satellite-based production monitoring by NASS for the past decade. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been seen as the most pragmatic input source for modeling yields primarily based on its daily revisit capabilities and reasonable ground sample resolution. The research methods presented here will be broad but provides a summary of what is useful and adoptable with satellite imagery in terms of crop yield estimation. Corn and soybeans will be of particular focus but other major staple crops like wheat and rice will also be presented. NASS will demonstrate that while MODIS provides a slew of vegetation related products, the traditional normalized difference vegetation index (NDVI) is still ideal. Results using land surface temperature products, also generated from MODIS, will also be shown. Beyond the MODIS data itself, NASS research has also focused efforts on understanding a

  11. Estimation of runoff mitigation by morphologically different cover crop root systems

    Yu, Yang; Loiskandl, Willibald; Kaul, Hans-Peter; Himmelbauer, Margarita; Wei, Wei; Chen, Liding; Bodner, Gernot

    2016-07-01

    Hydrology is a major driver of biogeochemical processes underlying the distinct productivity of different biomes, including agricultural plantations. Understanding factors governing water fluxes in soil is therefore a key target for hydrological management. Our aim was to investigate changes in soil hydraulic conductivity driven by morphologically different root systems of cover crops and their impact on surface runoff. Root systems of twelve cover crop species were characterized and the corresponding hydraulic conductivity was measured by tension infiltrometry. Relations of root traits to Gardner's hydraulic conductivity function were determined and the impact on surface runoff was estimated using HYDRUS 2D. The species differed in both rooting density and root axes thickness, with legumes distinguished by coarser axes. Soil hydraulic conductivity was changed particularly in the plant row where roots are concentrated. Specific root length and median root radius were the best predictors for hydraulic conductivity changes. For an intensive rainfall simulation scenario up to 17% less rainfall was lost by surface runoff in case of the coarsely rooted legumes Melilotus officinalis and Lathyrus sativus, and the densely rooted Linum usitatissimum. Cover crops with coarse root axes and high rooting density enhance soil hydraulic conductivity and effectively reduce surface runoff. An appropriate functional root description can contribute to targeted cover crop selection for efficient runoff mitigation.

  12. Parameter values for the estimation of radionuclide transfer to major food crops in Korea

    Choi, Yong-Ho; Lim, Kwang-Muk; Jun, In; Keum, Dong-Kwon; Lee, Chang-Woo

    2008-01-01

    This paper summarizes the results of the radiotracer experiments and field studies performed in Korea for the past 20 years to obtain parameter values for estimating the environmental transfer of radionuclides to food crops. With regards to direct plant contamination, the interception fractions, weathering half-lives and translocation factors of Cs, Sr, Mn, Co and Ru were measured for depositions at different growth stages of selected food crops. In order to investigate an indirect contamination pathway, the soil-to-plant transfer factors (TF m , dimensionless) of Cs, Sr, Mn, Co and/or Zn were measured for rice, Chinese cabbage, radish, soybean, barley, lettuce and so on in one or more soils. In addition, the transfer factors (TF a , m 2 kg -1 ) based on a deposition density were also measured following depositions at different times during the growth periods of several food crops. Particularly for rice and Chinese cabbage, tritium experiments were also carried out for the TF a . The obtained parameter values varied considerably with the soils, crops, radionuclides and deposition times. These data would be applicable to both normal and acute releases not only in Korea but also in many other countries. (author)

  13. Illegal markets: Estimates of global proceeds

    Marinković Darko M.

    2015-01-01

    Full Text Available Illegal markets represent a phenomenon of considerable economic, political and social significance whose annual income exceeds the value of a thousand billion USD. Illegal market participants are beyond the reach of government institutions and rule of law while social connections and personal acquaintances play an important role of functional substitute. In the last decade there was a significant increase of illegal trafficking of narcotics, people, fire arms, counterfeit products and natural resources. Both selling and purchase of these as well as other kinds of products and services at illegal markets are generally characterized by high level of organization and presence of strong criminal groups and networks. Although these activities existed in the past their present scope and geographic distribution are without precedent. Measuring unlawful financial flows at illegal markets represents quite a complex task. Various estimates are the result of inexistence of uniform and generally accepted methodology. In addition to this, the special problem is also the consensus of market actors, because of which the phenomenon of illegal markets and distribution of products and services at these markets is rather hidden. The paper defines and analyzes the key features of illegal markets, the role of organized crime at illegal markets, as well as the estimates of the values of financial flows at the markets of counterfeit products, narcotics, and people as goods, or human organs and sexual services, weapons, tobacco products and dirty money.

  14. Poppy Crop Height and Capsule Volume Estimation from a Single UAS Flight

    Faheem Iqbal

    2017-06-01

    Full Text Available The objective of this study was to estimate poppy plant height and capsule volume with remote sensing using an Unmanned Aircraft System (UAS. Data were obtained from field measurements and UAS flights over two poppy crops at Cambridge and Cressy in Tasmania. Imagery acquired from the UAS was used to produce dense point clouds using structure from motion (SfM and multi-view stereopsis (MVS techniques. Dense point clouds were used to generate a digital surface model (DSM and orthophoto mosaic. An RGB index was derived from the orthophoto to extract the bare ground spaces. This bare ground space mask was used to filter the points on the ground, and a digital terrain model (DTM was interpolated from these points. Plant height values were estimated by subtracting the DSM and DTM to generate a Crop Height Model (CHM. UAS-derived plant height (PH and field measured PH in Cambridge were strongly correlated with R2 values ranging from 0.93 to 0.97 for Transect 1 and Transect 2, respectively, while at Cressy results from a single flight provided R2 of 0.97. Therefore, the proposed method can be considered an important step towards crop surface model (CSM generation from a single UAS flight in situations where a bare ground DTM is unavailable. High correlations were found between UAS-derived PH and poppy capsule volume (CV at capsule formation stage (R2 0.74, with relative error of 19.62%. Results illustrate that plant height can be reliably estimated for poppy crops based on a single UAS flight and can be used to predict opium capsule volume at capsule formation stage.

  15. Global biodiversity loss: Exaggerated versus realistic estimates

    John C. Briggs

    2016-06-01

    Full Text Available For the past 50 years, the public has been made to feel guilty about the tragedy of human-caused biodiversity loss due to the extinction of hundreds or thousands of species every year. Numerous articles and books from the scientific and popular press and publicity on the internet have contributed to a propaganda wave about our grievous loss and the beginning of a sixth mass extinction. However, within the past few years, questions have arisen about the validity of the data which led to the doom scenario. Here I show that, for the past 500 years, terrestrial animals (insects and vertebrates have been losing less than two species per year due to human causes. The majority of the extinctions have occurred on oceanic islands with little effect on continental ecology. In the marine environment, losses have also been very low. At the same time, speciation has continued to occur and biodiversity gain by this means may have equaled or even surpassed the losses. While species loss is not, so far, a global conservation problem, ongoing population declines within thousands of species that are at risk on land and in the sea constitute an extinction debt that will be paid unless those species can be rescued.

  16. The Space-Time Variation of Global Crop Yields, Detecting Simultaneous Outliers and Identifying the Teleconnections with Climatic Patterns

    Najafi, E.; Devineni, N.; Pal, I.; Khanbilvardi, R.

    2017-12-01

    An understanding of the climate factors that influence the space-time variability of crop yields is important for food security purposes and can help us predict global food availability. In this study, we address how the crop yield trends of countries globally were related to each other during the last several decades and the main climatic variables that triggered high/low crop yields simultaneously across the world. Robust Principal Component Analysis (rPCA) is used to identify the primary modes of variation in wheat, maize, sorghum, rice, soybeans, and barley yields. Relations between these modes of variability and important climatic variables, especially anomalous sea surface temperature (SSTa), are examined from 1964 to 2010. rPCA is also used to identify simultaneous outliers in each year, i.e. systematic high/low crop yields across the globe. The results demonstrated spatiotemporal patterns of these crop yields and the climate-related events that caused them as well as the connection of outliers with weather extremes. We find that among climatic variables, SST has had the most impact on creating simultaneous crop yields variability and yield outliers in many countries. An understanding of this phenomenon can benefit global crop trade networks.

  17. Governing the management and use of pooled microbial genetic resources: Lessons from the global crop commons

    Michael Halewood

    2010-01-01

    Full Text Available The paper highlights lessons learned over the last thirty years establishing a governance structure for the global crop commons that are of relevance to current champions of the microbial commons. It argues that the political, legal and biophysical situation in which microbial genetic resources (and their users are located today are similar to the situation of plant genetic resources in the mid-1990s, before the International Treaty on Plant Genetic Resources was negotiated. Consequently, the paper suggests that it may be useful to look to the model of global network of ex situ plant genetic resources collections as a precedent to follow – even if only loosely – in developing an intergovernmentally endorsed legal substructure and governance framework for the microbial commons.

  18. CropWatch agroclimatic indicators (CWAIs) for weather impact assessment on global agriculture

    Gommes, René; Wu, Bingfang; Zhang, Ning; Feng, Xueliang; Zeng, Hongwei; Li, Zhongyuan; Chen, Bo

    2017-02-01

    CropWatch agroclimatic indicators (CWAIs) are a monitoring tool developed by the CropWatch global crop monitoring system in the Chinese Academy of Sciences (CAS; http://www.cropwatch.com.cn, Wu et al Int J Digital Earth 7(2):113-137, 2014, Wu et al Remote Sens 7:3907-3933, 2015). Contrary to most other environmental and agroclimatic indicators, they are "agronomic value-added", i.e. they are spatial values averaged over agricultural areas only and they include a weighting that enhances the contribution of the areas with the largest production potential. CWAIs can be computed for any time interval (starting from dekads) and yield one synthetic value per variable over a specific area and time interval, for instance a national annual value. Therefore, they are very compatible with socio-economic and other variables that are usually reported at regular time intervals over administrative units, such as national environmental or agricultural statistics. Two of the CWAIs are satellite-based (RAIN and Photosynthetically Active radiation, PAR) while the third is ground based (TEMP, air temperature); capitals are used when specifically referring to CWAIs rather than the climate variables in general. The paper first provides an overview of some common agroclimatic indicators, describing their procedural, systemic and normative features in subsequent sections, following the terminology of Binder et al Environ Impact Assess Rev 30:71-81 (2010). The discussion focuses on the systemic and normative aspects: the CWAIs are assessed in terms of their coherent description of the agroclimatic crop environment, at different spatial scales (systemic). The final section shows that the CWAIs retain key statistical properties of the underlying climate variables and that they can be compared to a reference value and used as monitoring and early warning variables (normative).

  19. CropWatch agroclimatic indicators (CWAIs) for weather impact assessment on global agriculture.

    Gommes, René; Wu, Bingfang; Zhang, Ning; Feng, Xueliang; Zeng, Hongwei; Li, Zhongyuan; Chen, Bo

    2017-02-01

    CropWatch agroclimatic indicators (CWAIs) are a monitoring tool developed by the CropWatch global crop monitoring system in the Chinese Academy of Sciences (CAS; www.cropwatch.com.cn , Wu et al Int J Digital Earth 7(2):113-137, 2014, Wu et al Remote Sens 7:3907-3933, 2015). Contrary to most other environmental and agroclimatic indicators, they are "agronomic value-added", i.e. they are spatial values averaged over agricultural areas only and they include a weighting that enhances the contribution of the areas with the largest production potential. CWAIs can be computed for any time interval (starting from dekads) and yield one synthetic value per variable over a specific area and time interval, for instance a national annual value. Therefore, they are very compatible with socio-economic and other variables that are usually reported at regular time intervals over administrative units, such as national environmental or agricultural statistics. Two of the CWAIs are satellite-based (RAIN and Photosynthetically Active radiation, PAR) while the third is ground based (TEMP, air temperature); capitals are used when specifically referring to CWAIs rather than the climate variables in general. The paper first provides an overview of some common agroclimatic indicators, describing their procedural, systemic and normative features in subsequent sections, following the terminology of Binder et al Environ Impact Assess Rev 30:71-81 (2010). The discussion focuses on the systemic and normative aspects: the CWAIs are assessed in terms of their coherent description of the agroclimatic crop environment, at different spatial scales (systemic). The final section shows that the CWAIs retain key statistical properties of the underlying climate variables and that they can be compared to a reference value and used as monitoring and early warning variables (normative).

  20. Floods and food security: A method to estimate the effect of inundation on crops availability

    Pacetti, Tommaso; Caporali, Enrica; Rulli, Maria Cristina

    2017-12-01

    The inner connections between floods and food security are extremely relevant, especially in developing countries where food availability can be highly jeopardized by extreme events that damage the primary access to food, i.e. agriculture. A method for the evaluation of the effects of floods on food supply, consisting of the integration of remote sensing data, agricultural statistics and water footprint databases, is proposed and applied to two different case studies. Based on the existing literature related to extreme floods, the events in Bangladesh (2007) and in Pakistan (2010) have been selected as exemplary case studies. Results show that the use of remote sensing data combined with other sources of onsite information is particularly useful to assess the effects of flood events on food availability. The damages caused by floods on agricultural areas are estimated in terms of crop losses and then converted into lost calories and water footprint as complementary indicators. Method results are fully repeatable; whereas, for remote sensed data the sources of data are valid worldwide and the data regarding land use and crops characteristics are strongly site specific, which need to be carefully evaluated. A sensitivity analysis has been carried out for the water depth critical on the crops in Bangladesh, varying the assumed level by ±20%. The results show a difference in the energy content losses estimation of 12% underlying the importance of an accurate data choice.

  1. Productivity and carbon dioxide exchange of leguminous crops: estimates from flux tower measurements

    Gilmanov, Tagir G.; Baker, John M.; Bernacchi, Carl J.; Billesbach, David P.; Burba, George G.; Castro, Saulo; Chen, Jiquan; Eugster, Werner; Fischer, Marc L.; Gamon, John A.; Gebremedhin, Maheteme T.; Glenn, Aaron J.; Griffis, Timothy J.; Hatfield, Jerry L.; Heuer, Mark W.; Howard, Daniel M.; Leclerc, Monique Y.; Loescher, Henry W.; Marloie, Oliver; Meyers, Tilden P.; Olioso, Albert; Phillips, Rebecca L.; Prueger, John H.; Skinner, R. Howard; Suyker, Andrew E.; Tenuta, Mario; Wylie, Bruce K.

    2014-01-01

    Net CO2 exchange data of legume crops at 17 flux tower sites in North America and three sites in Europe representing 29 site-years of measurements were partitioned into gross photosynthesis and ecosystem respiration by using the nonrectangular hyperbolic light-response function method. The analyses produced net CO2 exchange data and new ecosystem-scale ecophysiological parameter estimates for legume crops determined at diurnal and weekly time steps. Dynamics and annual totals of gross photosynthesis, ecosystem respiration, and net ecosystem production were calculated by gap filling with multivariate nonlinear regression. Comparison with the data from grain crops obtained with the same method demonstrated that CO2 exchange rates and ecophysiological parameters of legumes were lower than those of maize (Zea mays L.) but higher than for wheat (Triticum aestivum L.) crops. Year-round annual legume crops demonstrated a broad range of net ecosystem production, from sinks of 760 g CO2 m–2 yr–1 to sources of –2100 g CO2 m–2 yr–1, with an average of –330 g CO2 m–2 yr–1, indicating overall moderate CO2–source activity related to a shorter period of photosynthetic uptake and metabolic costs of N2 fixation. Perennial legumes (alfalfa, Medicago sativa L.) were strong sinks for atmospheric CO2, with an average net ecosystem production of 980 (range 550–1200) g CO2 m–2 yr–1.

  2. SEBAL Model Using to Estimate Irrigation Water Efficiency & Water Requirement of Alfalfa Crop

    Zeyliger, Anatoly; Ermolaeva, Olga

    2013-04-01

    The sustainability of irrigation is a complex and comprehensive undertaking, requiring an attention to much more than hydraulics, chemistry, and agronomy. A special combination of human, environmental, and economic factors exists in each irrigated region and must be recognized and evaluated. A way to evaluate the efficiency of irrigation water use for crop production is to consider the so-called crop-water production functions, which express the relation between the yield of a crop and the quantity of water applied to it or consumed by it. The term has been used in a somewhat ambiguous way. Some authors have defined the Crop-Water Production Functions between yield and the total amount of water applied, whereas others have defined it as a relation between yield and seasonal evapotranspiration (ET). In case of high efficiency of irrigation water use the volume of water applied is less than the potential evapotranspiration (PET), then - assuming no significant change of soil moisture storage from beginning of the growing season to its end-the volume of water may be roughly equal to ET. In other case of low efficiency of irrigation water use the volume of water applied exceeds PET, then the excess of volume of water applied over PET must go to either augmenting soil moisture storage (end-of-season moisture being greater than start-of-season soil moisture) or to runoff or/and deep percolation beyond the root zone. In presented contribution some results of a case study of estimation of biomass and leaf area index (LAI) for irrigated alfalfa by SEBAL algorithm will be discussed. The field study was conducted with aim to compare ground biomass of alfalfa at some irrigated fields (provided by agricultural farm) at Saratov and Volgograd Regions of Russia. The study was conducted during vegetation period of 2012 from April till September. All the operations from importing the data to calculation of the output data were carried by eLEAF company and uploaded in Fieldlook web

  3. WHO Initiative to Estimate the Global Burden of Foodborne Diseases

    Havelaar, Arie H.; Cawthorne, Amy; Angulo, Fred

    2013-01-01

    BackgroundThe public health impact of foodborne diseases globally is unknown. The WHO Initiative to Estimate the Global Burden of Foodborne Diseases was launched out of the need to fill this data gap. It is anticipated that this effort will enable policy makers and other stakeholders to set...... appropriate, evidence-informed priorities in the area of food safety. MethodsThe Initiative aims to provide estimates on the global burden of foodborne diseases by age, sex, and region; strengthen country capacity for conducting burden of foodborne disease assessments in parallel with food safety policy...

  4. Similar Estimates of Temperature Impacts on Global Wheat Yield by Three Independent Methods

    Liu, Bing; Asseng, Senthold; Muller, Christoph; Ewart, Frank; Elliott, Joshua; Lobell, David B.; Martre, Pierre; Ruane, Alex C.; Wallach, Daniel; Jones, James W.; hide

    2016-01-01

    The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify 'method uncertainty' in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.

  5. Similar estimates of temperature impacts on global wheat yield by three independent methods

    Liu, Bing; Asseng, Senthold; Müller, Christoph; Ewert, Frank; Elliott, Joshua; Lobell, David B.; Martre, Pierre; Ruane, Alex C.; Wallach, Daniel; Jones, James W.; Rosenzweig, Cynthia; Aggarwal, Pramod K.; Alderman, Phillip D.; Anothai, Jakarat; Basso, Bruno; Biernath, Christian; Cammarano, Davide; Challinor, Andy; Deryng, Delphine; Sanctis, Giacomo De; Doltra, Jordi; Fereres, Elias; Folberth, Christian; Garcia-Vila, Margarita; Gayler, Sebastian; Hoogenboom, Gerrit; Hunt, Leslie A.; Izaurralde, Roberto C.; Jabloun, Mohamed; Jones, Curtis D.; Kersebaum, Kurt C.; Kimball, Bruce A.; Koehler, Ann-Kristin; Kumar, Soora Naresh; Nendel, Claas; O'Leary, Garry J.; Olesen, Jørgen E.; Ottman, Michael J.; Palosuo, Taru; Prasad, P. V. Vara; Priesack, Eckart; Pugh, Thomas A. M.; Reynolds, Matthew; Rezaei, Ehsan E.; Rötter, Reimund P.; Schmid, Erwin; Semenov, Mikhail A.; Shcherbak, Iurii; Stehfest, Elke; Stöckle, Claudio O.; Stratonovitch, Pierre; Streck, Thilo; Supit, Iwan; Tao, Fulu; Thorburn, Peter; Waha, Katharina; Wall, Gerard W.; Wang, Enli; White, Jeffrey W.; Wolf, Joost; Zhao, Zhigan; Zhu, Yan

    2016-12-01

    The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify `method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.

  6. Estimating the global incidence of traumatic spinal cord injury.

    Fitzharris, M; Cripps, R A; Lee, B B

    2014-02-01

    Population modelling--forecasting. To estimate the global incidence of traumatic spinal cord injury (TSCI). An initiative of the International Spinal Cord Society (ISCoS) Prevention Committee. Regression techniques were used to derive regional and global estimates of TSCI incidence. Using the findings of 31 published studies, a regression model was fitted using a known number of TSCI cases as the dependent variable and the population at risk as the single independent variable. In the process of deriving TSCI incidence, an alternative TSCI model was specified in an attempt to arrive at an optimal way of estimating the global incidence of TSCI. The global incidence of TSCI was estimated to be 23 cases per 1,000,000 persons in 2007 (179,312 cases per annum). World Health Organization's regional results are provided. Understanding the incidence of TSCI is important for health service planning and for the determination of injury prevention priorities. In the absence of high-quality epidemiological studies of TSCI in each country, the estimation of TSCI obtained through population modelling can be used to overcome known deficits in global spinal cord injury (SCI) data. The incidence of TSCI is context specific, and an alternative regression model demonstrated how TSCI incidence estimates could be improved with additional data. The results highlight the need for data standardisation and comprehensive reporting of national level TSCI data. A step-wise approach from the collation of conventional epidemiological data through to population modelling is suggested.

  7. Global climate change increases risk of crop yield losses and food insecurity in the tropical Andes.

    Tito, Richard; Vasconcelos, Heraldo L; Feeley, Kenneth J

    2018-02-01

    One of the greatest current challenges to human society is ensuring adequate food production and security for a rapidly growing population under changing climatic conditions. Climate change, and specifically rising temperatures, will alter the suitability of areas for specific crops and cultivation systems. In order to maintain yields, farmers may be forced to change cultivation practices, the timing of cultivation, or even the type of crops grown. Alternatively, farmers can change the location where crops are cultivated (e.g., to higher elevations) to track suitable climates (in which case the plants will have to grow in different soils), as cultivated plants will otherwise have to tolerate warmer temperatures and possibly face novel enemies. We simulated these two last possible scenarios (for temperature increases of 1.3°C and 2.6°C) in the Peruvian Andes through a field experiment in which several traditionally grown varieties of potato and maize were planted at different elevations (and thus temperatures) using either the local soil or soil translocated from higher elevations. Maize production declined by 21%-29% in response to new soil conditions. The production of maize and potatoes declined by >87% when plants were grown under warmer temperatures, mainly as a result of the greater incidence of novel pests. Crop quality and value also declined under simulated migration and warming scenarios. We estimated that local farmers may experience severe economic losses of up to 2,300 US$ ha -1  yr -1 . These findings reveal that climate change is a real and imminent threat to agriculture and that there is a pressing need to develop effective management strategies to reduce yield losses and prevent food insecurity. Importantly, such strategies should take into account the influences of non-climatic and/or biotic factors (e.g., novel pests) on plant development. © 2017 John Wiley & Sons Ltd.

  8. Estimates of global cyanobacterial biomass and its distribution

    Garcia-Pichel, Ferran; Belnap, Jayne; Neuer, Susanne; Schanz, Ferdinand

    2003-01-01

    We estimated global cyanobacterial biomass in the main reservoirs of cyanobacteria on Earth: marine and freshwater plankton, arid land soil crusts, and endoliths. Estimates were based on typical population density values as measured during our research, or as obtained from literature surveys, which were then coupled with data on global geographical area coverage. Among the marine plankton, the global biomass of Prochlorococcus reaches 120 × 1012 grams of carbon (g C), and that of Synechoccus some 43 × 1012 g C. This makes Prochlorococcus and Synechococcus, in that order, the most abundant cyanobacteria on Earth. Tropical marine blooms of Trichodesmium account for an additional 10 × 1012 g C worldwide. In terrestrial environments, the mass of cyanobacteria in arid land soil crusts is estimated to reach 54 × 1012 g C and that of arid land endolithic communities an additional 14 × 1012 g C. The global biomass of planktic cyanobacteria in lakes is estimated to be around 3 × 1012 g C. Our conservative estimates, which did not include some potentially significant biomass reservoirs such as polar and subarctic areas, topsoils in subhumid climates, and shallow marine and freshwater benthos, indicate that the total global cyanobacterial biomass is in the order of 3 × 1014 g C, surpassing a thousand million metric tons (1015 g) of wet biomass.

  9. Estimation of the Carbon Footprint and Global Warming Potential in Rice Production Systems

    Dastan, S.; Soltani, F.; Noormohamadi, G.; Madani, H.; Yadi, R.

    2016-01-01

    Optimal management approaches can be adopted in order to increase crop productivity and lower the carbon footprint of grain products. The objective of this study was to estimate the carbon (C) footprint and global warming potential of rice production systems. In this experiment, rice production systems (including SRI, improved and conventional) were studied. All activities, field operations and data in production methods and at different input rates were monitored and recorded during 2012. Results showed that average global warming potential across production systems was equal to 2803.25 kg CO 2 -eq ha-1. The highest and least global warming potential were observed in the SRI and conventional systems, respectively. global warming potential per unit energy input was the least and most in SRI and conventional systems, respectively. Also, the SRI and conventional systems had the maximum and minimum global warming potential per unit energy output, respectively. SRI and conventional system had the greatest and least global warming potential per unit energy output, respectively. Therefore, the optimal management approach found in SRI resulted in a reduction in GHGs, global warming potential and the carbon footprint.

  10. Modeling soil organic carbon dynamics and their driving factors in the main global cereal cropping systems

    Wang, Guocheng; Zhang, Wen; Sun, Wenjuan; Li, Tingting; Han, Pengfei

    2017-10-01

    Changes in the soil organic carbon (SOC) stock are determined by the balance between the carbon input from organic materials and the output from the decomposition of soil C. The fate of SOC in cropland soils plays a significant role in both sustainable agricultural production and climate change mitigation. The spatiotemporal changes of soil organic carbon in croplands in response to different carbon (C) input management and environmental conditions across the main global cereal systems were studied using a modeling approach. We also identified the key variables that drive SOC changes at a high spatial resolution (0.1° × 0.1°) and over a long timescale (54 years from 1961 to 2014). A widely used soil C turnover model (RothC) and state-of-the-art databases of soil and climate variables were used in the present study. The model simulations suggested that, on a global average, the cropland SOC density increased at annual rates of 0.22, 0.45 and 0.69 Mg C ha-1 yr-1 under crop residue retention rates of 30, 60 and 90 %, respectively. Increasing the quantity of C input could enhance soil C sequestration or reduce the rate of soil C loss, depending largely on the local soil and climate conditions. Spatially, under a specific crop residue retention rate, relatively higher soil C sinks were found across the central parts of the USA, western Europe, and the northern regions of China. Relatively smaller soil C sinks occurred in the high-latitude regions of both the Northern and Southern hemispheres, and SOC decreased across the equatorial zones of Asia, Africa and America. We found that SOC change was significantly influenced by the crop residue retention rate (linearly positive) and the edaphic variable of initial SOC content (linearly negative). Temperature had weak negative effects, and precipitation had significantly negative impacts on SOC changes. The results can help guide carbon input management practices to effectively mitigate climate change through soil C

  11. Modeling soil organic carbon dynamics and their driving factors in the main global cereal cropping systems

    G. Wang

    2017-10-01

    Full Text Available Changes in the soil organic carbon (SOC stock are determined by the balance between the carbon input from organic materials and the output from the decomposition of soil C. The fate of SOC in cropland soils plays a significant role in both sustainable agricultural production and climate change mitigation. The spatiotemporal changes of soil organic carbon in croplands in response to different carbon (C input management and environmental conditions across the main global cereal systems were studied using a modeling approach. We also identified the key variables that drive SOC changes at a high spatial resolution (0.1°  ×  0.1° and over a long timescale (54 years from 1961 to 2014. A widely used soil C turnover model (RothC and state-of-the-art databases of soil and climate variables were used in the present study. The model simulations suggested that, on a global average, the cropland SOC density increased at annual rates of 0.22, 0.45 and 0.69 Mg C ha−1 yr−1 under crop residue retention rates of 30, 60 and 90 %, respectively. Increasing the quantity of C input could enhance soil C sequestration or reduce the rate of soil C loss, depending largely on the local soil and climate conditions. Spatially, under a specific crop residue retention rate, relatively higher soil C sinks were found across the central parts of the USA, western Europe, and the northern regions of China. Relatively smaller soil C sinks occurred in the high-latitude regions of both the Northern and Southern hemispheres, and SOC decreased across the equatorial zones of Asia, Africa and America. We found that SOC change was significantly influenced by the crop residue retention rate (linearly positive and the edaphic variable of initial SOC content (linearly negative. Temperature had weak negative effects, and precipitation had significantly negative impacts on SOC changes. The results can help guide carbon input management practices to

  12. Global stereo matching algorithm based on disparity range estimation

    Li, Jing; Zhao, Hong; Gu, Feifei

    2017-09-01

    The global stereo matching algorithms are of high accuracy for the estimation of disparity map, but the time-consuming in the optimization process still faces a curse, especially for the image pairs with high resolution and large baseline setting. To improve the computational efficiency of the global algorithms, a disparity range estimation scheme for the global stereo matching is proposed to estimate the disparity map of rectified stereo images in this paper. The projective geometry in a parallel binocular stereo vision is investigated to reveal a relationship between two disparities at each pixel in the rectified stereo images with different baselines, which can be used to quickly obtain a predicted disparity map in a long baseline setting estimated by that in the small one. Then, the drastically reduced disparity ranges at each pixel under a long baseline setting can be determined by the predicted disparity map. Furthermore, the disparity range estimation scheme is introduced into the graph cuts with expansion moves to estimate the precise disparity map, which can greatly save the cost of computing without loss of accuracy in the stereo matching, especially for the dense global stereo matching, compared to the traditional algorithm. Experimental results with the Middlebury stereo datasets are presented to demonstrate the validity and efficiency of the proposed algorithm.

  13. Paddy crop yield estimation in Kashmir Himalayan rice bowl using remote sensing and simulation model.

    Muslim, Mohammad; Romshoo, Shakil Ahmad; Rather, A Q

    2015-06-01

    The Kashmir Himalayan region of India is expected to be highly prone to the change in agricultural land use because of its geo-ecological fragility, strategic location vis-à-vis the Himalayan landscape, its trans-boundary river basins, and inherent socio-economic instabilities. Food security and sustainability of the region are thus greatly challenged by these impacts. The effect of future climate change, increased competition for land and water, labor from non-agricultural sectors, and increasing population adds to this complex problem. In current study, paddy rice yield at regional level was estimated using GIS-based environment policy integrated climate (GEPIC) model. The general approach of current study involved combining regional level crop database, regional soil data base, farm management data, and climatic data outputs with GEPIC model. The simulated yield showed that estimated production to be 4305.55 kg/ha (43.05 q h(-1)). The crop varieties like Jhelum, K-39, Chenab, China 1039, China-1007, and Shalimar rice-1 grown in plains recorded average yield of 4783.3 kg/ha (47.83 q ha(-1)). Meanwhile, high altitude areas with varieties like Kohsaar, K-78 (Barkat), and K-332 recorded yield of 4102.2 kg/ha (41.02 q ha(-1)). The observed and simulated yield showed a good match with R (2) = 0.95, RMSE = 132.24 kg/ha, respectively.

  14. Reference crop evapotranspiration estimate using high-resolution meteorological network's data

    C. Lussana

    2009-10-01

    Full Text Available Water management authorities need detailed information about each component of the hydrological balance. This document presents a method to estimate the evapotranspiration rate, initialized in order to obtain the reference crop evapotranspiration rate (ET0. By using an Optimal Interpolation (OI scheme, the hourly observations of several meteorological variables, measured by a high-resolution local meteorological network, are interpolated over a regular grid. The analysed meteorological fields, containing detailed meteorological information, enter a model for turbulent heat fluxes estimation based on Monin-Obukhov surface layer similarity theory. The obtained ET0 fields are then post-processed and disseminated to the users.

  15. Estimating current and future global urban domestic material consumption

    Baynes, Timothy Malcolm; Kaviti Musango, Josephine

    2018-06-01

    Urban material resource requirements are significant at the global level and these are expected to expand with future urban population growth. However, there are no global scale studies on the future material consumption of urban areas. This paper provides estimates of global urban domestic material consumption (DMC) in 2050 using three approaches based on: current gross statistics; a regression model; and a transition theoretic logistic model. All methods use UN urban population projections and assume a simple ‘business-as-usual’ scenario wherein historical aggregate trends in income and material flow continue into the future. A collation of data for 152 cities provided a year 2000 world average DMC/capita estimate, 12 tons/person/year (±22%), which we combined with UN population projections to produce a first-order estimation of urban DMC at 2050 of ~73 billion tons/year (±22%). Urban DMC/capita was found to be significantly correlated (R 2 > 0.9) to urban GDP/capita and area per person through a power law relation used to obtain a second estimate of 106 billion tons (±33%) in 2050. The inelastic exponent of the power law indicates a global tendency for relative decoupling of direct urban material consumption with increasing income. These estimates are global and influenced by the current proportion of developed-world cities in the global population of cities (and in our sample data). A third method employed a logistic model of transitions in urban DMC/capita with regional resolution. This method estimated global urban DMC to rise from approximately 40 billion tons/year in 2010 to ~90 billion tons/year in 2050 (modelled range: 66–111 billion tons/year). DMC/capita across different regions was estimated to converge from a range of 5–27 tons/person/year in the year 2000 to around 8–17 tons/person/year in 2050. The urban population does not increase proportionally during this period and thus the global average DMC/capita increases from ~12 to ~14 tons

  16. Estimating the global prevalence of transthyretin familial amyloid polyneuropathy

    Waddington‐Cruz, Márcia; Botteman, Marc F.; Carter, John A.; Chopra, Avijeet S.; Hopps, Markay; Stewart, Michelle; Fallet, Shari; Amass, Leslie

    2018-01-01

    ABSTRACT Introduction: This study sought to estimate the global prevalence of transthyretin familial amyloid polyneuropathy (ATTR‐FAP). Methods: Prevalence estimates and information supporting prevalence calculations was extracted from records yielded by reference‐database searches (2005–2016), conference proceedings, and nonpeer reviewed sources. Prevalence was calculated as prevalence rate multiplied by general population size, then extrapolated to countries without prevalence estimates but with reported cases. Results: Searches returned 3,006 records; 1,001 were fully assessed and 10 retained, yielding prevalence for 10 “core” countries, then extrapolated to 32 additional countries. ATTR‐FAP prevalence in core countries, extrapolated countries, and globally was 3,762 (range 3639–3884), 6424 (range, 1,887–34,584), and 10,186 (range, 5,526–38,468) persons, respectively. Discussion: The mid global prevalence estimate (10,186) approximates the maximum commonly accepted estimate (5,000–10,000). The upper limit (38,468) implies potentially higher prevalence. These estimates should be interpreted carefully because contributing evidence was heterogeneous and carried an overall moderate risk of bias. This highlights the requirement for increasing rare‐disease epidemiological assessment and clinician awareness. Muscle Nerve 57: 829–837, 2018 PMID:29211930

  17. Estimation of available water capacity components of two-layered soils using crop model inversion: Effect of crop type and water regime

    Sreelash, K.; Buis, Samuel; Sekhar, M.; Ruiz, Laurent; Kumar Tomer, Sat; Guérif, Martine

    2017-03-01

    Characterization of the soil water reservoir is critical for understanding the interactions between crops and their environment and the impacts of land use and environmental changes on the hydrology of agricultural catchments especially in tropical context. Recent studies have shown that inversion of crop models is a powerful tool for retrieving information on root zone properties. Increasing availability of remotely sensed soil and vegetation observations makes it well suited for large scale applications. The potential of this methodology has however never been properly evaluated on extensive experimental datasets and previous studies suggested that the quality of estimation of soil hydraulic properties may vary depending on agro-environmental situations. The objective of this study was to evaluate this approach on an extensive field experiment. The dataset covered four crops (sunflower, sorghum, turmeric, maize) grown on different soils and several years in South India. The components of AWC (available water capacity) namely soil water content at field capacity and wilting point, and soil depth of two-layered soils were estimated by inversion of the crop model STICS with the GLUE (generalized likelihood uncertainty estimation) approach using observations of surface soil moisture (SSM; typically from 0 to 10 cm deep) and leaf area index (LAI), which are attainable from radar remote sensing in tropical regions with frequent cloudy conditions. The results showed that the quality of parameter estimation largely depends on the hydric regime and its interaction with crop type. A mean relative absolute error of 5% for field capacity of surface layer, 10% for field capacity of root zone, 15% for wilting point of surface layer and root zone, and 20% for soil depth can be obtained in favorable conditions. A few observations of SSM (during wet and dry soil moisture periods) and LAI (within water stress periods) were sufficient to significantly improve the estimation of AWC

  18. Dependency of global primary bioenergy crop potentials in 2050 on food systems, yields, biodiversity conservation and political stability.

    Erb, Karl-Heinz; Haberl, Helmut; Plutzar, Christoph

    2012-08-01

    The future bioenergy crop potential depends on (1) changes in the food system (food demand, agricultural technology), (2) political stability and investment security, (3) biodiversity conservation, (4) avoidance of long carbon payback times from deforestation, and (5) energy crop yields. Using a biophysical biomass-balance model, we analyze how these factors affect global primary bioenergy potentials in 2050. The model calculates biomass supply and demand balances for eleven world regions, eleven food categories, seven food crop types and two livestock categories, integrating agricultural forecasts and scenarios with a consistent global land use and NPP database. The TREND scenario results in a global primary bioenergy potential of 77 EJ/yr, alternative assumptions on food-system changes result in a range of 26-141 EJ/yr. Exclusion of areas for biodiversity conservation and inaccessible land in failed states reduces the bioenergy potential by up to 45%. Optimistic assumptions on future energy crop yields increase the potential by up to 48%, while pessimistic assumptions lower the potential by 26%. We conclude that the design of sustainable bioenergy crop production policies needs to resolve difficult trade-offs such as food vs. energy supply, renewable energy vs. biodiversity conservation or yield growth vs. reduction of environmental problems of intensive agriculture.

  19. On global error estimation and control for initial value problems

    J. Lang (Jens); J.G. Verwer (Jan)

    2007-01-01

    textabstractThis paper addresses global error estimation and control for initial value problems for ordinary differential equations. The focus lies on a comparison between a novel approach based onthe adjoint method combined with a small sample statistical initialization and the classical approach

  20. On global error estimation and control for initial value problems

    Lang, J.; Verwer, J.G.

    2007-01-01

    Abstract. This paper addresses global error estimation and control for initial value problems for ordinary differential equations. The focus lies on a comparison between a novel approach based on the adjoint method combined with a small sample statistical initialization and the classical approach

  1. Empirical Models for the Estimation of Global Solar Radiation in ...

    Empirical Models for the Estimation of Global Solar Radiation in Yola, Nigeria. ... and average daily wind speed (WS) for the interval of three years (2010 – 2012) measured using various instruments for Yola of recorded data collected from the Center for Atmospheric Research (CAR), Anyigba are presented and analyzed.

  2. Estimating the true global burden of mental illness.

    Vigo, Daniel; Thornicroft, Graham; Atun, Rifat

    2016-02-01

    We argue that the global burden of mental illness is underestimated and examine the reasons for under-estimation to identify five main causes: overlap between psychiatric and neurological disorders; the grouping of suicide and self-harm as a separate category; conflation of all chronic pain syndromes with musculoskeletal disorders; exclusion of personality disorders from disease burden calculations; and inadequate consideration of the contribution of severe mental illness to mortality from associated causes. Using published data, we estimate the disease burden for mental illness to show that the global burden of mental illness accounts for 32·4% of years lived with disability (YLDs) and 13·0% of disability-adjusted life-years (DALYs), instead of the earlier estimates suggesting 21·2% of YLDs and 7·1% of DALYs. Currently used approaches underestimate the burden of mental illness by more than a third. Our estimates place mental illness a distant first in global burden of disease in terms of YLDs, and level with cardiovascular and circulatory diseases in terms of DALYs. The unacceptable apathy of governments and funders of global health must be overcome to mitigate the human, social, and economic costs of mental illness. Copyright © 2016 Elsevier Ltd. All rights reserved.

  3. Global building inventory for earthquake loss estimation and risk management

    Jaiswal, Kishor; Wald, David; Porter, Keith

    2010-01-01

    We develop a global database of building inventories using taxonomy of global building types for use in near-real-time post-earthquake loss estimation and pre-earthquake risk analysis, for the U.S. Geological Survey’s Prompt Assessment of Global Earthquakes for Response (PAGER) program. The database is available for public use, subject to peer review, scrutiny, and open enhancement. On a country-by-country level, it contains estimates of the distribution of building types categorized by material, lateral force resisting system, and occupancy type (residential or nonresidential, urban or rural). The database draws on and harmonizes numerous sources: (1) UN statistics, (2) UN Habitat’s demographic and health survey (DHS) database, (3) national housing censuses, (4) the World Housing Encyclopedia and (5) other literature.

  4. Estimating 20-year land-use change and derived CO2 emissions associated with crops, pasture and forestry in Brazil and each of its 27 states.

    Novaes, Renan M L; Pazianotto, Ricardo A A; Brandão, Miguel; Alves, Bruno J R; May, André; Folegatti-Matsuura, Marília I S

    2017-09-01

    Land-use change (LUC) in Brazil has important implications on global climate change, ecosystem services and biodiversity, and agricultural expansion plays a critical role in this process. Concerns over these issues have led to the need for estimating the magnitude and impacts associated with that, which are increasingly reported in the environmental assessment of products. Currently, there is an extensive debate on which methods are more appropriate for estimating LUC and related emissions and regionalized estimates are lacking for Brazil, which is a world leader in agricultural production (e.g. food, fibres and bioenergy). We developed a method for estimating scenarios of past 20-year LUC and derived CO 2 emission rates associated with 64 crops, pasture and forestry in Brazil as whole and in each of its 27 states, based on time-series statistics and in accordance with most used carbon-footprinting standards. The scenarios adopted provide a range between minimum and maximum rates of CO 2 emissions from LUC according to different possibilities of land-use transitions, which can have large impacts in the results. Specificities of Brazil, like multiple cropping and highly heterogeneous carbon stocks, are also addressed. The highest CO 2 emission rates are observed in the Amazon biome states and crops with the highest rates are those that have undergone expansion in this region. Some states and crops showing large agricultural areas have low emissions associated, especially in southern and eastern Brazil. Native carbon stocks and time of agricultural expansion are the most decisive factors to the patterns of emissions. Some implications on LUC estimation methods and standards and on agri-environmental policies are discussed. © 2017 John Wiley & Sons Ltd.

  5. Correlation Dimension Estimates of Global and Local Temperature Data.

    Wang, Qiang

    1995-11-01

    The author has attempted to detect the presence of low-dimensional deterministic chaos in temperature data by estimating the correlation dimension with the Hill estimate that has been recently developed by Mikosch and Wang. There is no convincing evidence of low dimensionality with either global dataset (Southern Hemisphere monthly average temperatures from 1858 to 1984) or local temperature dataset (daily minimums at Auckland, New Zealand). Any apparent reduction in the dimension estimates appears to be due large1y, if not entirely, to effects of statistical bias, but neither is it a purely random stochastic process. The dimension of the climatic attractor may be significantly larger than 10.

  6. Can we use photography to estimate radiation interception by a crop canopy?

    Chakwizira, E; Meenken, E D; George, M J; Fletcher, A L

    2015-03-01

    Accuracy of determining radiation interception, and hence radiation use efficiency, depends on the method of measuring photosynthetically active radiation intercepted. Methods vary, from expensive instruments such as Sunfleck ceptometers to simple methods such as digital photography. However, before universal use of digital photography there is need to determine its reliability and compare it with conventional, but expensive, methods. In a series of experiments at Lincoln, New Zealand, canopy development for barley, wheat, white clover and four forage brassica species was determined using both digital photographs and Sunfleck ceptometer. Values obtained were used to calculate conversion coefficient (Kf/Ki) ratios between the two methods. Digital photographs were taken at 45° and 90° for barley, wheat and white clover and at only 90° for brassicas. There was an interaction of effects of crop and cultivar for the cereal crops. Barley closed canopies earlier than wheat, and 'Emir' barley and 'Stettler' wheat had consistently higher canopy cover than 'Golden Promise' and 'HY459', respectively. Canopy cover was consistently larger at 45° than 90° for cereals. However, for white clover, the angle of digital photography was not important. There was also an interaction between effects of species and method of determining canopy cover for brassicas. Photographs gave higher cover values than ceptometer for forage rape and turnip, but the relationship was variable for forage kale and swede. Kf/Ki ratios of 1.0-1.10 for cereals, white clover and forage rape and turnip show that digital photographs can be used to estimated radiation interception, in place of Sunfleck ceptometer, for these crops. © 2014 German Botanical Society and The Royal Botanical Society of the Netherlands.

  7. Simply obtained global radiation, soil temperature and soil moisture in an alley cropping system in semi-arid Kenya

    Mungai, D.N.; Stigter, C.J.; Coulson, C.L.; Ng'ang'a, J.K.

    2000-01-01

    Global radiation, soil temperature and soil moisture data were obtained from a 4-6 year old Cassia siamea/maize (CM) alley cropping (or hedgerow intercropping) system, at a semi-arid site at Machakos, Kenya, in the late eighties. With the growing need to explore and manage variations in

  8. Comparison of net global warming potential and greenhouse gas intensity affected by management practices in two dryland cropping sites

    Little is known about the effect of management practices on net global warming potential (GWP) and greenhouse gas intensity (GHGI) that account for all sources and sinks of greenhouse gas (GHG) emissions in dryland cropping systems. The objective of this study was to compare the effect of a combinat...

  9. Net global warming potential and greenhouse gas intensity influenced by irrigation, tillage, crop rotation, and nitrogen fertilization

    Little information exists about sources and sinks of greenhouse gases (GHGs) affected by management practices to account for net emissions from agroecosystems. We evaluated the effects of irrigation, tillage, crop rotation, and N fertilization on net global warming potential (GWP) and greenhouse gas...

  10. Estimation of Evapotranspiration and Crop Coefficients of Tendone Vineyards Using Multi-Sensor Remote Sensing Data in a Mediterranean Environment

    Silvia Vanino

    2015-11-01

    Full Text Available The sustainable management of water resources plays a key role in Mediterranean viticulture, characterized by scarcity and competition of available water. This study focuses on estimating the evapotranspiration and crop coefficients of table grapes vineyards trained on overhead “tendone” systems in the Apulia region (Italy. Maximum vineyard transpiration was estimated by adopting the “direct” methodology for ETp proposed by the Food and Agriculture Organization in Irrigation and Drainage Paper No. 56, with crop parameters estimated from Landsat 8 and RapidEye satellite data in combination with ground-based meteorological data. The modeling results of two growing seasons (2013 and 2014 indicated that canopy growth, seasonal and 10-day sums evapotranspiration values were strictly related to thermal requirements and rainfall events. The estimated values of mean seasonal daily evapotranspiration ranged between 4.2 and 4.1 mm·d−1, while midseason estimated values of crop coefficients ranged from 0.88 to 0.93 in 2013, and 1.02 to 1.04 in 2014, respectively. The experimental evapotranspiration values calculated represent the maximum value in absence of stress, so the resulting crop coefficients should be used with some caution. It is concluded that the retrieval of crop parameters and evapotranspiration derived from remotely-sensed data could be helpful for downscaling to the field the local weather conditions and agronomic practices and thus may be the basis for supporting grape growers and irrigation managers.

  11. Estimated flows of gases and carbon within CEEF ecosystem composed of human, crops and goats

    Tako, Y.; Komatsubara, O.; Honda, G.; Arai, R.; Nitta, K.

    The Closed Ecology Experiment Facilities (CEEF) can be used as a test bed for Controlled Ecological Life Support Systems (CELSS), because technologies developed for the CEEF system facilitate self-sufficient material circulation necessary for long term missions such as Lunar and Mars exploration. In the experiment conducted under closed condition in FY2003, rice and soybeans were cultivated sequentially in two chambers and a chamber, each having a cultivation area of 30 m2 and floor area of 43 m2, inside the Plantation Module with artificial lighting of the CEEF. In the chamber having a cultivation area of 60 m2 and floor area of 65 m2, inside the Plantation Module with natural and artificial lighting, peanuts and safflowers were also cultivated. Stable transplant (or seeding) and harvest of each crop were maintained during a month. Flows of CO2, O2 and carbon to and from the crops were analyzed during the stable cultivation period. Simulated works and stay in the CEEF lasting five days were conducted two times under ventilating condition in FY2003. Gas exchange of human was estimated using heart rate data collected during the experiments and correlation between gas exchange rate and heart rate. Gas exchange rate and carbon balance of female goats were determined using an open-flow measurement system with a gastight chamber. From these results, flows of gases and carbon in the CEEF were discussed.

  12. Estimation of diffuse from measured global solar radiation

    Moriarty, W.W.

    1991-01-01

    A data set of quality controlled radiation observations from stations scattered throughout Australia was formed and further screened to remove residual doubtful observations. It was then divided into groups by solar elevation, and used to find average relationships for each elevation group between relative global radiation (clearness index - the measured global radiation expressed as a proportion of the radiation on a horizontal surface at the top of the atmosphere) and relative diffuse radiation. Clear-cut relationships were found, which were then fitted by polynomial expressions giving the relative diffuse radiation as a function of relative global radiation and solar elevation. When these expressions were used to estimate the diffuse radiation from the global, the results had a slightly smaller spread of errors than those from an earlier technique given by Spencer. It was found that the errors were related to cloud amount, and further relationships were developed giving the errors as functions of global radiation, solar elevation, and the fraction of sky obscured by high cloud and by opaque (low and middle level) cloud. When these relationships were used to adjust the first estimates of diffuse radiation, there was a considerable reduction in the number of large errors

  13. High-resolution global grids of revised Priestley-Taylor and Hargreaves-Samani coefficients for assessing ASCE-standardized reference crop evapotranspiration and solar radiation

    Aschonitis, Vassilis G.; Papamichail, Dimitris; Demertzi, Kleoniki; Colombani, Nicolo; Mastrocicco, Micol; Ghirardini, Andrea; Castaldelli, Giuseppe; Fano, Elisa-Anna

    2017-08-01

    The objective of the study is to provide global grids (0.5°) of revised annual coefficients for the Priestley-Taylor (P-T) and Hargreaves-Samani (H-S) evapotranspiration methods after calibration based on the ASCE (American Society of Civil Engineers)-standardized Penman-Monteith method (the ASCE method includes two reference crops: short-clipped grass and tall alfalfa). The analysis also includes the development of a global grid of revised annual coefficients for solar radiation (Rs) estimations using the respective Rs formula of H-S. The analysis was based on global gridded climatic data of the period 1950-2000. The method for deriving annual coefficients of the P-T and H-S methods was based on partial weighted averages (PWAs) of their mean monthly values. This method estimates the annual values considering the amplitude of the parameter under investigation (ETo and Rs) giving more weight to the monthly coefficients of the months with higher ETo values (or Rs values for the case of the H-S radiation formula). The method also eliminates the effect of unreasonably high or low monthly coefficients that may occur during periods where ETo and Rs fall below a specific threshold. The new coefficients were validated based on data from 140 stations located in various climatic zones of the USA and Australia with expanded observations up to 2016. The validation procedure for ETo estimations of the short reference crop showed that the P-T and H-S methods with the new revised coefficients outperformed the standard methods reducing the estimated root mean square error (RMSE) in ETo values by 40 and 25 %, respectively. The estimations of Rs using the H-S formula with revised coefficients reduced the RMSE by 28 % in comparison to the standard H-S formula. Finally, a raster database was built consisting of (a) global maps for the mean monthly ETo values estimated by ASCE-standardized method for both reference crops, (b) global maps for the revised annual coefficients of the P

  14. 1km Global Terrestrial Carbon Flux: Estimations and Evaluations

    Murakami, K.; Sasai, T.; Kato, S.; Saito, M.; Matsunaga, T.; Hiraki, K.; Maksyutov, S. S.

    2017-12-01

    Estimating global scale of the terrestrial carbon flux change with high accuracy and high resolution is important to understand global environmental changes. Furthermore the estimations of the global spatiotemporal distribution may contribute to the political and social activities such as REDD+. In order to reveal the current state of terrestrial carbon fluxes covering all over the world and a decadal scale. The satellite-based diagnostic biosphere model is suitable for achieving this purpose owing to observing on the present global land surface condition uniformly at some time interval. In this study, we estimated the global terrestrial carbon fluxes with 1km grids by using the terrestrial biosphere model (BEAMS). And we evaluated our new carbon flux estimations on various spatial scales and showed the transition of forest carbon stocks in some regions. Because BEAMS required high resolution meteorological data and satellite data as input data, we made 1km interpolated data using a kriging method. The data used in this study were JRA-55, GPCP, GOSAT L4B atmospheric CO2 data as meteorological data, and MODIS land product as land surface satellite data. Interpolating process was performed on the meteorological data because of insufficient resolution, but not on MODIS data. We evaluated our new carbon flux estimations using the flux tower measurement (FLUXNET2015 Datasets) in a point scale. We used 166 sites data for evaluating our model results. These flux sites are classified following vegetation type (DBF, EBF, ENF, mixed forests, grass lands, croplands, shrub lands, Savannas, wetlands). In global scale, the BEAMS estimations was underestimated compared to the flux measurements in the case of carbon uptake and release. The monthly variations of NEP showed relatively high correlations in DBF and mixed forests, but the correlation coefficients of EBF, ENF, and grass lands were less than 0.5. In the meteorological factors, air temperature and solar radiation showed

  15. Estimation of water consumption of tomato crops planted in rock wool bed in greenhouse

    Ito, K.; Senge, M.; Iwama, K.; Hashimoto, I.

    2002-01-01

    For estimating the crop water consumption, it is necessary to determine meteorological data in greenhouse from open field data and calculate potential evaporation. In this study, temperature, humidity, wind velocity and solar radiation were measured in greenhouse as well as in open field. Then, we compared the meteorological data of greenhouse with that of open field. Results of the comparison differed from the reference values of the Official Manual (1997). Humidity during heating period and wind velocity in the greenhouse cannot be evaluated from the steps of the Official Manual. We applied the original equation that was derived in this observation to calculate the potential evaporation in the greenhouse. It became apparent that the potential evaporation could be estimated using open field data. A portion of irrigated water was consumed by vegetation and remainder was discharged from rock wool bed. Mean daily water consumption during the measurement period was 2.50(mm/d), with a monthly maximum occurring in July with 3.54(mm/d). Discharged water amounted to 9% of irrigated water. Tomato's crop coeffieiency with rock wool cultivation was calculated by potential evaporation and water consumption. In this field, this value was smaller than those recorded in the Official Manual. The amount of irrigation was same in all segments of the greenhouse. However, water consumption was affected by incident energy. A portion of discharged water (5% of irrigation water in this greenhouse) could not be saved because there existed a differential volume need for some plants which consumed more water in relation to others

  16. Estimation of Evapotranspiration from Fields with and without Cover Crops Using Remote Sensing and in situ Methods

    Christopher Hay

    2012-11-01

    Full Text Available Estimation of actual evapotranspiration (ETa based on remotely sensed imagery is very valuable in agricultural regions where ETa rates can vary greatly from field to field. This research utilizes the image processing model METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration to estimate late season, post-harvest ETa rates from fields with a cover crop planted after a cash crop (in this case, a rye/radish/pea mixture planted after spring wheat. Remotely sensed EToF (unit-less fraction of grass-based reference ET, ETo maps were generated using Erdas Imagine software for a 260 km2 area in northeastern South Dakota, USA. Meteorological information was obtained from a Bowen-Ratio Energy Balance System (BREBS located within the image. Nine image dates were used for the growing season, from May through October. Five of those nine were captured during the cover crop season. METRIC was found to successfully differentiate between fields with and without cover crops. In a blind comparison, METRIC compared favorably with the estimated ETa rates found using the BREBS (ETλE, with a difference in total estimated ETa for the cover crop season of 7%.

  17. Rapid estimation of the economic consequences of global earthquakes

    Jaiswal, Kishor; Wald, David J.

    2011-01-01

    The U.S. Geological Survey's (USGS) Prompt Assessment of Global Earthquakes for Response (PAGER) system, operational since mid 2007, rapidly estimates the most affected locations and the population exposure at different levels of shaking intensities. The PAGER system has significantly improved the way aid agencies determine the scale of response needed in the aftermath of an earthquake. For example, the PAGER exposure estimates provided reasonably accurate assessments of the scale and spatial extent of the damage and losses following the 2008 Wenchuan earthquake (Mw 7.9) in China, the 2009 L'Aquila earthquake (Mw 6.3) in Italy, the 2010 Haiti earthquake (Mw 7.0), and the 2010 Chile earthquake (Mw 8.8). Nevertheless, some engineering and seismological expertise is often required to digest PAGER's exposure estimate and turn it into estimated fatalities and economic losses. This has been the focus of PAGER's most recent development. With the new loss-estimation component of the PAGER system it is now possible to produce rapid estimation of expected fatalities for global earthquakes (Jaiswal and others, 2009). While an estimate of earthquake fatalities is a fundamental indicator of potential human consequences in developing countries (for example, Iran, Pakistan, Haiti, Peru, and many others), economic consequences often drive the responses in much of the developed world (for example, New Zealand, the United States, and Chile), where the improved structural behavior of seismically resistant buildings significantly reduces earthquake casualties. Rapid availability of estimates of both fatalities and economic losses can be a valuable resource. The total time needed to determine the actual scope of an earthquake disaster and to respond effectively varies from country to country. It can take days or sometimes weeks before the damage and consequences of a disaster can be understood both socially and economically. The objective of the U.S. Geological Survey's PAGER system is

  18. Global, Persistent, Real-time Multi-sensor Automated Satellite Image Analysis and Crop Forecasting in Commercial Cloud

    Brumby, S. P.; Warren, M. S.; Keisler, R.; Chartrand, R.; Skillman, S.; Franco, E.; Kontgis, C.; Moody, D.; Kelton, T.; Mathis, M.

    2016-12-01

    Cloud computing, combined with recent advances in machine learning for computer vision, is enabling understanding of the world at a scale and at a level of space and time granularity never before feasible. Multi-decadal Earth remote sensing datasets at the petabyte scale (8×10^15 bits) are now available in commercial cloud, and new satellite constellations will generate daily global coverage at a few meters per pixel. Public and commercial satellite observations now provide a wide range of sensor modalities, from traditional visible/infrared to dual-polarity synthetic aperture radar (SAR). This provides the opportunity to build a continuously updated map of the world supporting the academic community and decision-makers in government, finanace and industry. We report on work demonstrating country-scale agricultural forecasting, and global-scale land cover/land, use mapping using a range of public and commercial satellite imagery. We describe processing over a petabyte of compressed raw data from 2.8 quadrillion pixels (2.8 petapixels) acquired by the US Landsat and MODIS programs over the past 40 years. Using commodity cloud computing resources, we convert the imagery to a calibrated, georeferenced, multiresolution tiled format suited for machine-learning analysis. We believe ours is the first application to process, in less than a day, on generally available resources, over a petabyte of scientific image data. We report on work combining this imagery with time-series SAR collected by ESA Sentinel 1. We report on work using this reprocessed dataset for experiments demonstrating country-scale food production monitoring, an indicator for famine early warning. We apply remote sensing science and machine learning algorithms to detect and classify agricultural crops and then estimate crop yields and detect threats to food security (e.g., flooding, drought). The software platform and analysis methodology also support monitoring water resources, forests and other general

  19. CHANGE DETECTION OF CROPPING PATTERN IN PADDY FIELD USING MULTI SPECTRAL SATELLITE DATA FOR ESTIMATING IRRIGATION WATER NEEDS

    Rizatus Shofiyati1

    2012-10-01

    Full Text Available This paper investigates the use of multi spectral satellite data for cropping pattern monitoring in paddy field. The southern coastal of Citarum watershed, West Java Province was selected as study sites. The analysis used in this study is identifying crop pattern based on growth stages of wetland paddy and other crops by investi-gating the characteristic of Normalized Differen-ce Vegetation Indices (NDVI and Wetness of Tasseled Cap Transformation (TCT derived from 14 scenes of Landsat TM date 1988 to 2001. In general, the phenological of growth stages of wetland paddy can be used to distinguish with other seasonal crops. The research results indicate that multi spectral satellite data has a great potential for identi-fication and monitoring cropping pattern in paddy field. Specific character of NDVI and Wetness can also produce a map of cropping pattern in paddy field that is useful to monitor agricultural land condition. The cropping pattern can also be used to estimate irrigation water needed of paddy field in the area. Expected implication of the information obtained from this analysis is useful for guiding more appropriate planning and better agricultural management.

  20. Estimating perception of scene layout properties from global image features.

    Ross, Michael G; Oliva, Aude

    2010-01-08

    The relationship between image features and scene structure is central to the study of human visual perception and computer vision, but many of the specifics of real-world layout perception remain unknown. We do not know which image features are relevant to perceiving layout properties, or whether those features provide the same information for every type of image. Furthermore, we do not know the spatial resolutions required for perceiving different properties. This paper describes an experiment and a computational model that provides new insights on these issues. Humans perceive the global spatial layout properties such as dominant depth, openness, and perspective, from a single image. This work describes an algorithm that reliably predicts human layout judgments. This model's predictions are general, not specific to the observers it trained on. Analysis reveals that the optimal spatial resolutions for determining layout vary with the content of the space and the property being estimated. Openness is best estimated at high resolution, depth is best estimated at medium resolution, and perspective is best estimated at low resolution. Given the reliability and simplicity of estimating the global layout of real-world environments, this model could help resolve perceptual ambiguities encountered by more detailed scene reconstruction schemas.

  1. Applicability of empirical correlations for estimating global solar radiation

    Gopinathan, K.K.; Baholo, M.

    1987-01-01

    Three empirical models suggested by different investigators, for estimating monthly mean daily global radiation on a horizontal surface, are compared statistically to test their universal applicability. The models thus compared are those suggested by Rietveld, Glover and McCulloch and Gopinathan. The models are compared by calculating the root mean square error, mean bias error and mean relative percentage error values. The model suggested by Gopinathan yields the best results in terms of root mean square, mean bias and mean percentage errors. The model by Rietveld is the second best and the model by Glover and McCulloch comes at third place. However, the differences in the magnitude of errors among the three models are very small and all the three models can be considered to be accurate for global radiation estimation for any location in the world

  2. Estimating Global Burden of Disease due to congenital anomaly

    Boyle, Breidge; Addor, Marie-Claude; Arriola, Larraitz

    2018-01-01

    OBJECTIVE: To validate the estimates of Global Burden of Disease (GBD) due to congenital anomaly for Europe by comparing infant mortality data collected by EUROCAT registries with the WHO Mortality Database, and by assessing the significance of stillbirths and terminations of pregnancy for fetal...... the burden of disease due to congenital anomaly, and thus declining YLL over time may obscure lack of progress in primary, secondary and tertiary prevention....

  3. Estimation Of The Spatial Distribution Of Crop Coefficient (Kc) From Landsat Satellite Imagery

    Abou EI-Magd, I.H.

    2009-01-01

    Single crop coefficient factor (K c ) is an essential component for crop water allocation for efficient irrigation scheduling and irrigation water management. Kc is basically defined as the ratio of actual evapotranspiration and grass/alfalfa reference evapotranspiration and always measured by lysimeter in localized area in the field, which then generalized on the whole irrigated land. The lack of precise information about the crop coefficient particularly in our country together with both small sized fields and heterogeneity of agricultural crops calls for developing a new methodology for computing a real time crop coefficient from remotely sensed data. This paper discusses the methodology developed for obtaining a real time single crop coefficient from Landsat Satellite ETM + 7 imageries. The methodology was applied and optimized on one irrigation field with two different dates and crop cover in the northern Delta of Egypt

  4. The Role of Transgenic Crops in the Future of Global Food and Feed

    O. Škubna; H. Řezbová

    2012-01-01

    The paper is aimed on the problematic of biotech crops planting (GM, transgenic crops). The main aim of this paper is to analyze the trends in the main biotech crops planting groups in the sense of their use for food and feed in the future. The selected groups of biotech crops analyzed in this article are soybeans, maize (corn), cotton and rapeseed (canola). The used methods are chain and basic indexes and regression analysis of times series/ trend data - for predicting on next four years (20...

  5. Assimilation of Remotely Sensed Soil Moisture Profiles into a Crop Modeling Framework for Reliable Yield Estimations

    Mishra, V.; Cruise, J.; Mecikalski, J. R.

    2017-12-01

    Much effort has been expended recently on the assimilation of remotely sensed soil moisture into operational land surface models (LSM). These efforts have normally been focused on the use of data derived from the microwave bands and results have often shown that improvements to model simulations have been limited due to the fact that microwave signals only penetrate the top 2-5 cm of the soil surface. It is possible that model simulations could be further improved through the introduction of geostationary satellite thermal infrared (TIR) based root zone soil moisture in addition to the microwave deduced surface estimates. In this study, root zone soil moisture estimates from the TIR based Atmospheric Land Exchange Inverse (ALEXI) model were merged with NASA Soil Moisture Active Passive (SMAP) based surface estimates through the application of informational entropy. Entropy can be used to characterize the movement of moisture within the vadose zone and accounts for both advection and diffusion processes. The Principle of Maximum Entropy (POME) can be used to derive complete soil moisture profiles and, fortuitously, only requires a surface boundary condition as well as the overall mean moisture content of the soil column. A lower boundary can be considered a soil parameter or obtained from the LSM itself. In this study, SMAP provided the surface boundary while ALEXI supplied the mean and the entropy integral was used to tie the two together and produce the vertical profile. However, prior to the merging, the coarse resolution (9 km) SMAP data were downscaled to the finer resolution (4.7 km) ALEXI grid. The disaggregation scheme followed the Soil Evaporative Efficiency approach and again, all necessary inputs were available from the TIR model. The profiles were then assimilated into a standard agricultural crop model (Decision Support System for Agrotechnology, DSSAT) via the ensemble Kalman Filter. The study was conducted over the Southeastern United States for the

  6. Towards Global Simulation of Irrigation in a Land Surface Model: Multiple Cropping and Rice Paddy in Southeast Asia

    Beaudoing, Hiroko Kato; Rodell, Matthew; Ozdogan, Mutlu

    2010-01-01

    Agricultural land use significantly influences the surface water and energy balances. Effects of irrigation on land surface states and fluxes include repartitioning of latent and sensible heat fluxes, an increase in net radiation, and an increase in soil moisture and runoff. We are working on representing irrigation practices in continental- to global-scale land surface simulation in NASA's Global Land Data Assimilation System (GLDAS). Because agricultural practices across the nations are diverse, and complex, we are attempting to capture the first-order reality of the regional practices before achieving a global implementation. This study focuses on two issues in Southeast Asia: multiple cropping and rice paddy irrigation systems. We first characterize agricultural practices in the region (i.e., crop types, growing seasons, and irrigation) using the Global data set of monthly irrigated and rainfed crop areas around the year 2000 (MIRCA2000) dataset. Rice paddy extent is identified using remote sensing products. Whether irrigated or rainfed, flooded fields need to be represented and treated explicitly. By incorporating these properties and processes into a physically based land surface model, we are able to quantify the impacts on the simulated states and fluxes.

  7. A Global Estimate of the Number of Coral Reef Fishers.

    Louise S L Teh

    Full Text Available Overfishing threatens coral reefs worldwide, yet there is no reliable estimate on the number of reef fishers globally. We address this data gap by quantifying the number of reef fishers on a global scale, using two approaches - the first estimates reef fishers as a proportion of the total number of marine fishers in a country, based on the ratio of reef-related to total marine fish landed values. The second estimates reef fishers as a function of coral reef area, rural coastal population, and fishing pressure. In total, we find that there are 6 million reef fishers in 99 reef countries and territories worldwide, of which at least 25% are reef gleaners. Our estimates are an improvement over most existing fisher population statistics, which tend to omit accounting for gleaners and reef fishers. Our results suggest that slightly over a quarter of the world's small-scale fishers fish on coral reefs, and half of all coral reef fishers are in Southeast Asia. Coral reefs evidently support the socio-economic well-being of numerous coastal communities. By quantifying the number of people who are employed as reef fishers, we provide decision-makers with an important input into planning for sustainable coral reef fisheries at the appropriate scale.

  8. A Global Estimate of the Number of Coral Reef Fishers.

    Teh, Louise S L; Teh, Lydia C L; Sumaila, U Rashid

    2013-01-01

    Overfishing threatens coral reefs worldwide, yet there is no reliable estimate on the number of reef fishers globally. We address this data gap by quantifying the number of reef fishers on a global scale, using two approaches - the first estimates reef fishers as a proportion of the total number of marine fishers in a country, based on the ratio of reef-related to total marine fish landed values. The second estimates reef fishers as a function of coral reef area, rural coastal population, and fishing pressure. In total, we find that there are 6 million reef fishers in 99 reef countries and territories worldwide, of which at least 25% are reef gleaners. Our estimates are an improvement over most existing fisher population statistics, which tend to omit accounting for gleaners and reef fishers. Our results suggest that slightly over a quarter of the world's small-scale fishers fish on coral reefs, and half of all coral reef fishers are in Southeast Asia. Coral reefs evidently support the socio-economic well-being of numerous coastal communities. By quantifying the number of people who are employed as reef fishers, we provide decision-makers with an important input into planning for sustainable coral reef fisheries at the appropriate scale.

  9. Damage severity estimation from the global stiffness decrease

    Nitescu, C; Gillich, G R; Manescu, T; Korka, Z I; Abdel Wahab, M

    2017-01-01

    In actual damage detection methods, localization and severity estimation can be treated separately. The severity is commonly estimated using fracture mechanics approach, with the main disadvantage of involving empirically deduced relations. In this paper, a damage severity estimator based on the global stiffness reduction is proposed. This feature is computed from the deflections of the intact and damaged beam, respectively. The damage is always located where the bending moment achieves maxima. If the damage is positioned elsewhere on the beam, its effect becomes lower, because the stress is produced by a diminished bending moment. It is shown that the global stiffness reduction produced by a crack is the same for all beams with a similar cross-section, regardless of the boundary conditions. One mathematical relation indicating the severity and another indicating the effect of removing damage from the beam. Measurements on damaged beams with different boundary conditions and cross-sections are carried out, and the location and severity are found using the proposed relations. These comparisons prove that the proposed approach can be used to accurately compute the severity estimator. (paper)

  10. Evaluation of Various Methods for Estimating Global Solar Radiation in the Southeastern United States

    Woli, Prem; Paz, Joel O.

    2012-05-01

    Global solar radiation Rg is an important input for crop models to simulate crop responses. Because the scarcity of long and continuous records of Rg is a serious limitation in many countries, Rg is estimated using models. For crop-model application, empirical Rg models that use commonly measured meteorological variables, such as temperature and precipitation, are generally preferred. Although a large number of models of this kind exist, few have been evaluated for conditions in the United States. This study evaluated the performances of 16 empirical, temperature- and/or precipitation-based Rg models for the southeastern United States. By taking into account spatial distribution and data availability, 30 locations in the region were selected and their daily weather data spanning eight years obtained. One-half of the data was used for calibrating the models, and the other half was used for evaluation. For each model, location-specific parameter values were estimated through regressions. Models were evaluated for each location using the root-mean-square error and the modeling efficiency as goodness-of-fit measures. Among the models that use temperature or precipitation as the input variable, the Mavromatis model showed the best performance. The piecewise linear regression based Wu et al. model (WP) performed best not only among the models that use both temperature and precipitation but also among the 16 models evaluated, mainly because it has separate relationships for low and high radiation levels. The modeling efficiency of WP was from ~5% to more than 100% greater than those of the other models, depending on models and locations.

  11. A priori estimates of global solutions of superlinear parabolic systems

    Julius Pacuta

    2016-04-01

    Full Text Available We consider the parabolic system $ u_{t}-\\Delta u = u^{r}v^{p}$, $v_{t}-\\Delta v = u^{q}v^{s}$ in $\\Omega\\times(0,\\infty$, complemented by the homogeneous Dirichlet boundary conditions and the initial conditions $(u,v(\\cdot,0 = (u_{0},v_{0}$ in $\\Omega$, where $\\Omega $ is a smooth bounded domain in $ \\mathbb{R}^{N} $ and $ u_{0},v_{0}\\in L^{\\infty}(\\Omega$ are nonnegative functions. We find conditions on $ p,q,r,s $ guaranteeing a priori estimates of nonnegative classical global solutions. More precisely every such solution is bounded by a constant depending on suitable norm of the initial data. Our proofs are based on bootstrap in weighted Lebesgue spaces, universal estimates of auxiliary functions and estimates of the Dirichlet heat kernel.

  12. Global Precipitation Measurement (GPM) Core Observatory Falling Snow Estimates

    Skofronick Jackson, G.; Kulie, M.; Milani, L.; Munchak, S. J.; Wood, N.; Levizzani, V.

    2017-12-01

    Retrievals of falling snow from space represent an important data set for understanding and linking the Earth's atmospheric, hydrological, and energy cycles. Estimates of falling snow must be captured to obtain the true global precipitation water cycle, snowfall accumulations are required for hydrological studies, and without knowledge of the frozen particles in clouds one cannot adequately understand the energy and radiation budgets. This work focuses on comparing the first stable falling snow retrieval products (released May 2017) for the Global Precipitation Measurement (GPM) Core Observatory (GPM-CO), which was launched February 2014, and carries both an active dual frequency (Ku- and Ka-band) precipitation radar (DPR) and a passive microwave radiometer (GPM Microwave Imager-GMI). Five separate GPM-CO falling snow retrieval algorithm products are analyzed including those from DPR Matched (Ka+Ku) Scan, DPR Normal Scan (Ku), DPR High Sensitivity Scan (Ka), combined DPR+GMI, and GMI. While satellite-based remote sensing provides global coverage of falling snow events, the science is relatively new, the different on-orbit instruments don't capture all snow rates equally, and retrieval algorithms differ. Thus a detailed comparison among the GPM-CO products elucidates advantages and disadvantages of the retrievals. GPM and CloudSat global snowfall evaluation exercises are natural investigative pathways to explore, but caution must be undertaken when analyzing these datasets for comparative purposes. This work includes outlining the challenges associated with comparing GPM-CO to CloudSat satellite snow estimates due to the different sampling, algorithms, and instrument capabilities. We will highlight some factors and assumptions that can be altered or statistically normalized and applied in an effort to make comparisons between GPM and CloudSat global satellite falling snow products as equitable as possible.

  13. Redefinition and global estimation of basal ecosystem respiration rate

    Yuan, W.; Luo, Y.; Li, X.; Liu, S.; Yu, G.; Zhou, T.; Bahn, M.; Black, A.; Desai, A.R.; Cescatti, A.; Marcolla, B.; Jacobs, C.; Chen, J.; Aurela, M.; Bernhofer, C.; Gielen, B.; Bohrer, G.; Cook, D.R.; Dragoni, D.; Dunn, A.L.; Gianelle, D.; Grnwald, T.; Ibrom, A.; Leclerc, M.Y.; Lindroth, A.; Liu, H.; Marchesini, L.B.; Montagnani, L.; Pita, G.; Rodeghiero, M.; Rodrigues, A.; Starr, G.; Stoy, Paul C.

    2011-01-01

    Basal ecosystem respiration rate (BR), the ecosystem respiration rate at a given temperature, is a common and important parameter in empirical models for quantifying ecosystem respiration (ER) globally. Numerous studies have indicated that BR varies in space. However, many empirical ER models still use a global constant BR largely due to the lack of a functional description for BR. In this study, we redefined BR to be ecosystem respiration rate at the mean annual temperature. To test the validity of this concept, we conducted a synthesis analysis using 276 site-years of eddy covariance data, from 79 research sites located at latitudes ranging from ∼3°S to ∼70°N. Results showed that mean annual ER rate closely matches ER rate at mean annual temperature. Incorporation of site-specific BR into global ER model substantially improved simulated ER compared to an invariant BR at all sites. These results confirm that ER at the mean annual temperature can be considered as BR in empirical models. A strong correlation was found between the mean annual ER and mean annual gross primary production (GPP). Consequently, GPP, which is typically more accurately modeled, can be used to estimate BR. A light use efficiency GPP model (i.e., EC-LUE) was applied to estimate global GPP, BR and ER with input data from MERRA (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate resolution Imaging Spectroradiometer). The global ER was 103 Pg C yr −1, with the highest respiration rate over tropical forests and the lowest value in dry and high-latitude areas.

  14. Greenhouse Gases Emission and Global Warming Potential as Affected by Chemical Inputs for Main Cultivated Crops in Kerman Province: - Horticultural Crops

    Nasibe Pourghasemian

    2017-12-01

    Full Text Available Introduction The latest report of the IPCC states that future emissions of greenhouse gases (GHGs will continue to increase and will be the main cause of global climatic changes, as well as Iran. The three greenhouse gases associated with agriculture are CO2, CH4, and N2O. Chemical inputs consumption in agriculture has increased annually, while more intensive use of energy led to some important human health and environmental problems such as greenhouse gas emissions and global warming. Therefore, it is necessary to reduce the application of chemical inputs in agricultural systems. Agriculture contributes significantly to atmospheric GHG emissions, with 14% of the global net CO2 emissions coming from this sector. Chemical inputs have a major role in this hazards. There is even less data on CO2, N2O, and CH4 gas emission analysis as affected by cultivating various crops in Kerman province. Therefore, this study was conducted to assess the GHGs emission and Global warming Potential GWP caused by chemical inputs (various chemical fertilizers and pesticides for cultivating potato, onion and watermelon in some regions of Kerman province at 2011-2012 growth season. Material and Methods The study was conducted in Kerman province of Iran. Data of planting area, application rates of the chemical inputs and other different parameter were collected from potato, onion and watermelon growers by using a face to face questionnaire in 2014 for different regions of Kerman(Bardsir, Bam, Jiroft, Kerman, Ravar, Rafsanjan and Sirjan. In addition to the data obtained by surveys, previous studies of related organization (Agricultural Ministry of Kerman were also utilized during the study. Farm random sampling was done within whole population and the sample size was determined by proper equations. The amounts of GHG emissions from chemical inputs in the studied crops were calculated by using CO2, N2O and CH4 emissions coefficient of chemical inputs. Then the amount of

  15. Parameter estimation of a two-horizon soil profile by combining crop canopy and surface soil moisture observations using GLUE

    Sreelash, K.; Sekhar, M.; Ruiz, L.; Tomer, S. K.; Guérif, M.; Buis, S.; Durand, P.; Gascuel-Odoux, C.

    2012-08-01

    SummaryEstimation of soil parameters by inverse modeling using observations on either surface soil moisture or crop variables has been successfully attempted in many studies, but difficulties to estimate root zone properties arise when heterogeneous layered soils are considered. The objective of this study was to explore the potential of combining observations on surface soil moisture and crop variables - leaf area index (LAI) and above-ground biomass for estimating soil parameters (water holding capacity and soil depth) in a two-layered soil system using inversion of the crop model STICS. This was performed using GLUE method on a synthetic data set on varying soil types and on a data set from a field experiment carried out in two maize plots in South India. The main results were (i) combination of surface soil moisture and above-ground biomass provided consistently good estimates with small uncertainity of soil properties for the two soil layers, for a wide range of soil paramater values, both in the synthetic and the field experiment, (ii) above-ground biomass was found to give relatively better estimates and lower uncertainty than LAI when combined with surface soil moisture, especially for estimation of soil depth, (iii) surface soil moisture data, either alone or combined with crop variables, provided a very good estimate of the water holding capacity of the upper soil layer with very small uncertainty whereas using the surface soil moisture alone gave very poor estimates of the soil properties of the deeper layer, and (iv) using crop variables alone (else above-ground biomass or LAI) provided reasonable estimates of the deeper layer properties depending on the soil type but provided poor estimates of the first layer properties. The robustness of combining observations of the surface soil moisture and the above-ground biomass for estimating two layer soil properties, which was demonstrated using both synthetic and field experiments in this study, needs now to

  16. Estimation of Global Vegetation Productivity from Global LAnd Surface Satellite Data

    Tao Yu

    2018-02-01

    Full Text Available Accurately estimating vegetation productivity is important in research on terrestrial ecosystems, carbon cycles and climate change. Eight-day gross primary production (GPP and annual net primary production (NPP are contained in MODerate Resolution Imaging Spectroradiometer (MODIS products (MOD17, which are considered the first operational datasets for monitoring global vegetation productivity. However, the cloud-contaminated MODIS leaf area index (LAI and Fraction of Photosynthetically Active Radiation (FPAR retrievals may introduce some considerable errors to MODIS GPP and NPP products. In this paper, global eight-day GPP and eight-day NPP were first estimated based on Global LAnd Surface Satellite (GLASS LAI and FPAR products. Then, GPP and NPP estimates were validated by FLUXNET GPP data and BigFoot NPP data and were compared with MODIS GPP and NPP products. Compared with MODIS GPP, a time series showed that estimated GLASS GPP in our study was more temporally continuous and spatially complete with smoother trajectories. Validated with FLUXNET GPP and BigFoot NPP, we demonstrated that estimated GLASS GPP and NPP achieved higher precision for most vegetation types.

  17. Religious affiliation at time of death - Global estimates and projections.

    Skirbekk, Vegard; Todd, Megan; Stonawski, Marcin

    2018-03-01

    Religious affiliation influences societal practices regarding death and dying, including palliative care, religiously acceptable health service procedures, funeral rites and beliefs about an afterlife. We aimed to estimate and project religious affiliation at the time of death globally, as this information has been lacking. We compiled data on demographic information and religious affiliation from more than 2500 surveys, registers and censuses covering 198 nations/territories. We present estimates of religious affiliation at the time of death as of 2010, projections up to and including 2060, taking into account trends in mortality, religious conversion, intergenerational transmission of religion, differential fertility, and gross migration flows, by age and sex. We find that Christianity continues to be the most common religion at death, although its share will fall from 37% to 31% of global deaths between 2010 and 2060. The share of individuals identifying as Muslim at the time of death increases from 21% to 24%. The share of religiously unaffiliated will peak at 17% in 2035 followed by a slight decline thereafter. In specific regions, such as Europe, the unaffiliated share will continue to rises from 14% to 21% throughout the period. Religious affiliation at the time of death is changing globally, with distinct regional patterns. This could affect spatial variation in healthcare and social customs relating to death and dying.

  18. Global solar radiation estimation in Lavras region, Minas Gerais

    Dantas, A.A.A.; Carvalho, L.G. de; Ferreira, E.

    2003-01-01

    The objective of this work was the determination of the ''a'' and '' b'' constants of the Angstrom linear model in order to estimate the global solar radiation in Lavras, MG. The work was carried out in the Climatological Station of Lavras (ECP/INMET/UFLA), at the Federal University of Lavras, from December 2001 to November 2002, through insolation daily data and global solar radiation daily records. The ''a'' and '' b'' constants, that express the atmospheric transmitance, were obtained by regression analysis of those data. The obtained equation, Qg/Qt = 0,23 + 0,49 presented a determination coefficient of 0,89. The results are smaller than those suggested by the recommendations that uses the local latitude. According to the results, its possible to indicate the values of 0,23 and 0,49 to be used as the ''a'' and '' b'' constants on the Angstrom equation to estimate the global solar radiation in Lavras, MG. (author) [pt

  19. Towards a globally optimized crop distribution: Integrating water use, nutrition, and economic value

    Davis, K. F.; Seveso, A.; Rulli, M. C.; D'Odorico, P.

    2016-12-01

    Human demand for crop production is expected to increase substantially in the coming decades as a result of population growth, richer diets and biofuel use. In order for food production to keep pace, unprecedented amounts of resources - water, fertilizers, energy - will be required. This has led to calls for `sustainable intensification' in which yields are increased on existing croplands while seeking to minimize impacts on water and other agricultural resources. Recent studies have quantified aspects of this, showing that there is a large potential to improve crop yields and increase harvest frequencies to better meet human demand. Though promising, both solutions would necessitate large additional inputs of water and fertilizer in order to be achieved under current technologies. However, the question of whether the current distribution of crops is, in fact, the best for realizing sustainable production has not been considered to date. To this end, we ask: Is it possible to increase crop production and economic value while minimizing water demand by simply growing crops where soil and climate conditions are best suited? Here we use maps of yields and evapotranspiration for 14 major food crops to identify differences between current crop distributions and where they can most suitably be planted. By redistributing crops across currently cultivated lands, we determine the potential improvements in calorie (+12%) and protein (+51%) production, economic output (+41%) and water demand (-5%). This approach can also incorporate the impact of future climate on cropland suitability, and as such, be used to provide optimized cropping patterns under climate change. Thus, our study provides a novel tool towards achieving sustainable intensification that can be used to recommend optimal crop distributions in the face of a changing climate while simultaneously accounting for food security, freshwater resources, and livelihoods.

  20. Improving Crop Yield and Nutrient Use Efficiency via Biofertilization—A Global Meta-analysis

    Lukas Schütz

    2018-01-01

    Full Text Available The application of microbial inoculants (biofertilizers is a promising technology for future sustainable farming systems in view of rapidly decreasing phosphorus stocks and the need to more efficiently use available nitrogen (N. Various microbial taxa are currently used as biofertilizers, based on their capacity to access nutrients from fertilizers and soil stocks, to fix atmospheric nitrogen, to improve water uptake or to act as biocontrol agents. Despite the existence of a considerable knowledge on effects of specific taxa of biofertilizers, a comprehensive quantitative assessment of the performance of biofertilizers with different traits such as phosphorus solubilization and N fixation applied to various crops at a global scale is missing. We conducted a meta-analysis to quantify benefits of biofertilizers in terms of yield increase, nitrogen and phosphorus use efficiency, based on 171 peer reviewed publications that met eligibility criteria. Major findings are: (i the superiority of biofertilizer performance in dry climates over other climatic regions (yield response: dry climate +20.0 ± 1.7%, tropical climate +14.9 ± 1.2%, oceanic climate +10.0 ± 3.7%, continental climate +8.5 ± 2.4%; (ii meta-regression analyses revealed that yield response due to biofertilizer application was generally small at low soil P levels; efficacy increased along higher soil P levels in the order arbuscular mycorrhizal fungi (AMF, P solubilizers, and N fixers; (iii meta-regressions showed that the success of inoculation with AMF was greater at low organic matter content and at neutral pH. Our comprehensive analysis provides a basis and guidance for proper choice and application of biofertilizers.

  1. Improving Crop Yield and Nutrient Use Efficiency via Biofertilization—A Global Meta-analysis

    Schütz, Lukas; Gattinger, Andreas; Meier, Matthias; Müller, Adrian; Boller, Thomas; Mäder, Paul; Mathimaran, Natarajan

    2018-01-01

    The application of microbial inoculants (biofertilizers) is a promising technology for future sustainable farming systems in view of rapidly decreasing phosphorus stocks and the need to more efficiently use available nitrogen (N). Various microbial taxa are currently used as biofertilizers, based on their capacity to access nutrients from fertilizers and soil stocks, to fix atmospheric nitrogen, to improve water uptake or to act as biocontrol agents. Despite the existence of a considerable knowledge on effects of specific taxa of biofertilizers, a comprehensive quantitative assessment of the performance of biofertilizers with different traits such as phosphorus solubilization and N fixation applied to various crops at a global scale is missing. We conducted a meta-analysis to quantify benefits of biofertilizers in terms of yield increase, nitrogen and phosphorus use efficiency, based on 171 peer reviewed publications that met eligibility criteria. Major findings are: (i) the superiority of biofertilizer performance in dry climates over other climatic regions (yield response: dry climate +20.0 ± 1.7%, tropical climate +14.9 ± 1.2%, oceanic climate +10.0 ± 3.7%, continental climate +8.5 ± 2.4%); (ii) meta-regression analyses revealed that yield response due to biofertilizer application was generally small at low soil P levels; efficacy increased along higher soil P levels in the order arbuscular mycorrhizal fungi (AMF), P solubilizers, and N fixers; (iii) meta-regressions showed that the success of inoculation with AMF was greater at low organic matter content and at neutral pH. Our comprehensive analysis provides a basis and guidance for proper choice and application of biofertilizers. PMID:29375594

  2. Improving Crop Yield and Nutrient Use Efficiency via Biofertilization-A Global Meta-analysis.

    Schütz, Lukas; Gattinger, Andreas; Meier, Matthias; Müller, Adrian; Boller, Thomas; Mäder, Paul; Mathimaran, Natarajan

    2017-01-01

    The application of microbial inoculants (biofertilizers) is a promising technology for future sustainable farming systems in view of rapidly decreasing phosphorus stocks and the need to more efficiently use available nitrogen (N). Various microbial taxa are currently used as biofertilizers, based on their capacity to access nutrients from fertilizers and soil stocks, to fix atmospheric nitrogen, to improve water uptake or to act as biocontrol agents. Despite the existence of a considerable knowledge on effects of specific taxa of biofertilizers, a comprehensive quantitative assessment of the performance of biofertilizers with different traits such as phosphorus solubilization and N fixation applied to various crops at a global scale is missing. We conducted a meta-analysis to quantify benefits of biofertilizers in terms of yield increase, nitrogen and phosphorus use efficiency, based on 171 peer reviewed publications that met eligibility criteria. Major findings are: (i) the superiority of biofertilizer performance in dry climates over other climatic regions (yield response: dry climate +20.0 ± 1.7%, tropical climate +14.9 ± 1.2%, oceanic climate +10.0 ± 3.7%, continental climate +8.5 ± 2.4%); (ii) meta-regression analyses revealed that yield response due to biofertilizer application was generally small at low soil P levels; efficacy increased along higher soil P levels in the order arbuscular mycorrhizal fungi (AMF), P solubilizers, and N fixers; (iii) meta-regressions showed that the success of inoculation with AMF was greater at low organic matter content and at neutral pH. Our comprehensive analysis provides a basis and guidance for proper choice and application of biofertilizers.

  3. Cereal Crops Are not Created Equal: Wheat Consumption Associated with Obesity Prevalence Globally and Regionally

    Wenpeng You

    2016-05-01

    Full Text Available Background: Cereals have been extensively advocated as the beneficial food group in terms of body weight management, but each staple cereal crop may contribute in different ways. Studies of the association between wheat availability and risk of obesity are controversial. This study aimed to test the global and regional association between wheat availability as reported by FAO and obesity prevalence at a population level. FAO does not distinguish between whole grain wheat and refined wheat. Methods: Population-specific data from 170 countries on prevalence of obesity, availabilities of mixed cereals, wheat, rice, maize, meat, sugar, fat, soy and calories and GDP are obtained from the UN agencies. All variables were measured as per capita per day (or per year. Each country is treated as an individual subject. SPSS v. 22 is used to analyse these data for all the 170 countries and official country groupings (regions using non parametric and parametric correlations, including partial correlation analysis. Results: Pearson’s correlation coefficient analysis showed that obesity prevalence is positively associated with wheat availability (r = 0.500, p < 0.001, but is inversely associated with availabilities of total cereals (r = -0.132, p = 0.087, rice (r = -0.405, p < 0.001 and maize (r = -0.227, p = 0.004. These associations remain in partial correlation model when we keep availabilities of meat, fat, sugar, soy, caloric intake and GDP statistically constant. Overall, positive associations between wheat availability and obesity prevalence remain in different regions. Maize and mixed cereal availabilities do not show independent associations with the obesity prevalence. Conclusions: Our study suggests that wheat availability is an independent predictor of the obesity prevalence both worldwide and with special regard to the regions of Africa, Americas and Asia. Future studies should distinguish between possible influence of whole grain and ultra

  4. Global parameter estimation for thermodynamic models of transcriptional regulation.

    Suleimenov, Yerzhan; Ay, Ahmet; Samee, Md Abul Hassan; Dresch, Jacqueline M; Sinha, Saurabh; Arnosti, David N

    2013-07-15

    Deciphering the mechanisms involved in gene regulation holds the key to understanding the control of central biological processes, including human disease, population variation, and the evolution of morphological innovations. New experimental techniques including whole genome sequencing and transcriptome analysis have enabled comprehensive modeling approaches to study gene regulation. In many cases, it is useful to be able to assign biological significance to the inferred model parameters, but such interpretation should take into account features that affect these parameters, including model construction and sensitivity, the type of fitness calculation, and the effectiveness of parameter estimation. This last point is often neglected, as estimation methods are often selected for historical reasons or for computational ease. Here, we compare the performance of two parameter estimation techniques broadly representative of local and global approaches, namely, a quasi-Newton/Nelder-Mead simplex (QN/NMS) method and a covariance matrix adaptation-evolutionary strategy (CMA-ES) method. The estimation methods were applied to a set of thermodynamic models of gene transcription applied to regulatory elements active in the Drosophila embryo. Measuring overall fit, the global CMA-ES method performed significantly better than the local QN/NMS method on high quality data sets, but this difference was negligible on lower quality data sets with increased noise or on data sets simplified by stringent thresholding. Our results suggest that the choice of parameter estimation technique for evaluation of gene expression models depends both on quality of data, the nature of the models [again, remains to be established] and the aims of the modeling effort. Copyright © 2013 Elsevier Inc. All rights reserved.

  5. A Global and Spatially Explicit Assessment of Climate Change Impacts on Crop Production and Consumptive Water Use

    Liu, Junguo; Folberth, Christian; Yang, Hong; Röckström, Johan; Abbaspour, Karim; Zehnder, Alexander J. B.

    2013-01-01

    Food security and water scarcity have become two major concerns for future human's sustainable development, particularly in the context of climate change. Here we present a comprehensive assessment of climate change impacts on the production and water use of major cereal crops on a global scale with a spatial resolution of 30 arc-minutes for the 2030s (short term) and the 2090s (long term), respectively. Our findings show that impact uncertainties are higher on larger spatial scales (e.g., global and continental) but lower on smaller spatial scales (e.g., national and grid cell). Such patterns allow decision makers and investors to take adaptive measures without being puzzled by a highly uncertain future at the global level. Short-term gains in crop production from climate change are projected for many regions, particularly in African countries, but the gains will mostly vanish and turn to losses in the long run. Irrigation dependence in crop production is projected to increase in general. However, several water poor regions will rely less heavily on irrigation, conducive to alleviating regional water scarcity. The heterogeneity of spatial patterns and the non-linearity of temporal changes of the impacts call for site-specific adaptive measures with perspectives of reducing short- and long-term risks of future food and water security. PMID:23460901

  6. A global and spatially explicit assessment of climate change impacts on crop production and consumptive water use.

    Junguo Liu

    Full Text Available Food security and water scarcity have become two major concerns for future human's sustainable development, particularly in the context of climate change. Here we present a comprehensive assessment of climate change impacts on the production and water use of major cereal crops on a global scale with a spatial resolution of 30 arc-minutes for the 2030s (short term and the 2090s (long term, respectively. Our findings show that impact uncertainties are higher on larger spatial scales (e.g., global and continental but lower on smaller spatial scales (e.g., national and grid cell. Such patterns allow decision makers and investors to take adaptive measures without being puzzled by a highly uncertain future at the global level. Short-term gains in crop production from climate change are projected for many regions, particularly in African countries, but the gains will mostly vanish and turn to losses in the long run. Irrigation dependence in crop production is projected to increase in general. However, several water poor regions will rely less heavily on irrigation, conducive to alleviating regional water scarcity. The heterogeneity of spatial patterns and the non-linearity of temporal changes of the impacts call for site-specific adaptive measures with perspectives of reducing short- and long-term risks of future food and water security.

  7. Global Carleman estimates for degenerate parabolic operators with applications

    Cannarsa, P; Vancostenoble, J

    2016-01-01

    Degenerate parabolic operators have received increasing attention in recent years because they are associated with both important theoretical analysis, such as stochastic diffusion processes, and interesting applications to engineering, physics, biology, and economics. This manuscript has been conceived to introduce the reader to global Carleman estimates for a class of parabolic operators which may degenerate at the boundary of the space domain, in the normal direction to the boundary. Such a kind of degeneracy is relevant to study the invariance of a domain with respect to a given stochastic diffusion flow, and appears naturally in climatology models.

  8. Global and regional emission estimates for HCFC-22

    E. Saikawa

    2012-11-01

    Full Text Available HCFC-22 (CHClF2, chlorodifluoromethane is an ozone-depleting substance (ODS as well as a significant greenhouse gas (GHG. HCFC-22 has been used widely as a refrigerant fluid in cooling and air-conditioning equipment since the 1960s, and it has also served as a traditional substitute for some chlorofluorocarbons (CFCs controlled under the Montreal Protocol. A low frequency record on tropospheric HCFC-22 since the late 1970s is available from measurements of the Southern Hemisphere Cape Grim Air Archive (CGAA and a few Northern Hemisphere air samples (mostly from Trinidad Head using the Advanced Global Atmospheric Gases Experiment (AGAGE instrumentation and calibrations. Since the 1990s high-frequency, high-precision, in situ HCFC-22 measurements have been collected at these AGAGE stations. Since 1992, the Global Monitoring Division of the National Oceanic and Atmospheric Administration/Earth System Research Laboratory (NOAA/ESRL has also collected flasks on a weekly basis from remote sites across the globe and analyzed them for a suite of halocarbons including HCFC-22. Additionally, since 2006 flasks have been collected approximately daily at a number of tower sites across the US and analyzed for halocarbons and other gases at NOAA. All results show an increase in the atmospheric mole fractions of HCFC-22, and recent data show a growth rate of approximately 4% per year, resulting in an increase in the background atmospheric mole fraction by a factor of 1.7 from 1995 to 2009. Using data on HCFC-22 consumption submitted to the United Nations Environment Programme (UNEP, as well as existing bottom-up emission estimates, we first create globally-gridded a priori HCFC-22 emissions over the 15 yr since 1995. We then use the three-dimensional chemical transport model, Model for Ozone and Related Chemical Tracers version 4 (MOZART v4, and a Bayesian inverse method to estimate global as well as regional annual emissions. Our inversion indicates

  9. Estimation of Crop Coefficient of Corn (Kccorn under Climate Change Scenarios Using Data Mining Technique

    Kampanad Bhaktikul

    2012-01-01

    Full Text Available The main objectives of this study are to determine the crop coefficient of corn (Kccorn using data mining technique under climate change scenarios, and to develop the guidelines for future water management based on climate change scenarios. Variables including date, maximum temperature, minimum temperature, precipitation, humidity, wind speed, and solar radiation from seven meteorological stations during 1991 to 2000 were used. Cross-Industry Standard Process for Data Mining (CRISP-DM was applied for data collection and analyses. The procedures compose of investigation of input data, model set up using Artificial Neural Networks (ANNs, model evaluation, and finally estimation of the Kccorn. Three climate change scenarios of carbon dioxide (CO2 concentration level: 360 ppm, 540 ppm, and 720 ppm were set. The results indicated that the best number of node of input layer - hidden layer - output layer was 7-13-1. The correlation coefficient of model was 0.99. The predicted Kccorn revealed that evapotranspiration (ETcorn pattern will be changed significantly upon CO2 concentration level. From the model predictions, ETcorn will be decreased 3.34% when CO2 increased from 360 ppm to 540 ppm. For the double CO2 concentration from 360 ppm to 720 ppm, ETcorn will be increased 16.13%. The future water management guidelines to cope with the climate change are suggested.

  10. Estimating the Global Burden of Endemic Canine Rabies

    Hampson, Katie; Coudeville, Laurent; Lembo, Tiziana; Sambo, Maganga; Kieffer, Alexia; Attlan, Michaël; Barrat, Jacques; Blanton, Jesse D.; Briggs, Deborah J.; Cleaveland, Sarah; Costa, Peter; Freuling, Conrad M.; Hiby, Elly; Knopf, Lea; Leanes, Fernando; Meslin, François-Xavier; Metlin, Artem; Miranda, Mary Elizabeth; Müller, Thomas; Nel, Louis H.; Recuenco, Sergio; Rupprecht, Charles E.; Schumacher, Carolin; Taylor, Louise; Vigilato, Marco Antonio Natal; Zinsstag, Jakob; Dushoff, Jonathan

    2015-01-01

    Background Rabies is a notoriously underreported and neglected disease of low-income countries. This study aims to estimate the public health and economic burden of rabies circulating in domestic dog populations, globally and on a country-by-country basis, allowing an objective assessment of how much this preventable disease costs endemic countries. Methodology/Principal Findings We established relationships between rabies mortality and rabies prevention and control measures, which we incorporated into a model framework. We used data derived from extensive literature searches and questionnaires on disease incidence, control interventions and preventative measures within this framework to estimate the disease burden. The burden of rabies impacts on public health sector budgets, local communities and livestock economies, with the highest risk of rabies in the poorest regions of the world. This study estimates that globally canine rabies causes approximately 59,000 (95% Confidence Intervals: 25-159,000) human deaths, over 3.7 million (95% CIs: 1.6-10.4 million) disability-adjusted life years (DALYs) and 8.6 billion USD (95% CIs: 2.9-21.5 billion) economic losses annually. The largest component of the economic burden is due to premature death (55%), followed by direct costs of post-exposure prophylaxis (PEP, 20%) and lost income whilst seeking PEP (15.5%), with only limited costs to the veterinary sector due to dog vaccination (1.5%), and additional costs to communities from livestock losses (6%). Conclusions/Significance This study demonstrates that investment in dog vaccination, the single most effective way of reducing the disease burden, has been inadequate and that the availability and affordability of PEP needs improving. Collaborative investments by medical and veterinary sectors could dramatically reduce the current large, and unnecessary, burden of rabies on affected communities. Improved surveillance is needed to reduce uncertainty in burden estimates and to

  11. HIV due to female sex work: regional and global estimates.

    Annette Prüss-Ustün

    Full Text Available Female sex workers (FSWs are at high risk of HIV infection. Our objective was to determine the proportion of HIV prevalence in the general female adult population that is attributable to the occupational exposure of female sex work, due to unprotected sexual intercourse.Population attributable fractions of HIV prevalence due to female sex work were estimated for 2011. A systematic search was conducted to retrieve required input data from available sources. Data gaps of HIV prevalence in FSWs for 2011 were filled using multilevel modeling and multivariate linear regression. The fraction of HIV attributable to female sex work was estimated as the excess HIV burden in FSWs deducting the HIV burden in FSWs due to injecting drug use.An estimated fifteen percent of HIV in the general female adult population is attributable to (unsafe female sex work. The region with the highest attributable fraction is Sub Saharan Africa, but the burden is also substantial for the Caribbean, Latin America and South and Southeast Asia. We estimate 106,000 deaths from HIV are a result of female sex work globally, 98,000 of which occur in Sub-Saharan Africa. If HIV prevalence in other population groups originating from sexual contact with FSWs had been considered, the overall attributable burden would probably be much larger.Female sex work is an important contributor to HIV transmission and the global HIV burden. Effective HIV prevention measures exist and have been successfully targeted at key populations in many settings. These must be scaled up.FSWs suffer from high HIV burden and are a crucial core population for HIV transmission. Surveillance, prevention and treatment of HIV in FSWs should benefit both this often neglected vulnerable group and the general population.

  12. HIV Due to Female Sex Work: Regional and Global Estimates

    Prüss-Ustün, Annette; Wolf, Jennyfer; Driscoll, Tim; Degenhardt, Louisa; Neira, Maria; Calleja, Jesus Maria Garcia

    2013-01-01

    Introduction Female sex workers (FSWs) are at high risk of HIV infection. Our objective was to determine the proportion of HIV prevalence in the general female adult population that is attributable to the occupational exposure of female sex work, due to unprotected sexual intercourse. Methods Population attributable fractions of HIV prevalence due to female sex work were estimated for 2011. A systematic search was conducted to retrieve required input data from available sources. Data gaps of HIV prevalence in FSWs for 2011 were filled using multilevel modeling and multivariate linear regression. The fraction of HIV attributable to female sex work was estimated as the excess HIV burden in FSWs deducting the HIV burden in FSWs due to injecting drug use. Results An estimated fifteen percent of HIV in the general female adult population is attributable to (unsafe) female sex work. The region with the highest attributable fraction is Sub Saharan Africa, but the burden is also substantial for the Caribbean, Latin America and South and Southeast Asia. We estimate 106,000 deaths from HIV are a result of female sex work globally, 98,000 of which occur in Sub-Saharan Africa. If HIV prevalence in other population groups originating from sexual contact with FSWs had been considered, the overall attributable burden would probably be much larger. Discussion Female sex work is an important contributor to HIV transmission and the global HIV burden. Effective HIV prevention measures exist and have been successfully targeted at key populations in many settings. These must be scaled up. Conclusion FSWs suffer from high HIV burden and are a crucial core population for HIV transmission. Surveillance, prevention and treatment of HIV in FSWs should benefit both this often neglected vulnerable group and the general population. PMID:23717432

  13. Estimating the global burden of endemic canine rabies.

    Katie Hampson

    2015-04-01

    Full Text Available Rabies is a notoriously underreported and neglected disease of low-income countries. This study aims to estimate the public health and economic burden of rabies circulating in domestic dog populations, globally and on a country-by-country basis, allowing an objective assessment of how much this preventable disease costs endemic countries.We established relationships between rabies mortality and rabies prevention and control measures, which we incorporated into a model framework. We used data derived from extensive literature searches and questionnaires on disease incidence, control interventions and preventative measures within this framework to estimate the disease burden. The burden of rabies impacts on public health sector budgets, local communities and livestock economies, with the highest risk of rabies in the poorest regions of the world. This study estimates that globally canine rabies causes approximately 59,000 (95% Confidence Intervals: 25-159,000 human deaths, over 3.7 million (95% CIs: 1.6-10.4 million disability-adjusted life years (DALYs and 8.6 billion USD (95% CIs: 2.9-21.5 billion economic losses annually. The largest component of the economic burden is due to premature death (55%, followed by direct costs of post-exposure prophylaxis (PEP, 20% and lost income whilst seeking PEP (15.5%, with only limited costs to the veterinary sector due to dog vaccination (1.5%, and additional costs to communities from livestock losses (6%.This study demonstrates that investment in dog vaccination, the single most effective way of reducing the disease burden, has been inadequate and that the availability and affordability of PEP needs improving. Collaborative investments by medical and veterinary sectors could dramatically reduce the current large, and unnecessary, burden of rabies on affected communities. Improved surveillance is needed to reduce uncertainty in burden estimates and to monitor the impacts of control efforts.

  14. Estimation of global solar radiation by means of sunshine duration

    Luis, Mazorra Aguiar; Felipe, Diaz Reyes [Electrical Engineering Dept., Las Palmas de Gran Canaria Univ. (U.L.P.G.C.), Campus Univ. Tafira (Spain); Pilar, Navarro Rivero [Canary Islands Technological Inst. (I.T.C.), Gran Canaria (Spain)

    2008-07-01

    This paper analyses the relationship between global solar irradiation and sunshine duration with different estimation models for the island of Gran Canaria (Spain). These parameters were taken from six measurement stations around the Island, and selected for their reliability and the long period of time they covered. All data used in this paper were handed over by the Canary Islands Technological Institute (I.T.C.). As a first approach, it was decided to study the Angstrom lineal model. In order to improve the knowledge on solar resources, a Typical Meteorological Year (TMY) was created from all daily data. TMY shows differences between southern and northern locations, where Trade Winds generate clouds during the summer months. TMY resumes a data bank much longer than a year in duration, generating the characteristics for a year series of each location, for both irradiation and sunshine duration. To create the TMY, weighted means have been used to smooth high or low values. At first, Angstrom lineal model has been used to estimate solar global irradiation from sunshine duration values, using TMY. But the lineal model didn't reproduce satisfactory results when used to obtain global solar radiation from all daily sunshine duration data. For this reason, different models based in both parameters were used. The parameters estimation of this model was achieved both from TMY daily and monthly series and from all daily data for every location. Because of the weather stability all over the year in the Island, most of the daily data are concentrated in a close range, occasioning a deviation in the lineal equations. To avoid this deviation it was proposed to consider a limit condition data, taking into account values out of the main cloud of data. Additionally, different models were proposed (quadratic, cubic, logarithmic and exponential) to make a regression from all daily data. The best results were obtained with the exponential model proposed in this paper. The

  15. Meeting the global food demand of the future by engineering crop photosynthesis and yield potential.

    Long, Stephen P; Marshall-Colon, Amy; Zhu, Xin-Guang

    2015-03-26

    Increase in demand for our primary foodstuffs is outstripping increase in yields, an expanding gap that indicates large potential food shortages by mid-century. This comes at a time when yield improvements are slowing or stagnating as the approaches of the Green Revolution reach their biological limits. Photosynthesis, which has been improved little in crops and falls far short of its biological limit, emerges as the key remaining route to increase the genetic yield potential of our major crops. Thus, there is a timely need to accelerate our understanding of the photosynthetic process in crops to allow informed and guided improvements via in-silico-assisted genetic engineering. Potential and emerging approaches to improving crop photosynthetic efficiency are discussed, and the new tools needed to realize these changes are presented. Copyright © 2015 Elsevier Inc. All rights reserved.

  16. Similar estimates of temperature impacts on global wheat yield by three independent methods

    Liu, Bing; Asseng, Senthold; Müller, Christoph; Ewert, Frank; Elliott, Joshua; Lobell, David B.; Martre, Pierre; Ruane, Alex C.; Wallach, Daniel; Jones, James W.; Supit, Iwan; Wolf, Joost

    2016-01-01

    The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO 2 fertilization effects,

  17. Next biotech plants: new traits, crops, developers and technologies for addressing global challenges.

    Ricroch, Agnès E; Hénard-Damave, Marie-Cécile

    2016-08-01

    Most of the genetically modified (GM) plants currently commercialized encompass a handful of crop species (soybean, corn, cotton and canola) with agronomic characters (traits) directed against some biotic stresses (pest resistance, herbicide tolerance or both) and created by multinational companies. The same crops with agronomic traits already on the market today will continue to be commercialized, but there will be also a wider range of species with combined traits. The timeframe anticipated for market release of the next biotech plants will not only depend on science progress in research and development (R&D) in laboratories and fields, but also primarily on how demanding regulatory requirements are in countries where marketing approvals are pending. Regulatory constraints, including environmental and health impact assessments, have increased significantly in the past decades, delaying approvals and increasing their costs. This has sometimes discouraged public research entities and small and medium size plant breeding companies from using biotechnology and given preference to other technologies, not as stringently regulated. Nevertheless, R&D programs are flourishing in developing countries, boosted by the necessity to meet the global challenges that are food security of a booming world population while mitigating climate change impacts. Biotechnology is an instrument at the service of these imperatives and a wide variety of plants are currently tested for their high yield despite biotic and abiotic stresses. Many plants with higher water or nitrogen use efficiency, tolerant to cold, salinity or water submergence are being developed. Food security is not only a question of quantity but also of quality of agricultural and food products, to be available and accessible for the ones who need it the most. Many biotech plants (especially staple food) are therefore being developed with nutritional traits, such as biofortification in vitamins and metals. The main

  18. A Global Estimate of Seafood Consumption by Coastal Indigenous Peoples.

    Cisneros-Montemayor, Andrés M; Pauly, Daniel; Weatherdon, Lauren V; Ota, Yoshitaka

    2016-01-01

    Coastal Indigenous peoples rely on ocean resources and are highly vulnerable to ecosystem and economic change. Their challenges have been observed and recognized at local and regional scales, yet there are no global-scale analyses to inform international policies. We compile available data for over 1,900 coastal Indigenous communities around the world representing 27 million people across 87 countries. Based on available data at local and regional levels, we estimate a total global yearly seafood consumption of 2.1 million (1.5 million-2.8 million) metric tonnes by coastal Indigenous peoples, equal to around 2% of global yearly commercial fisheries catch. Results reflect the crucial role of seafood for these communities; on average, consumption per capita is 15 times higher than non-Indigenous country populations. These findings contribute to an urgently needed sense of scale to coastal Indigenous issues, and will hopefully prompt increased recognition and directed research regarding the marine knowledge and resource needs of Indigenous peoples. Marine resources are crucial to the continued existence of coastal Indigenous peoples, and their needs must be explicitly incorporated into management policies.

  19. Atmospheric Inversion of the Global Surface Carbon Flux with Consideration of the Spatial Distributions of US Crop Production and Consumption

    Fung, Jonathan Winston

    Carbon dioxide is taken up by crops during production and released back to the atmosphere at different geographical locations through respiration of consumed crop commodities. In this study, spatially distributed county-level US cropland net primary productivity, harvested biomass, changes in soil carbon, and human and livestock consumption data were integrated into the prior terrestrial biosphere flux generated by the Boreal Ecosystem Productivity Simulator (BEPS). A global time-dependent Bayesian synthesis inversion with a nested focus on North America was carried out based on CO2 observations at 210 stations. Overall, the inverted annual North American CO2 sink weakened by 6.5% over the period from 2002 to 2007 compared to simulations disregarding US crop statistical data. The US Midwest is found to be the major sink of 0.36±0.13 PgC yr-1 whereas the large sink in the US Southeast forests weakened to 0.16±0.12 PgC yr-1 partly due to local CO2 sources from crop consumption.

  20. Quantifying Uncertainty in Estimation of Potential Recharge in Tropical and Temperate Catchments using a Crop Model and Microwave Remote Sensing

    Krishnan Kutty, S.; Sekhar, M.; Ruiz, L.; Tomer, S. K.; Bandyopadhyay, S.; Buis, S.; Guerif, M.; Gascuel-odoux, C.

    2012-12-01

    Groundwater recharge in a semi-arid region is generally low, but could exhibit high spatial variability depending on the soil type and plant cover. The potential recharge (the drainage flux just beneath the root zone) is found to be sensitive to water holding capacity and rooting depth (Rushton, 2003). Simple water balance approaches for recharge estimation often fail to consider the effect of plant cover, growth phases and rooting depth. Hence a crop model based approach might be better suited to assess sensitivity of recharge for various crop-soil combinations in agricultural catchments. Martinez et al. (2009) using a root zone modelling approach to estimate groundwater recharge stressed that future studies should focus on quantifying the uncertainty in recharge estimates due to uncertainty in soil water parameters such as soil layers, field capacity, rooting depth etc. Uncertainty in the parameters may arise due to the uncertainties in retrieved variables (surface soil moisture and leaf area index) from satellite. Hence a good estimate of parameters as well as their uncertainty is essential for a reliable estimate of the potential recharge. In this study we focus on assessing the sensitivity of crop and soil types on the potential recharge by using a generic crop model STICS. The effect of uncertainty in the soil parameters on the estimates of recharge and its uncertainty is investigated. The multi-layer soil water parameters and their uncertainty is estimated by inversion of STICS model using the GLUE approach. Surface soil moisture and LAI either retrieved from microwave remote sensing data or measured in field plots (Sreelash et al., 2012) were found to provide good estimates of the soil water properties and therefore both these data sets were used in this study to estimate the parameters and the potential recharge for a combination of soil-crop systems. These investigations were made in two field experimental catchments. The first one is in the tropical semi

  1. Global estimates of evapotranspiration and gross primary production based on MODIS and global meteorology data

    Yuan, W.; Liu, S.; Yu, G.; Bonnefond, J.-M.; Chen, J.; Davis, K.; Desai, A.R.; Goldstein, Allen H.; Gianelle, D.; Rossi, F.; Suyker, A.E.; Verma, S.B.

    2010-01-01

    The simulation of gross primary production (GPP) at various spatial and temporal scales remains a major challenge for quantifying the global carbon cycle. We developed a light use efficiency model, called EC-LUE, driven by only four variables: normalized difference vegetation index (NDVI), photosynthetically active radiation (PAR), air temperature, and the Bowen ratio of sensible to latent heat flux. The EC-LUE model may have the most potential to adequately address the spatial and temporal dynamics of GPP because its parameters (i.e., the potential light use efficiency and optimal plant growth temperature) are invariant across the various land cover types. However, the application of the previous EC-LUE model was hampered by poor prediction of Bowen ratio at the large spatial scale. In this study, we substituted the Bowen ratio with the ratio of evapotranspiration (ET) to net radiation, and revised the RS-PM (Remote Sensing-Penman Monteith) model for quantifying ET. Fifty-four eddy covariance towers, including various ecosystem types, were selected to calibrate and validate the revised RS-PM and EC-LUE models. The revised RS-PM model explained 82% and 68% of the observed variations of ET for all the calibration and validation sites, respectively. Using estimated ET as input, the EC-LUE model performed well in calibration and validation sites, explaining 75% and 61% of the observed GPP variation for calibration and validation sites respectively.Global patterns of ET and GPP at a spatial resolution of 0.5° latitude by 0.6° longitude during the years 2000–2003 were determined using the global MERRA dataset (Modern Era Retrospective-Analysis for Research and Applications) and MODIS (Moderate Resolution Imaging Spectroradiometer). The global estimates of ET and GPP agreed well with the other global models from the literature, with the highest ET and GPP over tropical forests and the lowest values in dry and high latitude areas. However, comparisons with observed

  2. Estimation of yield and water requirements of maize crops combining high spatial and temporal resolution images with a simple crop model, in the perspective of the Sentinel-2 mission

    Battude, Marjorie; Bitar, Ahmad Al; Brut, Aurore; Cros, Jérôme; Dejoux, Jean-François; Huc, Mireille; Marais Sicre, Claire; Tallec, Tiphaine; Demarez, Valérie

    2016-04-01

    Water resources are under increasing pressure as a result of global change and of a raising competition among the different users (agriculture, industry, urban). It is therefore important to develop tools able to estimate accurately crop water requirements in order to optimize irrigation while maintaining acceptable production. In this context, remote sensing is a valuable tool to monitor vegetation development and water demand. This work aims at developing a robust and generic methodology mainly based on high resolution remote sensing data to provide accurate estimates of maize yield and water needs at the watershed scale. Evapotranspiration (ETR) and dry aboveground biomass (DAM) of maize crops were modeled using time series of GAI images used to drive a simple agro-meteorological crop model (SAFYE, Duchemin et al., 2005). This model is based on a leaf partitioning function (Maas, 1993) for the simulation of crop biomass and on the FAO-56 methodology for the ETR simulation. The model also contains a module to simulate irrigation. This study takes advantage of the SPOT4 and SPOT5 Take5 experiments initiated by CNES (http://www.cesbio.ups-tlse.fr/multitemp/). They provide optical images over the watershed from February to May 2013 and from April to August 2015 respectively, with a temporal and spatial resolution similar to future images from the Sentinel-2 and VENμS missions. This dataset was completed with LandSat8 and Deimos1 images in order to cover the whole growing season while reducing the gaps in remote sensing time series. Radiometric, geometric and atmospheric corrections were achieved by the THEIA land data center, and the KALIDEOS processing chain. The temporal dynamics of the green area index (GAI) plays a key role in soil-plant-atmosphere interactions and in biomass accumulation process. Consistent seasonal dynamics of the remotely sensed GAI was estimated by applying a radiative transfer model based on artificial neural networks (BVNET, Baret

  3. MIRCA2000—Global monthly irrigated and rainfed crop areas around the year 2000: A new high-resolution data set for agricultural and hydrological modeling

    Portmann, Felix T.; Siebert, Stefan; DöLl, Petra

    2010-03-01

    To support global-scale assessments that are sensitive to agricultural land use, we developed the global data set of monthly irrigated and rainfed crop areas around the year 2000 (MIRCA2000). With a spatial resolution of 5 arc min (about 9.2 km at the equator), MIRCA2000 provides both irrigated and rainfed crop areas of 26 crop classes for each month of the year. The data set covers all major food crops as well as cotton. Other crops are grouped into categories (perennial, annual, and fodder grasses). It represents multicropping systems and maximizes consistency with census-based national and subnational statistics. According to MIRCA2000, 25% of the global harvested areas are irrigated, with a cropping intensity (including fallow land) of 1.12, as compared to 0.84 for the sum of rainfed and irrigated harvested crops. For the dominant crops (rice (1.7 million km2 harvested area), wheat (2.1 million km2), and maize (1.5 million km2)), roughly 60%, 30%, and 20% of the harvested areas are irrigated, respectively, and half of the citrus, sugar cane, and cotton areas. While wheat and maize are the crops with the largest rainfed harvested areas (1.5 million km2 and 1.2 million km2, respectively), rice is clearly the crop with the largest irrigated harvested area (1.0 million km2), followed by wheat (0.7 million km2) and maize (0.3 million km2). Using MIRCA2000, 33% of global crop production and 44% of total cereal production were determined to come from irrigated agriculture.

  4. Estimating biophysical properties of coffee (Coffea canephora) plants with above-canopy field measurements, using CropSpec®

    Putra, Bayu T. Widjaja; Soni, Peeyush; Morimoto, Eiji; Pujiyanto, Pujiyanto

    2018-04-01

    Remote sensing technologies have been applied to many crops, but tree crops like Robusta coffee (Coffea canephora) under shade conditions require additional attention while making above-canopy measurements. The objective of this study was to determine how well chlorophyll and nitrogen status of Robusta coffee plants can be estimated with the laser-based (CropSpec®) active sensor. This study also identified appropriate vegetation indices for estimating Nitrogen content by above-canopy measurement, using near-infra red and red-edge bands. Varying light intensity and different background of the plants were considered in developing the indices. Field experiments were conducted involving different non-destructive tools (CropSpec® and SPAD-502 chlorophyll meter). Subsequently, Kjeldahl laboratory analyses were performed to determine the actual Nitrogen content of the plants with different ages and field conditions used in the non-destructive previous stage. Measurements were undertaken for assessing the biophysical properties of tree plant. The usefulness of near-infrared and red-edge bands from these sensors in measuring critical nitrogen levels of coffee plants by above-canopy measurement are investigated in this study.

  5. Opportunities and challenges for harvest weed seed control in global cropping systems.

    Walsh, Michael J; Broster, John C; Schwartz-Lazaro, Lauren M; Norsworthy, Jason K; Davis, Adam S; Tidemann, Breanne D; Beckie, Hugh J; Lyon, Drew J; Soni, Neeta; Neve, Paul; Bagavathiannan, Muthukumar V

    2017-11-28

    The opportunity to target weed seeds during grain harvest was established many decades ago following the introduction of mechanical harvesting and the recognition of high weed-seed retention levels at crop maturity; however, this opportunity remained largely neglected until more recently. The introduction and adoption of harvest weed seed control (HWSC) systems in Australia has been in response to widespread occurrence of herbicide-resistant weed populations. With diminishing herbicide resources and the need to maintain highly productive reduced tillage and stubble-retention practices, growers began to develop systems that targeted weed seeds during crop harvest. Research and development efforts over the past two decades have established the efficacy of HWSC systems in Australian cropping systems, where widespread adoption is now occurring. With similarly dramatic herbicide resistance issues now present across many of the world's cropping regions, it is timely for HWSC systems to be considered for inclusion in weed-management programs in these areas. This review describes HWSC systems and establishing the potential for this approach to weed control in several cropping regions. As observed in Australia, the inclusion of HWSC systems can reduce weed populations substantially reducing the potential for weed adaptation and resistance evolution. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

  6. Possible effects of climatic change on estimated crop yields in Canada: A review

    Stewart, R.

    1990-01-01

    An overview is presented of research relating to the possible effects of climatic change on crop yields in Canada. Possible changes in the long-term climate resulting from a doubling in atmospheric carbon dioxide would have a major impact on agriculture in Canada. The Goddard Institute for Space Studies' general circulation model suggests that Canada will be significantly warmer and somewhat drier, with an annual temperature increase of more than 3 degree C and an annual precipitation increase of 11-13%. The increased annual precipitation will not compensate for the increased temperatures, and most agricultural regions will be somewhat drier. Current crop varieties and cropping systems will be unsuitable. Existing varieties will be eliminated or moved north into more suitable agricultural areas. Longer growing season varieties or alternative crops will be required for existing agricultural areas. Production opportunities for hard and soft winter wheat, corn, soybeans, horticulture and tender fruits could be enhanced. 21 refs

  7. An automated multi-model based evapotranspiration estimation framework for understanding crop-climate interactions in India

    Bhattarai, N.; Jain, M.; Mallick, K.

    2017-12-01

    A remote sensing based multi-model evapotranspiration (ET) estimation framework is developed using MODIS and NASA Merra-2 reanalysis data for data poor regions, and we apply this framework to the Indian subcontinent. The framework eliminates the need for in-situ calibration data and hence estimates ET completely from space and is replicable across all regions in the world. Currently, six surface energy balance models ranging from widely-used SEBAL, METRIC, and SEBS to moderately-used S-SEBI, SSEBop, and a relatively new model, STIC1.2 are being integrated and validated. Preliminary analysis suggests good predictability of the models for estimating near- real time ET under clear sky conditions from various crop types in India with coefficient of determination 0.32-0.55 and percent bias -15%-28%, when compared against Bowen Ratio based ET estimates. The results are particularly encouraging given that no direct ground input data were used in the analysis. The framework is currently being extended to estimate seasonal ET across the Indian subcontinent using a model-ensemble approach that uses all available MODIS 8-day datasets since 2000. These ET products are being used to monitor inter-seasonal and inter-annual dynamics of ET and crop water use across different crop and irrigation practices in India. Particularly, the potential impacts of changes in precipitation patterns and extreme heat (e.g., extreme degree days) on seasonal crop water consumption is being studied. Our ET products are able to locate the water stress hotspots that need to be targeted with water saving interventions to maintain agricultural production in the face of climate variability and change.

  8. Estimating the Impact and Spillover Effect of Climate Change on Crop Yield in Northern Ghana.

    Botchway, E.

    2016-12-01

    In tropical regions of the world human-induced climate change is likely to impact negatively on crop yields. To investigate the impact of climate change and its spillover effect on mean and variance of crop yields in northern Ghana, the Just and Pope stochastic production function and the Spatial Durbin model were adopted. Surprisingly, the results suggest that both precipitation and average temperature have positive effects on mean crop yield during the wet season. Wet season average temperature has a significant spillover effect in the region, whereas precipitation during the wet season has only one significant spillover effect on maize yield. Wet season precipitation does not have a strong significant effect on crop yield despite the rainfed nature of agriculture in the region. Thus, even if there are losers and winners as a result of future climate change at the regional level, future crop yield would largely depend on future technological development in agriculture, which may improve yields over time despite the changing climate. We argue, therefore, that technical improvement in farm management such as improved seeds and fertilizers, conservation tillage and better pest control, may have a more significant role in increasing observed crop productivity levels over time. So investigating the relative importance of non-climatic factors on crop yield may shed more light on where appropriate interventions can help in improving crop yields. Climate change, also, needs to be urgently assessed at the level of the household, so that poor and vulnerable people dependent on agriculture can be appropriately targeted in research and development activities whose object is poverty alleviation.

  9. Global fire emissions estimates during 1997–2016

    G. R. van der Werf

    2017-09-01

    Full Text Available Climate, land use, and other anthropogenic and natural drivers have the potential to influence fire dynamics in many regions. To develop a mechanistic understanding of the changing role of these drivers and their impact on atmospheric composition, long-term fire records are needed that fuse information from different satellite and in situ data streams. Here we describe the fourth version of the Global Fire Emissions Database (GFED and quantify global fire emissions patterns during 1997–2016. The modeling system, based on the Carnegie–Ames–Stanford Approach (CASA biogeochemical model, has several modifications from the previous version and uses higher quality input datasets. Significant upgrades include (1 new burned area estimates with contributions from small fires, (2 a revised fuel consumption parameterization optimized using field observations, (3 modifications that improve the representation of fuel consumption in frequently burning landscapes, and (4 fire severity estimates that better represent continental differences in burning processes across boreal regions of North America and Eurasia. The new version has a higher spatial resolution (0.25° and uses a different set of emission factors that separately resolves trace gas and aerosol emissions from temperate and boreal forest ecosystems. Global mean carbon emissions using the burned area dataset with small fires (GFED4s were 2.2  ×  1015 grams of carbon per year (Pg C yr−1 during 1997–2016, with a maximum in 1997 (3.0 Pg C yr−1 and minimum in 2013 (1.8 Pg C yr−1. These estimates were 11 % higher than our previous estimates (GFED3 during 1997–2011, when the two datasets overlapped. This net increase was the result of a substantial increase in burned area (37 %, mostly due to the inclusion of small fires, and a modest decrease in mean fuel consumption (−19 % to better match estimates from field studies, primarily in savannas and

  10. Crop domestication, global human-mediated migration, and the unresolved role of geography in pest control

    Yolanda H. Chen

    2016-05-01

    Full Text Available Abstract Ecological pest management seeks to improve pest control through the manipulation of ecological processes that promote natural enemies and suppress pests. These approaches can involve cultural practices such as reduced tillage, increased use of non-crop plants that provide food and shelter for natural enemies, and intercropping to enhance the abundance and diversity of natural enemies. A major assumption of ecological pest management is that these activities can be equally effective for all insect herbivores. Here, I propose that these strategies may only be effective for a subset of pests and geographic regions because most insect pests have complex evolutionary histories that make them difficult to manage. I discuss how crop domestication and human-mediated migration are major evolutionary events that shape the geography of interactions between plants, herbivores, and natural enemies. Insect herbivores can evolve to be pests through three major modes: 1 herbivores associated with the crop wild ancestor may shift onto the domesticated crop, 2 herbivores may host-shift from native host plants onto an introduced crop, or 3 human-mediated migration can introduce insect pests into new cropping regions. The resulting geographic structure can influence the success of pest management by altering ecological factors such as: species distributions, patterns of biodiversity, community structure, and natural enemy attack rates. I discuss how the different modes of insect pest evolution structure a set of relevant questions and approaches for ecological pest management. By acknowledging how agricultural history and geography shape the ecology and evolution of insect pests, we may collectively develop a better capacity to identify where and how ecological pest management approaches can be most broadly effective.

  11. Spatial Rice Yield Estimation Based on MODIS and Sentinel-1 SAR Data and ORYZA Crop Growth Model

    Tri D. Setiyono

    2018-02-01

    Full Text Available Crop insurance is a viable solution to reduce the vulnerability of smallholder farmers to risks from pest and disease outbreaks, extreme weather events, and market shocks that threaten their household food and income security. In developing and emerging countries, the implementation of area yield-based insurance, the form of crop insurance preferred by clients and industry, is constrained by the limited availability of detailed historical yield records. Remote-sensing technology can help to fill this gap by providing an unbiased and replicable source of the needed data. This study is dedicated to demonstrating and validating the methodology of remote sensing and crop growth model-based rice yield estimation with the intention of historical yield data generation for application in crop insurance. The developed system combines MODIS and SAR-based remote-sensing data to generate spatially explicit inputs for rice using a crop growth model. MODIS reflectance data were used to generate multitemporal LAI maps using the inverted Radiative Transfer Model (RTM. SAR data were used to generate rice area maps using MAPScape-RICE to mask LAI map products for further processing, including smoothing with logistic function and running yield simulation using the ORYZA crop growth model facilitated by the Rice Yield Estimation System (Rice-YES. Results from this study indicate that the approach of assimilating MODIS and SAR data into a crop growth model can generate well-adjusted yield estimates that adequately describe spatial yield distribution in the study area while reliably replicating official yield data with root mean square error, RMSE, of 0.30 and 0.46 t ha−1 (normalized root mean square error, NRMSE of 5% and 8% for the 2016 spring and summer seasons, respectively, in the Red River Delta of Vietnam, as evaluated at district level aggregation. The information from remote-sensing technology was also useful for identifying geographic locations with

  12. Estimation of clear sky hourly global solar radiation in Iraq

    Al-Jumaily, Kais J.; Al-Zuhairi, Munya F.; Mahdi, Zahraa S. [Department of Atmospheric Sciences, College of Science, Al-Mustansiriyah University, Baghdad (Iraq)

    2012-07-01

    The availability of hourly solar radiation data is very important for applications utilizing solar energy and for climate and environmental aspects. The aim of this work is to use a simple model for estimating hourly global solar radiation under clear sky condition in Iraq. Calculations were compared with measurements obtained from local station in Baghdad city and from Meteosat satellite data for different locations in Iraq. The statistical test methods of the mean bias error (MBE), root mean square error (RMSE) and t-test were used to evaluate the performance of the model. Results indicated that a fairly good agreement exists between calculated and measured values for all locations in Iraq. Since the model is independent of any meteorological variable, it would be of a practical use for rural areas where no meteorological data are available.

  13. Crop coefficient approaches based on fixed estimates of leaf resistance are not appropriate for estimating water use of citrus

    Taylor, NJ

    2015-03-01

    Full Text Available necessitates the use of water use models. The FAO-56 procedure is a simple, convenient and reproducible method, but as canopy cover and height vary greatly among different orchards, crop coefficients may not be readily transferrable from one orchard to another...

  14. Variation in the estimations of ETo and crop water use due to the sensor accuracy of the meteorological variables

    R. Moratiel

    2013-06-01

    Full Text Available In agricultural ecosystems the use of evapotranspiration (ET to improve irrigation water management is generally widespread. Commonly, the crop ET (ETc is estimated by multiplying the reference crop evapotranspiration (ETo by a crop coefficient (Kc. Accurate estimation of ETo is critical because it is the main factor affecting the calculation of crop water use and water management. The ETo is generally estimated from recorded meteorological variables at reference weather stations. The main objective of this paper was assessing the effect of the uncertainty due to random noise in the sensors used for measurement of meteorological variables on the estimation of ETo, crop ET and net irrigation requirements of grain corn and alfalfa in three irrigation districts of the middle Ebro River basin. Five scenarios were simulated, four of them individually considering each recorded meteorological variable (temperature, relative humidity, solar radiation and wind speed and a fifth scenario combining together the uncertainty of all sensors. The uncertainty in relative humidity for irrigation districts Riegos del Alto Aragón (RAA and Bardenas (BAR, and temperature for irrigation district Canal de Aragón y Cataluña (CAC, were the two most important factors affecting the estimation of ETo, corn ET (ETc_corn, alfalfa ET (ETc_alf, net corn irrigation water requirements (IRncorn and net alfalfa irrigation water requirements (IRnalf. Nevertheless, this effect was never greater than ±0.5% over annual scale time. The wind speed variable (Scenario 3 was the third variable more influential in the fluctuations (± of evapotranspiration, followed by solar radiation. Considering the accuracy for all sensors over annual scale time, the variation was about ±1% of ETo, ETc_corn, ETc_alf, IRncorn, and IRnalf. The fluctuations of evapotranspiration were higher at shorter time scale. ETo daily fluctuation remained lower than 5 % during the growing season of corn and

  15. Multisource Estimation of Long-term Global Terrestrial Surface Radiation

    Peng, L.; Sheffield, J.

    2017-12-01

    Land surface net radiation is the essential energy source at the earth's surface. It determines the surface energy budget and its partitioning, drives the hydrological cycle by providing available energy, and offers heat, light, and energy for biological processes. Individual components in net radiation have changed historically due to natural and anthropogenic climate change and land use change. Decadal variations in radiation such as global dimming or brightening have important implications for hydrological and carbon cycles. In order to assess the trends and variability of net radiation and evapotranspiration, there is a need for accurate estimates of long-term terrestrial surface radiation. While large progress in measuring top of atmosphere energy budget has been made, huge discrepancies exist among ground observations, satellite retrievals, and reanalysis fields of surface radiation, due to the lack of observational networks, the difficulty in measuring from space, and the uncertainty in algorithm parameters. To overcome the weakness of single source datasets, we propose a multi-source merging approach to fully utilize and combine multiple datasets of radiation components separately, as they are complementary in space and time. First, we conduct diagnostic analysis of multiple satellite and reanalysis datasets based on in-situ measurements such as Global Energy Balance Archive (GEBA), existing validation studies, and other information such as network density and consistency with other meteorological variables. Then, we calculate the optimal weighted average of multiple datasets by minimizing the variance of error between in-situ measurements and other observations. Finally, we quantify the uncertainties in the estimates of surface net radiation and employ physical constraints based on the surface energy balance to reduce these uncertainties. The final dataset is evaluated in terms of the long-term variability and its attribution to changes in individual

  16. Modeling of the radiative energy balance within a crop canopy for estimating evapotranspiration: Studies on a row planted soybean canopy

    Nakano, Y.; Hirota, O.

    1990-01-01

    The spatial distribution and density of the leaf area within a crop canopy were used to estimate the radiational environment and evapotranspiration. Morphological measurements were pursued on the soybean stands in the early stage of growth when the two-dimensional foliage distribution pattern existed. The rectangular tube model was used to calculate the light absorption by parallel row of crops both short-wave radiation (direct and diffuse solar radiation, and scattered radiation by plant elements) and long-wave radiation (emanated radiation from the sky, ground and leaves). The simulated profiles are in close agreement with the experimentally measured short-wave and net radiation data. The evapotranspiration of a row was calcuated using a simulated net radiation. The model calculation also agreed well with the evapotranspiration estimated by the Bowen ratio method

  17. Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras

    Jian Zhang

    2017-10-01

    Full Text Available Consumer-grade cameras are being increasingly used for remote sensing applications in recent years. However, the performance of this type of cameras has not been systematically tested and well documented in the literature. The objective of this research was to evaluate the performance of original and resolution-reduced images taken from two consumer-grade cameras, a RGB camera and a modified near-infrared (NIR camera, for crop identification and leaf area index (LAI estimation. Airborne RGB and NIR images taken over a 6.5-square-km cropping area were mosaicked and aligned to create a four-band mosaic with a spatial resolution of 0.4 m. The spatial resolution of the mosaic was then reduced to 1, 2, 4, 10, 15 and 30 m for comparison. Six supervised classifiers were applied to the RGB images and the four-band images for crop identification, and 10 vegetation indices (VIs derived from the images were related to ground-measured LAI. Accuracy assessment showed that maximum likelihood applied to the 0.4-m images achieved an overall accuracy of 83.3% for the RGB image and 90.4% for the four-band image. Regression analysis showed that the 10 VIs explained 58.7% to 83.1% of the variability in LAI. Moreover, spatial resolutions at 0.4, 1, 2 and 4 m achieved better classification results for both crop identification and LAI prediction than the coarser spatial resolutions at 10, 15 and 30 m. The results from this study indicate that imagery from consumer-grade cameras can be a useful data source for crop identification and canopy cover estimation.

  18. Global health worker salary estimates: an econometric analysis of global earnings data.

    Serje, Juliana; Bertram, Melanie Y; Brindley, Callum; Lauer, Jeremy A

    2018-01-01

    Human resources are consistently cited as a leading contributor to health care costs; however the availability of internationally comparable data on health worker earnings for all countries is a challenge for estimating the costs of health care services. This paper describes an econometric model using cross sectional earnings data from the International Labour Organization (ILO) that the World Health Organizations (WHO)-Choosing Interventions that are Cost-effective programme (CHOICE) has used to prepare estimates of health worker earnings (in 2010 USD) for all WHO member states. The ILO data contained 324 observations of earnings data across 4 skill levels for 193 countries. Using this data, along with the assumption that data were missing not at random, we used a Heckman two stage selection model to estimate earning data for each of the 4 skill levels for all WHO member states. It was possible to develop a prediction model for health worker earnings for all countries for which GDP data was available. Health worker earnings vary both within country due to skill level, as well as across countries. As a multiple of GDP per capita, earnings show a negative correlation with GDP-that is lower income countries pay their health workers relatively more than higher income countries. Limited data on health worker earnings is a limiting factor in estimating the costs of global health programmes. It is hoped that these estimates will support robust health care intervention costings and projections of resources needs over the Sustainable Development Goal period.

  19. Global estimates of high-level brain drain and deficit.

    Ioannidis, John P A

    2004-06-01

    Brain drain, the international migration of scientists in search of better opportunities, has been a long-standing concern, but quantitative measurements are uncommon and limited to specific countries or disciplines. We need to understand brain drain at a global level and estimate the extent to which scientists born in countries with low opportunities never realize their potential. Data on 1523 of the most highly cited scientists for 1981-1999 are analyzed. Overall, 31.9% of these scientists did not reside in the country where they were born (range 18.1-54.6% across 21 different scientific fields). There was great variability across developed countries in the proportions of foreign-born resident scientists and emigrating scientists. Countries without a critical mass of native scientists lost most scientists to migration. This loss occurred in both developed and developing countries. Adjusting for population and using the U.S. as reference, the number of highly cited native-born scientists was at least 75% of the expected number in only 8 countries other than the U.S. It is estimated that approximately 94% of the expected top scientists worldwide have not been able to materialize themselves due to various adverse conditions. Scientific deficit is only likely to help perpetuate these adverse conditions.

  20. Leishmaniasis worldwide and global estimates of its incidence.

    Jorge Alvar

    Full Text Available As part of a World Health Organization-led effort to update the empirical evidence base for the leishmaniases, national experts provided leishmaniasis case data for the last 5 years and information regarding treatment and control in their respective countries and a comprehensive literature review was conducted covering publications on leishmaniasis in 98 countries and three territories (see 'Leishmaniasis Country Profiles Text S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13, S14, S15, S16, S17, S18, S19, S20, S21, S22, S23, S24, S25, S26, S27, S28, S29, S30, S31, S32, S33, S34, S35, S36, S37, S38, S39, S40, S41, S42, S43, S44, S45, S46, S47, S48, S49, S50, S51, S52, S53, S54, S55, S56, S57, S58, S59, S60, S61, S62, S63, S64, S65, S66, S67, S68, S69, S70, S71, S72, S73, S74, S75, S76, S77, S78, S79, S80, S81, S82, S83, S84, S85, S86, S87, S88, S89, S90, S91, S92, S93, S94, S95, S96, S97, S98, S99, S100, S101'. Additional information was collated during meetings conducted at WHO regional level between 2007 and 2011. Two questionnaires regarding epidemiology and drug access were completed by experts and national program managers. Visceral and cutaneous leishmaniasis incidence ranges were estimated by country and epidemiological region based on reported incidence, underreporting rates if available, and the judgment of national and international experts. Based on these estimates, approximately 0.2 to 0.4 cases and 0.7 to 1.2 million VL and CL cases, respectively, occur each year. More than 90% of global VL cases occur in six countries: India, Bangladesh, Sudan, South Sudan, Ethiopia and Brazil. Cutaneous leishmaniasis is more widely distributed, with about one-third of cases occurring in each of three epidemiological regions, the Americas, the Mediterranean basin, and western Asia from the Middle East to Central Asia. The ten countries with the highest estimated case counts, Afghanistan, Algeria, Colombia, Brazil, Iran, Syria, Ethiopia, North

  1. CO2 uptake and ecophysiological parameters of the grain crops of midcontinent North America: estimates from flux tower measurements

    Gilmanov, Tagir; Wylie, Bruce; Tieszen, Larry; Meyers, Tilden P.; Baron, Vern S.; Bernacchi, Carl J.; Billesbach, David P.; Burba, George G.; Fischer, Marc L.; Glenn, Aaron J.; Hanan, Niall P.; Hatfield, Jerry L.; Heuer, Mark W.; Hollinger, Steven E.; Howard, Daniel M.; Matamala, Roser; Prueger, John H.; Tenuta, Mario; Young, David G.

    2013-01-01

    We analyzed net CO2 exchange data from 13 flux tower sites with 27 site-years of measurements over maize and wheat fields across midcontinent North America. A numerically robust “light-soil temperature-VPD”-based method was used to partition the data into photosynthetic assimilation and ecosystem respiration components. Year-round ecosystem-scale ecophysiological parameters of apparent quantum yield, photosynthetic capacity, convexity of the light response, respiration rate parameters, ecological light-use efficiency, and the curvature of the VPD-response of photosynthesis for maize and wheat crops were numerically identified and interpolated/extrapolated. This allowed us to gap-fill CO2 exchange components and calculate annual totals and budgets. VPD-limitation of photosynthesis was systematically observed in grain crops of the region (occurring from 20 to 120 days during the growing season, depending on site and year), determined by the VPD regime and the numerical value of the curvature parameter of the photosynthesis-VPD-response, σVPD. In 78% of the 27 site-years of observations, annual gross photosynthesis in these crops significantly exceeded ecosystem respiration, resulting in a net ecosystem production of up to 2100 g CO2 m−2 year−1. The measurement-based photosynthesis, respiration, and net ecosystem production data, as well as the estimates of the ecophysiological parameters, provide an empirical basis for parameterization and validation of mechanistic models of grain crop production in this economically and ecologically important region of North America.

  2. Techniques for the estimation of global irradiation from sunshine duration and global irradiation estimation for Italian locations

    Jain, P.C.

    1984-04-01

    Angstrom equation H=H 0 (a+bS/S 0 ) has been fitted using the least-square method to the global irradiation and the sunshine duration data of 31 Italian locations for the duration 1965-1974. Three more linear equations: i) the equation H'=H 0 (a+bS/S 0 ), obtained by incorporating the effect of the multiple reflections between the earth's surface and the atmosphere, ii) the equation H=H 0 (a+bS/S' 0 ), obtained by incorporating the effect of not burning of the sunshine recorder chart when the elevation of the sun is less than 5 deg., and iii) the equation H'=H 0 (a+bS/S' 0 ), obtained by incorporating both the above effects simultaneously, have also each been fitted to the same data. Good correlation with correlation coefficients around 0.9 or more are obtained for most of the locations with all the four equations. Substantial spatial scatter is obtained in the values of the regression parameters. The use of any of the three latter equations does not result in any advantage over that of the simpler Angstrom equation; it neither results in a decrease in the spatial scatter in the values of the regression parameters nor does it yield better correlation. The computed values of the regression parameters in the Angstrom equation yield estimates of the global irradiation that are on the average within +- 4% of the measured values for most of the locations. (author)

  3. Winter wheat yield estimation of remote sensing research based on WOFOST crop model and leaf area index assimilation

    Chen, Yanling; Gong, Adu; Li, Jing; Wang, Jingmei

    2017-04-01

    Accurate crop growth monitoring and yield predictive information are significant to improve the sustainable development of agriculture and ensure the security of national food. Remote sensing observation and crop growth simulation models are two new technologies, which have highly potential applications in crop growth monitoring and yield forecasting in recent years. However, both of them have limitations in mechanism or regional application respectively. Remote sensing information can not reveal crop growth and development, inner mechanism of yield formation and the affection of environmental meteorological conditions. Crop growth simulation models have difficulties in obtaining data and parameterization from single-point to regional application. In order to make good use of the advantages of these two technologies, the coupling technique of remote sensing information and crop growth simulation models has been studied. Filtering and optimizing model parameters are key to yield estimation by remote sensing and crop model based on regional crop assimilation. Winter wheat of GaoCheng was selected as the experiment object in this paper. And then the essential data was collected, such as biochemical data and farmland environmental data and meteorological data about several critical growing periods. Meanwhile, the image of environmental mitigation small satellite HJ-CCD was obtained. In this paper, research work and major conclusions are as follows. (1) Seven vegetation indexes were selected to retrieve LAI, and then linear regression model was built up between each of these indexes and the measured LAI. The result shows that the accuracy of EVI model was the highest (R2=0.964 at anthesis stage and R2=0.920 at filling stage). Thus, EVI as the most optimal vegetation index to predict LAI in this paper. (2) EFAST method was adopted in this paper to conduct the sensitive analysis to the 26 initial parameters of the WOFOST model and then a sensitivity index was constructed

  4. The green, blue and grey water footprint of crops and derived crop products

    Mekonnen, Mesfin; Hoekstra, Arjen Ysbert

    2011-01-01

    This study quantifies the green, blue and grey water footprint of global crop production in a spatially-explicit way for the period 1996–2005. The assessment improves upon earlier research by taking a high-resolution approach, estimating the water footprint of 126 crops at a 5 by 5 arc minute grid.

  5. Effects of nitrogen application rates on net annual global warming potential and greenhouse gas intensity in double-rice cropping systems of the Southern China.

    Chen, Zhongdu; Chen, Fu; Zhang, Hailin; Liu, Shengli

    2016-12-01

    The net global warming potential (NGWP) and net greenhouse gas intensity (NGHGI) of double-rice cropping systems are not well documented. We measured the NGWP and NGHGI including soil organic carbon (SOC) change and indirect emissions (IE) from double-crop rice fields with fertilizing systems in Southern China. These experiments with three different nitrogen (N) application rates since 2012 are as follows: 165 kgN ha -1 for early rice and 225 kgN ha -1 for late rice (N1), which was the local N application rates as the control; 135 kgN ha -1 for early rice and 180 kgN ha -1 for late rice (N2, 20 % reduction); and 105 kgN ha -1 for early rice and 135 kgN ha -1 for late rice (N3, 40 % reduction). Results showed that yields increased with the increase of N application rate, but without significant difference between N1 and N2 plots. Annual SOC sequestration rate under N1 was estimated to be 1.15 MgC ha -1  year -1 , which was higher than those under other fertilizing systems. Higher N application tended to increase CH 4 emissions during the flooded rice season and significantly increased N 2 O emissions from drained soils during the nonrice season, ranking as N1 > N2 > N3 with significant difference (P < 0.05). Two-year average IE has a huge contribution to GHG emissions mainly coming from the higher N inputs in the double-rice cropping system. Reducing N fertilizer usage can effectively decrease the NGWP and NGHGI in the double-rice cropping system, with the lowest NGHGI obtained in the N2 plot (0.99 kg CO 2 -eq kg -1 yield year -1 ). The results suggested that agricultural economic viability and GHG mitigation can be simultaneously achieved by properly reducing N fertilizer application in double-rice cropping systems.

  6. A Global-Scale Estimate of Ecosystem Services from Urban Agriculture: Understanding Incentives for Natural Capital in Cities

    Clinton, N.; Stuhlmacher, M.; Miles, A.; Uludere, N.; Wagner, M.; Georgescu, M.; Herwig, C.; Gong, P.

    2017-12-01

    Despite substantial interest in urban agriculture, little is known about the aggregate benefits conferred by natural capital for growing food in cities. Here we perform a scenario-based analysis to quantify ecosystem services from adoption of urban agriculture at varying intensity. To drive the scenarios, we created global-scale estimates of vacant land, rooftop and building surface area, at one kilometer resolution, from remotely sensed and modeled geospatial data. We used national scale agricultural reports, climate and other geospatial data at global scale to estimate agricultural production and economic returns, storm-water avoidance, energy savings from avoided heating and cooling costs, and ecosystem services provided by nitrogen sequestration, pollination and biocontrol of pests. The results indicate that vacant lands, followed by rooftops, represent the largest opportunities for natural capital put to agricultural use in urban areas. Ecosystem services from putting such spaces to productive use are dominated by agricultural returns, but energy savings conferred by insulative characteristics of growth substrate also provide economic incentives. Storm water avoidance was estimated to be substantial, but no economic value was estimated. Relatively low economic returns were estimated from the other ecosystem services examined. In aggregate, approximately $10-100 billion in economic incentives, before costs, were estimated. The results showed that relatively developed, high-income countries stand the most to gain from urban agricultural adoption due to the unique combination of climate, crop mixture and crop prices. While the results indicate that urban agriculture is not a panacea for urban food security issues, there is potential to simultaneously ameliorate multiple issues around food, energy and water in urbanized areas.

  7. Assessing Agricultural Risks of Climate Change in the 21st Century in a Global Gridded Crop Model Intercomparison

    Rosenzweig, Cynthia E.; Elliott, Joshua; Deryng, Delphine; Ruane, Alex C.; Mueller, Christoph; Arneth, Almut; Boote, Kenneth J.; Folberth, Christian; Glotter, Michael; Khabarov, Nikolay

    2014-01-01

    Here we present the results from an intercomparison of multiple global gridded crop models (GGCMs) within the framework of the Agricultural Model Intercomparison and Improvement Project and the Inter-Sectoral Impacts Model Intercomparison Project. Results indicate strong negative effects of climate change, especially at higher levels of warming and at low latitudes; models that include explicit nitrogen stress project more severe impacts. Across seven GGCMs, five global climate models, and four representative concentration pathways, model agreement on direction of yield changes is found in many major agricultural regions at both low and high latitudes; however, reducing uncertainty in sign of response in mid-latitude regions remains a challenge. Uncertainties related to the representation of carbon dioxide, nitrogen, and high temperature effects demonstrated here show that further research is urgently needed to better understand effects of climate change on agricultural production and to devise targeted adaptation strategies.

  8. Quantitative estimation of the fluorescent parameters for crop leaves with the Bayesian inversion

    In this study, the fluorescent parameters of crop leaves were retrieved from the leaf hyperspectral measurements by inverting the FluorMODleaf model, which is a leaf-level fluorescence model that is based on the widely used and validated PROSPECT (leaf optical properties) model and can simulate the ...

  9. Estimation of the herbaceous standing crop of the grassy plains of ...

    This was done for the 01 Choro Oiroua area in the Masai Mara region of Kenya where no such data previously existed. The calibration data were analysed using a simple linear regression analysis, which gave a significant correlation between the square-root transformation of the mean disc height and the standing crop.

  10. Global regulatory framework for production and marketing of crops biofortified with vitamins and minerals.

    Mejia, Luis A; Dary, Omar; Boukerdenna, Hala

    2017-02-01

    Biofortification of crops is being introduced in several countries as a strategy to reduce micronutrient deficiencies. Biofortified products, with increased contents of micronutrients, are currently produced by conventional plant breeding, genetic modification, or nutrient-enhanced fertilization. Corn, rice, wheat, beans, pearl millet, sweet potato, and cassava have been biofortified with increased contents of provitamin A carotenoids, iron, or zinc. However, regulatory considerations are rare or nonexistent. The objective of this paper is to review the regulatory framework for production and marketing of biofortified crops in countries that have adopted this strategy. The information was identified using Internet search engines and websites of health and nutrition organizations and nongovernmental organizations and by consulting scientists and government authorities. Thus far, biofortified products introduced in Latin America, Africa, and Asia have been produced only by conventional breeding. Cultivars using other techniques are still under testing. The production and marketing of these products have been conducted without regulatory framework and under limited government control or regulatory guidance. Nevertheless, some countries have integrated biofortified crops into their nutrition agendas. Although improvements by conventional breeding have not been subject to regulations, when biofortification becomes expanded by including other techniques, an appropriate regulatory framework will be necessary. © 2016 New York Academy of Sciences.

  11. Mapping Multi-Cropped Land Use to Estimate Water Demand Using the California Pesticide Reporting Database

    Henson, W.; Baillie, M. N.; Martin, D.

    2017-12-01

    Detailed and dynamic land-use data is one of the biggest data deficiencies facing food and water security issues. Better land-use data results in improved integrated hydrologic models that are needed to look at the feedback between land and water use, specifically for adequately representing changes and dynamics in rainfall-runoff, urban and agricultural water demands, and surface fluxes of water (e.g., evapotranspiration, runoff, and infiltration). Currently, land-use data typically are compiled from annual (e.g., Crop Scape) or multi-year composites if mapped at all. While this approach provides information about interannual land-use practices, it does not capture the dynamic changes in highly developed agricultural lands prevalent in California agriculture such as (1) dynamic land-use changes from high frequency multi-crop rotations and (2) uncertainty in sub-annual crop distribution, planting times, and cropped areas. California has collected spatially distributed data for agricultural pesticide use since 1974 through the California Pesticide Information Portal (CalPIP). A method leveraging the CalPIP database has been developed to provide vital information about dynamic agricultural land use (e.g., crop distribution and planting times) and water demand issues in Salinas Valley, California, along the central coast. This 7 billion dollar/year agricultural area produces up to 50% of U.S. lettuce and broccoli. Therefore, effective and sustainable water resource development in the area must balance the needs of this essential industry, other beneficial uses, and the environment. This new tool provides a way to provide more dynamic crop data in hydrologic models. While the current application focuses on the Salinas Valley, the methods are extensible to all of California and other states with similar pesticide reporting. The improvements in representing variability in crop patterns and associated water demands increase our understanding of land-use change and

  12. Estimating trends in the global mean temperature record

    Poppick, Andrew; Moyer, Elisabeth J.; Stein, Michael L.

    2017-06-01

    Given uncertainties in physical theory and numerical climate simulations, the historical temperature record is often used as a source of empirical information about climate change. Many historical trend analyses appear to de-emphasize physical and statistical assumptions: examples include regression models that treat time rather than radiative forcing as the relevant covariate, and time series methods that account for internal variability in nonparametric rather than parametric ways. However, given a limited data record and the presence of internal variability, estimating radiatively forced temperature trends in the historical record necessarily requires some assumptions. Ostensibly empirical methods can also involve an inherent conflict in assumptions: they require data records that are short enough for naive trend models to be applicable, but long enough for long-timescale internal variability to be accounted for. In the context of global mean temperatures, empirical methods that appear to de-emphasize assumptions can therefore produce misleading inferences, because the trend over the twentieth century is complex and the scale of temporal correlation is long relative to the length of the data record. We illustrate here how a simple but physically motivated trend model can provide better-fitting and more broadly applicable trend estimates and can allow for a wider array of questions to be addressed. In particular, the model allows one to distinguish, within a single statistical framework, between uncertainties in the shorter-term vs. longer-term response to radiative forcing, with implications not only on historical trends but also on uncertainties in future projections. We also investigate the consequence on inferred uncertainties of the choice of a statistical description of internal variability. While nonparametric methods may seem to avoid making explicit assumptions, we demonstrate how even misspecified parametric statistical methods, if attuned to the

  13. The effects of global warming on fisheries: Simulation estimates

    Carlos A. Medel

    2016-04-01

    Full Text Available This paper develops two fisheries models in order to estimate the effect of global warming (GW on firm value. GW is defined as an increase in the average temperature of the Earth’s surface as a result of emissions. It is assumed that (i GW exists, and (ii higher temperatures negatively affect biomass. CO2 The literature on biology and GW supporting these two crucial assumptions is reviewed. The main argument presented is that temperature increase has two effects on biomass, both of which have an impact on firm value. First, higher temperatures cause biomass to oscillate. To measure the effect of biomass oscillation on firm value the model in [1] is modified to include water temperature as a variable. The results indicate that a 1 to 20% variation in biomass causes firm value to fall from 6 to 44%, respectively. Second, higher temperatures reduce biomass, and a modification of the model in [2] reveals that an increase in temperature anomaly between +1 and +8°C causes fishing firm value to decrease by 8 to 10%.

  14. Estimated Glomerular Filtration Rate; Laboratory Implementation and Current Global Status.

    Miller, W Greg; Jones, Graham R D

    2018-01-01

    In 2002, the Kidney Disease Outcomes Quality Initiative guidelines for identifying and treating CKD recommended that clinical laboratories report estimated glomerular filtration rate (eGFR) with every creatinine result to assist clinical practitioners to identify people with early-stage CKD. At that time, the original Modification of Diet in Renal Disease (MDRD) Study equation based on serum creatinine measurements was recommended for calculating eGFR. Because the MDRD Study equation was developed using a nonstandardized creatinine method, a Laboratory Working Group of the National Kidney Disease Education program was formed and implemented standardized calibration traceability for all creatinine methods from global manufacturers by approximately 2010. A modified MDRD Study equation for use with standardized creatinine was developed. The Chronic Kidney Disease Epidemiology Collaboration developed a new equation in 2009 that was more accurate than the MDRD Study equation at values above 60 mL/min/1.73 m 2 . As of 2017, reporting eGFR with creatinine is almost universal in many countries. A reference system for cystatin C became available in 2010, and manufacturers are in the process to standardize cystatin C assays. Equations for eGFR based on standardized cystatin C alone and with creatinine are now available from the Chronic Kidney Disease Epidemiology Collaboration and other groups. Copyright © 2017 National Kidney Foundation, Inc. Published by Elsevier Inc. All rights reserved.

  15. Estimated impact of global population growth on future wilderness extent

    Dumont, E.

    2012-06-01

    Wilderness areas in the world are threatened by the environmental impacts of the growing global human population. This study estimates the impact of birth rate on the future surface area of biodiverse wilderness and on the proportion of this area without major extinctions. The following four drivers are considered: human population growth (1), agricultural efficiency (2), groundwater drawdown by irrigation (3), and non-agricultural space used by humans (buildings, gardens, roads, etc.) (4). This study indicates that the surface area of biodiverse unmanaged land will reduce with about 5.4% between 2012 and 2050. Further, it indicates that the biodiverse land without major extinctions will reduce with about 10.5%. These percentages are based on a commonly used population trajectory which assumes that birth rates across the globe will reduce in a similar way as has occurred in the past in many developed countries. Future birth rate is however very uncertain. Plausible future birth rates lower than the expected rates lead to much smaller reductions in surface area of biodiverse unmanaged land (0.7% as opposed to 5.4%), and a reduction in the biodiverse land without major extinctions of about 5.6% (as opposed to 10.5%). This indicates that birth rate is an important factor influencing the quality and quantity of wilderness remaining in the future.

  16. Importance of pollinators in changing landscapes for world crops

    Klein, Alexandra-Maria; Vaissière, Bernard E; Cane, James H; Steffan-Dewenter, Ingolf; Cunningham, Saul A; Kremen, Claire; Tscharntke, Teja

    2006-01-01

    The extent of our reliance on animal pollination for world crop production for human food has not previously been evaluated and the previous estimates for countries or continents have seldom used primary data. In this review, we expand the previous estimates using novel primary data from 200 countries and found that fruit, vegetable or seed production from 87 of the leading global food crops is dependent upon animal pollination, while 28 crops do not rely upon animal pollination. However, glo...

  17. Identity-based estimation of greenhouse gas emissions from crop production

    Bennetzen, Eskild Hohlmann; Smith, Pete; Soussana, Jean-Francois

    2012-01-01

    reduction of emissions i.e. reducing emissions per unit of agricultural product rather than the absolute emissions per se. Hence the system productivity must be included in the same analysis. This paper presents the Kaya- Porter identity, derived from the Kaya identity, as a new way to calculate GHG...... (ha). These separate elements in the identity can be targeted in emissions reduction and mitigation policies and are useful to analyse past and current trends in emissions and to explore future scenarios. Using the Kaya-Porter identity we have performed a case study on Danish crop production and find...... emissions to have been reduced by 12% from 1992 to 2008, whilst yields per unit area have remained constant. Both land-based emissions and energy-based emissions have decreased, mainly due to a 41% reduction in nitrogen fertilizer use. The initial identity based analysis for crop production presented here...

  18. Development of estimation method for crop yield using MODIS satellite imagery data and process-based model for corn and soybean in US Corn-Belt region

    Lee, J.; Kang, S.; Jang, K.; Ko, J.; Hong, S.

    2012-12-01

    Crop productivity is associated with the food security and hence, several models have been developed to estimate crop yield by combining remote sensing data with carbon cycle processes. In present study, we attempted to estimate crop GPP and NPP using algorithm based on the LUE model and a simplified respiration model. The state of Iowa and Illinois was chosen as the study site for estimating the crop yield for a period covering the 5 years (2006-2010), as it is the main Corn-Belt area in US. Present study focuses on developing crop-specific parameters for corn and soybean to estimate crop productivity and yield mapping using satellite remote sensing data. We utilized a 10 km spatial resolution daily meteorological data from WRF to provide cloudy-day meteorological variables but in clear-say days, MODIS-based meteorological data were utilized to estimate daily GPP, NPP, and biomass. County-level statistics on yield, area harvested, and productions were used to test model predicted crop yield. The estimated input meteorological variables from MODIS and WRF showed with good agreements with the ground observations from 6 Ameriflux tower sites in 2006. For examples, correlation coefficients ranged from 0.93 to 0.98 for Tmin and Tavg ; from 0.68 to 0.85 for daytime mean VPD; from 0.85 to 0.96 for daily shortwave radiation, respectively. We developed county-specific crop conversion coefficient, i.e. ratio of yield to biomass on 260 DOY and then, validated the estimated county-level crop yield with the statistical yield data. The estimated corn and soybean yields at the county level ranged from 671 gm-2 y-1 to 1393 gm-2 y-1 and from 213 gm-2 y-1 to 421 gm-2 y-1, respectively. The county-specific yield estimation mostly showed errors less than 10%. Furthermore, we estimated crop yields at the state level which were validated against the statistics data and showed errors less than 1%. Further analysis for crop conversion coefficient was conducted for 200 DOY and 280 DOY

  19. The Impact of Measurement Error on Estimates of the Price Reaction to USDA Crop Reports

    Aulerich, Nicole M.; Irwin, Scott H.; Nelson, Carl H.

    2007-01-01

    This paper investigates the impact of USDA crop production reports in corn and soybean futures markets. The analysis is based on all corn and soybean production reports released over 1970-2006. The empirical analysis compares the typical OLS event study approach to the new Identification by Censoring (ITC) technique. Corn and soybean production reports are analyzed both separately and together for impact in corn and soybean futures prices. ITC proves to be the more useful method because it av...

  20. estimating water consumptive use for some crops under stress conditions using neutron scattering method

    Salama, M.A.A.A.

    2011-01-01

    Field experiment was conducted to study the influence of different levels of irrigation water salinity on actual evapotranspiration, water stress coefficient, yield and water use efficiency of both groundnut and wheat crops growing on sandy soil under trickle irrigation system located at 30 o 24 ' N latitude, 31 o 35 ' E longitude while the altitude is 20 m above the sea level.Four irrigation water salinity levels were used for both crops, they are; 2.4 (S 1 ), 2.7 (S 2 ), 3.3 (S 3 ) and 4.4 (S 4 ) dS m -1 , for groundnut and 4.9 (S 1 ), 6.3 (S 2 ), 8.7 (S 3 ) and 13 (S 4 ) dS m -1 , for wheat respectively besides a fresh water (FW) as a control treatment (0.5 dS m -1 ). Cattle manure was added as a soil amendment at a rate of 48 m 3 ha -1 . Neutron moisture meter was used to determine soil moisture content and depletion through the soil depths of 30, 45, 60, 75 and 90cm. Soil moisture content at 15 cm soil depth was determined gravimetrically. The applied irrigation water was 700 mm/season for groundnut and 550 mm/season for wheat based on 100 % of the recommended crop water requirements according to FAO No.33. (1979). The obtained results showed that the actual evapotranspiration (ET a ) and water stress coefficient (K s ) were slightly deceased by increasing the salinity of irrigation water especially under (S 4 ) irrigation salinity treatment for both crops.

  1. Exploring the Usefulness of MISR-HR Products to Estimate Maize Crop Extent and Using Field Evidence to Evaluate the Results in South Africa's Free State Province

    Verstraete, M. M.; Knox, N. M.; Hunt, L. A.; Kleyn, L.

    2014-12-01

    The MISR instrument on NASA's Terra platform has been operating for almost 15 years. Standard products are generated at a spatial resolution of 1.1 km or coarser, but a recently developed method to re-analyze the Level-1B2 data allows the retrieval of biogeophysical products at the native spatial resolution of the instrument (275 m). This development opens new opportunities to better address issues such as the management of agricultural production and food security. South African maize production is of great economic and social importance, not only nationally, but on the global market too, being one of the top ten maize producing countries. Seasonal maize production statistics are currently based on a combination of field measurements and estimates derived from manually digitizing high resolution imagery from the SPOT satellite. The field measurements are collected using the Producer Independent Crop Estimate System (PICES) developed by Crop Estimates Committee of the Department of Agriculture, Forestry and Fisheries. There is a strong desire to improve the quality of these statistics, to generate those earlier, and to automate the process to encompass larger areas. This paper will explore the feasibility of using the MISR-HR spectral and directional products, combined with the finer spatial resolution and the relatively frequent coverage afforded by that instrument, to address these needs. The study area is based in the Free State, South Africa, one of the primary maize growing areas in the country, and took place during the 2012-2013 summer growing season. The significance of the outcomes will be evaluated in the context of the 14+ years of available MISR data.

  2. Estimated effects of radioactive fallout on agricultural production in Sweden. Contamination of crop products

    Eriksson, Aake; Loensjoe, H.; Karlstroem, F.

    1994-01-01

    The study is part of a research project, 'Radioactivity problems within the food sector' performed in 1991-94 at the request of the National Board of Agriculture in Sweden by The National Research Establishment, Dept. of NBC Defence, and the Dept. of Radioecology and the Dept. of Biosystems and Technology, the latter two belonging to the Swedish Univ. of Agricultural Sciences. The aim of the study was to investigate the contamination levels that may occur in agricultural crop products in Sweden in a situation of radioactive fallout from the use of nuclear weapons. There is a risk for a major nuclide transport in agricultural systems by the feeds, mainly by pasture grass and silage and hay crops but also to some extent by grain crops. For that reason, cattle are expected to be important vectors of the fallout nuclides to the human diet, particularly in milk from dairy cattle but also in beef. The activity transport by grain to pig products may also be of some importance. 8 refs, 7 figs, 25 tabs

  3. Global warming likely reduces crop yield and water availability of the dryland cropping systems in the U.S. central Great Plains

    We investigated impacts of GCM-projected climate change on dryland crop rotations of wheat-fallow and wheat-corn-fallow in the Central Great Plains (Akron in Colorado, USA) using the CERES 4.0 crop modules in RZWQM2. The climate change scenarios for CO2, temperature, and precipitation were produced ...

  4. Evaluation of black carbon estimations in global aerosol models

    Y. Zhao

    2009-11-01

    Full Text Available We evaluate black carbon (BC model predictions from the AeroCom model intercomparison project by considering the diversity among year 2000 model simulations and comparing model predictions with available measurements. These model-measurement intercomparisons include BC surface and aircraft concentrations, aerosol absorption optical depth (AAOD retrievals from AERONET and Ozone Monitoring Instrument (OMI and BC column estimations based on AERONET. In regions other than Asia, most models are biased high compared to surface concentration measurements. However compared with (column AAOD or BC burden retreivals, the models are generally biased low. The average ratio of model to retrieved AAOD is less than 0.7 in South American and 0.6 in African biomass burning regions; both of these regions lack surface concentration measurements. In Asia the average model to observed ratio is 0.7 for AAOD and 0.5 for BC surface concentrations. Compared with aircraft measurements over the Americas at latitudes between 0 and 50N, the average model is a factor of 8 larger than observed, and most models exceed the measured BC standard deviation in the mid to upper troposphere. At higher latitudes the average model to aircraft BC ratio is 0.4 and models underestimate the observed BC loading in the lower and middle troposphere associated with springtime Arctic haze. Low model bias for AAOD but overestimation of surface and upper atmospheric BC concentrations at lower latitudes suggests that most models are underestimating BC absorption and should improve estimates for refractive index, particle size, and optical effects of BC coating. Retrieval uncertainties and/or differences with model diagnostic treatment may also contribute to the model-measurement disparity. Largest AeroCom model diversity occurred in northern Eurasia and the remote Arctic, regions influenced by anthropogenic sources. Changing emissions, aging, removal, or optical properties within a single model

  5. Hardware architecture design of a fast global motion estimation method

    Liang, Chaobing; Sang, Hongshi; Shen, Xubang

    2015-12-01

    VLSI implementation of gradient-based global motion estimation (GME) faces two main challenges: irregular data access and high off-chip memory bandwidth requirement. We previously proposed a fast GME method that reduces computational complexity by choosing certain number of small patches containing corners and using them in a gradient-based framework. A hardware architecture is designed to implement this method and further reduce off-chip memory bandwidth requirement. On-chip memories are used to store coordinates of the corners and template patches, while the Gaussian pyramids of both the template and reference frame are stored in off-chip SDRAMs. By performing geometric transform only on the coordinates of the center pixel of a 3-by-3 patch in the template image, a 5-by-5 area containing the warped 3-by-3 patch in the reference image is extracted from the SDRAMs by burst read. Patched-based and burst mode data access helps to keep the off-chip memory bandwidth requirement at the minimum. Although patch size varies at different pyramid level, all patches are processed in term of 3x3 patches, so the utilization of the patch-processing circuit reaches 100%. FPGA implementation results show that the design utilizes 24,080 bits on-chip memory and for a sequence with resolution of 352x288 and frequency of 60Hz, the off-chip bandwidth requirement is only 3.96Mbyte/s, compared with 243.84Mbyte/s of the original gradient-based GME method. This design can be used in applications like video codec, video stabilization, and super-resolution, where real-time GME is a necessity and minimum memory bandwidth requirement is appreciated.

  6. A New Global LAI Product and Its Use for Terrestrial Carbon Cycle Estimation

    Chen, J. M.; Liu, R.; Ju, W.; Liu, Y.

    2014-12-01

    For improving the estimation of the spatio-temporal dynamics of the terrestrial carbon cycle, a new time series of the leaf area index (LAI) is generated for the global land surface at 8 km resolution from 1981 to 2012 by combining AVHRR and MODIS satellite data. This product differs from existing LAI products in the following two aspects: (1) the non-random spatial distribution of leaves with the canopy is considered, and (2) the seasonal variation of the vegetation background is included. The non-randomness of the leaf spatial distribution in the canopy is considered using the second vegetation structural parameter named clumping index (CI), which quantifies the deviation of the leaf spatial distribution from the random case. Using the MODIS Bidirectional Reflectance Distribution Function product, a global map of CI is produced at 500 m resolution. In our LAI algorithm, CI is used to convert the effective LAI obtained from mono-angle remote sensing into the true LAI, otherwise LAI would be considerably underestimated. The vegetation background is soil in crop, grass and shrub but includes soil, grass, moss, and litter in forests. Through processing a large volume of MISR data from 2000 to 2010, monthly red and near-infrared reflectances of the vegetation background is mapped globally at 1 km resolution. This new LAI product has been validated extensively using ground-based LAI measurements distributed globally. In carbon cycle modeling, the use of CI in addition to LAI allows for accurate separation of sunlit and shaded leaves as an important step in terrestrial photosynthesis and respiration modeling. Carbon flux measurements over 100 sites over the globe are used to validate an ecosystem model named Boreal Ecosystem Productivity Simulator (BEPS). The validated model is run globally at 8 km resolution for the period from 1981 to 2012 using the LAI product and other spatial datasets. The modeled results suggest that changes in vegetation structure as quantified

  7. Heavy metals effects on forage crops yields and estimation of elements accumulation in plants as affected by soil

    Grytsyuk, N.; Arapis, G.; Perepelyatnikova, L.; Ivanova, T.; Vynograds'ka, V.

    2006-01-01

    Heavy metals (Cu, Cd, Pb, Zn) effect on the productivity of forage crops (clover and perennial cereal grasses) and their accumulation in plants, depending on the concentration of these elements in a soil, has been studied in micro-field experiments on three types of soil. The principle objective was to determine regularities of heavy metals migration in a soil-plant system aiming the estimation of permissible levels of heavy metals content in soils with the following elaboration of methods, which regulate the toxicants transfer to plants. Methods of field experiments, agrochemical and atomic absorption analysis were used. Results were statistically treated by Statistica 6.0, S-Plus 6. Experimental results have shown that the intensity of heavy metals accumulation in plants depends on the type of the soil, the species of plants, the physicochemical properties of heavy metals and their content in the soil. Logarithmic interdependency of heavy metals concentration in soils and their accumulation in plants is suggested. However, the strong correlation between the different heavy metals concentrations in the various soils and the yield of crops was not observed. Toxicants accumulation in crops decreased in time

  8. Heavy metals effects on forage crops yields and estimation of elements accumulation in plants as affected by soil.

    Grytsyuk, N; Arapis, G; Perepelyatnikova, L; Ivanova, T; Vynograds'ka, V

    2006-02-01

    Heavy metals (Cu, Cd, Pb, Zn) effect on the productivity of forage crops (clover and perennial cereal grasses) and their accumulation in plants, depending on the concentration of these elements in a soil, has been studied in micro-field experiments on three types of soil. The principle objective was to determine regularities of heavy metals migration in a soil-plant system aiming the estimation of permissible levels of heavy metals content in soils with the following elaboration of methods, which regulate the toxicants transfer to plants. Methods of field experiments, agrochemical and atomic absorption analysis were used. Results were statistically treated by Statistica 6.0, S-Plus 6. Experimental results have shown that the intensity of heavy metals accumulation in plants depends on the type of the soil, the species of plants, the physicochemical properties of heavy metals and their content in the soil. Logarithmic interdependency of heavy metals concentration in soils and their accumulation in plants is suggested. However, the strong correlation between the different heavy metals concentrations in the various soils and the yield of crops was not observed. Toxicants accumulation in crops decreased in time.

  9. Benefits of seasonal forecasts of crop yields

    Sakurai, G.; Okada, M.; Nishimori, M.; Yokozawa, M.

    2017-12-01

    Major factors behind recent fluctuations in food prices include increased biofuel production and oil price fluctuations. In addition, several extreme climate events that reduced worldwide food production coincided with upward spikes in food prices. The stabilization of crop yields is one of the most important tasks to stabilize food prices and thereby enhance food security. Recent development of technologies related to crop modeling and seasonal weather forecasting has made it possible to forecast future crop yields for maize and soybean. However, the effective use of these technologies remains limited. Here we present the potential benefits of seasonal crop-yield forecasts on a global scale for choice of planting day. For this purpose, we used a model (PRYSBI-2) that can well replicate past crop yields both for maize and soybean. This model system uses a Bayesian statistical approach to estimate the parameters of a basic process-based model of crop growth. The spatial variability of model parameters was considered by estimating the posterior distribution of the parameters from historical yield data by using the Markov-chain Monte Carlo (MCMC) method with a resolution of 1.125° × 1.125°. The posterior distributions of model parameters were estimated for each spatial grid with 30 000 MCMC steps of 10 chains each. By using this model and the estimated parameter distributions, we were able to estimate not only crop yield but also levels of associated uncertainty. We found that the global average crop yield increased about 30% as the result of the optimal selection of planting day and that the seasonal forecast of crop yield had a large benefit in and near the eastern part of Brazil and India for maize and the northern area of China for soybean. In these countries, the effects of El Niño and Indian Ocean dipole are large. The results highlight the importance of developing a system to forecast global crop yields.

  10. Impacts of climate change and climate extremes on major crops productivity in China at a global warming of 1.5 and 2.0 °C

    Chen, Yi; Zhang, Zhao; Tao, Fulu

    2018-05-01

    A new temperature goal of holding the increase in global average temperature well below 2 °C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5 °C above pre-industrial levels has been established in the Paris Agreement, which calls for an understanding of climate risk under 1.5 and 2.0 °C warming scenarios. Here, we evaluated the effects of climate change on growth and productivity of three major crops (i.e. maize, wheat, rice) in China during 2106-2115 in warming scenarios of 1.5 and 2.0 °C using a method of ensemble simulation with well-validated Model to capture the Crop-Weather relationship over a Large Area (MCWLA) family crop models, their 10 sets of optimal crop model parameters and 70 climate projections from four global climate models. We presented the spatial patterns of changes in crop growth duration, crop yield, impacts of heat and drought stress, as well as crop yield variability and the probability of crop yield decrease. Results showed that climate change would have major negative impacts on crop production, particularly for wheat in north China, rice in south China and maize across the major cultivation areas, due to a decrease in crop growth duration and an increase in extreme events. By contrast, with moderate increases in temperature, solar radiation, precipitation and atmospheric CO2 concentration, agricultural climate resources such as light and thermal resources could be ameliorated, which would enhance canopy photosynthesis and consequently biomass accumulations and yields. The moderate climate change would slightly worsen the maize growth environment but would result in a much more appropriate growth environment for wheat and rice. As a result, wheat, rice and maize yields would change by +3.9 (+8.6), +4.1 (+9.4) and +0.2 % (-1.7 %), respectively, in a warming scenario of 1.5 °C (2.0 °C). In general, the warming scenarios would bring more opportunities than risks for crop development and food

  11. Coupled Crop/Hydrology Model to Estimate Expanded Irrigation Impact on Water Resources

    Handyside, C. T.; Cruise, J.

    2017-12-01

    A coupled agricultural and hydrologic systems model is used to examine the environmental impact of irrigation in the Southeast. A gridded crop model for the Southeast is used to determine regional irrigation demand. This irrigation demand is used in a regional hydrologic model to determine the hydrologic impact of irrigation. For the Southeast to maintain/expand irrigated agricultural production and provide adaptation to climate change and climate variability it will require integrated agricultural and hydrologic system models that can calculate irrigation demand and the impact of the this demand on the river hydrology. These integrated models can be used as (1) historical tools to examine vulnerability of expanded irrigation to past climate extremes (2) future tools to examine the sustainability of expanded irrigation under future climate scenarios and (3) a real-time tool to allow dynamic water resource management. Such tools are necessary to assure stakeholders and the public that irrigation can be carried out in a sustainable manner. The system tools to be discussed include a gridded version of the crop modeling system (DSSAT). The gridded model is referred to as GriDSSAT. The irrigation demand from GriDSSAT is coupled to a regional hydrologic model developed by the Eastern Forest Environmental Threat Assessment Center of the USDA Forest Service) (WaSSI). The crop model provides the dynamic irrigation demand which is a function of the weather. The hydrologic model includes all other competing uses of water. Examples of use the crop model coupled with the hydrologic model include historical analyses which show the change in hydrology as additional acres of irrigated land are added to water sheds. The first order change in hydrology is computed in terms of changes in the Water Availability Stress Index (WASSI) which is the ratio of water demand (irrigation, public water supply, industrial use, etc.) and water availability from the hydrologic model. Also

  12. Differences in net global warming potential and greenhouse gas intensity between major rice-based cropping systems in China.

    Xiong, Zhengqin; Liu, Yinglie; Wu, Zhen; Zhang, Xiaolin; Liu, Pingli; Huang, Taiqing

    2015-12-02

    Double rice (DR) and upland crop-single rice (UR) systems are the major rice-based cropping systems in China, yet differences in net global warming potential (NGWP) and greenhouse gas intensity (GHGI) between the two systems are poorly documented. Accordingly, a 3-year field experiment was conducted to simultaneously measure methane (CH4) and nitrous oxide (N2O) emissions and changes in soil organic carbon (SOC) in oil rape-rice-rice and wheat-rice (representing DR and UR, respectively) systems with straw incorporation (0, 3 and 6 t/ha) during the rice-growing seasons. Compared with the UR system, the annual CH4, N2O, grain yield and NGWP were significantly increased in the DR system, though little effect on SOC sequestration or GHGI was observed without straw incorporation. Straw incorporation increased CH4 emission and SOC sequestration but had no significant effect on N2O emission in both systems. Averaged over the three study years, straw incorporation had no significant effect on NGWP and GHGI in the UR system, whereas these parameters were greatly increased in the DR system, i.e., by 108% (3 t/ha) and 180% (6 t/ha) for NGWP and 103% (3 t/ha) and 168% (6 t/ha) for GHGI.

  13. Differences in net global warming potential and greenhouse gas intensity between major rice-based cropping systems in China

    Xiong, Zhengqin; Liu, Yinglie; Wu, Zhen; Zhang, Xiaolin; Liu, Pingli; Huang, Taiqing

    2015-01-01

    Double rice (DR) and upland crop-single rice (UR) systems are the major rice-based cropping systems in China, yet differences in net global warming potential (NGWP) and greenhouse gas intensity (GHGI) between the two systems are poorly documented. Accordingly, a 3-year field experiment was conducted to simultaneously measure methane (CH4) and nitrous oxide (N2O) emissions and changes in soil organic carbon (SOC) in oil rape-rice-rice and wheat-rice (representing DR and UR, respectively) systems with straw incorporation (0, 3 and 6 t/ha) during the rice-growing seasons. Compared with the UR system, the annual CH4, N2O, grain yield and NGWP were significantly increased in the DR system, though little effect on SOC sequestration or GHGI was observed without straw incorporation. Straw incorporation increased CH4 emission and SOC sequestration but had no significant effect on N2O emission in both systems. Averaged over the three study years, straw incorporation had no significant effect on NGWP and GHGI in the UR system, whereas these parameters were greatly increased in the DR system, i.e., by 108% (3 t/ha) and 180% (6 t/ha) for NGWP and 103% (3 t/ha) and 168% (6 t/ha) for GHGI. PMID:26626733

  14. Detection of Irrigated Crops from Sentinel-1 and Sentinel-2 Data to Estimate Seasonal Groundwater Use in South India

    Sylvain Ferrant

    2017-11-01

    Full Text Available Indian agriculture relies on monsoon rainfall and irrigation from surface and groundwater. The interannual variability of monsoon rainfalls is high, which forces South Indian farmers to adapt their irrigated areas to local water availability. In this study, we have developed and tested a methodology for monitoring these spatiotemporal variations using Sentinel-1 and -2 observations over the Kudaliar catchment, Telangana State (~1000 km2. These free radar and optical data have been acquired since 2015 on a weekly basis over continental areas, at a high spatial resolution (10–20 m that is well adapted to the small areas of South Indian field crops. A machine learning algorithm, the Random Forest method, was used over three growing seasons (January to March and July to November 2016 and January to March 2017 to classify small patches of inundated rice paddy, maize, and other irrigated crops, as well as surface water stored in the small reservoirs scattered across the landscape. The crop production comprises only irrigated crops (less than 20% of the areas during the dry season (Rabi, December to March, to which rain-fed cotton is added to reach 60% of the areas during the monsoon season (Kharif, June to November. Sentinel-1 radar backscatter provides useful observations during the cloudy monsoon season. The lowest irrigated area totals were found during Rabi 2016 and Kharif 2016, accounting for 3.5 and 5% with moderate classification confusion. This confusion decreases with increasing areas of irrigated crops during Rabi 2017. During this season, 16% of rice and 6% of irrigated crops were detected after the exceptional rainfalls observed in September. Surface water in small surface reservoirs reached 3% of the total area, which corresponds to a high value. The use of both Sentinel datasets improves the method accuracy and strengthens our confidence in the resulting maps. This methodology shows the potential of automatically monitoring, in near

  15. Assessing energy efficiencies, economy, and global warming potential (GWP) effects of major crop production systems in Iran: a case study in East Azerbaijan province.

    Mohammadzadeh, Arash; Mahdavi Damghani, Abdolmajid; Vafabakhsh, Javad; Deihimfard, Reza

    2017-07-01

    Efficient use of energy in farming systems is one of the most important implications for decreasing greenhouse gas (GHG) emissions and mitigating global warming (GW). This paper describes the energy use patterns, analyze the economics, and report global warming potential effects of major crop production systems in East Azerbaijan province, Iran. For this purpose, 110 farmers whose main activity was major crop production in the region, including wheat, barley, carrot, tomato, onion, potato, alfalfa, corn silage, canola, and saffron, were surveyed. Some other data was obtained from the Ministry of Agriculture Jihad of Iran. Results showed that, in terms of total energy input, onion (87,556 Mj ha -1 ) and potato (80,869 Mj ha -1 ) production systems were more energy-intensive than other crops. Among the studied crops, the highest values of net return (6563.8 $ ha -1 ) and benefit/cost ratio (1.95) were related to carrot and corn silage production systems, respectively. Studies have also shown that onion and saffron production systems emit the highest (5332.6 kg CO2eq ha -1 ) and lowest (646.24 kg CO 2 eq ha -1 ) CO 2 eq. emission, respectively. When it was averaged across crops, diesel fuel accounted for the greatest GHG contribution with 43% of the total, followed by electric power (28%) and nitrogen fertilizer (21%). In the present study, eco-efficiency was calculated as a ratio of the gross production value and global warming potential effect for the studied crops. Out of all the studied crops, the highest values of eco-efficiency were calculated to be 8.65 $ kg CO 2 eq -1 for the saffron production system followed by the carrot (3.65 $ kg CO 2 eq -1 ) production. Generally, from the aspect of energy balance and use efficiency, the alfalfa production system was the best; however, from an economical point of view, the carrot production system was better than the other crops.

  16. Consequences of the cultivation of energy crops for the global nitrogen cycle

    Bouwman, A.F.; Grinsven, van J.J.M.; Eickhout, B.

    2010-01-01

    In this paper, we assess the global consequences of implementing first- and second-generation bioenergy in the coming five decades, focusing on the nitrogen cycle. We Use a climate mitigation scenario from the Organization for Economic Cooperation and Development's (OECD) Environmental Outlook, in

  17. Remote sensing based crop type mapping and evapotranspiration estimates at the farm level in arid regions of the globe

    Ozdogan, M.; Serrat-Capdevila, A.; Anderson, M. C.

    2017-12-01

    Despite increasing scarcity of freshwater resources, there is dearth of spatially explicit information on irrigation water consumption through evapotranspiration, particularly in semi-arid and arid geographies. Remote sensing, either alone or in combination with ground surveys, is increasingly being used for irrigation water management by quantifying evaporative losses at the farm level. Increased availability of observations, sophisticated algorithms, and access to cloud-based computing is also helping this effort. This presentation will focus on crop-specific evapotranspiration estimates at the farm level derived from remote sensing in a number of water-scarce regions of the world. The work is part of a larger effort to quantify irrigation water use and improve use efficiencies associated with several World Bank projects. Examples will be drawn from India, where groundwater based irrigation withdrawals are monitored with the help of crop type mapping and evapotranspiration estimates from remote sensing. Another example will be provided from a northern irrigation district in Mexico, where remote sensing is used for detailed water accounting at the farm level. These locations exemplify the success stories in irrigation water management with the help of remote sensing with the hope that spatially disaggregated information on evapotranspiration can be used as inputs for various water management decisions as well as for better water allocation strategies in many other water scarce regions.

  18. Development of dynamic wheat crop model in ISAM and estimation of impacts of environmental factors on wheat production in India

    Gahlot, S.; Lin, T. S.; Jain, A. K.; Baidya Roy, S.; Sehgal, V. K.; Dhakar, R.

    2017-12-01

    With changing environmental conditions, such as climate and elevated atmospheric CO2 concentrations, questions about food security can be answered by modeling crops based on our understanding of the dynamic crop growth processes and interactions between the crops and their environment in the form of carbon, water and energy fluxes. These interactions and their effect on cropland ecosystems are non-linear because of the feedback mechanisms. Hence, process-based modelling approach can be used to conduct numerical experiments to derive insights into these processes and interactive feedbacks. In this study we have implemented dynamic crop growth processes for wheat into a data-modeling framework, Integrated Science Assessment Model (ISAM), to estimate the impacts of different factors like CO2 fertilization, irrigation, nitrogen limitation and climate change on wheat in India. In specific, we have implemented wheat-specific phenology, C3 photosynthesis mechanism and phenology-specific carbon allocation schemes for assimilated carbon to leaf, stem, root and grain pools. Crop growth limiting stress factors like nutrients, temperature and light have been included. The impact of high temperatures on leaf senescence, anthesis and grain filling has been modeled and found to be causing significant reduction in yield in the recent years. Field data from an experimental wheat site located at the Indian Agricultural Research Institute (IARI), New Delhi, India has been collected for aboveground biomass and leaf area index (LAI) for two growing seasons 2014-15 and 2015-16. This data has been used to study the phenology, growing season length, thermal requirements and growth stages of wheat. Using the field data, the dynamic model for wheat has been evaluated for the site level seasonal variability in leaf area index (LAI) and aboveground biomass. The variations in carbon, water and energy fluxes, plant height and rooting depth have been analyzed on the site level. Model experiments

  19. Estimating Solar Energy Potential in Buildings on a Global Level

    Petrichenko, Ksenia

    2015-01-01

    This chapter contributes to the debate around net-zero energy concept from a global perspective. By means of comprehensive modelling, it analyses how much global building energy consumption could be reduced through utilisation of building-integrated solar energy technologies and energy......-efficiency improvements. Valuable insights on the locations and building types, in which it is feasible to achieve a net-zero level of energy performance through solar energy utilisation, are presented in world maps....

  20. Importance of pollinators in changing landscapes for world crops.

    Klein, Alexandra-Maria; Vaissière, Bernard E; Cane, James H; Steffan-Dewenter, Ingolf; Cunningham, Saul A; Kremen, Claire; Tscharntke, Teja

    2007-02-07

    The extent of our reliance on animal pollination for world crop production for human food has not previously been evaluated and the previous estimates for countries or continents have seldom used primary data. In this review, we expand the previous estimates using novel primary data from 200 countries and found that fruit, vegetable or seed production from 87 of the leading global food crops is dependent upon animal pollination, while 28 crops do not rely upon animal pollination. However, global production volumes give a contrasting perspective, since 60% of global production comes from crops that do not depend on animal pollination, 35% from crops that depend on pollinators, and 5% are unevaluated. Using all crops traded on the world market and setting aside crops that are solely passively self-pollinated, wind-pollinated or parthenocarpic, we then evaluated the level of dependence on animal-mediated pollination for crops that are directly consumed by humans. We found that pollinators are essential for 13 crops, production is highly pollinator dependent for 30, moderately for 27, slightly for 21, unimportant for 7, and is of unknown significance for the remaining 9. We further evaluated whether local and landscape-wide management for natural pollination services could help to sustain crop diversity and production. Case studies for nine crops on four continents revealed that agricultural intensification jeopardizes wild bee communities and their stabilizing effect on pollination services at the landscape scale.

  1. The family farm in a globalizing world: the role of crop science in alleviating poverty

    Lipton, Michael

    2005-01-01

    "The topic of family farms has been gaining prominence in the academic, policy, and donor communities in recent years. Small farms dominate the agricultural landscape in the developing world, providing the largest source of employment and income to the rural poor, yet smallholders remain highly susceptible to poverty and hunger. With the advance of globalization and greater integration of agricultural markets, the need for increases in agricultural productivity for family farms is particularl...

  2. Global and Country-Level Fragility to Major Disruptions in Crop Production

    Puma, M. J.; Wada, Y.; Chon, S. Y.; Cook, B. I.; Nordbotten, J. M.

    2016-12-01

    New food polices are needed to mitigate vulnerabilities in the global food system to unexpected and severe production losses. The starting point for developing such policies is the ability to quantify the potential range of food and economic losses associated with major food-production shocks. Although individual major shock events are unpredictable, it is possible to quantify the relative vulnerabilities of the global food system as a whole and of individual countries within the system to production shocks. Here we combine a scale for food disruptions, which links the magnitude for a production shock with the corresponding short-term food and economic losses for that event (analogous to the well-known Richter magnitude scale for earthquakes), with country-level food system metrics. We demonstrate the value of our approach using the recent El Niño event of 2015-2016. Ultimately, these metrics can be used as part of efforts to develop national and global level food policies to prepare for and mitigate possible food-supply disruptions.

  3. Modeling of Yield Estimation for The Main Crops in Iran Based on Mechanization Index (hp ha-1

    K Abbasi

    2014-09-01

    Full Text Available Agricultural mechanization is a method for transiting from traditional agriculture towards industrial and sustainable one. Due to the limitation of natural resources and increasing population we need to have economical production of agricultural crops. For reaching this destination; agricultural mechanization has a remarkable role. So it is necessary to have an extensive view for mechanization, because with the help of mechanization the agricultural inputs such as seeds, fertilizer and even water and soil can effectively be managed for an economical and sustainable production. This study has been carried out in many provinces of Iran. The data of agricultural tractors and cereal combine harvesters were firstly gathered by means of questionnaire. The tractors were categorized in four power levels of less than 45, 45 to 80, 80 to 110, and more than 110 hp. In addition, it was also carried out for cereal combine harvesters; it was in three power levels, i.e. between 100 to 110, 110 to 155 and 155 to 210 horse-power in 3 ages, i.e. less than 13, between 13 to 20, and more than 20 years. Information regarding to cultivation areas, production volume, and yield of main crops gathered from statistics of Ministry of Jihad-e-Agriculture. Then agriculture mechanization level index (hp ha-1 in each province was calculated. Four main crops including irrigated and rain-fed wheat and irrigated and rain-fed barley, which met the required criteria to be used in the model, were statistically analyzed. Correlation analysis was carried out in order to get an effective model between yield of the four main crops in Iran and agriculture mechanization level index. Pearson correlation index showed that there is a direct and significant correlation between these variables. Subsequently, outliers were identified in order to get a model with necessary efficiency to predict the yield through mechanization level index, by scatter diagram and estimating regression lines in 1

  4. Performance of Sayigh's universal formula in the estimation of global solar radiation in Ghana

    Oduro Afriyie, K.

    1995-10-01

    The performance of Sayigh's universal formula for the estimation of global solar radiation is tested against that of Angstrom-Black model for 13 stations in Ghana, using monthly mean daily global solar radiation averaged over the years 1957-1981. Sayigh's model is found not to perform as credibility as the Angstrom-Black model in the estimation of monthly global solar radiation in Ghana. Of the 156 values of monthly global solar radiation estimated by Sayigh's model, 123 (or 78.8%) had discrepancies of more than 10% with the measured values. The corresponding value for the Angstrom-Black model was 7 (or 4.5%). (author). 5 refs

  5. Estimation of the global climate effect of brown carbon

    Zhang, A.; Wang, Y.; Zhang, Y.; Weber, R. J.; Song, Y.

    2017-12-01

    Carbonaceous aerosols significantly affect global radiative forcing and climate through absorption and scattering of sunlight. Black carbon (BC) and brown carbon (BrC) are light-absorbing carbonaceous aerosols. The global distribution and climate effect of BrC is uncertain. A recent study suggests that BrC absorption is comparable to BC in the upper troposphere over biomass burning region and that the resulting heating tends to stabilize the atmosphere. Yet current climate models do not include proper treatments of BrC. In this study, we derived a BrC global biomass burning emission inventory from Global Fire Emissions Database 4 (GFED4) and developed a BrC module in the Community Atmosphere Model version 5 (CAM5) of Community Earth System Model (CESM) model. The model simulations compared well to BrC observations of the Studies of Emissions, Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) and Deep Convective Clouds and Chemistry Project (DC-3) campaigns and includes BrC bleaching. Model results suggested that BrC in the upper troposphere due to convective transport is as important an absorber as BC globally. Upper tropospheric BrC radiative forcing is particularly significant over the tropics, affecting the atmosphere stability and Hadley circulation.

  6. Estimation of daily global solar radiation as a function of the solar energy potential at soil surface

    Pereira, A.B.; Vrisman, A.L.; Galvani, E.

    2002-01-01

    The solar radiation received at the surface of the earth, apart from its relevance to several daily human activities, plays an important role in the growth and development of plants. The aim of the current work was to develop and gauge an estimation model for the evaluation of the global solar radiation flux density as a function of the solar energy potential at soil surface. Radiometric data were collected at Ponta Grossa, PR, Brazil (latitude 25°13' S, longitude 50°03' W, altitude 880 m). Estimated values of solar energy potential obtained as a function of only one measurement taken at solar noon time were confronted with those measured by a Robitzsch bimetalic actinograph, for days that presented insolation ratios higher than 0.85. This data set was submitted to a simple linear regression analysis, having been obtained a good adjustment between observed and calculated values. For the estimation of the coefficients a and b of Angström's equation, the method based on the solar energy potential at soil surface was used for the site under study. The methodology was efficient to assess the coefficients, aiming at the determination of the global solar radiation flux density, whith quickness and simplicity, having also found out that the criterium for the estimation of the solar energy potential is equivalent to that of the classical methodology of Angström. Knowledge of the available solar energy potential and global solar radiation flux density is of great importance for the estimation of the maximum atmospheric evaporative demand, of water consumption by irrigated crops, and also for building solar engineering equipment, such as driers, heaters, solar ovens, refrigerators, etc [pt

  7. Impact of Galileo on Global Ionosphere Map Estimation

    Undetermined, U.

    2006-01-01

    The upcoming GNSS Galileo, with its new satellite geometry and frequency plan, will not only bring many benefits for navigation and positioning but also help to improve ionosphere delay estimation. This paper investigates ionosphere estimation with Galileo and compares it with the results from

  8. Redefinition and global estimation of basal ecosystem respiration rate

    Yuan, Wenping [College of Global Change and Earth System Science, Beijing Normal University, Beijing, China; Luo, Yiqi [Department of Botany and Microbiology, University of Oklahoma, Norman, Oklahoma, USA; Li, Xianglan [College of Global Change and Earth System Science, Beijing Normal University, Beijing, China; Liu, Shuguang; Yu, Guirui [Key Laboratory of Ecosystem Network Observation and Modeling, Synthesis Research Center of Chinese Ecosystem Research Network, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Zhou, Tao [State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing, China; Bahn, Michael [Institute of Ecology, University of Innsbruck, Innsbruck, Austria; Black, Andy [Faculty of Land and Food Systems, University of British Columbia, Vancouver, B. C., Canada; Desai, Ankur R. [Atmospheric and Oceanic Sciences Department, Center for Climatic Research, Nelson Institute for Environmental Studies, University of Wisconsin-Madison, Madison, Wisconsin, USA; Cescatti, Alessandro [Institute for Environment and Sustainability, Joint Research Centre, European Commission, Ispra, Italy; Marcolla, Barbara [Sustainable Agro-ecosystems and Bioresources Department, Fondazione Edmund Mach-IASMA Research and Innovation Centre, San Michele all' Adige, Italy; Jacobs, Cor [Alterra, Earth System Science-Climate Change, Wageningen University, Wageningen, Netherlands; Chen, Jiquan [Department of Earth, Ecological, and Environmental Sciences, University of Toledo, Toledo, Ohio, USA; Aurela, Mika [Climate and Global Change Research, Finnish Meteorological Institute, Helsinki, Finland; Bernhofer, Christian [Chair of Meteorology, Institute of Hydrology and Meteorology, Technische Universität Dresden, Dresden, Germany; Gielen, Bert [Department of Biology, University of Antwerp, Wilrijk, Belgium; Bohrer, Gil [Department of Civil, Environmental, and Geodetic Engineering, Ohio State University, Columbus, Ohio, USA; Cook, David R. [Climate Research Section, Environmental Science Division, Argonne National Laboratory, Argonne, Illinois, USA; Dragoni, Danilo [Department of Geography, Indiana University, Bloomington, Indiana, USA; Dunn, Allison L. [Department of Physical and Earth Sciences, Worcester State College, Worcester, Massachusetts, USA; Gianelle, Damiano [Sustainable Agro-ecosystems and Bioresources Department, Fondazione Edmund Mach-IASMA Research and Innovation Centre, San Michele all' Adige, Italy; Grünwald, Thomas [Chair of Meteorology, Institute of Hydrology and Meteorology, Technische Universität Dresden, Dresden, Germany; Ibrom, Andreas [Risø DTU National Laboratory for Sustainable Energy, Biosystems Division, Technical University of Denmark, Roskilde, Denmark; Leclerc, Monique Y. [Department of Crop and Soil Sciences, College of Agricultural and Environmental Sciences, University of Georgia, Griffin, Georgia, USA; Lindroth, Anders [Geobiosphere Science Centre, Physical Geography and Ecosystems Analysis, Lund University, Lund, Sweden; Liu, Heping [Laboratory for Atmospheric Research, Department of Civil and Environmental Engineering, Washington State University, Pullman, Washington, USA; Marchesini, Luca Belelli [Department for Innovation in Biological, Agro-Food and Forest Systems, University of Tuscia, Viterbo, Italy; Montagnani, Leonardo; Pita, Gabriel [Department of Mechanical Engineering, Instituto Superior Técnico, Lisbon, Portugal; Rodeghiero, Mirco [Sustainable Agro-ecosystems and Bioresources Department, Fondazione Edmund Mach-IASMA Research and Innovation Centre, San Michele all' Adige, Italy; Rodrigues, Abel [Unidade de Silvicultura e Produtos Florestais, Instituto Nacional dos Recursos Biológicos, Oeiras, Portugal; Starr, Gregory [Department of Biological Sciences, University of Alabama, Tuscaloosa, Alabama, USA; Stoy, Paul C. [Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, Montana, USA

    2011-10-13

    Basal ecosystem respiration rate (BR), the ecosystem respiration rate at a given temperature, is a common and important parameter in empirical models for quantifying ecosystem respiration (ER) globally. Numerous studies have indicated that BR varies in space. However, many empirical ER models still use a global constant BR largely due to the lack of a functional description for BR. In this study, we redefined BR to be ecosystem respiration rate at the mean annual temperature. To test the validity of this concept, we conducted a synthesis analysis using 276 site-years of eddy covariance data, from 79 research sites located at latitudes ranging from ~3°S to ~70°N. Results showed that mean annual ER rate closely matches ER rate at mean annual temperature. Incorporation of site-specific BR into global ER model substantially improved simulated ER compared to an invariant BR at all sites. These results confirm that ER at the mean annual

  9. Net global warming potential and greenhouse gas intensity as affected by different water management strategies in Chinese double rice-cropping systems.

    Wu, Xiaohong; Wang, Wei; Xie, Xiaoli; Yin, Chunmei; Hou, Haijun; Yan, Wende; Wang, Guangjun

    2018-01-15

    This study provides a complete account of global warming potential (GWP) and greenhouse gas intensity (GHGI) in relation to a long-term water management experiment in Chinese double-rice cropping systems. The three strategies of water management comprised continuous (year-round) flooding (CF), flooding during the rice season but with drainage during the midseason and harvest time (F-D-F), and irrigation only for flooding during transplanting and the tillering stage (F-RF). The CH 4 and N 2 O fluxes were measured with the static chamber method. Soil organic carbon (SOC) sequestration rates were estimated based on the changes in the carbon stocks during 1998-2014. Longer periods of soil flooding led to increased CH 4 emissions, reduced N 2 O emissions, and enhanced SOC sequestration. The net GWPs were 22,497, 8,895, and 1,646 kg CO 2 -equivalent ha -1 yr -1 for the CF, F-D-F, and F-RF, respectively. The annual rice grain yields were comparable between the F-D-F and CF, but were reduced significantly (by 13%) in the F-RF. The GHGIs were 2.07, 0.87, and 0.18 kg CO 2 -equivalent kg -1 grain yr -1 for the CF, F-D-F, and F-RF, respectively. These results suggest that F-D-F could be used to maintain the grain yields and simultaneously mitigate the climatic impact of double rice-cropping systems.

  10. Estimating global cropland production from 1961 to 2010

    Han, Pengfei; Zeng, Ning; Zhao, Fang; Lin, Xiaohui

    2017-09-01

    Global cropland net primary production (NPP) has tripled over the last 50 years, contributing 17-45 % to the increase in global atmospheric CO2 seasonal amplitude. Although many regional-scale comparisons have been made between statistical data and modeling results, long-term national comparisons across global croplands are scarce due to the lack of detailed spatiotemporal management data. Here, we conducted a simulation study of global cropland NPP from 1961 to 2010 using a process-based model called Vegetation-Global Atmosphere-Soil (VEGAS) and compared the results with Food and Agriculture Organization of the United Nations (FAO) statistical data on both continental and country scales. According to the FAO data, the global cropland NPP was 1.3, 1.8, 2.2, 2.6, 3.0, and 3.6 PgC yr-1 in the 1960s, 1970s, 1980s, 1990s, 2000s, and 2010s, respectively. The VEGAS model captured these major trends on global and continental scales. The NPP increased most notably in the US Midwest, western Europe, and the North China Plain and increased modestly in Africa and Oceania. However, significant biases remained in some regions such as Africa and Oceania, especially in temporal evolution. This finding is not surprising as VEGAS is the first global carbon cycle model with full parameterization representing the Green Revolution. To improve model performance for different major regions, we modified the default values of management intensity associated with the agricultural Green Revolution differences across various regions to better match the FAO statistical data at the continental level and for selected countries. Across all the selected countries, the updated results reduced the RMSE from 19.0 to 10.5 TgC yr-1 (˜ 45 % decrease). The results suggest that these regional differences in model parameterization are due to differences in socioeconomic development. To better explain the past changes and predict the future trends, it is important to calibrate key parameters on regional

  11. Combating a Global Threat to a Clonal Crop: Banana Black Sigatoka Pathogen Pseudocercospora fijiensis (Synonym Mycosphaerella fijiensis) Genomes Reveal Clues for Disease Control

    Arango Isaza, Rafael E.; Diaz-Trujillo, Caucasella; Dhillon, Braham; Aerts, Andrea; Carlier, Jean; Crane, Charles F.; V. de Jong, Tristan; de Vries, Ineke; Dietrich, Robert; Farmer, Andrew D.; Fortes Fereira, Claudia; Garcia, Suzana; Guzman, Mauricio; Hamelin, Richard C.; Lindquist, Erika A.; Mehrabi, Rahim; Quiros, Olman; Schmutz, Jeremy; Shapiro, Harris; Reynolds, Elizabeth; Scalliet, Gabriel; Souza Manoel, Jr.; Stergiopoulos, Ioannis; Van der Lee, Theo A. J.; De Wit, Pierre J. G. M.; Zapater, Marie-Françoise; Zwiers, Lute-Harm; Grigoriev, Igor V.; Goodwin, Stephen B.; Kema, Gert H. J.

    2016-01-01

    Black Sigatoka or black leaf streak disease, caused by the ascomycete fungus Pseudocercospora fijiensis, inflicts huge costs on banana producers, due to crop losses and expenses for disease control. The global banana export trade relies on Cavendish clones that are highly susceptible to P.

  12. Estimation of Crop Gross Primary Production (GPP): I. Impact of MODIS Observation Footprint and Impact of Vegetation BRDF Characteristics

    Zhang, Qingyuan; Cheng, Yen-Ben; Lyapustin, Alexei I.; Wang, Yujie; Xiao, Xiangming; Suyker, Andrew; Verma, Shashi; Tan, Bin; Middleton, Elizabeth M.

    2014-01-01

    Accurate estimation of gross primary production (GPP) is essential for carbon cycle and climate change studies. Three AmeriFlux crop sites of maize and soybean were selected for this study. Two of the sites were irrigated and the other one was rainfed. The normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), the green band chlorophyll index (CIgreen), and the green band wide dynamic range vegetation index (WDRVIgreen) were computed from the moderate resolution imaging spectroradiometer (MODIS) surface reflectance data. We examined the impacts of the MODIS observation footprint and the vegetation bidirectional reflectance distribution function (BRDF) on crop daily GPP estimation with the four spectral vegetation indices (VIs - NDVI, EVI, WDRVIgreen and CIgreen) where GPP was predicted with two linear models, with and without offset: GPP = a × VI × PAR and GPP = a × VI × PAR + b. Model performance was evaluated with coefficient of determination (R2), root mean square error (RMSE), and coefficient of variation (CV). The MODIS data were filtered into four categories and four experiments were conducted to assess the impacts. The first experiment included all observations. The second experiment only included observations with view zenith angle (VZA) = 35? to constrain growth of the footprint size,which achieved a better grid cell match with the agricultural fields. The third experiment included only forward scatter observations with VZA = 35?. The fourth experiment included only backscatter observations with VZA = 35?. Overall, the EVI yielded the most consistently strong relationships to daily GPP under all examined conditions. The model GPP = a × VI × PAR + b had better performance than the model GPP = a × VI × PAR, and the offset was significant for most cases. Better performance was obtained for the irrigated field than its counterpart rainfed field. Comparison of experiment 2 vs. experiment 1 was used to examine the observation

  13. Saving lives in health: global estimates and country measurement.

    Daniel Low-Beer

    2013-10-01

    Full Text Available Daniel Low-Beer and colleagues provide a response from The Global Fund on the PLOS Medicine article by David McCoy and colleagues critiquing their lives saved assessment models. Please see later in the article for the Editors' Summary.

  14. Estimated migration rates under scenarios of global climate change.

    Jay R. Malcolm; Adam Markham; Ronald P. Neilson; Michael. Oaraci

    2002-01-01

    Greefihouse-induced warming and resulting shifts in climatic zones may exceed the migration capabilities of some species. We used fourteen combinations of General Circulation Models (GCMs) and Global Vegetation Models (GVMs) to investigate possible migration rates required under CO2 doubled climatic forcing.

  15. Redefinition and global estimation of basal ecosystem respiration rate

    Yuan, Wenping; Luo, Yiqi; Li, Xianglan

    2011-01-01

    Basal ecosystem respiration rate (BR), the ecosystem respiration rate at a given temperature, is a common and important parameter in empirical models for quantifying ecosystem respiration (ER) globally. Numerous studies have indicated that BR varies in space. However, many empirical ER models sti...

  16. Bowen ratio/energy balance technique for estimating crop net CO2 assimilation, and comparison with a canopy chamber

    Held, A. A.; Steduto, P.; Orgaz, F.; Matista, A.; Hsiao, T. C.

    1990-12-01

    This paper describes a Bowen ratio/energy balance (BREB) system which, in conjunction with an infra-red gas analyzer (IRGA), is referred to as BREB+ and is used to estimate evapotranspiration ( ET) and net CO2 flux ( NCF) over crop canopies. The system is composed of a net radiometer, soil heat flux plates, two psychrometers based on platinum resistance thermometers (PRT), bridge circuits to measure resistances, an IRGA, air pumps and switching valves, and a data logger. The psychrometers are triple shielded and aspirated, and with aspiration also between the two inner shields. High resistance (1 000 ohm) PRT's are used for dry and wet bulbs to minimize errors due to wiring and connector resistances. A high (55 K ohm) fixed resistance serves as one arm of the resistance bridge to ensure linearity in output signals. To minimize gaps in data, to allow measurements at short (e.g., 5 min) intervals, and to simplify operation, the psychrometers were fixed at their upper and lower position over the crop and not alternated. Instead, the PRT's, connected to the bridge circuit and the data logger, were carefully calibrated together. Field tests using a common air source showed appartent effects of the local environment around each psychrometer on the temperatures measured. ET rates estimated with the BREB system were compared to those measured with large lysimeters. Daily totals agreed within 5%. There was a tendency, however, for the lysimeter measurements to lag behind the BREB measurements. Daily patterns of NCF estimated with the BREB+ system are consistent with expectations from theories and data in the literature. Side-by-side comparisons with a stirred Mylar canopy chamber showed similar NCF patterns. On the other hand, discrepancies between the results of the two methods were quite marked in the morning or afternoon on certain dates. Part of the discrepancies may be attributed to inaccuracies in the psychrometric temperature measurements. Other possible causes

  17. Current Status and Perspectives for the Estimation of Crop Water Requirements from Earth Observation

    Guido D’Urso

    2010-06-01

    Full Text Available This paper presents an overview of current techniques and recent developments in the application of Earth Observationdata for assessing crop water requirements. During recent years there has been much progress in understandingland surface-atmosphere processes and their parameterisation in the management of land and water resources.This knowledge can be combined with the potentiality of Earth Observation techniques from space, whichare able to provide detailed information for monitoring agricultural systems.As today, two main developments in the field of Earth Observation data acquisition and analysis have occurred:a availability of new generations of sensors, with enhanced spectral and spatial resolution;b detailed knowledge of the processes that determine the response of land surface as detected from remote sensorsin different regions of the electromagnetic spectrum.These advancements have made possible a “quantitative” approach in the interpretation of Earth Observation data,ready for being transferred to operative applications i.e. for irrigation scheduling and water management. Thispaper presents a review of current applications of optical data in the visible and near infrared spectral regions, withparticular emphasis to the experiences developed by the author within AQUATER and other research projectsproject.

  18. A Unified Experimental Approach for Estimation of Irrigationwater and Nitrate Leaching in Tree Crops

    Hopmans, J. W.; Kandelous, M. M.; Moradi, A. B.

    2014-12-01

    Groundwater quality is specifically vulnerable in irrigated agricultural lands in California and many other(semi-)arid regions of the world. The routine application of nitrogen fertilizers with irrigation water in California is likely responsible for the high nitrate concentrations in groundwater, underlying much of its main agricultural areas. To optimize irrigation/fertigation practices, it is essential that irrigation and fertilizers are applied at the optimal concentration, place, and time to ensure maximum root uptake and minimize leaching losses to the groundwater. The applied irrigation water and dissolved fertilizer, as well as root growth and associated nitrate and water uptake, interact with soil properties and fertilizer source(s) in a complex manner that cannot easily be resolved. It is therefore that coupled experimental-modeling studies are required to allow for unraveling of the relevant complexities that result from typical field-wide spatial variations of soil texture and layering across farmer-managed fields. We present experimental approaches across a network of tree crop orchards in the San Joaquin Valley, that provide the necessary soil data of soil moisture, water potential and nitrate concentration to evaluate and optimize irrigation water management practices. Specifically, deep tensiometers were used to monitor in-situ continuous soil water potential gradients, for the purpose to compute leaching fluxes of water and nitrate at both the individual tree and field scale.

  19. A modelling approach to estimate the European biofuel production: from crops to biofuels

    Clodic, Melissa [Institute National de la Recherche Agronomique (IFP/INRA), Paris (France). Instituto Frances do Petroleo

    2008-07-01

    Today, in the context of energy competition and climate change, biofuels are promoted as a renewable resource to diversify the energy supply. However, biofuel development remains controversial. Here, we will present a way to make an environmental and economic cost and benefit analysis of European biofuels, from the crops until the marketed products, by using a linear programming optimization modelling approach. To make this European biofuel production model, named AGRAF, possible, we decided to use different independent linear programming optimization models which represent the separate parts of the process: European agricultural production, production of transforming industries and refinery production. To model the agricultural and the refining sections, we have chosen to improve existing and experimented models by adding a biofuel production part. For the transforming industry, we will create a new partial equilibrium model which will represent stake holders such as Sofiproteol, Stereos, etc. Data will then be exchanged between the models to coordinate all the biofuel production steps. Here, we will also focus on spatialization in order to meet certain of our requirements, such as the exchange flux analysis or the determination of transport costs, usually important in an industrial optimization model. (author)

  20. Developing in situ non-destructive estimates of crop biomass to address issues of scale in remote sensing

    Marshall, Michael T.; Thenkabail, Prasad S.

    2015-01-01

    Ground-based estimates of aboveground wet (fresh) biomass (AWB) are an important input for crop growth models. In this study, we developed empirical equations of AWB for rice, maize, cotton, and alfalfa, by combining several in situ non-spectral and spectral predictors. The non-spectral predictors included: crop height (H), fraction of absorbed photosynthetically active radiation (FAPAR), leaf area index (LAI), and fraction of vegetation cover (FVC). The spectral predictors included 196 hyperspectral narrowbands (HNBs) from 350 to 2500 nm. The models for rice, maize, cotton, and alfalfa included H and HNBs in the near infrared (NIR); H, FAPAR, and HNBs in the NIR; H and HNBs in the visible and NIR; and FVC and HNBs in the visible; respectively. In each case, the non-spectral predictors were the most important, while the HNBs explained additional and statistically significant predictors, but with lower variance. The final models selected for validation yielded an R2 of 0.84, 0.59, 0.91, and 0.86 for rice, maize, cotton, and alfalfa, which when compared to models using HNBs alone from a previous study using the same spectral data, explained an additional 12%, 29%, 14%, and 6% in AWB variance. These integrated models will be used in an up-coming study to extrapolate AWB over 60 × 60 m transects to evaluate spaceborne multispectral broad bands and hyperspectral narrowbands.

  1. Developing in situ Non-Destructive Estimates of Crop Biomass to Address Issues of Scale in Remote Sensing

    Michael Marshall

    2015-01-01

    Full Text Available Ground-based estimates of aboveground wet (fresh biomass (AWB are an important input for crop growth models. In this study, we developed empirical equations of AWB for rice, maize, cotton, and alfalfa, by combining several in situ non-spectral and spectral predictors. The non-spectral predictors included: crop height (H, fraction of absorbed photosynthetically active radiation (FAPAR, leaf area index (LAI, and fraction of vegetation cover (FVC. The spectral predictors included 196 hyperspectral narrowbands (HNBs from 350 to 2500 nm. The models for rice, maize, cotton, and alfalfa included H and HNBs in the near infrared (NIR; H, FAPAR, and HNBs in the NIR; H and HNBs in the visible and NIR; and FVC and HNBs in the visible; respectively. In each case, the non-spectral predictors were the most important, while the HNBs explained additional and statistically significant predictors, but with lower variance. The final models selected for validation yielded an R2 of 0.84, 0.59, 0.91, and 0.86 for rice, maize, cotton, and alfalfa, which when compared to models using HNBs alone from a previous study using the same spectral data, explained an additional 12%, 29%, 14%, and 6% in AWB variance. These integrated models will be used in an up-coming study to extrapolate AWB over 60 × 60 m transects to evaluate spaceborne multispectral broad bands and hyperspectral narrowbands.

  2. Discrete non-parametric kernel estimation for global sensitivity analysis

    Senga Kiessé, Tristan; Ventura, Anne

    2016-01-01

    This work investigates the discrete kernel approach for evaluating the contribution of the variance of discrete input variables to the variance of model output, via analysis of variance (ANOVA) decomposition. Until recently only the continuous kernel approach has been applied as a metamodeling approach within sensitivity analysis framework, for both discrete and continuous input variables. Now the discrete kernel estimation is known to be suitable for smoothing discrete functions. We present a discrete non-parametric kernel estimator of ANOVA decomposition of a given model. An estimator of sensitivity indices is also presented with its asymtotic convergence rate. Some simulations on a test function analysis and a real case study from agricultural have shown that the discrete kernel approach outperforms the continuous kernel one for evaluating the contribution of moderate or most influential discrete parameters to the model output. - Highlights: • We study a discrete kernel estimation for sensitivity analysis of a model. • A discrete kernel estimator of ANOVA decomposition of the model is presented. • Sensitivity indices are calculated for discrete input parameters. • An estimator of sensitivity indices is also presented with its convergence rate. • An application is realized for improving the reliability of environmental models.

  3. Origins of food crops connect countries worldwide

    Achicanoy, Harold A.; Bjorkman, Anne D.; Navarro-Racines, Carlos; Guarino, Luigi; Flores-Palacios, Ximena; Engels, Johannes M. M.; Wiersema, John H.; Dempewolf, Hannes; Sotelo, Steven; Ramírez-Villegas, Julian; Castañeda-Álvarez, Nora P.; Fowler, Cary; Jarvis, Andy; Rieseberg, Loren H.; Struik, Paul C.

    2016-01-01

    Research into the origins of food plants has led to the recognition that specific geographical regions around the world have been of particular importance to the development of agricultural crops. Yet the relative contributions of these different regions in the context of current food systems have not been quantified. Here we determine the origins (‘primary regions of diversity’) of the crops comprising the food supplies and agricultural production of countries worldwide. We estimate the degree to which countries use crops from regions of diversity other than their own (‘foreign crops’), and quantify changes in this usage over the past 50 years. Countries are highly interconnected with regard to primary regions of diversity of the crops they cultivate and/or consume. Foreign crops are extensively used in food supplies (68.7% of national food supplies as a global mean are derived from foreign crops) and production systems (69.3% of crops grown are foreign). Foreign crop usage has increased significantly over the past 50 years, including in countries with high indigenous crop diversity. The results provide a novel perspective on the ongoing globalization of food systems worldwide, and bolster evidence for the importance of international collaboration on genetic resource conservation and exchange.

  4. Online Global Land Surface Temperature Estimation from Landsat

    David Parastatidis

    2017-11-01

    Full Text Available This study explores the estimation of land surface temperature (LST for the globe from Landsat 5, 7 and 8 thermal infrared sensors, using different surface emissivity sources. A single channel algorithm is used for consistency among the estimated LST products, whereas the option of using emissivity from different sources provides flexibility for the algorithm’s implementation to any area of interest. The Google Earth Engine (GEE, an advanced earth science data and analysis platform, allows the estimation of LST products for the globe, covering the time period from 1984 to present. To evaluate the method, the estimated LST products were compared against two reference datasets: (a LST products derived from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer, as higher-level products based on the temperature-emissivity separation approach; (b Landsat LST data that have been independently produced, using different approaches. An overall RMSE (root mean square error of 1.52 °C was observed and it was confirmed that the accuracy of the LST product is dependent on the emissivity; different emissivity sources provided different LST accuracies, depending on the surface cover. The LST products, for the full Landsat 5, 7 and 8 archives, are estimated “on-the-fly” and are available on-line via a web application.

  5. Improved crop residue cover estimates by coupling spectral indices for residue and moisture

    Remote sensing assessment of soil residue cover (fR) and tillage intensity will improve our predictions of the impact of agricultural practices and promote sustainable management. Spectral indices for estimating fR are sensitive to soil and residue water content, therefore, the uncertainty of estima...

  6. Phase estimation for global defocus correction in optical coherence tomography

    Jensen, Mikkel; Israelsen, Niels Møller; Podoleanu, Adrian

    2017-01-01

    In this work we investigate three techniques for estimation of the non-linear phase present due to defocus in opticalcoherence tomography, and apply them with the angular spectrum method. The techniques are: Least squarestting the of unwrapped phase of the angular spectrum, iterative optimization......, and sub-aperture correlations. The estimated phase of a single en-face image is used to extrapolate the non-linear phase at all depths, whichin the end can be used to correct the entire 3-D tomogram, and any other tomogram from the same system.......In this work we investigate three techniques for estimation of the non-linear phase present due to defocus in opticalcoherence tomography, and apply them with the angular spectrum method. The techniques are: Least squarestting the of unwrapped phase of the angular spectrum, iterative optimization...

  7. Estimation of radiation hazard of global 85Kr

    Vasilenko, I.Ya.; Moskalev, Yu.I.; Istomina, A.G.

    1979-01-01

    The data on sources and levels of the 85 Kr biosphere contamination are presented on the basis of generalization and analysis of literature. The potential irradiation doses for people are calculated and the biological estimation of the hazard of 85 Kr accumulation in the atmosphere up to 2050 is given taking into account the prospects for development of nuclear power engineering. The basis of the estimation is the radionuclide blastomogeneous and genetic effect. The conclusion is made that the prospects for development of nuclear power engineering do not lead to any sufficient increase in the number of malignant tumors and genetic abnormalities caused by 85 Kr radiation comparing with their natural frequency

  8. Global drought watch from space at work: Crop losses and food security

    Kogan, F.

    2012-12-01

    Drought is one of the most adverse environmental disasters. It affects countries economies, environment a very large number of people in the world. Only in the USA drought costs taxpayers nearly $6 billion each year. Drought is a very unusual phenomenon because unlike other environmental disaster it starts unnoticeably, develop cumulatively, the impact is also cumulative and by the time when the effect of drought is observable it is too late to mitigate the consequences. Therefore, it is difficult to mitigate droughts using in situ data. The National Oceanic and Atmospheric Administration (NOAA) developed new method for drought detection and monitoring from reflectance measured by the Advanced Very High Resolution Radiometer flown on NOAA polar-orbiting operational environmental satellites. The method calculates Vegetation Health (VH) indices, which estimate vegetation condition (health) on a scale from extreme stress to favorable conditions based on intensity of greenness, vigor and thermal condition of vegetation canopy. The VH is estimated every week for each 4 by 4 km earth surface and is delivered to the NOAA/NESDIS web site in digital and color-coded form. The web site address is the following http://www.star.nesdis.noaa.gov/smcd/emb/vci/VH/index.php In addition to drought and vegetation health monitoring, the VH indices are applied in agriculture, forestry, mosquito-borne diseases, climate, invasive species and others. During the first seven months of 2009, drought was observed in the southern US (especially Texas), Argentina (very intensive drought), some of the countries of sub-Sahara Africa, India (central and eastern), Kazakhstan and Australia.

  9. Global weighted estimates for second-order nondivergence elliptic ...

    Fengping Yao

    2018-03-21

    Mar 21, 2018 ... One of the key a priori estimates in the theory of second-order elliptic .... It is well known that the maximal functions satisfy strong p–p .... Here we prove the following auxiliary result, which will be a crucial ingredient in the proof.

  10. estimation of global solar radiation from sunshine hours for warri

    DJFLEX

    Multiple linear regression models were developed to estimate the monthly daily sunshine hours using four parameters during a period of eleven years (1997 to 2007) for Warri, Nigeria (Latitude of 5o. 34' 21.0''); the parameters include, Relative Humidity, Maximum and Minimum Temperature, Rainfall and Wind Speed.

  11. Estimation of global solar radiation from sunshine hours for Warri ...

    Multiple linear regression models were developed to estimate the monthly daily sunshine hours using four parameters during a period of eleven years (1997 to 2007) for Warri, Nigeria (Latitude of 5o 34' 21.0''); the parameters include, Relative Humidity, Maximum and Minimum Temperature, Rainfall and Wind Speed.

  12. Nitrate leaching from sandy loam soils under a double-cropping forage system estimated from suction-probe measurements.

    Trindade, H.; Coutinho, J.; Beusichem, van M.L.; Scholefield, D.; Moreira, N.

    1997-01-01

    Nitrate leaching from a double-cropping forage system was measured over a 2-year period (June 1994–May 1996) in the Northwest region of Portugal using ceramic cup samplers. The crops were grown for silage making and include maize (from May to September) and a winter crop (rest of the year)

  13. Alternate bearing in citrus: changes in the expression of flowering control genes and in global gene expression in ON- versus OFF-crop trees.

    Shalom, Liron; Samuels, Sivan; Zur, Naftali; Shlizerman, Lyudmila; Zemach, Hanita; Weissberg, Mira; Ophir, Ron; Blumwald, Eduardo; Sadka, Avi

    2012-01-01

    Alternate bearing (AB) is the process in fruit trees by which cycles of heavy yield (ON crop) one year are followed by a light yield (OFF crop) the next. Heavy yield usually reduces flowering intensity the following year. Despite its agricultural importance, how the developing crop influences the following year's return bloom and yield is not fully understood. It might be assumed that an 'AB signal' is generated in the fruit, or in another organ that senses fruit presence, and moves into the bud to determine its fate-flowering or vegetative growth. The bud then responds to fruit presence by altering regulatory and metabolic pathways. Determining these pathways, and when they are altered, might indicate the nature of this putative AB signal. We studied bud morphology, the expression of flowering control genes, and global gene expression in ON- and OFF-crop buds. In May, shortly after flowering and fruit set, OFF-crop buds were already significantly longer than ON-crop buds. The number of differentially expressed genes was higher in May than at the other tested time points. Processes differentially expressed between ON- and OFF-crop trees included key metabolic and regulatory pathways, such as photosynthesis and secondary metabolism. The expression of genes of trehalose metabolism and flavonoid metabolism was validated by nCounter technology, and the latter was confirmed by metabolomic analysis. Among genes induced in OFF-crop trees was one homologous to SQUAMOSA PROMOTER BINDING-LIKE (SPL), which controls juvenile-to-adult and annual phase transitions, regulated by miR156. The expression pattern of SPL-like, miR156 and other flowering control genes suggested that fruit load affects bud fate, and therefore development and metabolism, a relatively long time before the flowering induction period. Results shed light on some of the metabolic and regulatory processes that are altered in ON and OFF buds.

  14. Crop maize evapotranspiration; 2: ratios between the evapotranspiration to class A pan evaporation, to the reference evapotranspiration and to global solar radiation, at three sowing dates

    Matzenauer, R.; Bergamashi, H.; Berlato, M.A.

    1998-01-01

    Water availability is the most limiting factor for growth and grain yield of maize in the State of Rio Grande do Sul, Brazil, reducing frequently this production. Therefore, studies involving the determination of the water requirements are important for irrigation management to minimize the water availability problem. The main objective of this study was to calculate ratios between the maize crop evapotranspiration (ETm) to the class A pan evaporation (Eo), to the reference evapotranspiration (ETo) and to global solar radiation (Rs), in order to obtain ralations between ETm/Eo, ETm/ETo and ETm/Rs, at different crop stages for three different sowing dates. Field experiments were carried out at the Experimental Station of Taquari/RS, 29°48’ of south latitude, 51°49’of west longitude, and 76m of altitude, from 1976/77 to 1988/89. ETm was measured using drainage lysimeters (Thornthwaite-Mather type). The average ratio between ETm and Eo for whole crop cycle (from sowing to physiological maturity) was 0.66, 0.72, and 0.68, respectively, in crops sown on September, October, and November. The average ratio between ETm and ETo for whole crop cycle was 0.74, 0.81, and 0.8, in crops sown on September, October, and November, while the average ratio between ETm and Rs was 0.45, 0.51, and 0.49 for the same sowing dates. The higher average values of crop coefficients occured from tasseling to the milk grain stage, when ETm/Eo was 0.81, 0.92, and 0.81; ETm/ETo was 0.97, 1.05, and 0.96, whereas ETm/Rs was 0.6, 0.68, and 0.6 for crops sown on September, October, and November, respectively [pt

  15. Greenhouse Gas Emissions and Global Warming Potential of Traditional and Diversified Tropical Rice Rotation Systems including Impacts of Upland Crop Management Practices i.e. Mulching and Inter-crop Cultivation

    Janz, Baldur; Weller, Sebastian; Kraus, David; Wassmann, Reiner; Butterbach-Bahl, Klaus; Kiese, Ralf

    2016-04-01

    Paddy rice cultivation is increasingly challenged by irrigation water scarcity, while at the same time changes in demand (e.g. changes in diets or increasing demand for biofuels) will feed back on agricultural practices. These factors are changing traditional cropping patterns from flooded double-rice systems to the introduction of well-aerated upland crop systems in the dry season. Emissions of methane (CH4) are expected to decrease, while emissions of nitrous oxide (N2O) will increase and soil organic carbon (SOC) stocks will most likely be volatilized in the form of carbon dioxide (CO2). We measured greenhouse gas (GHG) emissions at the International Rice Research Institute (IRRI) in the Philippines to provide a comparative assessment of the global warming potentials (GWP) as well as yield scaled GWPs of different crop rotations and to evaluate mitigation potentials or risks of new management practices i.e. mulching and inter-crop cultivation. New management practices of mulching and intercrop cultivation will also have the potential to change SOC dynamics, thus can play the key role in contributing to the GWP of upland cropping systems. To present, more than three years of continuous measurement data of CH4 and N2O emissions in double-rice cropping (R-R) and paddy rice rotations diversified with either maize (R-M) or aerobic rice (R-A) in upland cultivation have been collected. Introduction of upland crops in the dry season reduced irrigation water use and CH4 emissions by 66-81% and 95-99%, respectively. Moreover, for practices including upland crops, CH4 emissions in the subsequent wet season with paddy rice were reduced by 54-60%. Although annual N2O emissions increased twice- to threefold in the diversified systems, the strong reduction of CH4 led to a significantly lower (pbalance but also with regard to soil fertility. New upland crop management practices where first implemented during land-preparation for dry season (July) 2015 where i) 6t/ha rice straw

  16. Comparison of two global digital algorithms for Minkowski tensor estimation

    The geometry of real world objects can be described by Minkowski tensors. Algorithms have been suggested to approximate Minkowski tensors if only a binary image of the object is available. This paper presents implementations of two such algorithms. The theoretical convergence properties...... are confirmed by simulations on test sets, and recommendations for input arguments of the algorithms are given. For increasing resolutions, we obtain more accurate estimators for the Minkowski tensors. Digitisations of more complicated objects are shown to require higher resolutions....

  17. A global analysis of alternative tillage and crop establishment practices for economically and environmentally efficient rice production.

    Chakraborty, Debashis; Ladha, Jagdish Kumar; Rana, Dharamvir Singh; Jat, Mangi Lal; Gathala, Mahesh Kumar; Yadav, Sudhir; Rao, Adusumilli Narayana; Ramesha, Mugadoli S; Raman, Anitha

    2017-08-24

    Alternative tillage and rice establishment options should aim at less water and labor to produce similar or improved yields compared with traditional puddled-transplanted rice cultivation. The relative performance of these practices in terms of yield, water input, and economics varies across rice-growing regions. A global meta and mixed model analysis was performed, using a dataset involving 323 on-station and 9 on-farm studies (a total of 3878 paired data), to evaluate the yield, water input, greenhouse gas emissions, and cost and net return with five major tillage/crop establishment options. Shifting from transplanting to direct-seeding was advantageous but the change from conventional to zero or reduced tillage reduced yields. Direct-seeded rice under wet tillage was the best alternative with yield advantages of 1.3-4.7% (p Direct-seeding under zero tillage was another potential alternative with high savings in water input and cost of cultivation, with no yield penalty. The alternative practices reduced methane emissions but increased nitrous oxide emissions. Soil texture plays a key role in relative yield advantages, and therefore refinement of the practice to suit a specific agro-ecosystem is needed.

  18. Crop yield, genetic parameter estimation and selection of sacha inchi in central Amazon

    Mágno Sávio Ferreira Valente

    2017-06-01

    Full Text Available In Brazil, sacha inchi oil is produced by hand from plant materials with no breeding or detailed information about the chemical composition of seeds. In addition, most of the current information on the agronomic traits of this species originates from research carried out in the Peruvian Amazon. In order to promote the research and cultivation of sacha inchi in the Brazilian territory, this study aimed to analyze, in the central Amazon region, different accessions of this oilseed for characteristics of production and quality of fruits and seeds, as well as to estimate genetic parameters, through mixed models, with identification of superior accessions, for breeding purposes. A total of 37 non-domesticated accessions were evaluated in a randomized block design, with five replications and two plants per plot. The average oil content in seeds was 29.07 % and unsaturated fatty acids amounted to 91.5 % of the total fat content. For the yield traits, the estimates of individual broad-sense heritability were moderate (~0.33, while the heritability based on the average of progenies resulted in a selective accuracy of approximately 0.85. The use of the selection index provided simultaneous gains for yield traits (> 40 % and oil yield. A high genetic variability was observed for the main traits of commercial interest for the species, as well as promising perspectives for the development of superior varieties for agro-industrial use.

  19. Cost analysis of small hydroelectric power plants components and preliminary estimation of global cost

    Basta, C.; Olive, W.J.; Antunes, J.S.

    1990-01-01

    An analysis of cost for each components of Small Hydroelectric Power Plant, taking into account the real costs of these projects is shown. It also presents a global equation which allows a preliminary estimation of cost for each construction. (author)

  20. Estimation of Subdaily Polar Motion with the Global Positioning System During the Spoch '92 Campaign

    Ibanez-Meier, R.; Freedman, A. P.; Herring, T. A.; Gross, R. S.; Lichten, S. M.; Lindqwister, U. J.

    1994-01-01

    Data collected over six days from a worldwide Global Positioning System (GPS) tracking network during the Epoch '92 campaign are used to estimate variations of the Earth's pole position every 30 minutes.

  1. Global Burden of Leptospirosis: Estimated in Terms of Disability Adjusted Life Years

    Torgerson, Paul R.; Hagan, José E.; Costa, Federico; Calcagno, Juan; Kane, Michael; Martinez-Silveira, Martha S.; Goris, Marga G. A.; Stein, Claudia; Ko, Albert I.; Abela-Ridder, Bernadette

    2015-01-01

    Background Leptospirosis, a spirochaetal zoonosis, occurs in diverse epidemiological settings and affects vulnerable populations, such as rural subsistence farmers and urban slum dwellers. Although leptospirosis can cause life-threatening disease, there is no global burden of disease estimate in

  2. Estimated Costs of Continuing Operations in Iraq and Other Operations of the Global War on Terrorism

    Holtz-Eakin, Douglas

    2004-01-01

    At the request of Senator Conrad, the Congressional Budget Office (CBO) has estimated the costs of military operations in Iraq and Afghanistan and other operations associated with the global war on terrorism (GWOT...

  3. Distancing from experienced self: how global versus local perception affects estimation of psychological distance

    Liberman, N.; Förster, J.

    2009-01-01

    In 4 studies, the authors examined the prediction derived from construal level theory (CLT) that higher level of perceptual construal would enhance estimated egocentric psychological distance. The authors primed participants with global perception, local perception, or both (the control condition).

  4. Quantitative Estimation of Above Ground Crop Biomass using Ground-based, Airborne and Spaceborne Low Frequency Polarimetric Synthetic Aperture Radar

    Koyama, C.; Watanabe, M.; Shimada, M.

    2016-12-01

    Estimation of crop biomass is one of the important challenges in environmental remote sensing related to agricultural as well as hydrological and meteorological applications. Usually passive optical data (photographs, spectral data) operating in the visible and near-infrared bands is used for such purposes. The virtue of optical remote sensing for yield estimation, however, is rather limited as the visible light can only provide information about the chemical characteristics of the canopy surface. Low frequency microwave signals with wavelength longer 20 cm have the potential to penetrate through the canopy and provide information about the whole vertical structure of vegetation from the top of the canopy down to the very soil surface. This phenomenon has been well known and exploited to detect targets under vegetation in the military radar application known as FOPEN (foliage penetration). With the availability of polarimetric interferometric SAR data the use PolInSAR techniques to retrieve vertical vegetation structures has become an attractive tool. However, PolInSAR is still highly experimental and suitable data is not yet widely available. In this study we focus on the use of operational dual-polarization L-band (1.27 GHz) SAR which is since the launch of Japan's Advanced Land Observing Satellite (ALOS, 2006-2011) available worldwide. Since 2014 ALOS-2 continues to deliver such kind of partial polarimetric data for the entire land surface. In addition to these spaceborne data sets we use airborne L-band SAR data acquired by the Japanese Pi-SAR-L2 as well as ultra-wideband (UWB) ground based SAR data operating in the frequency range from 1-4 GHz. By exploiting the complex dual-polarization [C2] Covariance matrix information, the scattering contributions from the canopy can be well separated from the ground reflections allowing for the establishment of semi-empirical relationships between measured radar reflectivity and the amount of fresh-weight above

  5. Estimation of global network statistics from incomplete data.

    Catherine A Bliss

    Full Text Available Complex networks underlie an enormous variety of social, biological, physical, and virtual systems. A profound complication for the science of complex networks is that in most cases, observing all nodes and all network interactions is impossible. Previous work addressing the impacts of partial network data is surprisingly limited, focuses primarily on missing nodes, and suggests that network statistics derived from subsampled data are not suitable estimators for the same network statistics describing the overall network topology. We generate scaling methods to predict true network statistics, including the degree distribution, from only partial knowledge of nodes, links, or weights. Our methods are transparent and do not assume a known generating process for the network, thus enabling prediction of network statistics for a wide variety of applications. We validate analytical results on four simulated network classes and empirical data sets of various sizes. We perform subsampling experiments by varying proportions of sampled data and demonstrate that our scaling methods can provide very good estimates of true network statistics while acknowledging limits. Lastly, we apply our techniques to a set of rich and evolving large-scale social networks, Twitter reply networks. Based on 100 million tweets, we use our scaling techniques to propose a statistical characterization of the Twitter Interactome from September 2008 to November 2008. Our treatment allows us to find support for Dunbar's hypothesis in detecting an upper threshold for the number of active social contacts that individuals maintain over the course of one week.

  6. New global fire emission estimates and evaluation of volatile organic compounds

    C. Wiedinmyer; L. K. Emmons; S. K. Akagi; R. J. Yokelson; J. J. Orlando; J. A. Al-Saadi; A. J. Soja

    2010-01-01

    A daily, high-resolution, global fire emissions model has been built to estimate emissions from open burning for air quality modeling applications: The Fire INventory from NCAR (FINN version 1). The model framework uses daily fire detections from the MODIS instruments and updated emission factors, specifically for speciated non-methane organic compounds (NMOC). Global...

  7. Contribution of milk production to global greenhouse gas emissions. An estimation based on typical farms.

    Hagemann, Martin; Ndambi, Asaah; Hemme, Torsten; Latacz-Lohmann, Uwe

    2012-02-01

    Studies on the contribution of milk production to global greenhouse gas (GHG) emissions are rare (FAO 2010) and often based on crude data which do not appropriately reflect the heterogeneity of farming systems. This article estimates GHG emissions from milk production in different dairy regions of the world based on a harmonised farm data and assesses the contribution of milk production to global GHG emissions. The methodology comprises three elements: (1) the International Farm Comparison Network (IFCN) concept of typical farms and the related globally standardised dairy model farms representing 45 dairy regions in 38 countries; (2) a partial life cycle assessment model for estimating GHG emissions of the typical dairy farms; and (3) standard regression analysis to estimate GHG emissions from milk production in countries for which no typical farms are available in the IFCN database. Across the 117 typical farms in the 38 countries analysed, the average emission rate is 1.50 kg CO(2) equivalents (CO(2)-eq.)/kg milk. The contribution of milk production to the global anthropogenic emissions is estimated at 1.3 Gt CO(2)-eq./year, accounting for 2.65% of total global anthropogenic emissions (49 Gt; IPCC, Synthesis Report for Policy Maker, Valencia, Spain, 2007). We emphasise that our estimates of the contribution of milk production to global GHG emissions are subject to uncertainty. Part of the uncertainty stems from the choice of the appropriate methods for estimating emissions at the level of the individual animal.

  8. Estimating the Cross-Shelf Export of Riverine Materials: Part 2. Estimates of Global Freshwater and Nutrient Export

    Izett, Jonathan G.; Fennel, Katja

    2018-02-01

    Rivers deliver large amounts of fresh water, nutrients, and other terrestrially derived materials to the coastal ocean. Where inputs accumulate on the shelf, harmful effects such as hypoxia and eutrophication can result. In contrast, where export to the open ocean is efficient riverine inputs contribute to global biogeochemical budgets. Assessing the fate of riverine inputs is difficult on a global scale. Global ocean models are generally too coarse to resolve the relatively small scale features of river plumes. High-resolution regional models have been developed for individual river plume systems, but it is impractical to apply this approach globally to all rivers. Recently, generalized parameterizations have been proposed to estimate the export of riverine fresh water to the open ocean (Izett & Fennel, 2018, https://doi.org/10.1002/2017GB005667; Sharples et al., 2017, https://doi.org/10.1002/2016GB005483). Here the relationships of Izett and Fennel, https://doi.org/10.1002/2017GB005667 are used to derive global estimates of open-ocean export of fresh water and dissolved inorganic silicate, dissolved organic carbon, and dissolved organic and inorganic phosphorus and nitrogen. We estimate that only 15-53% of riverine fresh water reaches the open ocean directly in river plumes; nutrient export is even less efficient because of processing on continental shelves. Due to geographic differences in riverine nutrient delivery, dissolved silicate is the most efficiently exported to the open ocean (7-56.7%), while dissolved inorganic nitrogen is the least efficiently exported (2.8-44.3%). These results are consistent with previous estimates and provide a simple way to parameterize export to the open ocean in global models.

  9. Impacts on Water Management and Crop Production of Regional Cropping System Adaptation to Climate Change

    Zhong, H.; Sun, L.; Tian, Z.; Liang, Z.; Fischer, G.

    2014-12-01

    China is one of the most populous and fast developing countries, also faces a great pressure on grain production and food security. Multi-cropping system is widely applied in China to fully utilize agro-climatic resources and increase land productivity. As the heat resource keep improving under climate warming, multi-cropping system will also shifting northward, and benefit crop production. But water shortage in North China Plain will constrain the adoption of new multi-cropping system. Effectiveness of multi-cropping system adaptation to climate change will greatly depend on future hydrological change and agriculture water management. So it is necessary to quantitatively express the water demand of different multi-cropping systems under climate change. In this paper, we proposed an integrated climate-cropping system-crops adaptation framework, and specifically focused on: 1) precipitation and hydrological change under future climate change in China; 2) the best multi-cropping system and correspondent crop rotation sequence, and water demand under future agro-climatic resources; 3) attainable crop production with water constraint; and 4) future water management. In order to obtain climate projection and precipitation distribution, global climate change scenario from HADCAM3 is downscaled with regional climate model (PRECIS), historical climate data (1960-1990) was interpolated from more than 700 meteorological observation stations. The regional Agro-ecological Zone (AEZ) model is applied to simulate the best multi-cropping system and crop rotation sequence under projected climate change scenario. Finally, we use the site process-based DSSAT model to estimate attainable crop production and the water deficiency. Our findings indicate that annual land productivity may increase and China can gain benefit from climate change if multi-cropping system would be adopted. This study provides a macro-scale view of agriculture adaptation, and gives suggestions to national

  10. Canaryseed Crop

    Maximiliano Cogliatti

    2012-03-01

    Full Text Available Canaryseed (Phalaris canariensis L. is a graminaceous crop species with production practices and cycle similar to those of other winter cereal crops such as spring wheat (Triticum aestivum L. and oat (Avena sativa L.. Currently its grains are used almost exclusively as feed for birds, alone or mixed with other grains like millet, sunflower seed, and flaxseed. Canaryseed is a genuine cereal with a unique composition that suggests its potential for food use. P. canariensis is cultivated in many areas of temperate climates. Currently, its production is concentrated in the southwestern provinces of Canada (Alberta, Saskatchewan and Manitoba and on a smaller scale in Argentina, Thailand and Australia. Globally it is considered to be a minor crop with regional relevance, with a production about of 250000 tonnes per year, which restricts private investment and public research on its genetic and technological improvement. For this reason, the type of crop management that is applied to this species largely depends on innovations made in other similar crops. This work provides an updated summary of the available information on the species: its requirements, distribution, genetic resources, cultivation practices, potential uses, marketing and other topics of interest to researchers and producers.

  11. Utilization of Landsat-8 data for the estimation of carrot and maize crop water footprint under the arid climate of Saudi Arabia.

    Madugundu, Rangaswamy; Al-Gaadi, Khalid A; Tola, ElKamil; Hassaballa, Abdalhaleem A; Kayad, Ahmed G

    2018-01-01

    The crop Water Footprint (WF) can provide a comprehensive knowledge of the use of water through the demarcation of the amount of the water consumed by different crops. The WF has three components: green (WFg), blue (WFb) and grey (WFgr) water footprints. The WFg refers to the rainwater stored in the root zone soil layer and is mainly utilized for agricultural, horticultural and forestry production. The WFb, however, is the consumptive use of water from surface or groundwater resources and mainly deals with irrigated agriculture, industry, domestic water use, etc. While the WFgr is the amount of fresh water required to assimilate pollutants resulting from the use of fertilizers/agrochemicals. This study was conducted on six agricultural fields in the Eastern region of Saudi Arabia, during the period from December 2015 to December 2016, to investigate the spatiotemporal variation of the WF of silage maize and carrot crops. The WF of each crop was estimated in two ways, namely agro-meteorological (WFAgro) and remote sensing (WFRS) methods. The blue, green and grey components of WFAgro were computed with the use of weather station/Eddy covariance measurements and field recorded crop yield datasets. The WFRS estimated by applying surface energy balance principles on Landsat-8 imageries. However, due to non-availability of Landsat-8 data on the event of rainy days, this study was limited to blue component (WFRS-b). The WFAgro of silage maize was found to range from 3545 m3 t-1 to 4960 m3 t-1; on an average, the WFAgro-g, WFAgro-b, and WFAgro-gr are composed of < 1%, 77%, and 22%, respectively. In the case of carrot, the WFAgro ranged between 297 m3 t-1 and 502 m3 t-1. The WFAgro-g of carrot crop was estimated at <1%, while WFAgro-b and WFAgro-gr was 67% and 32%, respectively. The WFAgro-b is occupied as a major portion in WF of silage maize (77%) and carrot (68%) crops. This is due to the high crop water demand combined with a very erratic rainfall, the irrigation is

  12. Utilization of Landsat-8 data for the estimation of carrot and maize crop water footprint under the arid climate of Saudi Arabia.

    Rangaswamy Madugundu

    Full Text Available The crop Water Footprint (WF can provide a comprehensive knowledge of the use of water through the demarcation of the amount of the water consumed by different crops. The WF has three components: green (WFg, blue (WFb and grey (WFgr water footprints. The WFg refers to the rainwater stored in the root zone soil layer and is mainly utilized for agricultural, horticultural and forestry production. The WFb, however, is the consumptive use of water from surface or groundwater resources and mainly deals with irrigated agriculture, industry, domestic water use, etc. While the WFgr is the amount of fresh water required to assimilate pollutants resulting from the use of fertilizers/agrochemicals. This study was conducted on six agricultural fields in the Eastern region of Saudi Arabia, during the period from December 2015 to December 2016, to investigate the spatiotemporal variation of the WF of silage maize and carrot crops. The WF of each crop was estimated in two ways, namely agro-meteorological (WFAgro and remote sensing (WFRS methods. The blue, green and grey components of WFAgro were computed with the use of weather station/Eddy covariance measurements and field recorded crop yield datasets. The WFRS estimated by applying surface energy balance principles on Landsat-8 imageries. However, due to non-availability of Landsat-8 data on the event of rainy days, this study was limited to blue component (WFRS-b. The WFAgro of silage maize was found to range from 3545 m3 t-1 to 4960 m3 t-1; on an average, the WFAgro-g, WFAgro-b, and WFAgro-gr are composed of < 1%, 77%, and 22%, respectively. In the case of carrot, the WFAgro ranged between 297 m3 t-1 and 502 m3 t-1. The WFAgro-g of carrot crop was estimated at <1%, while WFAgro-b and WFAgro-gr was 67% and 32%, respectively. The WFAgro-b is occupied as a major portion in WF of silage maize (77% and carrot (68% crops. This is due to the high crop water demand combined with a very erratic rainfall, the

  13. Global sensitivity and uncertainty analysis of the nitrate leaching and crop yield simulation under different water and nitrogen management practices

    Agricultural system models have become important tools in studying water and nitrogen (N) dynamics, as well as crop growth, under different management practices. Complexity in input parameters often leads to significant uncertainty when simulating dynamic processes such as nitrate leaching or crop y...

  14. Global Expanded Nutrient Supply (GENuS Model: A New Method for Estimating the Global Dietary Supply of Nutrients.

    Matthew R Smith

    Full Text Available Insufficient data exist for accurate estimation of global nutrient supplies. Commonly used global datasets contain key weaknesses: 1 data with global coverage, such as the FAO food balance sheets, lack specific information about many individual foods and no information on micronutrient supplies nor heterogeneity among subnational populations, while 2 household surveys provide a closer approximation of consumption, but are often not nationally representative, do not commonly capture many foods consumed outside of the home, and only provide adequate information for a few select populations. Here, we attempt to improve upon these datasets by constructing a new model--the Global Expanded Nutrient Supply (GENuS model--to estimate nutrient availabilities for 23 individual nutrients across 225 food categories for thirty-four age-sex groups in nearly all countries. Furthermore, the model provides historical trends in dietary nutritional supplies at the national level using data from 1961-2011. We determine supplies of edible food by expanding the food balance sheet data using FAO production and trade data to increase food supply estimates from 98 to 221 food groups, and then estimate the proportion of major cereals being processed to flours to increase to 225. Next, we estimate intake among twenty-six demographic groups (ages 20+, both sexes in each country by using data taken from the Global Dietary Database, which uses nationally representative surveys to relate national averages of food consumption to individual age and sex-groups; for children and adolescents where GDD data does not yet exist, average calorie-adjusted amounts are assumed. Finally, we match food supplies with nutrient densities from regional food composition tables to estimate nutrient supplies, running Monte Carlo simulations to find the range of potential nutrient supplies provided by the diet. To validate our new method, we compare the GENuS estimates of nutrient supplies against

  15. Adoption of an unmanned helicopter for low-altitude remote sensing to estimate yield and total biomass of a rice crop

    A radio-controlled unmanned helicopter-based LARS (Low-Altitude Remote Sensing) platform was used to acquire quality images of high spatial and temporal resolution, in order to estimate yield and total biomass of a rice crop (Oriza Sativa, L.). Fifteen rice field plots with five N-treatments (0, 33,...

  16. Global estimation of CO emissions using three sets of satellite data for burned area

    Jain, Atul K.

    Using three sets of satellite data for burned areas together with the tree cover imagery and a biogeochemical component of the Integrated Science Assessment Model (ISAM) the global emissions of CO and associated uncertainties are estimated for the year 2000. The available fuel load (AFL) is calculated using the ISAM biogeochemical model, which accounts for the aboveground and surface fuel removed by land clearing for croplands and pasturelands, as well as the influence on fuel load of various ecosystem processes (such as stomatal conductance, evapotranspiration, plant photosynthesis and respiration, litter production, and soil organic carbon decomposition) and important feedback mechanisms (such as climate and fertilization feedback mechanism). The ISAM estimated global total AFL in the year 2000 was about 687 Pg AFL. All forest ecosystems account for about 90% of the global total AFL. The estimated global CO emissions based on three global burned area satellite data sets (GLOBSCAR, GBA, and Global Fire Emissions Database version 2 (GFEDv2)) for the year 2000 ranges between 320 and 390 Tg CO. Emissions from open fires are highest in tropical Africa, primarily due to forest cutting and burning. The estimated overall uncertainty in global CO emission is about ±65%, with the highest uncertainty occurring in North Africa and Middle East region (±99%). The results of this study suggest that the uncertainties in the calculated emissions stem primarily from the area burned data.

  17. Greenhouse Gases Emission and Global Warming Potential as Affected by Chemical Inputs for Main Cultivated Crops in Kerman Province: - Horticultural Crops

    Nasibe Pourghasemian; Rooholla Moradi

    2017-01-01

    Introduction The latest report of the IPCC states that future emissions of greenhouse gases (GHGs) will continue to increase and will be the main cause of global climatic changes, as well as Iran. The three greenhouse gases associated with agriculture are CO2, CH4, and N2O. Chemical inputs consumption in agriculture has increased annually, while more intensive use of energy led to some important human health and environmental problems such as greenhouse gas emissions and global warming. Th...

  18. Scaling up stomatal conductance from leaf to canopy using a dual-leaf model for estimating crop evapotranspiration.

    Risheng Ding

    Full Text Available The dual-source Shuttleworth-Wallace model has been widely used to estimate and partition crop evapotranspiration (λET. Canopy stomatal conductance (Gsc, an essential parameter of the model, is often calculated by scaling up leaf stomatal conductance, considering the canopy as one single leaf in a so-called "big-leaf" model. However, Gsc can be overestimated or underestimated depending on leaf area index level in the big-leaf model, due to a non-linear stomatal response to light. A dual-leaf model, scaling up Gsc from leaf to canopy, was developed in this study. The non-linear stomata-light relationship was incorporated by dividing the canopy into sunlit and shaded fractions and calculating each fraction separately according to absorbed irradiances. The model includes: (1 the absorbed irradiance, determined by separately integrating the sunlit and shaded leaves with consideration of both beam and diffuse radiation; (2 leaf area for the sunlit and shaded fractions; and (3 a leaf conductance model that accounts for the response of stomata to PAR, vapor pressure deficit and available soil water. In contrast to the significant errors of Gsc in the big-leaf model, the predicted Gsc using the dual-leaf model had a high degree of data-model agreement; the slope of the linear regression between daytime predictions and measurements was 1.01 (R2 = 0.98, with RMSE of 0.6120 mm s-1 for four clear-sky days in different growth stages. The estimates of half-hourly λET using the dual-source dual-leaf model (DSDL agreed well with measurements and the error was within 5% during two growing seasons of maize with differing hydrometeorological and management strategies. Moreover, the estimates of soil evaporation using the DSDL model closely matched actual measurements. Our results indicate that the DSDL model can produce more accurate estimation of Gsc and λET, compared to the big-leaf model, and thus is an effective alternative approach for estimating and

  19. Estimated crop loss due to coconut mite and financial analysis of controlling the pest using the acaricide abamectin.

    Rezende, Daniela; Melo, José W S; Oliveira, José E M; Gondim, Manoel G C

    2016-07-01

    Reducing the losses caused by Aceria guerreronis Keifer has been an arduous task for farmers. However, there are no detailed studies on losses that simultaneously analyse correlated parameters, and very few studies that address the economic viability of chemical control, the main strategy for managing this pest. In this study the objectives were (1) to estimate the crop loss due to coconut mite and (2) to perform a financial analysis of acaricide application to control the pest. For this, the following parameters were evaluated: number and weight of fruits, liquid albumen volume, and market destination of plants with and without monthly abamectin spraying (three harvests). The costs involved in the chemical control of A. guerreronis were also quantified. Higher A. guerreronis incidence on plants resulted in a 60 % decrease in the mean number of fruits harvested per bunch and a 28 % decrease in liquid albumen volume. Mean fruit weight remained unaffected. The market destination of the harvested fruit was also affected by higher A. guerreronis incidence. Untreated plants, with higher A. guerreronis infestation intensity, produced a lower proportion of fruit intended for fresh market and higher proportions of non-marketable fruit and fruit intended for industrial processing. Despite the costs involved in controlling A. guerreronis, the difference between the profit from the treated site and the untreated site was 18,123.50 Brazilian Real; this value represents 69.1 % higher profit at the treated site.

  20. Estimating the global clinical burden of Plasmodium falciparum malaria in 2007.

    Simon I Hay

    2010-06-01

    Full Text Available The epidemiology of malaria makes surveillance-based methods of estimating its disease burden problematic. Cartographic approaches have provided alternative malaria burden estimates, but there remains widespread misunderstanding about their derivation and fidelity. The aims of this study are to present a new cartographic technique and its application for deriving global clinical burden estimates of Plasmodium falciparum malaria for 2007, and to compare these estimates and their likely precision with those derived under existing surveillance-based approaches.In seven of the 87 countries endemic for P. falciparum malaria, the health reporting infrastructure was deemed sufficiently rigorous for case reports to be used verbatim. In the remaining countries, the mapped extent of unstable and stable P. falciparum malaria transmission was first determined. Estimates of the plausible incidence range of clinical cases were then calculated within the spatial limits of unstable transmission. A modelled relationship between clinical incidence and prevalence was used, together with new maps of P. falciparum malaria endemicity, to estimate incidence in areas of stable transmission, and geostatistical joint simulation was used to quantify uncertainty in these estimates at national, regional, and global scales. Combining these estimates for all areas of transmission risk resulted in 451 million (95% credible interval 349-552 million clinical cases of P. falciparum malaria in 2007. Almost all of this burden of morbidity occurred in areas of stable transmission. More than half of all estimated P. falciparum clinical cases and associated uncertainty occurred in India, Nigeria, the Democratic Republic of the Congo (DRC, and Myanmar (Burma, where 1.405 billion people are at risk. Recent surveillance-based methods of burden estimation were then reviewed and discrepancies in national estimates explored. When these cartographically derived national estimates were ranked

  1. LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status

    Optical remote sensing of crop nitrogen (N) status is developing into a powerful diagnostic tool that can improve N management decisions. Crop N status is a function of dry mass per unit area (W) and N concentration (%Na), which can be used to calculate N nutrition index (NNI),where NNI is %Na/%Nc (...

  2. Earth Observation-Based Operational Estimation of Soil Moisture and Evapotranspiration for Agricultural Crops in Support of Sustainable Water Management

    George P. Petropoulos

    2018-01-01

    Full Text Available Global information on the spatio-temporal variation of parameters driving the Earth’s terrestrial water and energy cycles, such as evapotranspiration (ET rates and surface soil moisture (SSM, is of key significance. The water and energy cycles underpin global food and water security and need to be fully understood as the climate changes. In the last few decades, Earth Observation (EO technology has played an increasingly important role in determining both ET and SSM. This paper reviews the state of the art in the use specifically of operational EO of both ET and SSM estimates. We discuss the key technical and operational considerations to derive accurate estimates of those parameters from space. The review suggests significant progress has been made in the recent years in retrieving ET and SSM operationally; yet, further work is required to optimize parameter accuracy and to improve the operational capability of services developed using EO data. Emerging applications on which ET/SSM operational products may be included in the context specifically in relation to agriculture are also highlighted; the operational use of those operational products in such applications remains to be seen.

  3. Combined equations for estimating global solar radiation: Projection of radiation field over Japan under global warming conditions by statistical downscaling

    Iizumi, T.; Nishimori, M.; Yokozawa, M.

    2008-01-01

    For this study, we developed a new statistical model to estimate the daily accumulated global solar radiation on the earth's surface and used the model to generate a high-resolution climate change scenario of the radiation field in Japan. The statistical model mainly relies on precipitable water vapor calculated from air temperature and relative humidity on the surface to estimate seasonal changes in global solar radiation. On the other hand, to estimate daily radiation fluctuations, the model uses either a diurnal temperature range or relative humidity. The diurnal temperature range, calculated from the daily maximum and minimum temperatures, and relative humidity is a general output of most climate models, and pertinent observation data are comparatively easy to access. The statistical model performed well when estimating the monthly mean value, daily fluctuation statistics, and regional differences in the radiation field in Japan. To project the change in the radiation field for the years 2081 to 2100, we applied the statistical model to the climate change scenario of a high-resolution Regional Climate Model with a 20-km mesh size (RCM20) developed at the Meteorological Research Institute based on the Special Report for Emission Scenario (SRES)-A2. The projected change shows the following tendency: global solar radiation will increase in the warm season and decrease in the cool season in many areas of Japan, indicating that global warming may cause changes in the radiation field in Japan. The generated climate change scenario for the radiation field is linked to long-term and short-term changes in air temperature and relative humidity obtained from the RCM20 and, consequently, is expected to complement the RCM20 datasets for an impact assessment study in the agricultural sector

  4. Advantage of hyperspectral EO-1 Hyperion over multispectral IKONOS, GeoEye-1, WorldView-2, Landsat ETM+, and MODIS vegetation indices in crop biomass estimation

    Marshall, Michael T.; Thenkabail, Prasad S.

    2015-01-01

    Crop biomass is increasingly being measured with surface reflectance data derived from multispectral broadband (MSBB) and hyperspectral narrowband (HNB) space-borne remotely sensed data to increase the accuracy and efficiency of crop yield models used in a wide array of agricultural applications. However, few studies compare the ability of MSBBs versus HNBs to capture crop biomass variability. Therefore, we used standard data mining techniques to identify a set of MSBB data from the IKONOS, GeoEye-1, Landsat ETM+, MODIS, WorldView-2 sensors and compared their performance with HNB data from the EO-1 Hyperion sensor in explaining crop biomass variability of four important field crops (rice, alfalfa, cotton, maize). The analysis employed two-band (ratio) vegetation indices (TBVIs) and multiband (additive) vegetation indices (MBVIs) derived from Singular Value Decomposition (SVD) and stepwise regression. Results demonstrated that HNB-derived TBVIs and MBVIs performed better than MSBB-derived TBVIs and MBVIs on a per crop basis and for the pooled data: overall, HNB TBVIs explained 5–31% greater variability when compared with various MSBB TBVIs; and HNB MBVIs explained 3–33% greater variability when compared with various MSBB MBVIs. The performance of MSBB MBVIs and TBVIs improved mildly, by combining spectral information across multiple sensors involving IKONOS, GeoEye-1, Landsat ETM+, MODIS, and WorldView-2. A number of HNBs that advance crop biomass modeling were determined. Based on the highest factor loadings on the first component of the SVD, the “red-edge” spectral range (700–740 nm) centered at 722 nm (bandwidth = 10 nm) stood out prominently, while five additional and distinct portions of the recorded spectral range (400–2500 nm) centered at 539 nm, 758 nm, 914 nm, 1130 nm, 1320 nm (bandwidth = 10 nm) were also important. The best HNB vegetation indices for crop biomass estimation involved 549 and 752 nm for rice (R2 = 0.91); 925 and 1104 nm for

  5. Evidence for a climate signal in trends of global crop yield variability over the past 50 years

    Osborne, T M; Wheeler, T R

    2013-01-01

    Low variability of crop production from year to year is desirable for many reasons, including reduced income risk and stability of supplies. Therefore, it is important to understand the nature of yield variability, whether it is changing through time, and how it varies between crops and regions. Previous studies have shown that national crop yield variability has changed in the past, with the direction and magnitude dependent on crop type and location. Whilst such studies acknowledge the importance of climate variability in determining yield variability, it has been assumed that its magnitude and its effect on crop production have not changed through time and, hence, that changes to yield variability have been due to non-climatic factors. We address this assumption by jointly examining yield and climate variability for three major crops (rice, wheat and maize) over the past 50 years. National yield time series and growing season temperature and precipitation were de-trended and related using multiple linear regression. Yield variability changed significantly in half of the crop–country combinations examined. For several crop–country combinations, changes in yield variability were related to changes in climate variability. (letter)

  6. Distancing from experienced self: how global-versus-local perception affects estimation of psychological distance.

    Liberman, Nira; Förster, Jens

    2009-08-01

    In 4 studies, the authors examined the prediction derived from construal level theory (CLT) that higher level of perceptual construal would enhance estimated egocentric psychological distance. The authors primed participants with global perception, local perception, or both (the control condition). Relative to the control condition, global processing made participants estimate larger psychological distances in time (Study 1), space (Study 2), social distance (Study 3), and hypotheticality (Study 4). Local processing had the opposite effect. Consistent with CLT, all studies show that the effect of global-versus-local processing did emerge when participants estimated egocentric distances, which are distances from the experienced self in the here and now, but did not emerge with temporal distances not from now (Study 1), spatial distances not from here (Study 2), social distances not from the self (Study 3), or hypothetical events that did not involve altering an experienced reality (Study 4).

  7. Economic impact analysis for global warming: Sensitivity analysis for cost and benefit estimates

    Ierland, E.C. van; Derksen, L.

    1994-01-01

    Proper policies for the prevention or mitigation of the effects of global warming require profound analysis of the costs and benefits of alternative policy strategies. Given the uncertainty about the scientific aspects of the process of global warming, in this paper a sensitivity analysis for the impact of various estimates of costs and benefits of greenhouse gas reduction strategies is carried out to analyze the potential social and economic impacts of climate change

  8. Global gradient estimates for divergence-type elliptic problems involving general nonlinear operators

    Cho, Yumi

    2018-05-01

    We study nonlinear elliptic problems with nonstandard growth and ellipticity related to an N-function. We establish global Calderón-Zygmund estimates of the weak solutions in the framework of Orlicz spaces over bounded non-smooth domains. Moreover, we prove a global regularity result for asymptotically regular problems which are getting close to the regular problems considered, when the gradient variable goes to infinity.

  9. Artificial intelligence techniques applied to hourly global irradiance estimation from satellite-derived cloud index

    Zarzalejo, L.F.; Ramirez, L.; Polo, J. [DER-CIEMAT, Madrid (Spain). Renewable Energy Dept.

    2005-07-01

    Artificial intelligence techniques, such as fuzzy logic and neural networks, have been used for estimating hourly global radiation from satellite images. The models have been fitted to measured global irradiance data from 15 Spanish terrestrial stations. Both satellite imaging data and terrestrial information from the years 1994, 1995 and 1996 were used. The results of these artificial intelligence models were compared to a multivariate regression based upon Heliosat I model. A general better behaviour was observed for the artificial intelligence models. (author)

  10. Artificial intelligence techniques applied to hourly global irradiance estimation from satellite-derived cloud index

    Zarzalejo, Luis F.; Ramirez, Lourdes; Polo, Jesus

    2005-01-01

    Artificial intelligence techniques, such as fuzzy logic and neural networks, have been used for estimating hourly global radiation from satellite images. The models have been fitted to measured global irradiance data from 15 Spanish terrestrial stations. Both satellite imaging data and terrestrial information from the years 1994, 1995 and 1996 were used. The results of these artificial intelligence models were compared to a multivariate regression based upon Heliosat I model. A general better behaviour was observed for the artificial intelligence models

  11. Impacts of multiple global environmental changes on African crop yield and water use efficiency: Implications to food and water security

    Pan, S.; Yang, J.; Zhang, J.; Xu, R.; Dangal, S. R. S.; Zhang, B.; Tian, H.

    2016-12-01

    Africa is one of the most vulnerable regions in the world to climate change and climate variability. Much concern has been raised about the impacts of climate and other environmental factors on water resource and food security through the climate-water-food nexus. Understanding the responses of crop yield and water use efficiency to environmental changes is particularly important because Africa is well known for widespread poverty, slow economic growth and agricultural systems particularly sensitive to frequent and persistent droughts. However, the lack of integrated understanding has limited our ability to quantify and predict the potential of Africa's agricultural sustainability and freshwater supply, and to better manage the system for meeting an increasing food demand in a way that is socially and environmentally or ecologically sustainable. By using the Dynamic Land Ecosystem Model (DLEM-AG2) driven by spatially-explicit information on land use, climate and other environmental changes, we have assessed the spatial and temporal patterns of crop yield, evapotranspiration (ET) and water use efficiency across entire Africa in the past 35 years (1980-2015) and the rest of the 21st century (2016-2099). Our preliminary results indicate that African crop yield in the past three decades shows an increasing trend primarily due to cropland expansion (about 50%), elevated atmospheric CO2 concentration, and nitrogen deposition. However, crop yield shows substantially spatial and temporal variation due to inter-annual and inter-decadal climate variability and spatial heterogeneity of environmental drivers. Climate extremes especially droughts and heat wave have largely reduced crop yield in the most vulnerable regions. Our results indicate that N fertilizer could be a major driver to improve food security in Africa. Future climate warming could reduce crop yield and shift cropland distribution. Our study further suggests that improving water use efficiency through land

  12. Improving Global Gross Primary Productivity Estimates by Computing Optimum Light Use Efficiencies Using Flux Tower Data

    Madani, Nima; Kimball, John S.; Running, Steven W.

    2017-11-01

    In the light use efficiency (LUE) approach of estimating the gross primary productivity (GPP), plant productivity is linearly related to absorbed photosynthetically active radiation assuming that plants absorb and convert solar energy into biomass within a maximum LUE (LUEmax) rate, which is assumed to vary conservatively within a given biome type. However, it has been shown that photosynthetic efficiency can vary within biomes. In this study, we used 149 global CO2 flux towers to derive the optimum LUE (LUEopt) under prevailing climate conditions for each tower location, stratified according to model training and test sites. Unlike LUEmax, LUEopt varies according to heterogeneous landscape characteristics and species traits. The LUEopt data showed large spatial variability within and between biome types, so that a simple biome classification explained only 29% of LUEopt variability over 95 global tower training sites. The use of explanatory variables in a mixed effect regression model explained 62.2% of the spatial variability in tower LUEopt data. The resulting regression model was used for global extrapolation of the LUEopt data and GPP estimation. The GPP estimated using the new LUEopt map showed significant improvement relative to global tower data, including a 15% R2 increase and 34% root-mean-square error reduction relative to baseline GPP calculations derived from biome-specific LUEmax constants. The new global LUEopt map is expected to improve the performance of LUE-based GPP algorithms for better assessment and monitoring of global terrestrial productivity and carbon dynamics.

  13. Integrating livestock manure with a corn-soybean bioenergy cropping system improves short-term carbon sequestration rates and net global warming potential

    Thelen, K.D.; Fronning, B.E.; Kravchenko, A.; Min, D.H.; Robertson, G.P. [Michigan State University, East Lansing, MI 48824 (United States)

    2010-07-15

    Carbon cycling and the global warming potential (GWP) of bioenergy cropping systems with complete biomass removal are of agronomic and environmental concern. Corn growers who plan to remove corn stover as a feedstock for the emerging cellulosic ethanol industry will benefit from carbon amendments such as manure and compost, to replace carbon removed with the corn stover. The objective of this research was to determine the effect of beef cattle feedlot manure and composted dairy manure on short-term carbon sequestration rates and net global warming potential (GWP) in a corn-soybean rotation with complete corn-stover removal. Field experiments consisting of a corn-soybean rotation with whole-plant corn harvest, were conducted near East Lansing, MI over a three-year period beginning in 2002. Compost and manure amendments raised soil carbon (C) at a level sufficient to overcome the C debt associated with manure production, manure collection and storage, land application, and post-application field emissions. The net GWP in carbon dioxide equivalents for the manure and compost amended cropping systems was -934 and -784 g m{sup -2} y{sup -1}, respectively, compared to 52 g m{sup -2} y{sup -1} for the non-manure amended synthetic fertilizer check. This work further substantiates the environmental benefits associated with renewable fuels and demonstrates that with proper management, the integration of livestock manures in biofuel cropping systems can enhance greenhouse gas (GHG) remediation. (author)

  14. Integrating livestock manure with a corn-soybean bioenergy cropping system improves short-term carbon sequestration rates and net global warming potential

    Thelen, K.D.; Fronning, B.E.; Kravchenko, A.; Min, D.H.; Robertson, G.P.

    2010-01-01

    Carbon cycling and the global warming potential (GWP) of bioenergy cropping systems with complete biomass removal are of agronomic and environmental concern. Corn growers who plan to remove corn stover as a feedstock for the emerging cellulosic ethanol industry will benefit from carbon amendments such as manure and compost, to replace carbon removed with the corn stover. The objective of this research was to determine the effect of beef cattle feedlot manure and composted dairy manure on short-term carbon sequestration rates and net global warming potential (GWP) in a corn-soybean rotation with complete corn-stover removal. Field experiments consisting of a corn-soybean rotation with whole-plant corn harvest, were conducted near East Lansing, MI over a three-year period beginning in 2002. Compost and manure amendments raised soil carbon (C) at a level sufficient to overcome the C debt associated with manure production, manure collection and storage, land application, and post-application field emissions. The net GWP in carbon dioxide equivalents for the manure and compost amended cropping systems was -934 and -784 g m -2 y -1 , respectively, compared to 52 g m -2 y -1 for the non-manure amended synthetic fertilizer check. This work further substantiates the environmental benefits associated with renewable fuels and demonstrates that with proper management, the integration of livestock manures in biofuel cropping systems can enhance greenhouse gas (GHG) remediation.

  15. Evaluating the use of sharpened land surface temperature for daily evapotranspiration estimation over irrigated crops in arid lands

    Rosas, Jorge

    2014-12-01

    Satellite remote sensing provides data on land surface characteristics, useful for mapping land surface energy fluxes and evapotranspiration (ET). Land-surface temperature (LST) derived from thermal infrared (TIR) satellite data has been reliably used as a remote indicator of ET and surface moisture status. However, TIR imagery usually operates at a coarser resolution than that of shortwave sensors on the same satellite platform, making it sometimes unsuitable for monitoring of field-scale crop conditions. This study applies the data mining sharpener (DMS; Gao et al., 2012) technique to data from the Moderate Resolution Imaging Spectroradiometer (MODIS), which sharpens the 1 km thermal data down to the resolution of the optical data (250-500 m) based on functional LST and reflectance relationships established using a flexible regression tree approach. The DMS approach adopted here has been enhanced/refined for application over irrigated farming areas located in harsh desert environments in Saudi Arabia. The sharpened LST data is input to an integrated modeling system that uses the Atmosphere-Land Exchange Inverse (ALEXI) model and associated flux disaggregation scheme (DisALEXI) in conjunction with model reanalysis data and remotely sensed data from polar orbiting (MODIS) and geostationary (MSG; Meteosat Second Generation) satellite platforms to facilitate daily estimates of evapotranspiration. Results are evaluated against available flux tower observations over irrigated maize near Riyadh in Saudi Arabia. Successful monitoring of field-scale changes in surface fluxes are of importance towards an efficient water use in areas where fresh water resources are scarce and poorly monitored. Gao, F.; Kustas, W.P.; Anderson, M.C. A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land. Remote Sens. 2012, 4, 3287-3319.

  16. A fuzzy regression with support vector machine approach to the estimation of horizontal global solar radiation

    Baser, Furkan; Demirhan, Haydar

    2017-01-01

    Accurate estimation of the amount of horizontal global solar radiation for a particular field is an important input for decision processes in solar radiation investments. In this article, we focus on the estimation of yearly mean daily horizontal global solar radiation by using an approach that utilizes fuzzy regression functions with support vector machine (FRF-SVM). This approach is not seriously affected by outlier observations and does not suffer from the over-fitting problem. To demonstrate the utility of the FRF-SVM approach in the estimation of horizontal global solar radiation, we conduct an empirical study over a dataset collected in Turkey and applied the FRF-SVM approach with several kernel functions. Then, we compare the estimation accuracy of the FRF-SVM approach to an adaptive neuro-fuzzy system and a coplot supported-genetic programming approach. We observe that the FRF-SVM approach with a Gaussian kernel function is not affected by both outliers and over-fitting problem and gives the most accurate estimates of horizontal global solar radiation among the applied approaches. Consequently, the use of hybrid fuzzy functions and support vector machine approaches is found beneficial in long-term forecasting of horizontal global solar radiation over a region with complex climatic and terrestrial characteristics. - Highlights: • A fuzzy regression functions with support vector machines approach is proposed. • The approach is robust against outlier observations and over-fitting problem. • Estimation accuracy of the model is superior to several existent alternatives. • A new solar radiation estimation model is proposed for the region of Turkey. • The model is useful under complex terrestrial and climatic conditions.

  17. Vaccine-associated paralytic poliomyelitis: a review of the epidemiology and estimation of the global burden.

    Platt, Lauren R; Estívariz, Concepción F; Sutter, Roland W

    2014-11-01

    Vaccine-associated paralytic poliomyelitis (VAPP) is a rare adverse event associated with oral poliovirus vaccine (OPV). This review summarizes the epidemiology and provides a global burden estimate. A literature review was conducted to abstract the epidemiology and calculate the risk of VAPP. A bootstrap method was applied to calculate global VAPP burden estimates. Trends in VAPP epidemiology varied by country income level. In the low-income country, the majority of cases occurred in individuals who had received >3 doses of OPV (63%), whereas in middle and high-income countries, most cases occurred in recipients after their first OPV dose or unvaccinated contacts (81%). Using all risk estimates, VAPP risk was 4.7 cases per million births (range, 2.4-9.7), leading to a global annual burden estimate of 498 cases (range, 255-1018). If the analysis is limited to estimates from countries that currently use OPV, the VAPP risk is 3.8 cases per million births (range, 2.9-4.7) and a burden of 399 cases (range, 306-490). Because many high-income countries have replaced OPV with inactivated poliovirus vaccine, the VAPP burden is concentrated in lower-income countries. The planned universal introduction of inactivated poliovirus vaccine is likely to substantially decrease the global VAPP burden by 80%-90%. © The Author 2014. Published by Oxford University Press on behalf of the Infectious Diseases Society of America. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  18. A critical review on the estimation of daily global solar radiation from sunshine duration

    Yorukoglu, Mehmet; Celik, Ali Naci

    2006-01-01

    Models such as the Angstroem-Prescott equation are used to estimate global solar radiation from sunshine duration. In the literature, researchers investigate either the goodness of the model itself or the goodness of the estimation of global solar radiation based on a set of statistical parameters such as R 2 , RMSE, MBE, MABE, MPE and MAPE. If the former is the objective, then the statistical analysis should naturally be based on H/H o - S/S o (the ratio of daily solar radiation to extraterrestrial daily solar radiation vs. the ratio of sunshine duration to day length). If the latter is investigated, then the statistical analysis should be based on H c - H m (calculated daily solar radiation vs. measured daily solar radiation). A literature survey undertaken in the present article showed that these two data sets are apt to be confused, drawing the statistical parameters to be used in assessment of the estimation model from the latter data set or the vice versa set. The statistical parameters are clearly derived from the basics for both of the data sets, and the inconsistencies caused by this confusion and other factors are exposed. A case study of the estimation models and global solar radiation estimation from sunshine duration is presented using five different models (linear, quadratic, cubic, logarithmic and exponential), which are the most common models used in the literature, based on 6 years long measured hourly global solar radiation data

  19. Global and Regional Estimates of Prevalent and Incident Herpes Simplex Virus Type 1 Infections in 2012.

    Katharine J Looker

    Full Text Available Herpes simplex virus type 1 (HSV-1 commonly causes orolabial ulcers, while HSV-2 commonly causes genital ulcers. However, HSV-1 is an increasing cause of genital infection. Previously, the World Health Organization estimated the global burden of HSV-2 for 2003 and for 2012. The global burden of HSV-1 has not been estimated.We fitted a constant-incidence model to pooled HSV-1 prevalence data from literature searches for 6 World Health Organization regions and used 2012 population data to derive global numbers of 0-49-year-olds with prevalent and incident HSV-1 infection. To estimate genital HSV-1, we applied values for the proportion of incident infections that are genital.We estimated that 3709 million people (range: 3440-3878 million aged 0-49 years had prevalent HSV-1 infection in 2012 (67%, with highest prevalence in Africa, South-East Asia and Western Pacific. Assuming 50% of incident infections among 15-49-year-olds are genital, an estimated 140 million (range: 67-212 million people had prevalent genital HSV-1 infection, most of which occurred in the Americas, Europe and Western Pacific.The global burden of HSV-1 infection is huge. Genital HSV-1 burden can be substantial but varies widely by region. Future control efforts, including development of HSV vaccines, should consider the epidemiology of HSV-1 in addition to HSV-2, and especially the relative contribution of HSV-1 to genital infection.

  20. Impact of climate change estimated through statistical downscaling on crop productivity and soil water balance in Southern Italy

    Ventrella, D.; Giglio, L.; Charfeddine, M.; Palatella, L.; Pizzigalli, C.; Vitale, D.; Paradisi, P.; Miglietta, M. M.; Rana, G.

    2010-09-01

    The climatic change induced by the global warming is expected to modify the agricultural activity and consequently the other social and economical sectors. In this context, an efficient management of the water resources is considered very important for Italy and in particular for Southern areas characterized by a typical Mediterranean climate in order to improve the economical and environmental sustainability of the agricultural activity. Climate warming could have a substantial impact on some agronomical practices as the choice of the crops to be included in the rotations, the sowing time and the irrigation scheduling. For a particular zone, the impact of climatic change on agricultural activity will depend also on the continuum "soil-plant-climate" and this continuum has to be included in the analysis for forecasting purposes. The Project CLIMESCO is structured in four workpackages (WP): (1) Identification of homogeneous areas, (2) Climatic change, (3) Optimization of water resources and (4) Scenarios analysis. In this study we applied a statistical downscaling method, Canonical Correlation Analysis after Principal Component Analysis filtering, to two sub-regions of agricultural interest in Sicily and Apulia (respectively, Delia basin and Capitanata). We adopt, as large scale predictors, the sea level pressure from the the EMULATE project dataset and the 1000 hPa temperature obtained from the NCEP reanalyses, while the predictands are monthly time series of maximum and minimum temperature and precipitation. As the crop growth models need daily datasets, a stochastic weather generator (the LARS-WG model) has been applied for this purpose. LARS-WG needs a preliminary calibration with daily time series of meteorological fields, that are available in the framework of CLIMESCO project. Then, the statistical relationships have been applied to two climate change scenarios (SRES A2 and B2), provided by three different GCM's: the Hadley Centre Coupled Model version 3 (Had

  1. Global Burden of Leptospirosis: Estimated in Terms of Disability Adjusted Life Years.

    Paul R Torgerson

    Full Text Available Leptospirosis, a spirochaetal zoonosis, occurs in diverse epidemiological settings and affects vulnerable populations, such as rural subsistence farmers and urban slum dwellers. Although leptospirosis can cause life-threatening disease, there is no global burden of disease estimate in terms of Disability Adjusted Life Years (DALYs available.We utilised the results of a parallel publication that reported global estimates of morbidity and mortality due to leptospirosis. We estimated Years of Life Lost (YLLs from age and gender stratified mortality rates. Years of Life with Disability (YLDs were developed from a simple disease model indicating likely sequelae. DALYs were estimated from the sum of YLLs and YLDs. The study suggested that globally approximately 2.90 million DALYs are lost per annum (UIs 1.25-4.54 million from the approximately annual 1.03 million cases reported previously. Males are predominantly affected with an estimated 2.33 million DALYs (UIs 0.98-3.69 or approximately 80% of the total burden. For comparison, this is over 70% of the global burden of cholera estimated by GBD 2010. Tropical regions of South and South-east Asia, Western Pacific, Central and South America, and Africa had the highest estimated leptospirosis disease burden.Leptospirosis imparts a significant health burden worldwide, which approach or exceed those encountered for a number of other zoonotic and neglected tropical diseases. The study findings indicate that highest burden estimates occur in resource-poor tropical countries, which include regions of Africa where the burden of leptospirosis has been under-appreciated and possibly misallocated to other febrile illnesses such as malaria.

  2. Identifying grain-size dependent errors on global forest area estimates and carbon studies

    Daolan Zheng; Linda S. Heath; Mark J. Ducey

    2008-01-01

    Satellite-derived coarse-resolution data are typically used for conducting global analyses. But the forest areas estimated from coarse-resolution maps (e.g., 1 km) inevitably differ from a corresponding fine-resolution map (such as a 30-m map) that would be closer to ground truth. A better understanding of changes in grain size on area estimation will improve our...

  3. Importance of representing optical depth variability for estimates of global line-shaped contrail radiative forcing.

    Kärcher, Bernd; Burkhardt, Ulrike; Ponater, Michael; Frömming, Christine

    2010-11-09

    Estimates of the global radiative forcing by line-shaped contrails differ mainly due to the large uncertainty in contrail optical depth. Most contrails are optically thin so that their radiative forcing is roughly proportional to their optical depth and increases with contrail coverage. In recent assessments, the best estimate of mean contrail radiative forcing was significantly reduced, because global climate model simulations pointed at lower optical depth values than earlier studies. We revise these estimates by comparing the probability distribution of contrail optical depth diagnosed with a climate model with the distribution derived from a microphysical, cloud-scale model constrained by satellite observations over the United States. By assuming that the optical depth distribution from the cloud model is more realistic than that from the climate model, and by taking the difference between the observed and simulated optical depth over the United States as globally representative, we quantify uncertainties in the climate model's diagnostic contrail parameterization. Revising the climate model results accordingly increases the global mean radiative forcing estimate for line-shaped contrails by a factor of 3.3, from 3.5 mW/m(2) to 11.6 mW/m(2) for the year 1992. Furthermore, the satellite observations and the cloud model point at higher global mean optical depth of detectable contrails than often assumed in radiative transfer (off-line) studies. Therefore, we correct estimates of contrail radiative forcing from off-line studies as well. We suggest that the global net radiative forcing of line-shaped persistent contrails is in the range 8-20 mW/m(2) for the air traffic in the year 2000.

  4. Insect pollination and self-incompatibility in edible and/or medicinal crops in southwestern China, a global hotspot of biodiversity.

    Ren, Zong-Xin; Wang, Hong; Bernhardt, Peter; Li, De-Zhu

    2014-10-01

    An increasing global demand for food, coupled with the widespread decline of pollinator diversity, remains an international concern in agriculture and genetic conservation. In particular, there are large gaps in the study of the pollination of economically important and traditionally grown species in China. Many plant species grown in China are both edible and used medicinally. The country retains extensive written records of agricultural and apicultural practices, facilitating contemporary studies of some important taxa. Here, we focus on Yunnan in southwestern China, a mega-biodiversity hotspot for medicinal/food plants. We used plant and insect taxa as model systems to understand the patterns and consequences of pollinator deficit to crops. We identified several gaps and limitations in research on the pollination ecology and breeding systems of domesticated taxa and their wild relatives in Yunnan and asked the following questions: (1) What is known about pollination systems of edible and medicinal plants in Yunnan? (2) What are the most important pollinators of Codonopsis subglobosa (Campanulaceae)? (3) How important are native pollinator species for maximizing yield in Chinese crops compared with the introduced Apis mellifera? We found that some crops that require cross-pollination now depend exclusively on hand pollination. Three domesticated crops are dependent primarily on the native but semidomesticated Apis cerana and the introduced A. mellifera. Other species of wild pollinators often play important roles for certain specialty crops (e.g., Vespa velutina pollinates Codonopsis subglobosa). We propose a more systematic and comprehensive approach to applied research in the future. © 2014 Botanical Society of America, Inc.

  5. Global CO2 efficiency: Country-wise estimates using a stochastic cost frontier

    Herrala, Risto; Goel, Rajeev K.

    2012-01-01

    This paper examines global carbon dioxide (CO 2 ) efficiency by employing a stochastic cost frontier analysis of about 170 countries in 1997 and 2007. The main contribution lies in providing a new approach to environmental efficiency estimation, in which the efficiency estimates quantify the distance from the policy objective of minimum emissions. We are able to examine a very large pool of nations and provide country-wise efficiency estimates. We estimate three econometric models, corresponding with alternative interpretations of the Cancun vision (Conference of the Parties 2011). The models reveal progress in global environmental efficiency during a preceding decade. The estimates indicate vast differences in efficiency levels, and efficiency changes across countries. The highest efficiency levels are observed in Africa and Europe, while the lowest are clustered around China. The largest efficiency gains were observed in central and eastern Europe. CO 2 efficiency also improved in the US and China, the two largest emitters, but their ranking in terms of CO 2 efficiency deteriorated. Policy implications are discussed. - Highlights: ► We estimate global environmental efficiency in line with the Cancun vision, using a stochastic cost frontier. ► The study covers 170 countries during a 10 year period, ending in 2007. ► The biggest improvements occurred in Europe, and efficiency falls in South America. ► The efficiency ranking of US and China, the largest emitters, deteriorated. ► In 2007, highest efficiency was observed in Africa and Europe, and the lowest around China.

  6. Applications of TRMM-based Multi-Satellite Precipitation Estimation for Global Runoff Simulation: Prototyping a Global Flood Monitoring System

    Hong, Yang; Adler, Robert F.; Huffman, George J.; Pierce, Harold

    2008-01-01

    Advances in flood monitoring/forecasting have been constrained by the difficulty in estimating rainfall continuously over space (catchment-, national-, continental-, or even global-scale areas) and flood-relevant time scale. With the recent availability of satellite rainfall estimates at fine time and space resolution, this paper describes a prototype research framework for global flood monitoring by combining real-time satellite observations with a database of global terrestrial characteristics through a hydrologically relevant modeling scheme. Four major components included in the framework are (1) real-time precipitation input from NASA TRMM-based Multi-satellite Precipitation Analysis (TMPA); (2) a central geospatial database to preprocess the land surface characteristics: water divides, slopes, soils, land use, flow directions, flow accumulation, drainage network etc.; (3) a modified distributed hydrological model to convert rainfall to runoff and route the flow through the stream network in order to predict the timing and severity of the flood wave, and (4) an open-access web interface to quickly disseminate flood alerts for potential decision-making. Retrospective simulations for 1998-2006 demonstrate that the Global Flood Monitor (GFM) system performs consistently at both station and catchment levels. The GFM website (experimental version) has been running at near real-time in an effort to offer a cost-effective solution to the ultimate challenge of building natural disaster early warning systems for the data-sparse regions of the world. The interactive GFM website shows close-up maps of the flood risks overlaid on topography/population or integrated with the Google-Earth visualization tool. One additional capability, which extends forecast lead-time by assimilating QPF into the GFM, also will be implemented in the future.

  7. Estimating pesticide emission fractions for use in LCA: A global consensus-building effort

    Fantke, Peter; Anton, Assumpcio; Basset-Mens, Claudine

    2016-01-01

    agreement on recommended default agricultural pesticide emission fractions to environmental media. Consensual decisions on the assessment framework are (a) primary distributions are used as inputs for LCIA, while further investigating how to assess secondary emissions, (b) framework and LCA application...... and application method scenarios will be based on sensitiv ity analysis, (g) default emission estimates for LCA will be derived from production-weighted averages, and (h) emission fractions will be reported spatially disaggregated. Recommendations for LCA practitioners and database developers are (a) LCA studies...... the field as part of technosphere and ecosphere, (e) fate and exposure processes should be included in LCIA (e.g. crop uptake), (f) default emission estimates should be used in absence of detailed scenario data, (g) and all assumptions should be reported. The recommended pesticide emission fractions results...

  8. Global Kalman filter approaches to estimate absolute angles of lower limb segments.

    Nogueira, Samuel L; Lambrecht, Stefan; Inoue, Roberto S; Bortole, Magdo; Montagnoli, Arlindo N; Moreno, Juan C; Rocon, Eduardo; Terra, Marco H; Siqueira, Adriano A G; Pons, Jose L

    2017-05-16

    In this paper we propose the use of global Kalman filters (KFs) to estimate absolute angles of lower limb segments. Standard approaches adopt KFs to improve the performance of inertial sensors based on individual link configurations. In consequence, for a multi-body system like a lower limb exoskeleton, the inertial measurements of one link (e.g., the shank) are not taken into account in other link angle estimations (e.g., foot). Global KF approaches, on the other hand, correlate the collective contribution of all signals from lower limb segments observed in the state-space model through the filtering process. We present a novel global KF (matricial global KF) relying only on inertial sensor data, and validate both this KF and a previously presented global KF (Markov Jump Linear Systems, MJLS-based KF), which fuses data from inertial sensors and encoders from an exoskeleton. We furthermore compare both methods to the commonly used local KF. The results indicate that the global KFs performed significantly better than the local KF, with an average root mean square error (RMSE) of respectively 0.942° for the MJLS-based KF, 1.167° for the matrical global KF, and 1.202° for the local KFs. Including the data from the exoskeleton encoders also resulted in a significant increase in performance. The results indicate that the current practice of using KFs based on local models is suboptimal. Both the presented KF based on inertial sensor data, as well our previously presented global approach fusing inertial sensor data with data from exoskeleton encoders, were superior to local KFs. We therefore recommend to use global KFs for gait analysis and exoskeleton control.

  9. Estimating N2O processes during grassland renewal and grassland conversion to maize cropping using N2O isotopocules

    Buchen, Caroline; Well, Reinhard; Flessa, Heinz; Fuß, Roland; Helfrich, Mirjam; Lewicka-Szczebak, Dominika

    2017-04-01

    Grassland break-up due to grassland renewal and grassland conversion to cropland can lead to a flush of mineral nitrogen from decomposition of the old grass sward and the decomposition of soil organic matter. Moreover, increased carbon and nitrogen mineralisation can result in enhanced nitrous oxide (N2O) emissions. As N2O is known to be an important greenhouse gas and a major precursor for ozone depletion, its emissions need to be mitigated by adjusting agricultural management practices. Therefore, it is necessary to understand the N2O processes involved, as well as the contribution of N2O reduction to N2. Apart from the widely used 15N gas flux method, natural abundance isotopic analysis of the four most abundant isotopocules of N2O species is a promising alternative to assess N2O production pathways. We used stable isotope analyses of soil-emitted N2O (δ18ON2O, δ15NN2Obulk and δ15NN2OSP= intramolecular distribution of 15N within the linear N2O molecule) with an isotopocule mapping approach to simultaneously estimate the magnitude of N2O reduction to N2 and the fraction of N2O originating from the bacterial denitrification pathway or fungal denitrification and/or nitrification. This approach is based on endmember areas of isotopic values for the N2O produced from different sources reported in the literature. For this purpose, we calculated two main scenarios with different assumptions for N2O produced: N2O is reduced to N2 before residual N2O is mixed with N2O of various sources (Scenario a) and vice versa (Scenario b). Based on this, we applied seven different scenario variations, where we evaluated the range of possible values for the potential N2O production pathways (heterotrophic bacterial denitrification and/or nitrifier denitrification and fungal denitrification and/or nitrification). This was done by using a range of isotopic endmember values and assuming different fractionation factors of N2O reduction in order to find the most reliable scenario

  10. Application of ANFIS and SVM Systems in Order to Estimate Monthly Reference Crop Evapotranspiration in the Northwest of Iran

    F. Ahmadi

    2016-10-01

    Full Text Available Introduction Crop evapotranspiration modeling process mainly performs with empirical methods, aerodynamic and energy balance. In these methods, the evapotranspiration is calculated based on the average values of meteorological parameters at different time steps. The linear models didn’t have a good performance in this field due to high variability of evapotranspiration and the researchers have turned to the use of nonlinear and intelligent models. For accurate estimation of this hydrologic variable, it should be spending much time and money to measure many data (19. Materials and Methods Recently the new hybrid methods have been developed by combining some of methods such as artificial neural networks, fuzzy logic and evolutionary computation, that called Soft Computing and Intelligent Systems. These soft techniques are used in various fields of engineering. A fuzzy neurosis is a hybrid system that incorporates the decision ability of fuzzy logic with the computational ability of neural network, which provides a high capability for modeling and estimating. Basically, the Fuzzy part is used to classify the input data set and determines the degree of membership (that each number can be laying between 0 and 1 and decisions for the next activity made based on a set of rules and move to the next stage. Adaptive Neuro-Fuzzy Inference Systems (ANFIS includes some parts of a typical fuzzy expert system which the calculations at each step is performed by the hidden layer neurons and the learning ability of the neural network has been created to increase the system information (9. SVM is a one of supervised learning methods which used for classification and regression affairs. This method was developed by Vapink (15 based on statistical learning theory. The SVM is a method for binary classification in an arbitrary characteristic space, so it is suitable for prediction problems (12. The SVM is originally a two-class Classifier that separates the classes

  11. Methodological framework for World Health Organization estimates of the global burden of foodborne disease

    B. Devleesschauwer (Brecht); J.A. Haagsma (Juanita); F.J. Angulo (Frederick); D.C. Bellinger (David); D. Cole (Dana); D. Döpfer (Dörte); A. Fazil (Aamir); E.M. Fèvre (Eric); H.J. Gibb (Herman); T. Hald (Tine); M.D. Kirk (Martyn); R.J. Lake (Robin); C. Maertens De Noordhout (Charline); C. Mathers (Colin); S.A. McDonald (Scott); S.M. Pires (Sara); N. Speybroeck (Niko); M.K. Thomas (Kate); D. Torgerson; F. Wu (Felicia); A.H. Havelaar (Arie); N. Praet (Nicolas)

    2015-01-01

    textabstractBackground: The Foodborne Disease Burden Epidemiology Reference Group (FERG) was established in 2007 by the World Health Organization to estimate the global burden of foodborne diseases (FBDs). This paper describes the methodological framework developed by FERG's Computational Task Force

  12. Can a sample of Landsat sensor scenes reliably estimate the global extent of tropical deforestation?

    R. L. Czaplewski

    2003-01-01

    Tucker and Townshend (2000) conclude that wall-to-wall coverage is needed to avoid gross errors in estimations of deforestation rates' because tropical deforestation is concentrated along roads and rivers. They specifically question the reliability of the 10% sample of Landsat sensor scenes used in the global remote sensing survey conducted by the Food and...

  13. Synthesizing Global and Local Datasets to Estimate Jurisdictional Forest Carbon Fluxes in Berau, Indonesia.

    Griscom, Bronson W; Ellis, Peter W; Baccini, Alessandro; Marthinus, Delon; Evans, Jeffrey S; Ruslandi

    2016-01-01

    Forest conservation efforts are increasingly being implemented at the scale of sub-national jurisdictions in order to mitigate global climate change and provide other ecosystem services. We see an urgent need for robust estimates of historic forest carbon emissions at this scale, as the basis for credible measures of climate and other benefits achieved. Despite the arrival of a new generation of global datasets on forest area change and biomass, confusion remains about how to produce credible jurisdictional estimates of forest emissions. We demonstrate a method for estimating the relevant historic forest carbon fluxes within the Regency of Berau in eastern Borneo, Indonesia. Our method integrates best available global and local datasets, and includes a comprehensive analysis of uncertainty at the regency scale. We find that Berau generated 8.91 ± 1.99 million tonnes of net CO2 emissions per year during 2000-2010. Berau is an early frontier landscape where gross emissions are 12 times higher than gross sequestration. Yet most (85%) of Berau's original forests are still standing. The majority of net emissions were due to conversion of native forests to unspecified agriculture (43% of total), oil palm (28%), and fiber plantations (9%). Most of the remainder was due to legal commercial selective logging (17%). Our overall uncertainty estimate offers an independent basis for assessing three other estimates for Berau. Two other estimates were above the upper end of our uncertainty range. We emphasize the importance of including an uncertainty range for all parameters of the emissions equation to generate a comprehensive uncertainty estimate-which has not been done before. We believe comprehensive estimates of carbon flux uncertainty are increasingly important as national and international institutions are challenged with comparing alternative estimates and identifying a credible range of historic emissions values.

  14. Estimation of Global 1km-grid Terrestrial Carbon Exchange Part I: Developing Inputs and Modelling

    Sasai, T.; Murakami, K.; Kato, S.; Matsunaga, T.; Saigusa, N.; Hiraki, K.

    2015-12-01

    Global terrestrial carbon cycle largely depends on a spatial pattern in land cover type, which is heterogeneously-distributed over regional and global scales. However, most studies, which aimed at the estimation of carbon exchanges between ecosystem and atmosphere, remained within several tens of kilometers grid spatial resolution, and the results have not been enough to understand the detailed pattern of carbon exchanges based on ecological community. Improving the sophistication of spatial resolution is obviously necessary to enhance the accuracy of carbon exchanges. Moreover, the improvement may contribute to global warming awareness, policy makers and other social activities. In this study, we show global terrestrial carbon exchanges (net ecosystem production, net primary production, and gross primary production) with 1km-grid resolution. As methodology for computing the exchanges, we 1) developed a global 1km-grid climate and satellite dataset based on the approach in Setoyama and Sasai (2013); 2) used the satellite-driven biosphere model (Biosphere model integrating Eco-physiological And Mechanistic approaches using Satellite data: BEAMS) (Sasai et al., 2005, 2007, 2011); 3) simulated the carbon exchanges by using the new dataset and BEAMS by the use of a supercomputer that includes 1280 CPU and 320 GPGPU cores (GOSAT RCF of NIES). As a result, we could develop a global uniform system for realistically estimating terrestrial carbon exchange, and evaluate net ecosystem production in each community level; leading to obtain highly detailed understanding of terrestrial carbon exchanges.

  15. A global building inventory for earthquake loss estimation and risk management

    Jaiswal, K.; Wald, D.; Porter, K.

    2010-01-01

    We develop a global database of building inventories using taxonomy of global building types for use in near-real-time post-earthquake loss estimation and pre-earthquake risk analysis, for the U.S. Geological Survey's Prompt Assessment of Global Earthquakes for Response (PAGER) program. The database is available for public use, subject to peer review, scrutiny, and open enhancement. On a country-by-country level, it contains estimates of the distribution of building types categorized by material, lateral force resisting system, and occupancy type (residential or nonresidential, urban or rural). The database draws on and harmonizes numerous sources: (1) UN statistics, (2) UN Habitat's demographic and health survey (DHS) database, (3) national housing censuses, (4) the World Housing Encyclopedia and (5) other literature. ?? 2010, Earthquake Engineering Research Institute.

  16. WHE-PAGER Project: A new initiative in estimating global building inventory and its seismic vulnerability

    Porter, K.A.; Jaiswal, K.S.; Wald, D.J.; Greene, M.; Comartin, Craig

    2008-01-01

    The U.S. Geological Survey’s Prompt Assessment of Global Earthquake’s Response (PAGER) Project and the Earthquake Engineering Research Institute’s World Housing Encyclopedia (WHE) are creating a global database of building stocks and their earthquake vulnerability. The WHE already represents a growing, community-developed public database of global housing and its detailed structural characteristics. It currently contains more than 135 reports on particular housing types in 40 countries. The WHE-PAGER effort extends the WHE in several ways: (1) by addressing non-residential construction; (2) by quantifying the prevalence of each building type in both rural and urban areas; (3) by addressing day and night occupancy patterns, (4) by adding quantitative vulnerability estimates from judgment or statistical observation; and (5) by analytically deriving alternative vulnerability estimates using in part laboratory testing.

  17. Estimation of Global 1km-grid Terrestrial Carbon Exchange Part II: Evaluations and Applications

    Murakami, K.; Sasai, T.; Kato, S.; Niwa, Y.; Saito, M.; Takagi, H.; Matsunaga, T.; Hiraki, K.; Maksyutov, S. S.; Yokota, T.

    2015-12-01

    Global terrestrial carbon cycle largely depends on a spatial pattern in land cover type, which is heterogeneously-distributed over regional and global scales. Many studies have been trying to reveal distribution of carbon exchanges between terrestrial ecosystems and atmosphere for understanding global carbon cycle dynamics by using terrestrial biosphere models, satellite data, inventory data, and so on. However, most studies remained within several tens of kilometers grid spatial resolution, and the results have not been enough to understand the detailed pattern of carbon exchanges based on ecological community and to evaluate the carbon stocks by forest ecosystems in each countries. Improving the sophistication of spatial resolution is obviously necessary to enhance the accuracy of carbon exchanges. Moreover, the improvement may contribute to global warming awareness, policy makers and other social activities. We show global terrestrial carbon exchanges (net ecosystem production, net primary production, and gross primary production) with 1km-grid resolution. The methodology for these estimations are shown in the 2015 AGU FM poster "Estimation of Global 1km-grid Terrestrial Carbon Exchange Part I: Developing Inputs and Modelling". In this study, we evaluated the carbon exchanges in various regions with other approaches. We used the satellite-driven biosphere model (BEAMS) as our estimations, GOSAT L4A CO2 flux data, NEP retrieved by NICAM and CarbonTracer2013 flux data, for period from Jun 2001 to Dec 2012. The temporal patterns for this period were indicated similar trends between BEAMS, GOSAT, NICAM, and CT2013 in many sub-continental regions. Then, we estimated the terrestrial carbon exchanges in each countries, and could indicated the temporal patterns of the exchanges in large carbon stock regions.Global terrestrial carbon cycle largely depends on a spatial pattern of land cover type, which is heterogeneously-distributed over regional and global scales. Many

  18. Transcriptomics-guided development of RNA interference strategies to manage whiteflies: a globally distributed vector of crop viruses

    Over 300 viruses are transmitted by the whitefly, Bemisia tabaci, with 90% of them belonging to the genus, Begomovirus. Begomoviruses are exclusively transmitted by whiteflies to a range of agriculture crops, resulting in billions of dollars lost annually, while jeopardizing food security worldwide....

  19. Estimating 40 years of nitrogen deposition in global biomes using the SCIAMACHY NO2 column

    Lu, Xuehe; Zhang, Xiuying; Liu, Jinxun; Jin, Jiaxin

    2016-01-01

    Owing to human activity, global nitrogen (N) cycles have been altered. In the past 100 years, global N deposition has increased. Currently, the monitoring and estimating of N deposition and the evaluation of its effects on global carbon budgets are the focus of many researchers. NO2 columns retrieved by space-borne sensors provide us with a new way of exploring global N cycles and these have the ability to estimate N deposition. However, the time range limitation of NO2 columns makes the estimation of long timescale N deposition difficult. In this study we used ground-based NOx emission data to expand the density of NO2columns, and 40 years of N deposition (1970–2009) was inverted using the multivariate linear model with expanded NO2 columns. The dynamic of N deposition was examined in both global and biome scales. The results show that the average N deposition was 0.34 g N m–2 year–1 in the 2000s, which was an increase of 38.4% compared with the 1970s’. The total N deposition in different biomes is unbalanced. N deposition is only 38.0% of the global total in forest biomes; this is made up of 25.9%, 11.3, and 0.7% in tropical, temperate, and boreal forests, respectively. As N-limited biomes, there was little increase of N deposition in boreal forests. However, N deposition has increased by a total of 59.6% in tropical forests and croplands, which are N-rich biomes. Such characteristics may influence the effects on global carbon budgets.

  20. The Global Burden of Latent Tuberculosis Infection: A Re-estimation Using Mathematical Modelling.

    Rein M G J Houben

    2016-10-01

    Full Text Available The existing estimate of the global burden of latent TB infection (LTBI as "one-third" of the world population is nearly 20 y old. Given the importance of controlling LTBI as part of the End TB Strategy for eliminating TB by 2050, changes in demography and scientific understanding, and progress in TB control, it is important to re-assess the global burden of LTBI.We constructed trends in annual risk in infection (ARI for countries between 1934 and 2014 using a combination of direct estimates of ARI from LTBI surveys (131 surveys from 1950 to 2011 and indirect estimates of ARI calculated from World Health Organisation (WHO estimates of smear positive TB prevalence from 1990 to 2014. Gaussian process regression was used to generate ARIs for country-years without data and to represent uncertainty. Estimated ARI time-series were applied to the demography in each country to calculate the number and proportions of individuals infected, recently infected (infected within 2 y, and recently infected with isoniazid (INH-resistant strains. Resulting estimates were aggregated by WHO region. We estimated the contribution of existing infections to TB incidence in 2035 and 2050. In 2014, the global burden of LTBI was 23.0% (95% uncertainty interval [UI]: 20.4%-26.4%, amounting to approximately 1.7 billion people. WHO South-East Asia, Western-Pacific, and Africa regions had the highest prevalence and accounted for around 80% of those with LTBI. Prevalence of recent infection was 0.8% (95% UI: 0.7%-0.9% of the global population, amounting to 55.5 (95% UI: 48.2-63.8 million individuals currently at high risk of TB disease, of which 10.9% (95% UI:10.2%-11.8% was isoniazid-resistant. Current LTBI alone, assuming no additional infections from 2015 onwards, would be expected to generate TB incidences in the region of 16.5 per 100,000 per year in 2035 and 8.3 per 100,000 per year in 2050. Limitations included the quantity and methodological heterogeneity of direct ARI

  1. Methodological Framework for World Health Organization Estimates of the Global Burden of Foodborne Disease.

    Brecht Devleesschauwer

    Full Text Available The Foodborne Disease Burden Epidemiology Reference Group (FERG was established in 2007 by the World Health Organization to estimate the global burden of foodborne diseases (FBDs. This paper describes the methodological framework developed by FERG's Computational Task Force to transform epidemiological information into FBD burden estimates.The global and regional burden of 31 FBDs was quantified, along with limited estimates for 5 other FBDs, using Disability-Adjusted Life Years in a hazard- and incidence-based approach. To accomplish this task, the following workflow was defined: outline of disease models and collection of epidemiological data; design and completion of a database template; development of an imputation model; identification of disability weights; probabilistic burden assessment; and estimating the proportion of the disease burden by each hazard that is attributable to exposure by food (i.e., source attribution. All computations were performed in R and the different functions were compiled in the R package 'FERG'. Traceability and transparency were ensured by sharing results and methods in an interactive way with all FERG members throughout the process.We developed a comprehensive framework for estimating the global burden of FBDs, in which methodological simplicity and transparency were key elements. All the tools developed have been made available and can be translated into a user-friendly national toolkit for studying and monitoring food safety at the local level.

  2. Estimating Climatological Bias Errors for the Global Precipitation Climatology Project (GPCP)

    Adler, Robert; Gu, Guojun; Huffman, George

    2012-01-01

    A procedure is described to estimate bias errors for mean precipitation by using multiple estimates from different algorithms, satellite sources, and merged products. The Global Precipitation Climatology Project (GPCP) monthly product is used as a base precipitation estimate, with other input products included when they are within +/- 50% of the GPCP estimates on a zonal-mean basis (ocean and land separately). The standard deviation s of the included products is then taken to be the estimated systematic, or bias, error. The results allow one to examine monthly climatologies and the annual climatology, producing maps of estimated bias errors, zonal-mean errors, and estimated errors over large areas such as ocean and land for both the tropics and the globe. For ocean areas, where there is the largest question as to absolute magnitude of precipitation, the analysis shows spatial variations in the estimated bias errors, indicating areas where one should have more or less confidence in the mean precipitation estimates. In the tropics, relative bias error estimates (s/m, where m is the mean precipitation) over the eastern Pacific Ocean are as large as 20%, as compared with 10%-15% in the western Pacific part of the ITCZ. An examination of latitudinal differences over ocean clearly shows an increase in estimated bias error at higher latitudes, reaching up to 50%. Over land, the error estimates also locate regions of potential problems in the tropics and larger cold-season errors at high latitudes that are due to snow. An empirical technique to area average the gridded errors (s) is described that allows one to make error estimates for arbitrary areas and for the tropics and the globe (land and ocean separately, and combined). Over the tropics this calculation leads to a relative error estimate for tropical land and ocean combined of 7%, which is considered to be an upper bound because of the lack of sign-of-the-error canceling when integrating over different areas with a

  3. Testing a statistical method of global mean palotemperature estimations in a long climate simulation

    Zorita, E.; Gonzalez-Rouco, F. [GKSS-Forschungszentrum Geesthacht GmbH (Germany). Inst. fuer Hydrophysik

    2001-07-01

    Current statistical methods of reconstructing the climate of the last centuries are based on statistical models linking climate observations (temperature, sea-level-pressure) and proxy-climate data (tree-ring chronologies, ice-cores isotope concentrations, varved sediments, etc.). These models are calibrated in the instrumental period, and the longer time series of proxy data are then used to estimate the past evolution of the climate variables. Using such methods the global mean temperature of the last 600 years has been recently estimated. In this work this method of reconstruction is tested using data from a very long simulation with a climate model. This testing allows to estimate the errors of the estimations as a function of the number of proxy data and the time scale at which the estimations are probably reliable. (orig.)

  4. Global Monitoring RSEM System for Crop Production by Incorporating Satellite-based Photosynthesis Rates and Anomaly Data of Sea Surface Temperature

    Kaneko, D.; Sakuma, H.

    2014-12-01

    The first author has been developing RSEM crop-monitoring system using satellite-based assessment of photosynthesis, incorporating meteorological conditions. Crop production comprises of several stages and plural mechanisms based on leaf photosynthesis, surface energy balance, and the maturing of grains after fixation of CO2, along with water exchange through soil vegetation-atmosphere transfer. Grain production in prime countries appears to be randomly perturbed regionally and globally. Weather for crop plants reflects turbulent phenomena of convective and advection flows in atmosphere and surface boundary layer. It has been difficult for scientists to simulate and forecast weather correctly for sufficiently long terms to crop harvesting. However, severely poor harvests related to continental events must originate from a consistent mechanism of abnormal energetic flow in the atmosphere through both land and oceans. It should be remembered that oceans have more than 100 times of energy storage compared to atmosphere and ocean currents represent gigantic energy flows, strongly affecting climate. Anomalies of Sea Surface Temperature (SST), globally known as El Niño, Indian Ocean dipole, and Atlantic Niño etc., affect the seasonal climate on a continental scale. The authors aim to combine monitoring and seasonal forecasting, considering such mechanisms through land-ocean biosphere transfer. The present system produces assessments for all continents, specifically monitoring agricultural fields of main crops. Historical regions of poor and good harvests are compared with distributions of SST anomalies, which are provided by NASA GSFC. Those comparisons fairly suggest that the Worst harvest in 1993 and the Best in 1994 relate to the offshore distribution of low temperature anomalies and high gaps in ocean surface temperatures. However, high-temperature anomalies supported good harvests because of sufficient solar radiation for photosynthesis, and poor harvests because

  5. A hybrid computational approach to estimate solar global radiation: An empirical evidence from Iran

    Mostafavi, Elham Sadat; Ramiyani, Sara Saeidi; Sarvar, Rahim; Moud, Hashem Izadi; Mousavi, Seyyed Mohammad

    2013-01-01

    This paper presents an innovative hybrid approach for the estimation of the solar global radiation. New prediction equations were developed for the global radiation using an integrated search method of genetic programming (GP) and simulated annealing (SA), called GP/SA. The solar radiation was formulated in terms of several climatological and meteorological parameters. Comprehensive databases containing monthly data collected for 6 years in two cities of Iran were used to develop GP/SA-based models. Separate models were established for each city. The generalization of the models was verified using a separate testing database. A sensitivity analysis was conducted to investigate the contribution of the parameters affecting the solar radiation. The derived models make accurate predictions of the solar global radiation and notably outperform the existing models. -- Highlights: ► A hybrid approach is presented for the estimation of the solar global radiation. ► The proposed method integrates the capabilities of GP and SA. ► Several climatological and meteorological parameters are included in the analysis. ► The GP/SA models make accurate predictions of the solar global radiation.

  6. The green, blue and grey water footprint of crops and derived crop products

    Mekonnen, M. M.; Hoekstra, A. Y.

    2011-05-01

    This study quantifies the green, blue and grey water footprint of global crop production in a spatially-explicit way for the period 1996-2005. The assessment improves upon earlier research by taking a high-resolution approach, estimating the water footprint of 126 crops at a 5 by 5 arc minute grid. We have used a grid-based dynamic water balance model to calculate crop water use over time, with a time step of one day. The model takes into account the daily soil water balance and climatic conditions for each grid cell. In addition, the water pollution associated with the use of nitrogen fertilizer in crop production is estimated for each grid cell. The crop evapotranspiration of additional 20 minor crops is calculated with the CROPWAT model. In addition, we have calculated the water footprint of more than two hundred derived crop products, including various flours, beverages, fibres and biofuels. We have used the water footprint assessment framework as in the guideline of the Water Footprint Network. Considering the water footprints of primary crops, we see that the global average water footprint per ton of crop increases from sugar crops (roughly 200 m3 ton-1), vegetables (300 m3 ton-1), roots and tubers (400 m3 ton-1), fruits (1000 m3 ton-1), cereals (1600 m3 ton-1), oil crops (2400 m3 ton-1) to pulses (4000 m3 ton-1). The water footprint varies, however, across different crops per crop category and per production region as well. Besides, if one considers the water footprint per kcal, the picture changes as well. When considered per ton of product, commodities with relatively large water footprints are: coffee, tea, cocoa, tobacco, spices, nuts, rubber and fibres. The analysis of water footprints of different biofuels shows that bio-ethanol has a lower water footprint (in m3 GJ-1) than biodiesel, which supports earlier analyses. The crop used matters significantly as well: the global average water footprint of bio-ethanol based on sugar beet amounts to 51 m3 GJ-1

  7. Incorrectly Interpreting the Carbon Mass Balance Technique Leads to Biased Emissions Estimates from Global Vegetation Fires

    Surawski, N. C.; Sullivan, A. L.; Roxburgh, S. H.; Meyer, M.; Polglase, P. J.

    2016-12-01

    Vegetation fires are a complex phenomenon and have a range of global impacts including influences on climate. Even though fire is a necessary disturbance for the maintenance of some ecosystems, a range of anthropogenically deleterious consequences are associated with it, such as damage to assets and infrastructure, loss of life, as well as degradation to air quality leading to negative impacts on human health. Estimating carbon emissions from fire relies on a carbon mass balance technique which has evolved with two different interpretations in the fire emissions community. Databases reporting global fire emissions estimates use an approach based on `consumed biomass' which is an approximation to the biogeochemically correct `burnt carbon' approach. Disagreement between the two methods occurs because the `consumed biomass' accounting technique assumes that all burnt carbon is volatilized and emitted. By undertaking a global review of the fraction of burnt carbon emitted to the atmosphere, we show that the `consumed biomass' accounting approach overestimates global carbon emissions by 4.0%, or 100 Teragrams, annually. The required correction is significant and represents 9% of the net global forest carbon sink estimated annually. To correctly partition burnt carbon between that emitted to the atmosphere and that remaining as a post-fire residue requires the post-burn carbon content to be estimated, which is quite often not undertaken in atmospheric emissions studies. To broaden our understanding of ecosystem carbon fluxes, it is recommended that the change in carbon content associated with burnt residues be accounted for. Apart from correctly partitioning burnt carbon between the emitted and residue pools, it enables an accounting approach which can assess the efficacy of fire management operations targeted at sequestering carbon from fire. These findings are particularly relevant for the second commitment period for the Kyoto protocol, since improved landscape fire

  8. Comparing Broad-Band and Red Edge-Based Spectral Vegetation Indices to Estimate Nitrogen Concentration of Crops Using Casi Data

    Wang, Yanjie; Liao, Qinhong; Yang, Guijun; Feng, Haikuan; Yang, Xiaodong; Yue, Jibo

    2016-06-01

    In recent decades, many spectral vegetation indices (SVIs) have been proposed to estimate the leaf nitrogen concentration (LNC) of crops. However, most of these indices were based on the field hyperspectral reflectance. To test whether they can be used in aerial remote platform effectively, in this work a comparison of the sensitivity between several broad-band and red edge-based SVIs to LNC is investigated over different crop types. By using data from experimental LNC values over 4 different crop types and image data acquired using the Compact Airborne Spectrographic Imager (CASI) sensor, the extensive dataset allowed us to evaluate broad-band and red edge-based SVIs. The result indicated that NDVI performed the best among the selected SVIs while red edge-based SVIs didn't show the potential for estimating the LNC based on the CASI data due to the spectral resolution. In order to search for the optimal SVIs, the band combination algorithm has been used in this work. The best linear correlation against the experimental LNC dataset was obtained by combining the 626.20nm and 569.00nm wavebands. These wavelengths correspond to the maximal chlorophyll absorption and reflection position region, respectively, and are known to be sensitive to the physiological status of the plant. Then this linear relationship was applied to the CASI image for generating an LNC map, which can guide farmers in the accurate application of their N fertilization strategies.

  9. Random balance designs for the estimation of first order global sensitivity indices

    Tarantola, S.; Gatelli, D.; Mara, T.A.

    2006-01-01

    We present two methods for the estimation of main effects in global sensitivity analysis. The methods adopt Satterthwaite's application of random balance designs in regression problems, and extend it to sensitivity analysis of model output for non-linear, non-additive models. Finite as well as infinite ranges for model input factors are allowed. The methods are easier to implement than any other method available for global sensitivity analysis, and reduce significantly the computational cost of the analysis. We test their performance on different test cases, including an international benchmark on safety assessment for nuclear waste disposal originally carried out by OECD/NEA

  10. Random balance designs for the estimation of first order global sensitivity indices

    Tarantola, S. [Joint Research Centre, European Commission, Institute of the Protection and Security of the Citizen, TP 361, Via E. Fermi 1, 21020 Ispra (Vatican City State, Holy See,) (Italy)]. E-mail: stefano.tarantola@jrc.it; Gatelli, D. [Joint Research Centre, European Commission, Institute of the Protection and Security of the Citizen, TP 361, Via E. Fermi 1, 21020 Ispra (VA) (Italy); Mara, T.A. [Laboratory of Industrial engineering, University of Reunion Island, BP 7151, 15 avenue Rene Cassin, 97 715 Saint-Denis (France)

    2006-06-15

    We present two methods for the estimation of main effects in global sensitivity analysis. The methods adopt Satterthwaite's application of random balance designs in regression problems, and extend it to sensitivity analysis of model output for non-linear, non-additive models. Finite as well as infinite ranges for model input factors are allowed. The methods are easier to implement than any other method available for global sensitivity analysis, and reduce significantly the computational cost of the analysis. We test their performance on different test cases, including an international benchmark on safety assessment for nuclear waste disposal originally carried out by OECD/NEA.

  11. Estimating a Global Hydrological Carrying Capacity Using GRACE Observed Water Stress

    An, K.; Reager, J. T.; Famiglietti, J. S.

    2013-12-01

    Global population is expected to reach 9 billion people by the year 2050, causing increased demands for water and potential threats to human security. This study attempts to frame the overpopulation problem through a hydrological resources lens by hypothesizing that observed groundwater trends should be directly attributed to human water consumption. This study analyzes the relationships between available blue water, population, and cropland area on a global scale. Using satellite data from NASA's Gravity Recovery and Climate Experiment (GRACE) along with land surface model data from the Global Land Data Assimilation System (GLDAS), a global groundwater depletion trend is isolated, the validity of which has been verified in many regional studies. By using the inherent distributions of these relationships, we estimate the regional populations that have exceeded their local hydrological carrying capacity. Globally, these populations sum to ~3.5 billion people that are living in presently water-stressed or potentially water-scarce regions, and we estimate total cropland is exceeding a sustainable threshold by about 80 million km^2. Key study areas such as the North China Plain, northwest India, and Mexico City were qualitatively chosen for further analysis of regional water resources and policies, based on our distributions of water stress. These case studies are used to verify the groundwater level changes seen in the GRACE trend . Tfor the many populous, arid regions of the world that have already begun to experience the strains of high water demand.he many populous, arid regions of the world have already begun to experience the strains of high water demand. It will take a global cooperative effort of improving domestic and agricultural use efficiency, and summoning a political will to prioritize environmental issues to adapt to a thirstier planet. Global Groundwater Depletion Trend (Mar 2003-Dec 2011)

  12. Estimating global "blue carbon" emissions from conversion and degradation of vegetated coastal ecosystems.

    Linwood Pendleton

    Full Text Available Recent attention has focused on the high rates of annual carbon sequestration in vegetated coastal ecosystems--marshes, mangroves, and seagrasses--that may be lost with habitat destruction ('conversion'. Relatively unappreciated, however, is that conversion of these coastal ecosystems also impacts very large pools of previously-sequestered carbon. Residing mostly in sediments, this 'blue carbon' can be released to the atmosphere when these ecosystems are converted or degraded. Here we provide the first global estimates of this impact and evaluate its economic implications. Combining the best available data on global area, land-use conversion rates, and near-surface carbon stocks in each of the three ecosystems, using an uncertainty-propagation approach, we estimate that 0.15-1.02 Pg (billion tons of carbon dioxide are being released annually, several times higher than previous estimates that account only for lost sequestration. These emissions are equivalent to 3-19% of those from deforestation globally, and result in economic damages of $US 6-42 billion annually. The largest sources of uncertainty in these estimates stems from limited certitude in global area and rates of land-use conversion, but research is also needed on the fates of ecosystem carbon upon conversion. Currently, carbon emissions from the conversion of vegetated coastal ecosystems are not included in emissions accounting or carbon market protocols, but this analysis suggests they may be disproportionally important to both. Although the relevant science supporting these initial estimates will need to be refined in coming years, it is clear that policies encouraging the sustainable management of coastal ecosystems could significantly reduce carbon emissions from the land-use sector, in addition to sustaining the well-recognized ecosystem services of coastal habitats.

  13. Estimating Global “Blue Carbon” Emissions from Conversion and Degradation of Vegetated Coastal Ecosystems

    Murray, Brian C.; Crooks, Stephen; Jenkins, W. Aaron; Sifleet, Samantha; Craft, Christopher; Fourqurean, James W.; Kauffman, J. Boone; Marbà, Núria; Megonigal, Patrick; Pidgeon, Emily; Herr, Dorothee; Gordon, David; Baldera, Alexis

    2012-01-01

    Recent attention has focused on the high rates of annual carbon sequestration in vegetated coastal ecosystems—marshes, mangroves, and seagrasses—that may be lost with habitat destruction (‘conversion’). Relatively unappreciated, however, is that conversion of these coastal ecosystems also impacts very large pools of previously-sequestered carbon. Residing mostly in sediments, this ‘blue carbon’ can be released to the atmosphere when these ecosystems are converted or degraded. Here we provide the first global estimates of this impact and evaluate its economic implications. Combining the best available data on global area, land-use conversion rates, and near-surface carbon stocks in each of the three ecosystems, using an uncertainty-propagation approach, we estimate that 0.15–1.02 Pg (billion tons) of carbon dioxide are being released annually, several times higher than previous estimates that account only for lost sequestration. These emissions are equivalent to 3–19% of those from deforestation globally, and result in economic damages of $US 6–42 billion annually. The largest sources of uncertainty in these estimates stems from limited certitude in global area and rates of land-use conversion, but research is also needed on the fates of ecosystem carbon upon conversion. Currently, carbon emissions from the conversion of vegetated coastal ecosystems are not included in emissions accounting or carbon market protocols, but this analysis suggests they may be disproportionally important to both. Although the relevant science supporting these initial estimates will need to be refined in coming years, it is clear that policies encouraging the sustainable management of coastal ecosystems could significantly reduce carbon emissions from the land-use sector, in addition to sustaining the well-recognized ecosystem services of coastal habitats. PMID:22962585

  14. Contributions of national and global health estimates to monitoring health-related sustainable development goals.

    Bundhamcharoen, Kanitta; Limwattananon, Supon; Kusreesakul, Khanitta; Tangcharoensathien, Viroj

    2016-01-01

    The millennium development goals triggered an increased demand for data on child and maternal mortalities for monitoring progress. With the advent of the sustainable development goals and growing evidence of an epidemiological transition toward non-communicable diseases, policymakers need data on mortality and disease trends and distribution to inform effective policies and support monitoring progress. Where there are limited capacities to produce national health estimates (NHEs), global health estimates (GHEs) can fill gaps for global monitoring and comparisons. This paper discusses lessons learned from Thailand's burden of disease (BOD) study on capacity development on NHEs and discusses the contributions and limitations of GHEs in informing policies at the country level. Through training and technical support by external partners, capacities are gradually strengthened and institutionalized to enable regular updates of BOD at national and subnational levels. Initially, the quality of cause-of-death reporting in death certificates was inadequate, especially for deaths occurring in the community. Verbal autopsies were conducted, using domestic resources, to determine probable causes of deaths occurring in the community. This method helped to improve the estimation of years of life lost. Since the achievement of universal health coverage in 2002, the quality of clinical data on morbidities has also considerably improved. There are significant discrepancies between the Global Burden of Disease 2010 study estimates for Thailand and the 1999 nationally generated BOD, especially for years of life lost due to HIV/AIDS, and the ranking of priority diseases. National ownership of NHEs and an effective interface between researchers and decision-makers contribute to enhanced country policy responses, whereas subnational data are intended to be used by various subnational partners. Although GHEs contribute to benchmarking country achievement compared with global health

  15. Yield trends and yield gap analysis of major crops in the world

    Hengsdijk, H.; Langeveld, J.W.A.

    2009-01-01

    This study aims to quantify the gap between current and potential yields of major crops in the world, and the production constraints that contribute to this yield gap. Using an expert-based evaluation of yield gaps and the literature, global and regional yields and yield trends of major crops are quantified, yield gaps evaluated by crop experts, current yield progress by breeding estimated, and different yield projections compared. Results show decreasing yield growth for wheat and rice, but ...

  16. The In Vitro Mass-Produced Model Mycorrhizal Fungus, Rhizophagus irregularis, Significantly Increases Yields of the Globally Important Food Security Crop Cassava

    Ceballos, Isabel; Ruiz, Michael; Fernández, Cristhian; Peña, Ricardo

    2013-01-01

    The arbuscular mycorrhizal symbiosis is formed between arbuscular mycorrhizal fungi (AMF) and plant roots. The fungi provide the plant with inorganic phosphate (P). The symbiosis can result in increased plant growth. Although most global food crops naturally form this symbiosis, very few studies have shown that their practical application can lead to large-scale increases in food production. Application of AMF to crops in the tropics is potentially effective for improving yields. However, a main problem of using AMF on a large-scale is producing cheap inoculum in a clean sterile carrier and sufficiently concentrated to cheaply transport. Recently, mass-produced in vitro inoculum of the model mycorrhizal fungus Rhizophagus irregularis became available, potentially making its use viable in tropical agriculture. One of the most globally important food plants in the tropics is cassava. We evaluated the effect of in vitro mass-produced R. irregularis inoculum on the yield of cassava crops at two locations in Colombia. A significant effect of R. irregularis inoculation on yield occurred at both sites. At one site, yield increases were observed irrespective of P fertilization. At the other site, inoculation with AMF and 50% of the normally applied P gave the highest yield. Despite that AMF inoculation resulted in greater food production, economic analyses revealed that AMF inoculation did not give greater return on investment than with conventional cultivation. However, the amount of AMF inoculum used was double the recommended dose and was calculated with European, not Colombian, inoculum prices. R. irregularis can also be manipulated genetically in vitro, leading to improved plant growth. We conclude that application of in vitro R. irregularis is currently a way of increasing cassava yields, that there is a strong potential for it to be economically profitable and that there is enormous potential to improve this efficiency further in the future. PMID:23950975

  17. The in vitro mass-produced model mycorrhizal fungus, Rhizophagus irregularis, significantly increases yields of the globally important food security crop cassava.

    Isabel Ceballos

    Full Text Available The arbuscular mycorrhizal symbiosis is formed between arbuscular mycorrhizal fungi (AMF and plant roots. The fungi provide the plant with inorganic phosphate (P. The symbiosis can result in increased plant growth. Although most global food crops naturally form this symbiosis, very few studies have shown that their practical application can lead to large-scale increases in food production. Application of AMF to crops in the tropics is potentially effective for improving yields. However, a main problem of using AMF on a large-scale is producing cheap inoculum in a clean sterile carrier and sufficiently concentrated to cheaply transport. Recently, mass-produced in vitro inoculum of the model mycorrhizal fungus Rhizophagus irregularis became available, potentially making its use viable in tropical agriculture. One of the most globally important food plants in the tropics is cassava. We evaluated the effect of in vitro mass-produced R. irregularis inoculum on the yield of cassava crops at two locations in Colombia. A significant effect of R. irregularis inoculation on yield occurred at both sites. At one site, yield increases were observed irrespective of P fertilization. At the other site, inoculation with AMF and 50% of the normally applied P gave the highest yield. Despite that AMF inoculation resulted in greater food production, economic analyses revealed that AMF inoculation did not give greater return on investment than with conventional cultivation. However, the amount of AMF inoculum used was double the recommended dose and was calculated with European, not Colombian, inoculum prices. R. irregularis can also be manipulated genetically in vitro, leading to improved plant growth. We conclude that application of in vitro R. irregularis is currently a way of increasing cassava yields, that there is a strong potential for it to be economically profitable and that there is enormous potential to improve this efficiency further in the future.

  18. Global estimate of lichen and bryophyte contributions to forest precipitation interception

    Van Stan, John; Porada, Philipp; Kleidon, Axel

    2017-04-01

    Interception of precipitation by forest canopies plays an important role in its partitioning to evaporation, transpiration and runoff. Field observations show arboreal lichens and bryophytes can substantially enhance forests' precipitation storage and evaporation. However, representations of canopy interception in global land surface models currently ignore arboreal lichen and bryophyte contributions. This study uses the lichen and bryophyte model (LiBry) to provide the first process-based modelling approach estimating these organisms' contributions to canopy water storage and evaporation. The global mean value of forest water storage capacity increased significantly from 0.87 mm to 1.33 mm by the inclusion of arboreal poikilohydric organisms. Global forest canopy evaporation of intercepted precipitation was also greatly enhanced by 44%. Ratio of total versus bare canopy global evaporation exceeded 2 in many forested regions. This altered global patterns in canopy water storage, evaporation, and ultimately the proportion of rainfall evaporated. A sensitivity analysis was also performed. Results indicate rainfall interception is of larger magnitude than previously reported by global land surface modelling work because of the important role of lichen and bryophytes in rainfall interception.

  19. Alternative U.S. biofuel mandates and global GHG emissions: The role of land use change, crop management and yield growth

    Mosnier, A.; Havlík, P.; Valin, H.; Baker, J.; Murray, B.; Feng, S.; Obersteiner, M.; McCarl, B.A.; Rose, S.K.; Schneider, U.A.

    2013-01-01

    We investigate the impacts of the U.S. renewable fuel standard (RFS2) and several alternative biofuel policy designs on global GHG emissions from land use change and agriculture over the 2010–2030 horizon. Analysis of the scenarios relies on GLOBIOM, a global, multi-sectoral economic model based on a detailed representation of land use. Our results reveal that RFS2 would substantially increase the portion of agricultural land needed for biofuel feedstock production. U.S. exports of most agricultural products would decrease as long as the biofuel target would increase leading to higher land conversion and nitrogen use globally. In fact, higher levels of the mandate mean lower net emissions within the U.S. but when the emissions from the rest of the world are considered, the US biofuel policy results in almost no change on GHG emissions for the RFS2 level and higher global GHG emissions for higher levels of the mandate or higher share of conventional corn-ethanol in the mandate. Finally, we show that if the projected crop productivity would be lower globally, the imbalance between domestic U.S. GHG savings and additional GHG emissions in the rest of the world would increase, thus deteriorating the net global impact of U.S. biofuel policies. - Highlights: ► We model the impact of the U.S. renewable fuel standard (RFS2). ► RFS2 would require more agricultural land and nitrogen globally. ► Increasing the mandates reduce GHG emissions within the U.S. ► Increasing the mandates increase GHG emissions in the rest of the world. ► Total GHG emissions increase with higher levels of mandate; higher share of corn-ethanol; lower productivity growth

  20. Approaches to Refining Estimates of Global Burden and Economics of Dengue

    Shepard, Donald S.; Undurraga, Eduardo A.; Betancourt-Cravioto, Miguel; Guzmán, María G.; Halstead, Scott B.; Harris, Eva; Mudin, Rose Nani; Murray, Kristy O.; Tapia-Conyer, Roberto; Gubler, Duane J.

    2014-01-01

    Dengue presents a formidable and growing global economic and disease burden, with around half the world's population estimated to be at risk of infection. There is wide variation and substantial uncertainty in current estimates of dengue disease burden and, consequently, on economic burden estimates. Dengue disease varies across time, geography and persons affected. Variations in the transmission of four different viruses and interactions among vector density and host's immune status, age, pre-existing medical conditions, all contribute to the disease's complexity. This systematic review aims to identify and examine estimates of dengue disease burden and costs, discuss major sources of uncertainty, and suggest next steps to improve estimates. Economic analysis of dengue is mainly concerned with costs of illness, particularly in estimating total episodes of symptomatic dengue. However, national dengue disease reporting systems show a great diversity in design and implementation, hindering accurate global estimates of dengue episodes and country comparisons. A combination of immediate, short-, and long-term strategies could substantially improve estimates of disease and, consequently, of economic burden of dengue. Suggestions for immediate implementation include refining analysis of currently available data to adjust reported episodes and expanding data collection in empirical studies, such as documenting the number of ambulatory visits before and after hospitalization and including breakdowns by age. Short-term recommendations include merging multiple data sources, such as cohort and surveillance data to evaluate the accuracy of reporting rates (by health sector, treatment, severity, etc.), and using covariates to extrapolate dengue incidence to locations with no or limited reporting. Long-term efforts aim at strengthening capacity to document dengue transmission using serological methods to systematically analyze and relate to epidemiologic data. As promising tools

  1. Assembling GHERG: Could "academic crowd-sourcing" address gaps in global health estimates?

    Rudan, Igor; Campbell, Harry; Marušić, Ana; Sridhar, Devi; Nair, Harish; Adeloye, Davies; Theodoratou, Evropi; Chan, Kit Yee

    2015-06-01

    In recent months, the World Health Organization (WHO), independent academic researchers, the Lancet and PLoS Medicine journals worked together to improve reporting of population health estimates. The new guidelines for accurate and transparent health estimates reporting (likely to be named GATHER), which are eagerly awaited, represent a helpful move that should benefit the field of global health metrics. Building on this progress and drawing from a tradition of Child Health Epidemiology Reference Group (CHERG)'s successful work model, we would like to propose a new initiative - "Global Health Epidemiology Reference Group" (GHERG). We see GHERG as an informal and entirely voluntary international collaboration of academic groups who are willing to contribute to improving disease burden estimates and respect the principles of the new guidelines - a form of "academic crowd-sourcing". The main focus of GHERG will be to identify the "gap areas" where not much information is available and/or where there is a lot of uncertainty present about the accuracy of the existing estimates. This approach should serve to complement the existing WHO and IHME estimates and to represent added value to both efforts.

  2. Estimating the approximation error when fixing unessential factors in global sensitivity analysis

    Sobol' , I.M. [Institute for Mathematical Modelling of the Russian Academy of Sciences, Moscow (Russian Federation); Tarantola, S. [Joint Research Centre of the European Commission, TP361, Institute of the Protection and Security of the Citizen, Via E. Fermi 1, 21020 Ispra (Italy)]. E-mail: stefano.tarantola@jrc.it; Gatelli, D. [Joint Research Centre of the European Commission, TP361, Institute of the Protection and Security of the Citizen, Via E. Fermi 1, 21020 Ispra (Italy)]. E-mail: debora.gatelli@jrc.it; Kucherenko, S.S. [Imperial College London (United Kingdom); Mauntz, W. [Department of Biochemical and Chemical Engineering, Dortmund University (Germany)

    2007-07-15

    One of the major settings of global sensitivity analysis is that of fixing non-influential factors, in order to reduce the dimensionality of a model. However, this is often done without knowing the magnitude of the approximation error being produced. This paper presents a new theorem for the estimation of the average approximation error generated when fixing a group of non-influential factors. A simple function where analytical solutions are available is used to illustrate the theorem. The numerical estimation of small sensitivity indices is discussed.

  3. Quantifying the Effects of Historical Land Cover Conversion Uncertainty on Global Carbon and Climate Estimates

    Di Vittorio, A. V.; Mao, J.; Shi, X.; Chini, L.; Hurtt, G.; Collins, W. D.

    2018-01-01

    Previous studies have examined land use change as a driver of global change, but the translation of land use change into land cover conversion has been largely unconstrained. Here we quantify the effects of land cover conversion uncertainty on the global carbon and climate system using the integrated Earth System Model. Our experiments use identical land use change data and vary land cover conversions to quantify associated uncertainty in carbon and climate estimates. Land cover conversion uncertainty is large, constitutes a 5 ppmv range in estimated atmospheric CO2 in 2004, and generates carbon uncertainty that is equivalent to 80% of the net effects of CO2 and climate and 124% of the effects of nitrogen deposition during 1850-2004. Additionally, land cover uncertainty generates differences in local surface temperature of over 1°C. We conclude that future studies addressing land use, carbon, and climate need to constrain and reduce land cover conversion uncertainties.

  4. Estimating Global Impervious Surface based on Social-economic Data and Satellite Observations

    Zeng, Z.; Zhang, K.; Xue, X.; Hong, Y.

    2016-12-01

    Impervious surface areas around the globe are expanding and significantly altering the surface energy balance, hydrology cycle and ecosystem services. Many studies have underlined the importance of impervious surface, r from hydrological modeling to contaminant transport monitoring and urban development estimation. Therefore accurate estimation of the global impervious surface is important for both physical and social sciences. Given the limited coverage of high spatial resolution imagery and ground survey, using satellite remote sensing and geospatial data to estimate global impervious areas is a practical approach. Based on the previous work of area-weighted imperviousness for north branch of the Chicago River provided by HDR, this study developed a method to determine the percentage of impervious surface using latest global land cover categories from multi-source satellite observations, population density and gross domestic product (GDP) data. Percent impervious surface at 30-meter resolution were mapped. We found that 1.33% of the CONUS (105,814 km2) and 0.475% of the land surface (640,370km2) are impervious surfaces. To test the utility and practicality of the proposed method, National Land Cover Database (NLCD) 2011 percent developed imperviousness for the conterminous United States was used to evaluate our results. The average difference between the derived imperviousness from our method and the NLCD data across CONUS is 1.14%, while difference between our results and the NLCD data are within ±1% over 81.63% of the CONUS. The distribution of global impervious surface map indicates that impervious surfaces are primarily concentrated in China, India, Japan, USA and Europe where are highly populated and/or developed. This study proposes a straightforward way of mapping global imperviousness, which can provide useful information for hydrologic modeling and other applications.

  5. Estimating the global conservation status of more than 15,000 Amazonian tree species

    ter Steege, H.; et al., [Unknown; Duivenvoorden, J.F.

    2015-01-01

    Estimates of extinction risk for Amazonian plant and animal species are rare and not often incorporated into land-use policy and conservation planning. We overlay spatial distribution models with historical and projected deforestation to show that at least 36% and up to 57% of all Amazonian tree species are likely to qualify as globally threatened under International Union for Conservation of Nature (IUCN) Red List criteria. If confirmed, these results would increase the number of threatened ...

  6. An Improved Global Wind Resource Estimate for Integrated Assessment Models: Preprint

    Eurek, Kelly [National Renewable Energy Lab. (NREL), Golden, CO (United States); Sullivan, Patrick [National Renewable Energy Lab. (NREL), Golden, CO (United States); Gleason, Michael [National Renewable Energy Lab. (NREL), Golden, CO (United States); Hettinger, Dylan [National Renewable Energy Lab. (NREL), Golden, CO (United States); Heimiller, Donna [National Renewable Energy Lab. (NREL), Golden, CO (United States); Lopez, Anthony [National Renewable Energy Lab. (NREL), Golden, CO (United States)

    2017-02-01

    This paper summarizes initial steps to improving the robustness and accuracy of global renewable resource and techno-economic assessments for use in integrated assessment models. We outline a method to construct country-level wind resource supply curves, delineated by resource quality and other parameters. Using mesoscale reanalysis data, we generate estimates for wind quality, both terrestrial and offshore, across the globe. Because not all land or water area is suitable for development, appropriate database layers provide exclusions to reduce the total resource to its technical potential. We expand upon estimates from related studies by: using a globally consistent data source of uniquely detailed wind speed characterizations; assuming a non-constant coefficient of performance for adjusting power curves for altitude; categorizing the distance from resource sites to the electric power grid; and characterizing offshore exclusions on the basis of sea ice concentrations. The product, then, is technical potential by country, classified by resource quality as determined by net capacity factor. Additional classifications dimensions are available, including distance to transmission networks for terrestrial wind and distance to shore and water depth for offshore. We estimate the total global wind generation potential of 560 PWh for terrestrial wind with 90% of resource classified as low-to-mid quality, and 315 PWh for offshore wind with 67% classified as mid-to-high quality. These estimates are based on 3.5 MW composite wind turbines with 90 m hub heights, 0.95 availability, 90% array efficiency, and 5 MW/km2 deployment density in non-excluded areas. We compare the underlying technical assumption and results with other global assessments.

  7. Global CO2 fluxes estimated from GOSAT retrievals of total column CO2

    S. Basu

    2013-09-01

    Full Text Available We present one of the first estimates of the global distribution of CO2 surface fluxes using total column CO2 measurements retrieved by the SRON-KIT RemoTeC algorithm from the Greenhouse gases Observing SATellite (GOSAT. We derive optimized fluxes from June 2009 to December 2010. We estimate fluxes from surface CO2 measurements to use as baselines for comparing GOSAT data-derived fluxes. Assimilating only GOSAT data, we can reproduce the observed CO2 time series at surface and TCCON sites in the tropics and the northern extra-tropics. In contrast, in the southern extra-tropics GOSAT XCO2 leads to enhanced seasonal cycle amplitudes compared to independent measurements, and we identify it as the result of a land–sea bias in our GOSAT XCO2 retrievals. A bias correction in the form of a global offset between GOSAT land and sea pixels in a joint inversion of satellite and surface measurements of CO2 yields plausible global flux estimates which are more tightly constrained than in an inversion using surface CO2 data alone. We show that assimilating the bias-corrected GOSAT data on top of surface CO2 data (a reduces the estimated global land sink of CO2, and (b shifts the terrestrial net uptake of carbon from the tropics to the extra-tropics. It is concluded that while GOSAT total column CO2 provide useful constraints for source–sink inversions, small spatiotemporal biases – beyond what can be detected using current validation techniques – have serious consequences for optimized fluxes, even aggregated over continental scales.

  8. Estimation of the Global Solar Energy Potential and Photovoltaic Cost with the use of Open Data

    Athina Korfiati

    2016-12-01

    Full Text Available There is an increasing demand for renewable electricity sources, due to the global efforts to reduce CO2 emissions. Despite the promising effects, only a limited amount of electricity is currently produced globally from solar power. In order to help countries realize the importance of tapping into solar energy, it is crucial to reveal the potential amount of electricity that could be thus produced. For this reason, open data were used to produce an interactive web map of the global solar energy potential. For the calculation of the potential, the top-down approach, generally used in the literature, was modified by introducing a better way of calculating rooftop areas, and accounting for temperature, which highly reduces PV panels’ efficiency. Mean annual temperature data were introduced to improve its accuracy, and an approach to estimate rooftop and façade areas as a function of GDP was developed. The current global solar potential technically available was estimated at about 613 PWh/y. Furthermore, the cost of photovoltaic generation was computed and extremely low values, 0.03 - 0.2 $/kWh, were derived.

  9. A new method to estimate global mass transport and its implication for sea level rise

    Yi, S.; Heki, K.

    2017-12-01

    Estimates of changes in global land mass by using GRACE observations can be achieved by two methods, a mascon method and a forward modeling method. However, results from these two methods show inconsistent secular trend. Sea level budget can be adopted to validate the consistency among observations of sea level rise by altimetry, steric change by the Argo project, and mass change by GRACE. Mascon products from JPL, GSFC and CSR are compared here, we find that all these three products cannot achieve a reconciled sea level budget, while this problem can be solved by a new forward modeling method. We further investigate the origin of this difference, and speculate that it is caused by the signal leakage from the ocean mass. Generally, it is well recognized that land signals leak into oceans, but it also happens the other way around. We stress the importance of correction of leakage from the ocean in the estimation of global land masses. Based on a reconciled sea level budget, we confirmed that global sea level rise has been accelerating significantly over 2005-2015, as a result of the ongoing global temperature increase.

  10. Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis

    Casadebaig, Pierre; Zheng, Bangyou; Chapman, Scott; Huth, Neil; Faivre, Robert; Chenu, Karine

    2016-01-01

    A crop can be viewed as a complex system with outputs (e.g. yield) that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background). The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90) was evaluated in a wide target population of environments (4 sites × 125 years), management practices (3 sowing dates × 3 nitrogen fertilization levels) and CO2 (2 levels). The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total). The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. Main (i.e. linear) and interaction (i.e. non-linear and interaction) sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model) improvement. PMID:26799483

  11. Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis.

    Pierre Casadebaig

    Full Text Available A crop can be viewed as a complex system with outputs (e.g. yield that are affected by inputs of genetic, physiology, pedo-climatic and management information. Application of numerical methods for model exploration assist in evaluating the major most influential inputs, providing the simulation model is a credible description of the biological system. A sensitivity analysis was used to assess the simulated impact on yield of a suite of traits involved in major processes of crop growth and development, and to evaluate how the simulated value of such traits varies across environments and in relation to other traits (which can be interpreted as a virtual change in genetic background. The study focused on wheat in Australia, with an emphasis on adaptation to low rainfall conditions. A large set of traits (90 was evaluated in a wide target population of environments (4 sites × 125 years, management practices (3 sowing dates × 3 nitrogen fertilization levels and CO2 (2 levels. The Morris sensitivity analysis method was used to sample the parameter space and reduce computational requirements, while maintaining a realistic representation of the targeted trait × environment × management landscape (∼ 82 million individual simulations in total. The patterns of parameter × environment × management interactions were investigated for the most influential parameters, considering a potential genetic range of +/- 20% compared to a reference cultivar. Main (i.e. linear and interaction (i.e. non-linear and interaction sensitivity indices calculated for most of APSIM-Wheat parameters allowed the identification of 42 parameters substantially impacting yield in most target environments. Among these, a subset of parameters related to phenology, resource acquisition, resource use efficiency and biomass allocation were identified as potential candidates for crop (and model improvement.

  12. Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global S