WorldWideScience

Sample records for crop model parameters

  1. Estimating winter wheat phenological parameters: Implications for crop modeling

    Science.gov (United States)

    Crop parameters, such as the timing of developmental events, are critical for accurate simulation results in crop simulation models, yet uncertainty often exists in determining the parameters. Factors contributing to the uncertainty include: a) sources of variation within a plant (i.e., within diffe...

  2. Use of remote sensing derived parameters in a crop model for biomass prediction of hay crop

    Science.gov (United States)

    El Hajj, Mohammad; Baghdadi, Nicolas; Cheviron, Bruno; Belaud, Gilles; Zribi, Mehrez

    2016-04-01

    Pre-harvest yield forecasting is a critical challenge for producers, especially for large agricultural areas. During previous decades, numerous crop models were developed to predict crop growth and yield at daily time, most often for wheat or maize, and also for grasslands. Crop models require several input parameters that describe soil properties (e.g. field capacity), plant characteristics (e.g. maximal rooting depth) and management options (e.g. sowing dates, irrigation and harvest dates), which are referred to as the soil, plant and management families of parameters. Remote sensing technology has been extensively applied to identify spatially distributed values of some of the accessible parameters in the soil, plant and management families. The aim of this study was to address the feasibility, merits and limitations of forcing remote-sensing-derived parameters (LAI values, harvest and irrigation dates) in the PILOTE crop model, targeting the Total Dry Matter (TDM) of hay crops. Results show that optical images are suitable to feed PILOTE with LAI values without inducing significant errors on the predicted Total Dry Matter (TDM) values (Root Mean Square Error "RMSE" = 0.41 t/ha and Mean Absolute Percentage Error "MAPE" = 22%). Moreover, optical images with revisit times lower than 16 days are adequate to feed PILOTE with remotely sensed harvest dates (RMSE < 0.44 t/ha, MAPE < 10.8%). Finally, feeding PILOTE with noisy irrigation dates that were estimated from SAR images also enabled reliable model predictions, at least when attaching a random uncertainty of "only" 3 days to the real known irrigation dates. The case of one or several undetected irrigations has also been explored, with the expected conclusion that undetected irrigations significantly affect model predictions only in dry periods. For the tested soil properties and climatic conditions, a maximum underestimation of TDM of approximately 1.55 t/ha (reference TDM of 3.43 t/ha) was observed in the second

  3. Monitor key parameters of winter wheat using Crop model

    Science.gov (United States)

    Jibo, Yue; Haikuan, Feng; Xiudong, Qi

    2016-11-01

    Estimation of biomass, canopy cover and yield is very important to agricultural decision Precision Farming. During the winter wheat growing season of 2013/2014, field measurements were conducted at Yangling District, Shaanxi Province at the jointing stage, heading stage and filling stage. AquaCrop model and Particle swarm optimization algorithm was used to find the global optimal simulation when the intermediate variable was the biomass. Through the simulation for each of the experimental data, biomass, canopy coverage and soil moisture were verification by ground measurements. Based on 8 sets of data, the simulation accuracy was calculated. The RMSE, nRMSE, MAE and R2 between simulation and measured biomass were 1.06 ton/ha, 11.92%, 0.90 ton/ha and 0.92. The RMSE, nRMSE, MAE and R2 between simulation and measured canopy cover were 8.92%, 9.84%, 7.84% and 0.66, respectively. The simulation results show that the AquaCrop model can help the decision making of winter wheat field in arid areas.

  4. Leaf photosynthesis and respiration of three bioenergy crops in relation to temperature and leaf nitrogen: how conserved are biochemical model parameters among crop species?

    NARCIS (Netherlands)

    Archontoulis, S.V.; Yin, X.; Vos, J.; Danalatos, N.G.; Struik, P.C.

    2012-01-01

    Given the need for parallel increases in food and energy production from crops in the context of global change, crop simulation models and data sets to feed these models with photosynthesis and respiration parameters are increasingly important. This study provides information on photosynthesis and r

  5. Leaf photosynthesis and respiration of three bioenergy crops in relation to temperature and leaf nitrogen: how conserved are biochemical model parameters among crop species?

    Science.gov (United States)

    Archontoulis, S. V.; Yin, X.; Vos, J.; Danalatos, N. G.; Struik, P. C.

    2012-01-01

    Given the need for parallel increases in food and energy production from crops in the context of global change, crop simulation models and data sets to feed these models with photosynthesis and respiration parameters are increasingly important. This study provides information on photosynthesis and respiration for three energy crops (sunflower, kenaf, and cynara), reviews relevant information for five other crops (wheat, barley, cotton, tobacco, and grape), and assesses how conserved photosynthesis parameters are among crops. Using large data sets and optimization techniques, the C3 leaf photosynthesis model of Farquhar, von Caemmerer, and Berry (FvCB) and an empirical night respiration model for tested energy crops accounting for effects of temperature and leaf nitrogen were parameterized. Instead of the common approach of using information on net photosynthesis response to CO2 at the stomatal cavity (An–Ci), the model was parameterized by analysing the photosynthesis response to incident light intensity (An–Iinc). Convincing evidence is provided that the maximum Rubisco carboxylation rate or the maximum electron transport rate was very similar whether derived from An–Ci or from An–Iinc data sets. Parameters characterizing Rubisco limitation, electron transport limitation, the degree to which light inhibits leaf respiration, night respiration, and the minimum leaf nitrogen required for photosynthesis were then determined. Model predictions were validated against independent sets. Only a few FvCB parameters were conserved among crop species, thus species-specific FvCB model parameters are needed for crop modelling. Therefore, information from readily available but underexplored An–Iinc data should be re-analysed, thereby expanding the potential of combining classical photosynthetic data and the biochemical model. PMID:22021569

  6. Optimal Estimation of Phenological Crop Model Parameters for Rice (Oryza sativa)

    Science.gov (United States)

    Sharifi, H.; Hijmans, R. J.; Espe, M.; Hill, J. E.; Linquist, B.

    2015-12-01

    Crop phenology models are important components of crop growth models. In the case of phenology models, generally only a few parameters are calibrated and default cardinal temperatures are used which can lead to a temperature-dependent systematic phenology prediction error. Our objective was to evaluate different optimization approaches in the Oryza2000 and CERES-Rice phenology sub-models to assess the importance of optimizing cardinal temperatures on model performance and systematic error. We used two optimization approaches: the typical single-stage (planting to heading) and three-stage model optimization (for planting to panicle initiation (PI), PI to heading (HD), and HD to physiological maturity (MT)) to simultaneously optimize all model parameters. Data for this study was collected over three years and six locations on seven California rice cultivars. A temperature-dependent systematic error was found for all cultivars and stages, however it was generally small (systematic error changes in cardinal temperature relative to the default values and thus optimization of cardinal temperatures did not affect systematic error or model performance. Compared to single stage optimization, three-stage optimization had little effect on determining time to PI or HD but significantly improved the precision in determining the time from HD to MT: the RMSE reduced from an average of 6 to 3.3 in Oryza2000 and from 6.6 to 3.8 in CERES-Rice. With regards to systematic error, we found a trade-off between RMSE and systematic error when optimization objective set to minimize RMSE or systematic error. Therefore, it is important to find the limits within which the trade-offs between RMSE and systematic error are acceptable, especially in climate change studies where this can prevent erroneous conclusions.

  7. Parameters sensitivity analysis for a~crop growth model applied to winter wheat in the Huanghuaihai Plain in China

    Science.gov (United States)

    Liu, M.; He, B.; Lü, A.; Zhou, L.; Wu, J.

    2014-06-01

    Parameters sensitivity analysis is a crucial step in effective model calibration. It quantitatively apportions the variation of model output to different sources of variation, and identifies how "sensitive" a model is to changes in the values of model parameters. Through calibration of parameters that are sensitive to model outputs, parameter estimation becomes more efficient. Due to uncertainties associated with yield estimates in a regional assessment, field-based models that perform well at field scale are not accurate enough to model at regional scale. Conducting parameters sensitivity analysis at the regional scale and analyzing the differences of parameter sensitivity between stations would make model calibration and validation in different sub-regions more efficient. Further, it would benefit the model applied to the regional scale. Through simulating 2000 × 22 samples for 10 stations in the Huanghuaihai Plain, this study discovered that TB (Optimal temperature), HI (Normal harvest index), WA (Potential radiation use efficiency), BN2 (Normal fraction of N in crop biomass at mid-season) and RWPC1 (Fraction of root weight at emergency) are more sensitive than other parameters. Parameters that determine nutrition supplement and LAI development have higher global sensitivity indices than first-order indices. For spatial application, soil diversity is crucial because soil is responsible for crop parameters sensitivity index differences between sites.

  8. Radiation model for row crops: I. Geometric view factors and parameter optimization

    Science.gov (United States)

    Row crops with partial cover result in different radiation partitioning to the soil and canopy compared with full cover; however, methods to account for partial cover have not been adequately investigated. The objectives of this study were to: (i) develop geometric view factors to account for the sp...

  9. A new modelling approach to simulate preferential flow and transport in water repellent porous media: Parameter sensitivity, and effects on crop growth and solute leaching

    NARCIS (Netherlands)

    Kramers, G.; Dam, van J.C.; Ritsema, C.J.; Stagnitti, F.; Oostindie, K.; Dekker, L.W.

    2005-01-01

    A modified version of the popular agrohydrological model SWAP has been used to evaluate modelling of soil water flow and crop growth at field situations in which water repellency causes preferential flow. The parameter sensitivity in such situations has been studied. Three options to model soil

  10. Interactive state-parameter estimation of a crop carbon mass balance model through the assimilation of observed winter wheat carbon flux and stock data

    Science.gov (United States)

    Sus, O.; Williams, M. D.; Gruenwald, T.

    2010-12-01

    Next to the consideration of land management practises, modelling the carbon balance of croplands requires a crop carbon budget model that realistically simulates photosynthesis, ecosystem respiration, soil carbon dynamics, and phenology dependant on crop-specific parameters and carbon allocation patterns. A crop carbon mass balance model is a tool which can aid to answer questions related to cropland carbon sequestration potential, best-practise recommendations, seasonal patterns and amplitude of net carbon exchange (NEE), and prediction of biomass growth and crop yield. However, land management complicates modelling of cropland NEE by largely determining the onset and length of the growing season of agricultural areas. Human decision making on crop cultivars, sowing and harvest dates, and management practices is difficult to simulate, and corresponding reliable data for larger spatial and temporal scales is still sparse. Crop carbon budget models require a specific set of parameters, some of which are poorly understood and are thus of empirical rather than mechanistic nature. Here, we present a study that deals with the assimilation of observations of both carbon flux and stock data into a crop C budget model (SPAc). Our data assimilation procedure (the Ensemble Kalman Filter, EnKF) aims at updating both model states and parameters, so that we will gain insight into optimized parameter values and carbon stock/flux estimates within quantified confidence limits. We obtained measured data of NEE, LAI, and leaf, root, stem, and storage organ dry mass for a winter wheat season in 2005/2006 from the CarboEurope Fluxnet site at Klingenberg/Germany. We conducted several model experiments, for each of which we assimilated a unique combination of data sources. We find that the assimilation of NEE data leads to reduced model error (observed vs. modelled NEE) compared to a model run without data assimilation (a reduction of ~15-20% of RMSE). The assimilated dry mass data on

  11. A GIS database for crop modelling

    NARCIS (Netherlands)

    Burrill, A.; Vossen, P.; Diepen, van C.A.

    1995-01-01

    The EC land information system has been combined with meteorological, topographical and crop parameter data, and with historical agricultural statistics, to produce an integrated database suitable as input to a European-level crop growth modelling system. The selection of variables to be included in

  12. Characterizing the dependence of vegetation model parameters on crop structure, incidence angle, and polarization at L-band

    DEFF Research Database (Denmark)

    Wigneron, J-P.; Pardé, M.; Waldteufel, P.

    2004-01-01

    To retrieve soil moisture over vegetation-covered areas from microwave radiometry, it is necessary to account for vegetation effects. At L-band, many retrieval approaches are based on a simple model that relies on two vegetation parameters: the optical depth (tau) and the single-scattering albedo......, wheat, grass, and alfalfa) based on L-band experimental datasets. The results should be useful for developing more accurate forward modeling and retrieval methods over mixed pixels including a variety of vegetation types....

  13. Cumulative and residual effects of potato cropping system management strategies on crop and soil health parameters

    Science.gov (United States)

    Soil and crop management practices can greatly affect parameters related to soil health, as well as crop productivity and disease development, and may provide options for more sustainable production. Different 3-yr potato cropping systems focused on specific management goals of soil conservation (SC...

  14. Irrigation modeling with AquaCrop

    Science.gov (United States)

    AquaCrop is a crop water productivity model developed by the Land and Water Division of UN-FAO. It simulates yield response to water of herbaceous crops, and is suited to address conditions where water is a key limiting factor in crop production. AquaCrop attempts to balance accuracy, simplicity, an...

  15. Parameterization models for pesticide exposure via crop consumption.

    Science.gov (United States)

    Fantke, Peter; Wieland, Peter; Juraske, Ronnie; Shaddick, Gavin; Itoiz, Eva Sevigné; Friedrich, Rainer; Jolliet, Olivier

    2012-12-04

    An approach for estimating human exposure to pesticides via consumption of six important food crops is presented that can be used to extend multimedia models applied in health risk and life cycle impact assessment. We first assessed the variation of model output (pesticide residues per kg applied) as a function of model input variables (substance, crop, and environmental properties) including their possible correlations using matrix algebra. We identified five key parameters responsible for between 80% and 93% of the variation in pesticide residues, namely time between substance application and crop harvest, degradation half-lives in crops and on crop surfaces, overall residence times in soil, and substance molecular weight. Partition coefficients also play an important role for fruit trees and tomato (Kow), potato (Koc), and lettuce (Kaw, Kow). Focusing on these parameters, we develop crop-specific models by parametrizing a complex fate and exposure assessment framework. The parametric models thereby reflect the framework's physical and chemical mechanisms and predict pesticide residues in harvest using linear combinations of crop, crop surface, and soil compartments. Parametric model results correspond well with results from the complex framework for 1540 substance-crop combinations with total deviations between a factor 4 (potato) and a factor 66 (lettuce). Predicted residues also correspond well with experimental data previously used to evaluate the complex framework. Pesticide mass in harvest can finally be combined with reduction factors accounting for food processing to estimate human exposure from crop consumption. All parametric models can be easily implemented into existing assessment frameworks.

  16. Crops Models for Varying Environmental Conditions

    Science.gov (United States)

    Jones, Harry; Cavazzoni, James; Keas, Paul

    2001-01-01

    New variable environment Modified Energy Cascade (MEC) crop models were developed for all the Advanced Life Support (ALS) candidate crops and implemented in SIMULINK. The MEC models are based on the Volk, Bugbee, and Wheeler Energy Cascade (EC) model and are derived from more recent Top-Level Energy Cascade (TLEC) models. The MEC models simulate crop plant responses to day-to-day changes in photosynthetic photon flux, photoperiod, carbon dioxide level, temperature, and relative humidity. The original EC model allows changes in light energy but uses a less accurate linear approximation. The simulation outputs of the new MEC models for constant nominal environmental conditions are very similar to those of earlier EC models that use parameters produced by the TLEC models. There are a few differences. The new MEC models allow setting the time for seed emergence, have realistic exponential canopy growth, and have corrected harvest dates for potato and tomato. The new MEC models indicate that the maximum edible biomass per meter squared per day is produced at the maximum allowed carbon dioxide level, the nominal temperatures, and the maximum light input. Reducing the carbon dioxide level from the maximum to the minimum allowed in the model reduces crop production significantly. Increasing temperature decreases production more than it decreases the time to harvest, so productivity in edible biomass per meter squared per day is greater at nominal than maximum temperatures, The productivity in edible biomass per meter squared per day is greatest at the maximum light energy input allowed in the model, but the edible biomass produced per light energy input unit is lower than at nominal light levels. Reducing light levels increases light and power use efficiency. The MEC models suggest we can adjust the light energy day-to- day to accommodate power shortages or Lise excess power while monitoring and controlling edible biomass production.

  17. Fuzzy logic technology for modeling of greenhouse crop transpiration rate

    Science.gov (United States)

    Deng, Lujuan; Wang, Huaishan

    2006-11-01

    The objective of this paper was present a reasonable greenhouse crop transpiration rate model for irrigation scheduling thereby to achieve the best effect, for example, water and energy economizing furthermore to make crop growing better. So it was essential to measure crop transpiration rate. Owing to the difficulty of obtaining accurate real time data of crop transpiration, it was commonly estimated from weather parameters. So the fuzzy logic model for estimation of greenhouse crop transpiration rate was developed. The model was made up of five sub-systems and three layers. There were nine input variables and one output variable. The results of comparison between measured and fuzzy model is inspirer. The squared correlation coefficient (r2) by fuzzy model method (r2=0.9302) is slightly higher than by FAO Penman-Monteith formula (r2=0.9213). The fuzzy logic crop transpiration rate model could be easily extended for irrigation decision-making.

  18. Parameterization Models for Pesticide Exposure via Crop Consumption

    DEFF Research Database (Denmark)

    Fantke, Peter; Wieland, Peter; Juraske, Ronnie

    2012-01-01

    An approach for estimating human exposure to pesticides via consumption of six important food crops is presented that can be used to extend multimedia models applied in health risk and life cycle impact assessment. We first assessed the variation of model output (pesticide residues per kg applied......) as a function of model input variables (substance, crop, and environmental properties) including their possible correlations using matrix algebra. We identified five key parameters responsible for between 80% and 93% of the variation in pesticide residues, namely time between substance application and crop......-specific models by parametrizing a complex fate and exposure assessment framework. The parametric models thereby reflect the framework’s physical and chemical mechanisms and predict pesticide residues in harvest using linear combinations of crop, crop surface, and soil compartments. Parametric model results...

  19. Connecting Biochemical Photosynthesis Models with Crop Models to Support Crop Improvement

    Science.gov (United States)

    Wu, Alex; Song, Youhong; van Oosterom, Erik J.; Hammer, Graeme L.

    2016-01-01

    The next advance in field crop productivity will likely need to come from improving crop use efficiency of resources (e.g., light, water, and nitrogen), aspects of which are closely linked with overall crop photosynthetic efficiency. Progress in genetic manipulation of photosynthesis is confounded by uncertainties of consequences at crop level because of difficulties connecting across scales. Crop growth and development simulation models that integrate across biological levels of organization and use a gene-to-phenotype modeling approach may present a way forward. There has been a long history of development of crop models capable of simulating dynamics of crop physiological attributes. Many crop models incorporate canopy photosynthesis (source) as a key driver for crop growth, while others derive crop growth from the balance between source- and sink-limitations. Modeling leaf photosynthesis has progressed from empirical modeling via light response curves to a more mechanistic basis, having clearer links to the underlying biochemical processes of photosynthesis. Cross-scale modeling that connects models at the biochemical and crop levels and utilizes developments in upscaling leaf-level models to canopy models has the potential to bridge the gap between photosynthetic manipulation at the biochemical level and its consequences on crop productivity. Here we review approaches to this emerging cross-scale modeling framework and reinforce the need for connections across levels of modeling. Further, we propose strategies for connecting biochemical models of photosynthesis into the cross-scale modeling framework to support crop improvement through photosynthetic manipulation.

  20. Connecting Biochemical Photosynthesis Models with Crop Models to Support Crop Improvement

    Directory of Open Access Journals (Sweden)

    Alex Wu

    2016-10-01

    Full Text Available The next advance in field crop productivity will likely need to come from improving crop use efficiency of resources (e.g. light, water and nitrogen, aspects of which are closely linked with overall crop photosynthetic efficiency. Progress in genetic manipulation of photosynthesis is confounded by uncertainties of consequences at crop level because of difficulties connecting across scales. Crop growth and development simulation models that integrate across biological levels of organization and use a gene-to-phenotype modelling approach may present a way forward. There has been a long history of development of crop models capable of simulating dynamics of crop physiological attributes. Many crop models incorporate canopy photosynthesis (source as a key driver for crop growth, while others derive crop growth from the balance between source- and sink-limitations. Modelling leaf photosynthesis has progressed from empirical modelling via light response curves to a more mechanistic basis, having clearer links to the underlying biochemical processes of photosynthesis. Cross-scale modelling that connects models at the biochemical and crop levels and utilises developments in upscaling leaf-level models to canopy models has the potential to bridge the gap between photosynthetic manipulation at the biochemical level and its consequences on crop productivity. Here we review approaches to this emerging cross-scale modelling framework and reinforce the need for connections across levels of modelling. Further, we propose strategies for connecting biochemical models of photosynthesis into the cross-scale modelling framework to support crop improvement through photosynthetic manipulation.

  1. Lumped-parameter models

    Energy Technology Data Exchange (ETDEWEB)

    Ibsen, Lars Bo; Liingaard, M.

    2006-12-15

    A lumped-parameter model represents the frequency dependent soil-structure interaction of a massless foundation placed on or embedded into an unbounded soil domain. In this technical report the steps of establishing a lumped-parameter model are presented. Following sections are included in this report: Static and dynamic formulation, Simple lumped-parameter models and Advanced lumped-parameter models. (au)

  2. Calibration of the Crop model in the Community Land Model

    Directory of Open Access Journals (Sweden)

    X. Zeng

    2013-01-01

    Full Text Available Farming is using more terrestrial ground with increases in population and the expanding use of agriculture for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. CLM-Crop development used measurements of gross primary productivity and net ecosystem exchange from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. In this paper we calibrate these values in order to provide a faithful projection in terms of both plant development and net carbon exchange, using a Markov chain Monte Carlo technique.

  3. Mathematical Modeling of the Agriculture Crop Technology

    Directory of Open Access Journals (Sweden)

    D. Drucioc

    1999-02-01

    Full Text Available The organized structure of computer system for economic and ecological estimation of agriculture crop technologies is described. The system is composed of six interconnected blocks. The linear, non-linear and stochastic mathematical models for machinery sizing and selection in farm-level cropping system is presented in the mathematical model block of computer system.

  4. Relations Between Red Edge Characteristics and Agronomic Parameters of Crops

    Institute of Scientific and Technical Information of China (English)

    TANG Yan-Lin; WANG Ren-Chao; HUANG Jing-Feng

    2004-01-01

    The hyperspectral reflectance of the canopy and the leaves on the main stem for six varieties, two each of rice, corn, and cotton crops, were measured at different growth stages with an ASD FieldSpec Pro FRTM to analyze red edge characteristics for leaf area indices (LAI), aboveground biomass, as well as the chlorophyll, carotenoid, and nitrogen shift' for λr of the leaf spectra for all 3 crops as the development stages progressed. For rice, corn, and cotton the LAI and fresh leaf mass had highly significant correlations (P < 0.01) to the red edge parameters λr, Dλr, and Sr of their canopy spectra. Additionally, for all crops the chlorophyll-a, chlorophyll-b, total chlorophyll, and carotenoid content of the leaves all had highly significant (P < 0.01) correlations to their λr.For rice, the nitrogen content of the leaves in g kg-1 and phytomass for a unit area of land in g m-2 also had a highly significant (P < 0.01) correlation to λr, Dλr, and Sr of the canopy spectra.

  5. Retrieving crop leaf area index by assimilation of MODIS data into a crop growth model

    Institute of Scientific and Technical Information of China (English)

    2010-01-01

    Leaf area index (LAI) is an important parameter in monitoring crop growth. One of the methods for retrieving LAI from remotely sensed observations is through inversion of canopy reflectance models. Many model inversion methods fail to account for variable LAI values at different crop growth stages. In this research, we use the crop growth model to describe the LAI changes with crop growth, and consider a priori LAI values at different crop growth stages as constraint information. The key approach of this research is to assimilate multiple canopy reflectance values observed at different growth stages and a priori LAI values into a coupled crop growth and radiative transfer model sequentially using a variational data assimilation algorithm. Adjoint method is used to minimize the cost function. Any other information source can be easily incorporated into the inversion cost function. The validation results show that the time series of MODIS canopy reflectance can greatly reduce the uncertainty of the inverted LAI values. Compared with MODIS LAI product at Changping and Shunyi Counties of Beijing, this method has significantly improved the estimated LAI temporal profile.

  6. Modeling the crop transpiration using an optimality-based approach

    Institute of Scientific and Technical Information of China (English)

    Stanislaus; J.Schymanski; Murugesu; Sivapalan

    2008-01-01

    Evapotranspiration constitutes more than 80% of the long-term water balance in Northern China.In this area,crop transpiration due to large areas of agriculture and irrigation is responsible for the majority of evapotranspiration.A model for crop transpiration is therefore essential for estimating the agricultural water consumption and understanding its feedback to the environment.However,most existing hydrological models usually calculate transpiration by relying on parameter calibration against local observations,and do not take into account crop feedback to the ambient environment.This study presents an optimality-based ecohydrology model that couples an ecological hypothesis,the photosynthetic process,stomatal movement,water balance,root water uptake and crop senescence,with the aim of predicting crop characteristics,CO2 assimilation and water balance based only on given meteorological data.Field experiments were conducted in the Weishan Irrigation District of Northern China to evaluate performance of the model.Agreement between simulation and measurement was achieved for CO2 assimilation,evapotranspiration and soil moisture content.The vegetation optimality was proven valid for crops and the model was applicable for both C3 and C4 plants.Due to the simple scheme of the optimality-based approach as well as its capability for modeling dynamic interactions between crops and the water cycle without prior vegetation information,this methodology is potentially useful to couple with the distributed hydrological model for application at the watershed scale.

  7. Effects of input uncertainty on cross-scale crop modeling

    Science.gov (United States)

    Waha, Katharina; Huth, Neil; Carberry, Peter

    2014-05-01

    The quality of data on climate, soils and agricultural management in the tropics is in general low or data is scarce leading to uncertainty in process-based modeling of cropping systems. Process-based crop models are common tools for simulating crop yields and crop production in climate change impact studies, studies on mitigation and adaptation options or food security studies. Crop modelers are concerned about input data accuracy as this, together with an adequate representation of plant physiology processes and choice of model parameters, are the key factors for a reliable simulation. For example, assuming an error in measurements of air temperature, radiation and precipitation of ± 0.2°C, ± 2 % and ± 3 % respectively, Fodor & Kovacs (2005) estimate that this translates into an uncertainty of 5-7 % in yield and biomass simulations. In our study we seek to answer the following questions: (1) are there important uncertainties in the spatial variability of simulated crop yields on the grid-cell level displayed on maps, (2) are there important uncertainties in the temporal variability of simulated crop yields on the aggregated, national level displayed in time-series, and (3) how does the accuracy of different soil, climate and management information influence the simulated crop yields in two crop models designed for use at different spatial scales? The study will help to determine whether more detailed information improves the simulations and to advise model users on the uncertainty related to input data. We analyse the performance of the point-scale crop model APSIM (Keating et al., 2003) and the global scale crop model LPJmL (Bondeau et al., 2007) with different climate information (monthly and daily) and soil conditions (global soil map and African soil map) under different agricultural management (uniform and variable sowing dates) for the low-input maize-growing areas in Burkina Faso/West Africa. We test the models' response to different levels of input

  8. Putting mechanisms into crop production models.

    Science.gov (United States)

    Boote, Kenneth J; Jones, James W; White, Jeffrey W; Asseng, Senthold; Lizaso, Jon I

    2013-09-01

    Crop growth models dynamically simulate processes of C, N and water balance on daily or hourly time-steps to predict crop growth and development and at season-end, final yield. Their ability to integrate effects of genetics, environment and crop management have led to applications ranging from understanding gene function to predicting potential impacts of climate change. The history of crop models is reviewed briefly, and their level of mechanistic detail for assimilation and respiration, ranging from hourly leaf-to-canopy assimilation to daily radiation-use efficiency is discussed. Crop models have improved steadily over the past 30-40 years, but much work remains. Improvements are needed for the prediction of transpiration response to elevated CO₂ and high temperature effects on phenology and reproductive fertility, and simulation of root growth and nutrient uptake under stressful edaphic conditions. Mechanistic improvements are needed to better connect crop growth to genetics and to soil fertility, soil waterlogging and pest damage. Because crop models integrate multiple processes and consider impacts of environment and management, they have excellent potential for linking research from genomics and allied disciplines to crop responses at the field scale, thus providing a valuable tool for deciphering genotype by environment by management effects. © 2013 John Wiley & Sons Ltd.

  9. Crop rotation modelling - A European model intercomparison

    DEFF Research Database (Denmark)

    Kollas, Chris; Kersebaum, Kurt C; Nendel, Claas;

    2015-01-01

    Diversification of crop rotations is considered an option to increase the resilience of European crop production under climate change. So far, however, many crop simulation studies have focused on predicting single crops in separate one-year simulations. Here, we compared the capability of fiftee...

  10. Deconstructing crop processes and models via identities

    DEFF Research Database (Denmark)

    Porter, John Roy; Christensen, Svend

    2013-01-01

    This paper is part review and part opinion piece; it has three parts of increasing novelty and speculation in approach. The first presents an overview of how some of the major crop simulation models approach the issue of simulating the responses of crops to changing climatic and weather variables......, mainly atmospheric CO2 concentration and increased and/or varying temperatures. It illustrates an important principle in models of a single cause having alternative effects and vice versa. The second part suggests some features, mostly missing in current crop models, that need to be included...

  11. Potato Production as Affected by Crop Parameters and Meteoro Logical Elements

    Science.gov (United States)

    Pereira, André B.; Villa Nova, Nilson A.; Pereira, Antonio R.

    Meteorological elements directly influence crop potential productivity, regulating its transpiration, photosynthesis, and respiration processes in such a way as to control the growth and development of the plants throughout their physiological mechanisms at a given site. The interaction of the meteorological factors with crop responses is complex and has been the target of attention of many researchers from all over the world. There is currently a great deal of interest in estimating crop productivity as a function of climate by means of different crop weather models in order to help growers choose planting locations and timing to produce high yields with good tuber quality under site-specific atmospheric conditions. In this manuscript an agrometeorological model based on maximum carbon dioxide assimilation rates for C3 plants, fraction of photosynthetically active radiation, air temperature, photoperiod duration, and crop parameters is assessed as to its performance under tropical conditions. Crop parameters include leaf areaand harvest indexes, dry matter content of potato tubers, and crop cycles to estimate potato potential yields. Productivity obtained with the cultivar Itararé, grown with adequate soil water supply conditions at four different sites in the State of São Paulo (Itararé, Piracicaba, TatuÍ, and São Manuel), Brazil, were used to test the model. The results showed thatthe agrometeorological model tested under the climatic conditions of the State of São Paulo in general underestimated irrigated potato yield by less than 10%.This justifies the recommendation to test the performance of the model in study in other climaticregions for different crops and genotypes under optimal irrigationconditions in further scientific investigations. We reached the conclusion that the agrometeorological model taking into account information on leaf area index, photoperiod duration, photosynthetically active radiation and air temperature is feasible to estimate

  12. Crop insurance: Risks and models of insurance

    Directory of Open Access Journals (Sweden)

    Čolović Vladimir

    2014-01-01

    Full Text Available The issue of crop protection is very important because of a variety of risks that could cause difficult consequences. One type of risk protection is insurance. The author in the paper states various models of insurance in some EU countries and the systems of subsidizing of insurance premiums by state. The author also gives a picture of crop insurance in the U.S., noting that in this country pays great attention to this matter. As for crop insurance in Serbia, it is not at a high level. The main problem with crop insurance is not only the risks but also the way of protection through insurance. The basic question that arises not only in the EU is the question is who will insure and protect crops. There are three possibilities: insurance companies under state control, insurance companies that are public-private partnerships or private insurance companies on a purely commercial basis.

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

    Energy Technology Data Exchange (ETDEWEB)

    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.

  14. Lumped-parameter models

    DEFF Research Database (Denmark)

    Ibsen, Lars Bo; Liingaard, Morten

    A lumped-parameter model represents the frequency dependent soil-structure interaction of a massless foundation placed on or embedded into an unbounded soil domain. The lumped-parameter model development have been reported by (Wolf 1991b; Wolf 1991a; Wolf and Paronesso 1991; Wolf and Paronesso 19...

  15. Response model parameter linking

    NARCIS (Netherlands)

    Barrett, Michelle Derbenwick

    2015-01-01

    With a few exceptions, the problem of linking item response model parameters from different item calibrations has been conceptualized as an instance of the problem of equating observed scores on different test forms. This thesis argues, however, that the use of item response models does not require

  16. Estimating Canopy Dark Respiration for Crop Models

    Science.gov (United States)

    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.

  17. Crop modelling for integrated assessment of risk to food production from climate change

    DEFF Research Database (Denmark)

    Ewert, F; Rötter, R P; Bindi, M

    2015-01-01

    The complexity of risks posed by climate change and possible adaptations for crop production has called for integrated assessment and modelling (IAM) approaches linking biophysical and economic models. This paper attempts to provide an overview of the present state of crop modelling to assess...... climate change risks to food production and to which extent crop models comply with IAM demands. Considerable progress has been made in modelling effects of climate variables, where crop models best satisfy IAM demands. Demands are partly satisfied for simulating commonly required assessment variables....... However, progress on the number of simulated crops, uncertainty propagation related to model parameters and structure, adaptations and scaling are less advanced and lagging behind IAM demands. The limitations are considered substantial and apply to a different extent to all crop models. Overcoming...

  18. Modeling olive-crop forecasting in Tunisia

    Science.gov (United States)

    Ben Dhiab, Ali; Ben Mimoun, Mehdi; Oteros, Jose; Garcia-Mozo, Herminia; Domínguez-Vilches, Eugenio; Galán, Carmen; Abichou, Mounir; Msallem, Monji

    2016-01-01

    Tunisia is the world's second largest olive oil-producing region after the European Union. This paper reports on the use of models to forecast local olive crops, using data for Tunisia's five main olive-producing areas: Mornag, Jemmel, Menzel Mhiri, Chaal, and Zarzis. Airborne pollen counts were monitored over the period 1993-2011 using a Cour trap. Forecasting models were constructed using agricultural data (harvest size in tonnes of fruit/year) and data for several weather-related and phenoclimatic variables (rainfall, humidity, temperature, Growing Degree Days, and Chilling). Analysis of these data revealed that the amount of airborne pollen emitted over the pollen season as a whole (i.e., the Pollen Index) was the variable most influencing harvest size. Findings for all local models also indicated that the amount, timing, and distribution of rainfall (except during blooming) had a positive impact on final olive harvests. Air temperature also influenced final crop yield in three study provinces (Menzel Mhiri, Chaal, and Zarzis), but with varying consequences: in the model constructed for Chaal, cumulative maximum temperature from budbreak to start of flowering contributed positively to yield; in the Menzel Mhiri model, cumulative average temperatures during fruit development had a positive impact on output; in Zarzis, by contrast, cumulative maximum temperature during the period prior to flowering negatively influenced final crop yield. Data for agricultural and phenoclimatic variables can be used to construct valid models to predict annual variability in local olive-crop yields; here, models displayed an accuracy of 98, 93, 92, 91, and 88 % for Zarzis, Mornag, Jemmel, Chaal, and Menzel Mhiri, respectively.

  19. Distributed Parameter Modelling Applications

    DEFF Research Database (Denmark)

    2011-01-01

    Here the issue of distributed parameter models is addressed. Spatial variations as well as time are considered important. Several applications for both steady state and dynamic applications are given. These relate to the processing of oil shale, the granulation of industrial fertilizers and the d......Here the issue of distributed parameter models is addressed. Spatial variations as well as time are considered important. Several applications for both steady state and dynamic applications are given. These relate to the processing of oil shale, the granulation of industrial fertilizers...... sands processing. The fertilizer granulation model considers the dynamics of MAP-DAP (mono and diammonium phosphates) production within an industrial granulator, that involves complex crystallisation, chemical reaction and particle growth, captured through population balances. A final example considers...

  20. Econometric models for predicting confusion crop ratios

    Science.gov (United States)

    Umberger, D. E.; Proctor, M. H.; Clark, J. E.; Eisgruber, L. M.; Braschler, C. B. (Principal Investigator)

    1979-01-01

    Results for both the United States and Canada show that econometric models can provide estimates of confusion crop ratios that are more accurate than historical ratios. Whether these models can support the LACIE 90/90 accuracy criterion is uncertain. In the United States, experimenting with additional model formulations could provide improved methods models in some CRD's, particularly in winter wheat. Improved models may also be possible for the Canadian CD's. The more aggressive province/state models outperformed individual CD/CRD models. This result was expected partly because acreage statistics are based on sampling procedures, and the sampling precision declines from the province/state to the CD/CRD level. Declining sampling precision and the need to substitute province/state data for the CD/CRD data introduced measurement error into the CD/CRD models.

  1. Modelling the effect of supplementary lighting on production and light utilisation efficiency of greenhouse crops.

    NARCIS (Netherlands)

    Koning, de J.C.M.

    1997-01-01

    The effect of supplementary lighting (SL) on dry matter production of greenhouse crops is predictable with ALSIM, a new crop growth model based on SUCROS87. The light utilization efficiency (LUE), defined as daily dry matter production divided by the daily photosynthetic photon flux is a parameter f

  2. Hortisim: a model for greenhouse crops and greenhouse climate

    NARCIS (Netherlands)

    Gijzen, H.; Heuvelink, E.; Challa, H.; Dayan, E.; Marcelis, L.F.M.; Cohen, S.; Fuchs, M.

    1998-01-01

    A combined model for crop production and climate in greenhouses, HORTISIM, was developed. Existing models, developed by several research groups, of various aspects of crop growth and greenhouse climate have been integrated. HORTISIM contains 7 submodels (Weather, Greenhouse Climate, Soil, Crop, Gree

  3. Dynamic Patterns, Parameters, and Climatic Response of CO2 Exchange of Agricultural Crops: Monocotyledons VS. Dicotyledons

    Science.gov (United States)

    Gilmanov, T. G.; Wylie, B. K.; Howard, D. M.

    2012-12-01

    Net CO2 exchange data from long-term flux tower measurements in monocotyledonous (wheat, maize) and dicotyledonous (soybeans, alfalfa, peas, peanuts) crops were partitioned into photosynthesis (P) and respiration (R) using the light-soil temperature-VPD response method. Analysis of the resulting time series of P and R revealed patterns of temporal and phenological dynamics in these plant groups. We established differences in ranges and dynamic patterns of P and R as well as CO2 exchange parameters (quantum yield, photosynthetic capacity, respiration rate, light-use efficiency, curvature of the VPD response). Weekly P and R data combined with remotely sensed 7-day eMODIS NDVI allow identification of the quasi-linear relationships between P, R, and NDVI, as well as estimation of parameters of NDVI response (start of the growing season, duration of the linearity period, slope of NDVI response). While the linear-like patterns occur early in the season, later the flux response to NDVI becomes less pronounced, and for the whole season the flux-NDVI relationship assumes a hysteresis-like pattern. Introduction of VPD and soil moisture limitation as well as phenological controls (growing degree days) leads to more flexible models for P and R in relation to NDVI and on-site drivers. These models allow mapping of the cropland CO2 exchange at regional and larger scales (e.g., the Great Plains). Significant relationships of the crop GPP to the seasonally integrated NDVI were also established, providing an opportunity for mapping of crop productivity using geographically distributed historic NDVI data. On the other hand, long time series (6 to 12 years and longer) of weekly P and R data lead to models of annual photosynthesis and respiration in response to climatic factors that may be used for prognostic purposes. We developed a model of maize GPP on the Great Plains in relation to the sum of temperatures above 5 °C and the hydrologic year precipitation. The model describes 75

  4. Modeling and control of greenhouse crop growth

    CERN Document Server

    Rodríguez, Francisco; Guzmán, José Luis; Ramírez-Arias, Armando

    2015-01-01

    A discussion of challenges related to the modeling and control of greenhouse crop growth, this book presents state-of-the-art answers to those challenges. The authors model the subsystems involved in successful greenhouse control using different techniques and show how the models obtained can be exploited for simulation or control design; they suggest ideas for the development of physical and/or black-box models for this purpose. Strategies for the control of climate- and irrigation-related variables are brought forward. The uses of PID control and feedforward compensators, both widely used in commercial tools, are summarized. The benefits of advanced control techniques—event-based, robust, and predictive control, for example—are used to improve on the performance of those basic methods. A hierarchical control architecture is developed governed by a high-level multiobjective optimization approach rather than traditional constrained optimization and artificial intelligence techniques.  Reference trajector...

  5. RICE YIELD ESTIMATION THROUGH ASSIMILATING SATELLITE DATA INTO A CROP SIMUMLATION MODEL

    Directory of Open Access Journals (Sweden)

    N. T. Son

    2016-06-01

    Full Text Available Rice is globally the most important food crop, feeding approximately half of the world’s population, especially in Asia where around half of the world’s poorest people live. Thus, advanced spatiotemporal information of rice crop yield during crop growing season is critically important for crop management and national food policy making. The main objective of this study was to develop an approach to integrate remotely sensed data into a crop simulation model (DSSAT for rice yield estimation in Taiwan. The data assimilation was processed to integrate biophysical parameters into DSSAT model for rice yield estimation using the particle swarm optimization (PSO algorithm. The cost function was constructed based on the differences between the simulated leaf area index (LAI and MODIS LAI, and the optimization process starts from an initial parameterization and accordingly adjusts parameters (e.g., planting date, planting population, and fertilizer amount in the crop simulation model. The fitness value obtained from the cost function determined whether the optimization algorithm had reached the optimum input parameters using a user-defined tolerance. The results of yield estimation compared with the government’s yield statistics indicated the root mean square error (RMSE of 11.7% and mean absolute error of 9.7%, respectively. This study demonstrated the applicability of satellite data assimilation into a crop simulation model for rice yield estimation, and the approach was thus proposed for crop yield monitoring purposes in the study region.

  6. Rice Yield Estimation Through Assimilating Satellite Data Into a Crop Simumlation Model

    Science.gov (United States)

    Son, N. T.; Chen, C. F.; Chen, C. R.; Chang, L. Y.; Chiang, S. H.

    2016-06-01

    Rice is globally the most important food crop, feeding approximately half of the world's population, especially in Asia where around half of the world's poorest people live. Thus, advanced spatiotemporal information of rice crop yield during crop growing season is critically important for crop management and national food policy making. The main objective of this study was to develop an approach to integrate remotely sensed data into a crop simulation model (DSSAT) for rice yield estimation in Taiwan. The data assimilation was processed to integrate biophysical parameters into DSSAT model for rice yield estimation using the particle swarm optimization (PSO) algorithm. The cost function was constructed based on the differences between the simulated leaf area index (LAI) and MODIS LAI, and the optimization process starts from an initial parameterization and accordingly adjusts parameters (e.g., planting date, planting population, and fertilizer amount) in the crop simulation model. The fitness value obtained from the cost function determined whether the optimization algorithm had reached the optimum input parameters using a user-defined tolerance. The results of yield estimation compared with the government's yield statistics indicated the root mean square error (RMSE) of 11.7% and mean absolute error of 9.7%, respectively. This study demonstrated the applicability of satellite data assimilation into a crop simulation model for rice yield estimation, and the approach was thus proposed for crop yield monitoring purposes in the study region.

  7. Modeling evolution of insect resistance to genetically modified crops

    OpenAIRE

    2015-01-01

    Genetically modified crops producing insecticidal proteins from Bacillus thuringiensis (Bt) for insect control have been planted on more than 200 million ha worldwide since 1996 [1]. Evolution of resistance by insect pests threatens the continued success of Bt crops [2, 3]. To delay pest resistance, refuges of non-Bt crops are planted near Bt crops to allow survival of susceptible pests [4, 5]. We used computer simulations of a population genetic model to determine if predictions from the the...

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

    Science.gov (United States)

    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.

  9. A GPS Backpack System for Mapping Soil and Crop Parameters in Agricultural Fields

    Science.gov (United States)

    Stafford, J. V.; Lebars, J. M.

    Farmers are having to gather increasing amounts of data on their soils and crops. Precision agriculture metre-by-metre is based on a knowledge of the spatial variation of soil and crop parameters across a field. The data has to be spatially located and GPS is an effective way of doing this. A backpack data logging system with GPS position tagging is described which has been designed to aid a fanner in the manual collection of data.

  10. Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts

    NARCIS (Netherlands)

    Wit, de A.J.W.; Diepen, van C.A.

    2007-01-01

    Uncertainty in spatial and temporal distribution of rainfall in regional crop yield simulations comprises a major fraction of the error on crop model simulation results. In this paper we used an Ensemble Kalman filter (EnKF) to assimilate coarse resolution satellite microwave sensor derived soil

  11. Future contributions of crop modelling : from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement

    NARCIS (Netherlands)

    Hammer, G.L.; Kropff, M.J.; Sinclair, T.R.; Porter, J.R.

    2002-01-01

    Crop modelling has evolved over the last 30 or so years in concert with advances in crop physiology, crop ecology and computing technology. Having reached a respectable degree of acceptance, it is appropriate to review briefly the course of developments in crop modelling and to project what might be

  12. Future contributions of crop modelling : from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement

    NARCIS (Netherlands)

    Hammer, G.L.; Kropff, M.J.; Sinclair, T.R.; Porter, J.R.

    2002-01-01

    Crop modelling has evolved over the last 30 or so years in concert with advances in crop physiology, crop ecology and computing technology. Having reached a respectable degree of acceptance, it is appropriate to review briefly the course of developments in crop modelling and to project what might be

  13. The impacts of data constraints on the predictive performance of a general process-based crop model (PeakN-crop v1.0)

    Science.gov (United States)

    Caldararu, Silvia; Purves, Drew W.; Smith, Matthew J.

    2017-04-01

    Improving international food security under a changing climate and increasing human population will be greatly aided by improving our ability to modify, understand and predict crop growth. What we predominantly have at our disposal are either process-based models of crop physiology or statistical analyses of yield datasets, both of which suffer from various sources of error. In this paper, we present a generic process-based crop model (PeakN-crop v1.0) which we parametrise using a Bayesian model-fitting algorithm to three different sources: data-space-based vegetation indices, eddy covariance productivity measurements and regional crop yields. We show that the model parametrised without data, based on prior knowledge of the parameters, can largely capture the observed behaviour but the data-constrained model greatly improves both the model fit and reduces prediction uncertainty. We investigate the extent to which each dataset contributes to the model performance and show that while all data improve on the prior model fit, the satellite-based data and crop yield estimates are particularly important for reducing model error and uncertainty. Despite these improvements, we conclude that there are still significant knowledge gaps, in terms of available data for model parametrisation, but our study can help indicate the necessary data collection to improve our predictions of crop yields and crop responses to environmental changes.

  14. Lessons from Climate Modeling on the Design and Use of Ensembles for Crop Modeling

    Science.gov (United States)

    Wallach, Daniel; Mearns, Linda O.; Ruane, Alexander C.; Roetter, Reimund P.; Asseng, Senthold

    2016-01-01

    Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor.

  15. Noah-MP-Crop: Introducing dynamic crop growth in the Noah-MP land surface model

    Science.gov (United States)

    Liu, Xing; Chen, Fei; Barlage, Michael; Zhou, Guangsheng; Niyogi, Dev

    2016-12-01

    Croplands are important in land-atmosphere interactions and in the modification of local and regional weather and climate; however, they are poorly represented in the current version of the coupled Weather Research and Forecasting/Noah with multiparameterization (Noah-MP) land surface modeling system. This study introduced dynamic corn (Zea mays) and soybean (Glycine max) growth simulations and field management (e.g., planting date) into Noah-MP and evaluated the enhanced model (Noah-MP-Crop) at field scales using crop biomass data sets, surface heat fluxes, and soil moisture observations. Compared to the generic dynamic vegetation and prescribed-leaf area index (LAI)-driven methods in Noah-MP, the Noah-MP-Crop showed improved performance in simulating leaf area index (LAI) and crop biomass. This model is able to capture the seasonal and annual variability of LAI and to differentiate corn and soybean in peak values of LAI as well as the length of growing seasons. Improved simulations of crop phenology in Noah-MP-Crop led to better surface heat flux simulations, especially in the early period of growing season where current Noah-MP significantly overestimated LAI. The addition of crop yields as model outputs expand the application of Noah-MP-Crop to regional agriculture studies. There are limitations in the use of current growing degree days (GDD) criteria to predict growth stages, and it is necessary to develop a new method that combines GDD with other environmental factors, to more accurately define crop growth stages. The capability introduced in Noah-MP allows further crop-related studies and development.

  16. Could a crop model be useful for improving sunflower crop management?

    Directory of Open Access Journals (Sweden)

    Flénet Francis

    2008-05-01

    Full Text Available In France, there is a need for improved sunflower crop management, in order to meet the greater requirement for oil by increasing both seed yields and the area of this crop. The objective of this article is to review the main characteristics of sunflower crop management in France and in other countries, in order to emphasize the need for improvement, and to evaluate if the recent advances in crop modelling could help to find solutions. In France, a better adaptation of crop management to water availability is needed, as well as a more efficient control of diseases without applying more fungicides. The results of these objectives would also trigger major improvements in other countries, but there is also a need to control insects and to adapt crop management to the goals of oil quality. The main sunflower crop models are reviewed in this article, with an emphasis on the most recent ones. Their ability to contribute to improving sunflower crop management, although they do not take into account diseases and insects, is discussed. Confidence in the decisions based on simulations, and the way to evaluate it, is also examined.

  17. Estimating crop yield using a satellite-based light use efficiency model

    DEFF Research Database (Denmark)

    Yuan, Wenping; Chen, Yang; Xia, Jiangzhou

    2016-01-01

    for simulating crops’ GPP. At both irrigated and rainfed sites, the EC-LUE model exhibits a similar level of performance. However, large errors are found when simulating yield based on crop harvest index. This analysis highlights the need to improve the representation of the harvest index and carbon allocation...... primary production (GPP) and yield of crops. The EC-LUE model can explain on average approximately 90% of the variability in GPP for 36 FLUXNET sites globally. The results indicate that a universal set of parameters, independent of crop species (except for C4 crops), can be adopted in the EC-LUE model...... for improving crop yield estimations from satellite-based methods....

  18. An integrated model for assessing both crop productivity and agricultural water resources at a large scale

    Science.gov (United States)

    Okada, M.; Sakurai, G.; Iizumi, T.; Yokozawa, M.

    2012-12-01

    Agricultural production utilizes regional resources (e.g. river water and ground water) as well as local resources (e.g. temperature, rainfall, solar energy). Future climate changes and increasing demand due to population increases and economic developments would intensively affect the availability of water resources for agricultural production. While many studies assessed the impacts of climate change on agriculture, there are few studies that dynamically account for changes in water resources and crop production. This study proposes an integrated model for assessing both crop productivity and agricultural water resources at a large scale. Also, the irrigation management to subseasonal variability in weather and crop response varies for each region and each crop. To deal with such variations, we used the Markov Chain Monte Carlo technique to quantify regional-specific parameters associated with crop growth and irrigation water estimations. We coupled a large-scale crop model (Sakurai et al. 2012), with a global water resources model, H08 (Hanasaki et al. 2008). The integrated model was consisting of five sub-models for the following processes: land surface, crop growth, river routing, reservoir operation, and anthropogenic water withdrawal. The land surface sub-model was based on a watershed hydrology model, SWAT (Neitsch et al. 2009). Surface and subsurface runoffs simulated by the land surface sub-model were input to the river routing sub-model of the H08 model. A part of regional water resources available for agriculture, simulated by the H08 model, was input as irrigation water to the land surface sub-model. The timing and amount of irrigation water was simulated at a daily step. The integrated model reproduced the observed streamflow in an individual watershed. Additionally, the model accurately reproduced the trends and interannual variations of crop yields. To demonstrate the usefulness of the integrated model, we compared two types of impact assessment of

  19. From field to globe: upscaling of crop growth modelling

    NARCIS (Netherlands)

    Bussel, van L.G.J.

    2011-01-01

    Recently, the scale of interest for application of crop growth models has extended to the region or even globe with time frames of 50-100 years. The application at larger scales of a crop growth model originally developed for a small scale without any adaptation might lead to errors and inaccuracies

  20. Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform.

    Science.gov (United States)

    Marshall-Colon, Amy; Long, Stephen P; Allen, Douglas K; Allen, Gabrielle; Beard, Daniel A; Benes, Bedrich; von Caemmerer, Susanne; Christensen, A J; Cox, Donna J; Hart, John C; Hirst, Peter M; Kannan, Kavya; Katz, Daniel S; Lynch, Jonathan P; Millar, Andrew J; Panneerselvam, Balaji; Price, Nathan D; Prusinkiewicz, Przemyslaw; Raila, David; Shekar, Rachel G; Shrivastava, Stuti; Shukla, Diwakar; Srinivasan, Venkatraman; Stitt, Mark; Turk, Matthew J; Voit, Eberhard O; Wang, Yu; Yin, Xinyou; Zhu, Xin-Guang

    2017-01-01

    Multi-scale models can facilitate whole plant simulations by linking gene networks, protein synthesis, metabolic pathways, physiology, and growth. Whole plant models can be further integrated with ecosystem, weather, and climate models to predict how various interactions respond to environmental perturbations. These models have the potential to fill in missing mechanistic details and generate new hypotheses to prioritize directed engineering efforts. Outcomes will potentially accelerate improvement of crop yield, sustainability, and increase future food security. It is time for a paradigm shift in plant modeling, from largely isolated efforts to a connected community that takes advantage of advances in high performance computing and mechanistic understanding of plant processes. Tools for guiding future crop breeding and engineering, understanding the implications of discoveries at the molecular level for whole plant behavior, and improved prediction of plant and ecosystem responses to the environment are urgently needed. The purpose of this perspective is to introduce Crops in silico (cropsinsilico.org), an integrative and multi-scale modeling platform, as one solution that combines isolated modeling efforts toward the generation of virtual crops, which is open and accessible to the entire plant biology community. The major challenges involved both in the development and deployment of a shared, multi-scale modeling platform, which are summarized in this prospectus, were recently identified during the first Crops in silico Symposium and Workshop.

  1. Modeling crop responses to environmental change

    Science.gov (United States)

    Rosenzweig, Cynthia

    1993-01-01

    Potential biophysical responses of crops to climate change are studied focusing on the primary environmental variables which define the limits to agricultural crop growth and production, and the principal methods for predicting climate change impacts on crop geography and production. It is concluded that the principal uncertainties in the prediction of the impacts of climate change on agriculture reside in the contribution of the direct effects of increasing CO2, in potential changes inclimate variability, and the effects of adjustments mechanisms in the context of climatic changes.

  2. Integrated modelling of crop production and nitrate leaching with the Daisy model.

    Science.gov (United States)

    Manevski, Kiril; Børgesen, Christen D; Li, Xiaoxin; Andersen, Mathias N; Abrahamsen, Per; Hu, Chunsheng; Hansen, Søren

    2016-01-01

    An integrated modelling strategy was designed and applied to the Soil-Vegetation-Atmosphere Transfer model Daisy for simulation of crop production and nitrate leaching under pedo-climatic and agronomic environment different than that of model original parameterisation. The points of significance and caution in the strategy are: •Model preparation should include field data in detail due to the high complexity of the soil and the crop processes simulated with process-based model, and should reflect the study objectives. Inclusion of interactions between parameters in a sensitivity analysis results in better account for impacts on outputs of measured variables.•Model evaluation on several independent data sets increases robustness, at least on coarser time scales such as month or year. It produces a valuable platform for adaptation of the model to new crops or for the improvement of the existing parameters set. On daily time scale, validation for highly dynamic variables such as soil water transport remains challenging. •Model application is demonstrated with relevance for scientists and regional managers. The integrated modelling strategy is applicable for other process-based models similar to Daisy. It is envisaged that the strategy establishes model capability as a useful research/decision-making, and it increases knowledge transferability, reproducibility and traceability.

  3. Simulation of potato yield in temperate condition by the AquaCrop model

    DEFF Research Database (Denmark)

    Razzaghi, Fatemeh; Zhenjiang, Zhou; Andersen, Mathias Neumann

    2017-01-01

    to calculate the soil water balance on a daily basis has become widespread in the last decades. Therefore, this study was performed to simulate potato yield, dry matter and soil water content under different water stress condition using the AquaCrop model. Three levels of irrigation comprising full irrigated...... was simulated using the AquaCrop model. Data from full irrigated treatment of 2014 was used for model calibration and data from 2013 (If, Id, and I0 treatments), 2014 (Id, and I0 treatments) and 2015 (If, Id, and I0 treatments) were used for model validation. The sensitivity analysis of different parameters...... showed that KcTr, HI0, CCX, calendar day from sowing to start of senescence and WP* had the most pronounced influence on tuber yield. The result showed that the AquaCrop model simulated soil water content, canopy cover and above-ground dry matter during the crop growth seasons with acceptable accuracy...

  4. Performance evaluation of selected crop yield-water use models for wheat crop

    Directory of Open Access Journals (Sweden)

    H. E. Igbadun

    2001-10-01

    Full Text Available Crop yield-water use models that provide useful information about the exact form of crop response to different amounts of water used by the crop throughout its growth stages and those that provide adequate information for decisions on optimal use of water in the farm were evaluated. Three crop yield models: Jensen (1968, Minhas et al., (1974 and Bras and Cordova (1981 additive type models were studied. Wheat (Triticum aestivum was planted at the Institute for Agricultural Research Farm during the 1995/96 and 1996/97 irrigation seasons of November to March. The data collected from the field experiments during the 1995/96 planting season were used to calibrate the models and their stress sensitivity factors estimated for four selected growth stages of the wheat crop. The ability of the model to predict grain yield of wheat with the estimated stress sensitivity factors was evaluated by comparing predicted grain yields by each model with those obtained in the field during the 1996/97 season. The three models performed fairly well in predicting grain yields, as the predicted results were not significantly different from the field measured grain yield at 5% level of significance.

  5. Contribution of crop models to adaptation in wheat

    DEFF Research Database (Denmark)

    Chenu, Karine; Porter, John Roy; Martre, Pierre

    2017-01-01

    With world population growing quickly, agriculture needs to produce more with fewer inputs while being environmentally friendly. In a context of changing environments, crop models are useful tools to simulate crop yields. Wheat (Triticum spp.) crop models have been evolving since the 1960s to tra......, and the upscaling of global change impacts. This review outlines the potential and limitations of modern wheat crop models in assisting agronomists, breeders, and policymakers to address the current and future challenges facing agriculture.......With world population growing quickly, agriculture needs to produce more with fewer inputs while being environmentally friendly. In a context of changing environments, crop models are useful tools to simulate crop yields. Wheat (Triticum spp.) crop models have been evolving since the 1960s...... to translate processes related to crop growth and development into mathematical equations. These have been used over decades for agronomic purposes, and have more recently incorporated advances in the modeling of environmental footprints, biotic constraints, trait and gene effects, climate change impact...

  6. Bioenergy crop models: Descriptions, data requirements and future challenges

    Energy Technology Data Exchange (ETDEWEB)

    Nair, S. Surendran [University of Tennessee, Knoxville (UTK); Kang, Shujiang [ORNL; Zhang, Xuesong [Pacific Northwest National Laboratory (PNNL); Miguez, Fernando [Iowa State University; Izaurralde, Dr. R. Cesar [Pacific Northwest National Laboratory (PNNL); Post, Wilfred M [ORNL; Dietze, Michael [University of Illinois, Urbana-Champaign; Lynd, L. [Dartmouth College; Wullschleger, Stan D [ORNL

    2012-01-01

    Field studies that address the production of lignocellulosic biomass as a source of renewable energy provide critical data for the development of bioenergy crop models. A literature survey revealed that 14 models have been used for simulating bioenergy crops including herbaceous and woody bioenergy crops, and for crassulacean acid metabolism (CAM) crops. These models simulate field-scale production of biomass for switchgrass (ALMANAC, EPIC, and Agro-BGC), miscanthus (MISCANFOR, MISCANMOD, and WIMOVAC), sugarcane (APSIM, AUSCANE, and CANEGRO), and poplar and willow (SECRETS and 3PG). Two models are adaptations of dynamic global vegetation models and simulate biomass yields of miscanthus and sugarcane at regional scales (Agro-IBIS and LPJmL). Although it lacks the complexity of other bioenergy crop models, the environmental productivity index (EPI) is the only model used to estimate biomass production of CAM (Agave and Opuntia) plants. Except for the EPI model, all models include representations of leaf area dynamics, phenology, radiation interception and utilization, biomass production, and partitioning of biomass to roots and shoots. A few models simulate soil water, nutrient, and carbon cycle dynamics, making them especially useful for assessing the environmental consequences (e.g., erosion and nutrient losses) associated with the large-scale deployment of bioenergy crops. The rapid increase in use of models for energy crop simulation is encouraging; however, detailed information on the influence of climate, soils, and crop management practices on biomass production is scarce. Thus considerable work remains regarding the parameterization and validation of process-based models for bioenergy crops; generation and distribution of high-quality field data for model development and validation; and implementation of an integrated framework for efficient, high-resolution simulations of biomass production for use in planning sustainable bioenergy systems.

  7. Global Gridded Crop Model Evaluation: Benchmarking, Skills, Deficiencies and Implications.

    Science.gov (United States)

    Muller, Christoph; Elliott, Joshua; Chryssanthacopoulos, James; Arneth, Almut; Balkovic, Juraj; Ciais, Philippe; Deryng, Delphine; Folberth, Christian; Glotter, Michael; Hoek, Steven; hide

    2017-01-01

    Crop models are increasingly used to simulate crop yields at the global scale, but so far there is no general framework on how to assess model performance. Here we evaluate the simulation results of 14 global gridded crop modeling groups that have contributed historic crop yield simulations for maize, wheat, rice and soybean to the Global Gridded Crop Model Intercomparison (GGCMI) of the Agricultural Model Intercomparison and Improvement Project (AgMIP). Simulation results are compared to reference data at global, national and grid cell scales and we evaluate model performance with respect to time series correlation, spatial correlation and mean bias. We find that global gridded crop models (GGCMs) show mixed skill in reproducing time series correlations or spatial patterns at the different spatial scales. Generally, maize, wheat and soybean simulations of many GGCMs are capable of reproducing larger parts of observed temporal variability (time series correlation coefficients (r) of up to 0.888 for maize, 0.673 for wheat and 0.643 for soybean at the global scale) but rice yield variability cannot be well reproduced by most models. Yield variability can be well reproduced for most major producing countries by many GGCMs and for all countries by at least some. A comparison with gridded yield data and a statistical analysis of the effects of weather variability on yield variability shows that the ensemble of GGCMs can explain more of the yield variability than an ensemble of regression models for maize and soybean, but not for wheat and rice. We identify future research needs in global gridded crop modeling and for all individual crop modeling groups. In the absence of a purely observation-based benchmark for model evaluation, we propose that the best performing crop model per crop and region establishes the benchmark for all others, and modelers are encouraged to investigate how crop model performance can be increased. We make our evaluation system accessible to all

  8. Modelling of soil salinity and halophyte crop production

    NARCIS (Netherlands)

    Vermue, E.; Metselaar, K.; Zee, van der S.E.A.T.M.

    2013-01-01

    In crop modelling the soil, plant and atmosphere system is regarded as a continuum with regard to root water uptake and transpiration. Crop production, often assumed to be linearly related with transpiration, depends on several factors, including water and nutrient availability and salinity. The

  9. Selecting crop models for decision making in wheat insurance

    NARCIS (Netherlands)

    Castaneda Vera, A.; Leffelaar, P.A.; Alvaro-Fuentes, J.; Cantero-Martinez, C.; Minguez, M.I.

    2015-01-01

    In crop insurance, the accuracy with which the insurer quantifies the actual risk is highly dependent on the availability on actual yield data. Crop models might be valuable tools to generate data on expected yields for risk assessment when no historical records are available. However, selecting a c

  10. Control and modelling of vertical temperature distribution in greenhouse crops

    NARCIS (Netherlands)

    Kempkes, F.L.K.; Bakker, J.C.; Braak, van de N.J.

    1998-01-01

    Based on physical transport processes (radiation, convection and latent heat transfer) a model has been developed to describe the vertical temperature distribution of a greenhouse crop. The radiation exchange factors between heating pipes, crop layers, soil and roof were determined as a function of

  11. Selecting crop models for decision making in wheat insurance

    NARCIS (Netherlands)

    Castaneda Vera, A.; Leffelaar, P.A.; Alvaro-Fuentes, J.; Cantero-Martinez, C.; Minguez, M.I.

    2015-01-01

    In crop insurance, the accuracy with which the insurer quantifies the actual risk is highly dependent on the availability on actual yield data. Crop models might be valuable tools to generate data on expected yields for risk assessment when no historical records are available. However, selecting a

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

    Science.gov (United States)

    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...

  13. AgMIP Training in Multiple Crop Models and Tools

    Science.gov (United States)

    Boote, Kenneth J.; Porter, Cheryl H.; Hargreaves, John; Hoogenboom, Gerrit; Thornburn, Peter; Mutter, Carolyn

    2015-01-01

    The Agricultural Model Intercomparison and Improvement Project (AgMIP) has the goal of using multiple crop models to evaluate climate impacts on agricultural production and food security in developed and developing countries. There are several major limitations that must be overcome to achieve this goal, including the need to train AgMIP regional research team (RRT) crop modelers to use models other than the ones they are currently familiar with, plus the need to harmonize and interconvert the disparate input file formats used for the various models. Two activities were followed to address these shortcomings among AgMIP RRTs to enable them to use multiple models to evaluate climate impacts on crop production and food security. We designed and conducted courses in which participants trained on two different sets of crop models, with emphasis on the model of least experience. In a second activity, the AgMIP IT group created templates for inputting data on soils, management, weather, and crops into AgMIP harmonized databases, and developed translation tools for converting the harmonized data into files that are ready for multiple crop model simulations. The strategies for creating and conducting the multi-model course and developing entry and translation tools are reviewed in this chapter.

  14. AquaCrop model simulation under different irrigation water and nitrogen strategies.

    Science.gov (United States)

    Khoshravesh, Mojtaba; Mostafazadeh-Fard, Behrouz; Heidarpour, Manouchehr; Kiani, Ali-Reza

    2013-01-01

    On a global scale, irrigated agriculture consumes about 72% of available freshwater resources. Deficit irrigation can be applied in the field to save irrigation water and still lead to acceptable crop production. The AquaCrop model is a simulation model for management of irrigation and nitrogen fertilizer. This model is a new model that is accurate, robust and requires fewer data inputs compared with the other models. The purpose of this study was to simulate canopy cover, grain yield and water use efficiency (WUE) for soybean using the AquaCrop model. A field line source sprinkler irrigation system was conducted under full and deficit irrigation using different nitrogen fertilizer applications during two cropping seasons for soybean at Gorgan province in Iran. The simulation results showed a reasonably accurate prediction of yield, canopy cover and WUE in all cases (error less than 23%). The simulated pattern of canopy progression over time was close to measured values, with Willmott's index of agreement for all the cases being ≥0.95 for different parameters. The AquaCrop model has the ability to simulate the WUE of soybean under different irrigation water and nitrogen applications. This model is a useful tool for managing the crop water productivity.

  15. Monitoring paddy rice crops through remote sensing: productivity estimation by light use efficiency model

    Science.gov (United States)

    Boschetti, Mirco; Mauri, Emanuela; Gadda, Chiara; Busetto, Lorenzo; Confalonieri, Roberto; Bocchi, Stefano; Brivio, Pietro A.

    2004-10-01

    Rice is one of the most important crops in the whole world, providing staple food for more than 3000 million people. For this reason FAO declared the year 2004 as The International Year of Rice promoting initiatives and researches on this valuable crop. Assessing the Net Primary Production (NPP) is fundamental to support a sustainable development and to give crop yield forecast essential to food security policy. Crop growth models can be useful tools for estimating growth, development and yield but require complex spatial distributed input parameters to produce valuable map. Light use efficiency (LUE) models, using satellite-borne data to achieve daily surface parameters, represent an alternative approach able to monitor differences in vegetation compound providing spatial distributed NPP maps. An experiment aimed at testing the capability of a LUE model using daily MODIS data to estimate rice crop production was conducted in a rice area of Northern Italy. Direct LAI measurements and indirect LAI2000 estimation were collected on different fields during the growing season to define a relationship with MODIS data. An hyperspectral MIVIS image was acquired in early July on the experimental site to provide high spatial resolution information on land cover distribution. LUE-NPP estimations on several fields were compared with CropSyst model outputs and field biomass measurements. A comparison of different methods performance is presented and relative advantages and drawbacks in spatialization are discussed.

  16. Assessing winter cover crop nutrient uptake efficiency using a water quality simulation model

    Science.gov (United States)

    Yeo, I.-Y.; Lee, S.; Sadeghi, A. M.; Beeson, P. C.; Hively, W. D.; McCarty, G. W.; Lang, M. W.

    2014-12-01

    Winter cover crops are an effective conservation management practice with potential to improve water quality. Throughout the Chesapeake Bay watershed (CBW), which is located in the mid-Atlantic US, winter cover crop use has been emphasized, and federal and state cost-share programs are available to farmers to subsidize the cost of cover crop establishment. The objective of this study was to assess the long-term effect of planting winter cover crops to improve water quality at the watershed scale (~ 50 km2) and to identify critical source areas of high nitrate export. A physically based watershed simulation model, Soil and Water Assessment Tool (SWAT), was calibrated and validated using water quality monitoring data to simulate hydrological processes and agricultural nutrient cycling over the period of 1990-2000. To accurately simulate winter cover crop biomass in relation to growing conditions, a new approach was developed to further calibrate plant growth parameters that control the leaf area development curve using multitemporal satellite-based measurements of species-specific winter cover crop performance. Multiple SWAT scenarios were developed to obtain baseline information on nitrate loading without winter cover crops and to investigate how nitrate loading could change under different winter cover crop planting scenarios, including different species, planting dates, and implementation areas. The simulation results indicate that winter cover crops have a negligible impact on the water budget but significantly reduce nitrate leaching to groundwater and delivery to the waterways. Without winter cover crops, annual nitrate loading from agricultural lands was approximately 14 kg ha-1, but decreased to 4.6-10.1 kg ha-1 with cover crops resulting in a reduction rate of 27-67% at the watershed scale. Rye was the most effective species, with a potential to reduce nitrate leaching by up to 93% with early planting at the field scale. Early planting of cover crops (~ 30

  17. Photovoltaic module parameters acquisition model

    Science.gov (United States)

    Cibira, Gabriel; Koščová, Marcela

    2014-09-01

    This paper presents basic procedures for photovoltaic (PV) module parameters acquisition using MATLAB and Simulink modelling. In first step, MATLAB and Simulink theoretical model are set to calculate I-V and P-V characteristics for PV module based on equivalent electrical circuit. Then, limited I-V data string is obtained from examined PV module using standard measurement equipment at standard irradiation and temperature conditions and stated into MATLAB data matrix as a reference model. Next, the theoretical model is optimized to keep-up with the reference model and to learn its basic parameters relations, over sparse data matrix. Finally, PV module parameters are deliverable for acquisition at different realistic irradiation, temperature conditions as well as series resistance. Besides of output power characteristics and efficiency calculation for PV module or system, proposed model validates computing statistical deviation compared to reference model.

  18. Mode choice model parameters estimation

    OpenAIRE

    Strnad, Irena

    2010-01-01

    The present work focuses on parameter estimation of two mode choice models: multinomial logit and EVA 2 model, where four different modes and five different trip purposes are taken into account. Mode choice model discusses the behavioral aspect of mode choice making and enables its application to a traffic model. Mode choice model includes mode choice affecting trip factors by using each mode and their relative importance to choice made. When trip factor values are known, it...

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

    Science.gov (United States)

    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

  20. Combining crop modelling with economic risk analysis for the evaluation of crop management strategies

    NARCIS (Netherlands)

    Lansigan, F.P.; Pandey, S.; Bouman, B.A.M.

    1997-01-01

    An agro-ecological approach is presented for the evaluation of alternative management options for rice production in the light of farmers attitudes towards risk. The approach combined eco-physiological crop growth modelling with stochastic dominance theory and is illustrated for a case study of rain

  1. Microbiological parameters as indicators of soil quality under various soil management and crop rotation systems in southern Brazil

    OpenAIRE

    FRANCHINI, J. C.; Crispino, C.C.; de Souza, R. A.; Torres, E; HUNGRIA, M.

    2007-01-01

    Metadata only record This article attempts to recognize soil parameters that can be used to monitor soil quality under different crop and soil management systems. The rates of CO2 emissions (soil respiration) were affected by variations in the sampling period, as well as in soil management and crop rotation. Considering all samples, CO2 emissions were 21% greater in conventional tillage. Soil microbial biomass was also influenced by sampling period and soil management, but not by crop rota...

  2. Climate Change Modelling and Its Roles to Chinese Crops Yield

    Institute of Scientific and Technical Information of China (English)

    JU Hui; LIN Er-da; Tim Wheeler; Andrew Challinor; JIANG Shuai

    2013-01-01

    Climate has been changing in the last fifty years in China and will continue to change regardless any efforts for mitigation. Agriculture is a climate-dependent activity and highly sensitive to climate changes and climate variability. Understanding the interactions between climate change and agricultural production is essential for society stable development of China. The first mission is to fully understand how to predict future climate and link it with agriculture production system. In this paper, recent studies both domestic and international are reviewed in order to provide an overall image of the progress in climate change researches. The methods for climate change scenarios construction are introduced. The pivotal techniques linking crop model and climate models are systematically assessed and climate change impacts on Chinese crops yield among model results are summarized. The study found that simulated productions of grain crop inherit uncertainty from using different climate models, emission scenarios and the crops simulation models. Moreover, studies have different spatial resolutions, and methods for general circulation model (GCM) downscaling which increase the uncertainty for regional impacts assessment. However, the magnitude of change in crop production due to climate change (at 700 ppm CO2 eq correct) appears within ±10%for China in these assessments. In most literatures, the three cereal crop yields showed decline under climate change scenarios and only wheat in some region showed increase. Finally, the paper points out several gaps in current researches which need more studies to shorten the distance for objective recognizing the impacts of climate change on crops. The uncertainty for crop yield projection is associated with climate change scenarios, CO2 fertilization effects and adaptation options. Therefore, more studies on the fields such as free air CO2 enrichment experiment and practical adaptations implemented need to be carried out.

  3. Consistent negative response of US crops to high temperatures in observations and crop models

    Science.gov (United States)

    Schauberger, Bernhard; Archontoulis, Sotirios; Arneth, Almut; Balkovic, Juraj; Ciais, Philippe; Deryng, Delphine; Elliott, Joshua; Folberth, Christian; Khabarov, Nikolay; Müller, Christoph; Pugh, Thomas A. M.; Rolinski, Susanne; Schaphoff, Sibyll; Schmid, Erwin; Wang, Xuhui; Schlenker, Wolfram; Frieler, Katja

    2017-04-01

    High temperatures are detrimental to crop yields and could lead to global warming-driven reductions in agricultural productivity. To assess future threats, the majority of studies used process-based crop models, but their ability to represent effects of high temperature has been questioned. Here we show that an ensemble of nine crop models reproduces the observed average temperature responses of US maize, soybean and wheat yields. Each day above 30°C diminishes maize and soybean yields by up to 6% under rainfed conditions. Declines observed in irrigated areas, or simulated assuming full irrigation, are weak. This supports the hypothesis that water stress induced by high temperatures causes the decline. For wheat a negative response to high temperature is neither observed nor simulated under historical conditions, since critical temperatures are rarely exceeded during the growing season. In the future, yields are modelled to decline for all three crops at temperatures above 30°C. Elevated CO2 can only weakly reduce these yield losses, in contrast to irrigation.

  4. Consistent negative response of US crops to high temperatures in observations and crop models

    Science.gov (United States)

    Schauberger, Bernhard; Archontoulis, Sotirios; Arneth, Almut; Balkovic, Juraj; Ciais, Philippe; Deryng, Delphine; Elliott, Joshua; Folberth, Christian; Khabarov, Nikolay; Müller, Christoph; Pugh, Thomas A. M.; Rolinski, Susanne; Schaphoff, Sibyll; Schmid, Erwin; Wang, Xuhui; Schlenker, Wolfram; Frieler, Katja

    2017-01-01

    High temperatures are detrimental to crop yields and could lead to global warming-driven reductions in agricultural productivity. To assess future threats, the majority of studies used process-based crop models, but their ability to represent effects of high temperature has been questioned. Here we show that an ensemble of nine crop models reproduces the observed average temperature responses of US maize, soybean and wheat yields. Each day >30 °C diminishes maize and soybean yields by up to 6% under rainfed conditions. Declines observed in irrigated areas, or simulated assuming full irrigation, are weak. This supports the hypothesis that water stress induced by high temperatures causes the decline. For wheat a negative response to high temperature is neither observed nor simulated under historical conditions, since critical temperatures are rarely exceeded during the growing season. In the future, yields are modelled to decline for all three crops at temperatures >30 °C. Elevated CO2 can only weakly reduce these yield losses, in contrast to irrigation.

  5. Implementation and calibration of the parameter-sparse Yield-SAFE model to predict production and land equivalent ratio in mixed tree and crop systems under two contrasting production situations in Europe

    NARCIS (Netherlands)

    Graves, A.R.; Burgess, P.J.; Palma, J.; Keesman, K.J.; Werf, van der W.; Dupraz, C.; Keulen, van H.; Herzog, F.; Mayus, M.

    2010-01-01

    Silvoarable agroforestry, the integration of trees and arable crops on the same area, has the potential to offer production, ecological, and societal benefits. However, the uptake of such systems in Europe has been limited by a combination of unsupportive policies and uncertainty concerning their

  6. Implementation and calibration of the parameter-sparse Yield-SAFE model to predict production and land equivalent ratio in mixed tree and crop systems under two contrasting production situations in Europe

    NARCIS (Netherlands)

    Graves, A.R.; Burgess, P.J.; Palma, J.; Keesman, K.J.; Werf, van der W.; Dupraz, C.; Keulen, van H.; Herzog, F.; Mayus, M.

    2010-01-01

    Silvoarable agroforestry, the integration of trees and arable crops on the same area, has the potential to offer production, ecological, and societal benefits. However, the uptake of such systems in Europe has been limited by a combination of unsupportive policies and uncertainty concerning their pr

  7. Simulating yield response of rice to salinity stress with the AquaCrop model.

    Science.gov (United States)

    Mondal, M Shahjahan; Saleh, Abul Fazal M; Razzaque Akanda, Md Abdur; Biswas, Sujit K; Md Moslehuddin, Abu Zofar; Zaman, Sinora; Lazar, Attila N; Clarke, Derek

    2015-06-01

    The FAO AquaCrop model has been widely applied throughout the world to simulate crop responses to deficit water applications. However, its application to saline conditions is not yet reported, though saline soils are common in coastal areas. In this study, we parameterized and tested AquaCrop to simulate rice yield under different salinity regimes. The data and information required in the model were collected through a field experiment at the Bangladesh Agricultural Research Institute, Gazipur. The experiment was conducted with the BRRI Dhan28, a popular boro rice variety in Bangladesh, with five levels of saline water irrigation, three replicates for each level. In addition, field monitoring was carried out at Satkhira in the southwest coastal region of Bangladesh to collect data and information based on farmers' practices and to further validate the model. The results indicated that the AquaCrop model with most of its default parameters could replicate the variation of rice yield with the variation of salinity reasonably well. The root mean square error and mean absolute error of the model yield were only 0.12 t per ha and 0.03 t per ha, respectively. The crop response versus soil salinity stress curve was found to be convex in shape with a lower threshold of 2 dS m(-1), an upper threshold of 10 dS m(-1) and a shape factor of 2.4. As the crop production system in the coastal belt of Bangladesh has become vulnerable to climate induced sea-level rise and the consequent increase in water and soil salinity, the AquaCrop would be a useful tool in assessing the potential impact of these future changes as well as other climatic parameters on rice yield in the coastal region.

  8. Support vector machine-based open crop model (SBOCM): Case of rice production in China.

    Science.gov (United States)

    Su, Ying-Xue; Xu, Huan; Yan, Li-Jiao

    2017-03-01

    Existing crop models produce unsatisfactory simulation results and are operationally complicated. The present study, however, demonstrated the unique advantages of statistical crop models for large-scale simulation. Using rice as the research crop, a support vector machine-based open crop model (SBOCM) was developed by integrating developmental stage and yield prediction models. Basic geographical information obtained by surface weather observation stations in China and the 1:1000000 soil database published by the Chinese Academy of Sciences were used. Based on the principle of scale compatibility of modeling data, an open reading frame was designed for the dynamic daily input of meteorological data and output of rice development and yield records. This was used to generate rice developmental stage and yield prediction models, which were integrated into the SBOCM system. The parameters, methods, error resources, and other factors were analyzed. Although not a crop physiology simulation model, the proposed SBOCM can be used for perennial simulation and one-year rice predictions within certain scale ranges. It is convenient for data acquisition, regionally applicable, parametrically simple, and effective for multi-scale factor integration. It has the potential for future integration with extensive social and economic factors to improve the prediction accuracy and practicability.

  9. Support vector machine-based open crop model (SBOCM: Case of rice production in China

    Directory of Open Access Journals (Sweden)

    Ying-xue Su

    2017-03-01

    Full Text Available Existing crop models produce unsatisfactory simulation results and are operationally complicated. The present study, however, demonstrated the unique advantages of statistical crop models for large-scale simulation. Using rice as the research crop, a support vector machine-based open crop model (SBOCM was developed by integrating developmental stage and yield prediction models. Basic geographical information obtained by surface weather observation stations in China and the 1:1000000 soil database published by the Chinese Academy of Sciences were used. Based on the principle of scale compatibility of modeling data, an open reading frame was designed for the dynamic daily input of meteorological data and output of rice development and yield records. This was used to generate rice developmental stage and yield prediction models, which were integrated into the SBOCM system. The parameters, methods, error resources, and other factors were analyzed. Although not a crop physiology simulation model, the proposed SBOCM can be used for perennial simulation and one-year rice predictions within certain scale ranges. It is convenient for data acquisition, regionally applicable, parametrically simple, and effective for multi-scale factor integration. It has the potential for future integration with extensive social and economic factors to improve the prediction accuracy and practicability.

  10. Modelling the effect of field management on crop water productivity and catchment hydrology

    Science.gov (United States)

    Van Gaelen, Hanne; Willems, Patrick; Diels, Jan; Raes, Dirk

    2014-05-01

    Upgrading crop water productivity (WPET) is crucial to assure food production in a future world, where simultaneously the world population grows and land and water resources become increasingly limited. Adapted field management is one of the key solutions to upgrade WPET for rainfed agriculture in drought prone regions. However field management strategies should be assessed considering their impact on a larger scale (catchment hydrology), and this for current and future climatic conditions. By linking a crop water productivity model (AquaCrop) to a lumped conceptual hydrological model (VHM), we aimed to develop a general modeling procedure to evaluate the impact of field management on WPET and catchment hydrology. To avoid disadvantages related to other model approaches, we specifically aimed at a procedure that (i) can be applied for both current and future climatic conditions, (ii) is widely applicable and generally relevant, i.e. also for developing countries, and (iii) requires a relatively small number of explicit parameters and mostly-intuitive input variables. The linkage between AquaCrop and VHM is tested for two catchments in Flanders with a high proportion of agricultural land. After the VHM model is calibrated and AquaCrop simulations are run for the different land units (crop-soil combinations) of the catchment, the response behaviour of the VHM unsaturated zone model and the AquaCrop soil water balance is compared. Differences are identified and interpreted and a final coupling of the two models is established trough the water balance of the unsaturated zone. Thereby the overland runoff and water percolation to the groundwater or subsurface flow are the most crucial linkage components. After both models are linked different field management scenarios can be investigated with respect to their effect on both WPET and catchment hydrology.

  11. Crop Yield and Area can be Reliably Estimated Using Farmer Supplied Yield Data, Remote Sensing and Crop Models in Australia.

    Science.gov (United States)

    Lawes, R.

    2016-12-01

    The Australian grain growing region is vast and occupies where some 25 million tonnes of wheat is produced from latitudes -27 to -42, where soils, crops and climates vary considerably. Predicting the area of individual crops is time consuming and currently conducted by survey, while yield estimates are derived from these areas and from information about grain receivables with little pre-harvest information available to industry. The existing approach fails to provide reliable, timely, small scale information about production. Similarly, previous attempts to predict yield using satellite derived information rely on information collected using the existing systems to calibrate models. We have developed a crop productivity and yield model - called C-Store Crop - that uses remotely sensed vegetation indices, along with site based rainfall, radiation and temperature information. Model calibration using 3000 points derived from farmer supplied yield maps for wheat, barley, canola and chickpea showed strong relationships (>70%) between modelled plant mass and observed crop yield at the paddock scale. C-Store Crop is being applied at 250m and 25m grid resolution. Farmer supplied yield data was also used to train a combination of Radar and Landsat images collected whilst the crop is growing to discriminate between crop types. Landsat information alone was unable to discriminate legume and cereal crops. Problems such as cloud prevented accessing appropriate scenes. Inclusion of Radar information reduced errors of commission and omission. By combining the C-Store Crop model with remote estimates of crop type, we anticipate predicting crop type and crop yield with uncertainty estimates across the Australian continent.

  12. Comparison of Statistical Models for Regional Crop Trial Analysis

    Institute of Scientific and Technical Information of China (English)

    ZHANG Qun-yuan; KONG Fan-ling

    2002-01-01

    Based on the review and comparison of main statistical analysis models for estimating varietyenvironment cell means in regional crop trials, a new statistical model, LR-PCA composite model was proposed, and the predictive precision of these models were compared by cross validation of an example data. Results showed that the order of model precision was LR-PCA model > AMMI model > PCA model > Treatment Means (TM) model > Linear Regression (LR) model > Additive Main Effects ANOVA model. The precision gain factor of LR-PCA model was 1.55, increasing by 8.4% compared with AMMI.

  13. Crop Yield Forecasted Model Based on Time Series Techniques

    Institute of Scientific and Technical Information of China (English)

    Li Hong-ying; Hou Yan-lin; Zhou Yong-juan; Zhao Hui-ming

    2012-01-01

    Traditional studies on potential yield mainly referred to attainable yield: the maximum yield which could be reached by a crop in a given environment. The new concept of crop yield under average climate conditions was defined in this paper, which was affected by advancement of science and technology. Based on the new concept of crop yield, the time series techniques relying on past yield data was employed to set up a forecasting model. The model was tested by using average grain yields of Liaoning Province in China from 1949 to 2005. The testing combined dynamic n-choosing and micro tendency rectification, and an average forecasting error was 1.24%. In the trend line of yield change, and then a yield turning point might occur, in which case the inflexion model was used to solve the problem of yield turn point.

  14. Early detection of crop injury from herbicide glyphosate by leaf biochemical parameter inversion

    Science.gov (United States)

    Zhao, Feng; Guo, Yiqing; Huang, Yanbo; Reddy, Krishna N.; Lee, Matthew A.; Fletcher, Reginald S.; Thomson, Steven J.

    2014-09-01

    Early detection of crop injury from herbicide glyphosate is of significant importance in crop management. In this paper, we attempt to detect glyphosate-induced crop injury by PROSPECT (leaf optical PROperty SPECTra model) inversion through leaf hyperspectral reflectance measurements for non-Glyphosate-Resistant (non-GR) soybean and non-GR cotton leaves. The PROSPECT model was inverted to retrieve chlorophyll content (Ca+b), equivalent water thickness (Cw), and leaf mass per area (Cm) from leaf hyperspectral reflectance spectra. The leaf stress conditions were then evaluated by examining the temporal variations of these biochemical constituents after glyphosate treatment. The approach was validated with greenhouse-measured datasets. Results indicated that the leaf injury caused by glyphosate treatments could be detected shortly after the spraying for both soybean and cotton by PROSPECT inversion, with Ca+b of the leaves treated with high dose solution decreasing more rapidly compared with leaves left untreated, whereas the Cw and Cm showed no obvious difference between treated and untreated leaves. For both non-GR soybean and non-GR cotton, the retrieved Ca+b values of the glyphosate treated plants from leaf hyperspectral data could be distinguished from that of the untreated plants within 48 h after the treatment, which could be employed as a useful indicator for glyphosate injury detection. These findings demonstrate the feasibility of applying the PROSPECT inversion technique for the early detection of leaf injury from glyphosate and its potential for agricultural plant status monitoring.

  15. MODELING OF AUTOMATION PROCESSES CONCERNING CROP CULTIVATION BY AVIATION

    OpenAIRE

    V. I. Ryabkov; V. A. Sichik

    2010-01-01

    The paper considers modeling of automation processes concerning crop cultivation by aviation. Processes that take place in three interconnected environments: human, technical and movable air objects are described by a model which is based on a set theory. Stochastic network theory of mass service systems for description of human-machine system of real time is proposed in the paper.

  16. Modelling biomass production and yield of horticultural crops: a review.

    NARCIS (Netherlands)

    Marcelis, L.F.M.; Heuvelink, E.; Goudriaan, J.

    1998-01-01

    Descriptive and explanatory modelling of biomass production and yield of horticultural crops is reviewed with special reference to the simulation of leaf area, light interception, dry matter (DM) production, DM partitioning and DM content. Most models for prediction of harvest date (timing of produc

  17. MeteoCrop DB: an agro-meteorological database coupled with crop models for studying climate change impacts on rice in Japan

    National Research Council Canada - National Science Library

    KUWAGATA, Tsuneo; YOSHIMOTO, Mayumi; ISHIGOOKA, Yasushi; HASEGAWA, Toshihiro; UTSUMI, Misako; NISHIMORI, Motoki; MASAKI, Yoshimitsu; SAITO, Osamu

    2011-01-01

    An agro-meteorological database coupled with crop models (MeteoCrop DB) has been developed for studying the impacts of climate change on rice (Oryza sativa L.) in Japan (http://MeteoCrop.dc.affrc.go.jp...

  18. Modelling impacts of climate change on arable crop diseases: progress, challenges and applications.

    Science.gov (United States)

    Newbery, Fay; Qi, Aiming; Fitt, Bruce Dl

    2016-08-01

    Combining climate change, crop growth and crop disease models to predict impacts of climate change on crop diseases can guide planning of climate change adaptation strategies to ensure future food security. This review summarises recent developments in modelling climate change impacts on crop diseases, emphasises some major challenges and highlights recent trends. The use of multi-model ensembles in climate change modelling and crop modelling is contributing towards measures of uncertainty in climate change impact projections but other aspects of uncertainty remain largely unexplored. Impact assessments are still concentrated on few crops and few diseases but are beginning to investigate arable crop disease dynamics at the landscape level.

  19. Impact of data quality and quantity and the calibration procedure on crop growth model calibration

    Science.gov (United States)

    Seidel, Sabine J.; Werisch, Stefan

    2014-05-01

    Crop growth models are a commonly used tool for impact assessment of climate variability and climate change on crop yields and water use. Process-based crop models rely on algorithms that approximate the main physiological plant processes by a set of equations containing several calibration parameters as well as basic underlying assumptions. It is well recognized that model calibration is essential to improve the accuracy and reliability of model predictions. However, model calibration and validation is often hindered by a limited quantity and quality of available data. Recent studies suggest that crop model parameters can only be derived from field experiments in which plant growth and development processes have been measured. To be able to achieve a reliable prediction of crop growth under irrigation or drought stress, the correct characterization of the whole soil-plant-atmosphere system is essential. In this context is the accurate simulation of crop development, yield and the soil water dynamics plays an important role. In this study we aim to investigate the importance of a site and cultivar-specific model calibration based on experimental data using the SVAT model Daisy. We investigate to which extent different data sets and different parameter estimation procedures affect particularly yield estimates, irrigation water demand and the soil water dynamics. The comprehensive experimental data has been derived from an experiment conducted in Germany where five irrigation regimes were imposed on cabbage. Data collection included continuous measurements of soil tension and soil water content in two plots at three depths, weekly measurements of LAI, plant heights, leaf-N-content, stomatal conductivity, biomass partitioning, rooting depth as well as harvested yields and duration of growing period. Three crop growth calibration strategies were compared: (1) manual calibration based on yield and duration of growing period, (2) manual calibration based on yield

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

    Science.gov (United States)

    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.

  1. Modeling perceptions of climatic risk in crop production.

    Science.gov (United States)

    Reinmuth, Evelyn; Parker, Phillip; Aurbacher, Joachim; Högy, Petra; Dabbert, Stephan

    2017-01-01

    In agricultural production, land-use decisions are components of economic planning that result in the strategic allocation of fields. Climate variability represents an uncertainty factor in crop production. Considering yield impact, climatic influence is perceived during and evaluated at the end of crop production cycles. In practice, this information is then incorporated into planning for the upcoming season. This process contributes to attitudes toward climate-induced risk in crop production. In the literature, however, the subjective valuation of risk is modeled as a risk attitude toward variations in (monetary) outcomes. Consequently, climatic influence may be obscured by political and market influences so that risk perceptions during the production process are neglected. We present a utility concept that allows the inclusion of annual risk scores based on mid-season risk perceptions that are incorporated into field-planning decisions. This approach is exemplified and implemented for winter wheat production in the Kraichgau, a region in Southwest Germany, using the integrated bio-economic simulation model FarmActor and empirical data from the region. Survey results indicate that a profitability threshold for this crop, the level of "still-good yield" (sgy), is 69 dt ha-1 (regional mean Kraichgau sample) for a given season. This threshold governs the monitoring process and risk estimators. We tested the modeled estimators against simulation results using ten projected future weather time series for winter wheat production. The mid-season estimators generally proved to be effective. This approach can be used to improve the modeling of planning decisions by providing a more comprehensive evaluation of field-crop response to climatic changes from an economic risk point of view. The methodology further provides economic insight in an agrometeorological context where prices for crops or inputs are lacking, but farmer attitudes toward risk should still be included in

  2. Validating the FAO AquaCrop model for irrigated and water deficient field maize

    Science.gov (United States)

    Accurate crop development models are important tools in evaluating the effects of water deficits on crop yield or productivity. The FAO AquaCrop model, predicting crop productivity and water requirement under water-limiting conditions, was calibrated and validated for maize (Zea mays L.) using six ...

  3. Effect of Correlations Between Model Parameters and Nuisance Parameters When Model Parameters are Fit to Data

    CERN Document Server

    Roe, Byron

    2013-01-01

    The effect of correlations between model parameters and nuisance parameters is discussed, in the context of fitting model parameters to data. Modifications to the usual $\\chi^2$ method are required. Fake data studies, as used at present, will not be optimum. Problems will occur for applications of the Maltoni-Schwetz \\cite{ms} theorem. Neutrino oscillations are used as examples, but the problems discussed here are general ones, which are often not addressed.

  4. Crop-climate models need an overhaul

    DEFF Research Database (Denmark)

    Rötter, Reimund P,; Carter, Timothy R.; Olesen, Jørgen Eivind

    2011-01-01

    Estimates o how much food we can grow in a warmer world are out of date. Reseachers need to swith to more rigorous multi-model ensembles.......Estimates o how much food we can grow in a warmer world are out of date. Reseachers need to swith to more rigorous multi-model ensembles....

  5. Parameterization and application of the AquaCrop model for simulating bioenergy crops in Oklahoma

    Science.gov (United States)

    Bilga, Navneet Kaur

    The objective of this study was to parameterize the AquaCrop model for two bioenergy crops, switchgrass and forage sorghum, using field measurements from Stillwater, Oklahoma in 2011. The parameterized model was then validated for additional sites at Chickasha and Woodward, Oklahoma. After parameterization at Stillwater, the simulated canopy cover closely matched the measured canopy cover dynamics with a RMSE of 6% in switchgrass and 5% in forage sorghum. The water stress thresholds for canopy expansion and stomatal conductance were similar for switchgrass and forage sorghum, but senescence was induced at 35% available water depletion for forage sorghum compared to 85% for switchgrass. The maximum rooting depth of switchgrass was estimated at 190 cm and that of forage sorghum at 120 cm. The normalized water productivity of switchgrass was found to be 14 g m-2, approximately half that of forage sorghum which was 27 g m-2. The parameterized model reasonably simulated soil water depletion at Stillwater (RMSE ethanol yields as a simulation study at Goodwell, Oklahoma. The corn, forage sorghum and switchgrass were simulated using AquaCrop five water levels: rainfed with initial soil moisture conditions of 60% available water capacity, 80% available water capacity, 100% available water capacity, and irrigation treatments at 70% allowable depletion, and at 50% allowable depletion. The simulation study was done over a period of ten years 2002-2011 to assess the long term performance. County average yields were consistent with simulated grain yields for corn under irrigated and rainfed conditions. Forage sorghum produced 30 % higher theoretical ethanol yields than corn under irrigated environments but not under rainfed environments. Switchgrass did not produce significantly higher theoretical ethanol yields than corn at any water level. Based on this modeling study, forage sorghum may have potential as an alternative to corn in the Oklahoma Panhandle given the advent of

  6. Modelling farmer uptake of perennial energy crops in the UK

    Energy Technology Data Exchange (ETDEWEB)

    Sherrington, Chris; Moran, Dominic [Scottish Agricultural College, West Mains Road, Edinburgh EH9 3JG (United Kingdom)

    2010-07-15

    The UK Biomass Strategy suggests that to reach the technical potential of perennial energy crops such as short rotation coppice (SRC) willow and miscanthus by 2020 requires 350,000 hectares of land. This represents a more than 20-fold increase on the current 15,546 hectares. Previous research has identified several barriers to adoption, including concerns over security of income from contracts. In addition, farmers perceive returns from these crops to be lower than for conventional crops. This paper uses a farm-level linear programming model to investigate theoretical uptake of energy crops at different gross margins under the assumption of a profit-maximising decision maker, and in the absence of known barriers to adoption. The findings suggest that while SRC willow, at current prices, remains less competitive, returns to miscanthus should have encouraged adoption on a wider scale than at present. This highlights the importance of the barriers to adoption. Recently announced contracts for miscanthus appear to offer a significant premium to farmers in order to encourage them to grow the crops. This raises the question of whether a more cost-effective approach would be for government to provide guarantees addressing farmers concerns including security of income from the contracts. Such an approach should encourage adoption at lower gross margins. (author)

  7. Overcoming Microsoft Excel's Weaknesses for Crop Model Building and Simulations

    Science.gov (United States)

    Sung, Christopher Teh Boon

    2011-01-01

    Using spreadsheets such as Microsoft Excel for building crop models and running simulations can be beneficial. Excel is easy to use, powerful, and versatile, and it requires the least proficiency in computer programming compared to other programming platforms. Excel, however, has several weaknesses: it does not directly support loops for iterative…

  8. Predicting weed problems in maize cropping by species distribution modelling

    Directory of Open Access Journals (Sweden)

    Bürger, Jana

    2014-02-01

    Full Text Available Increasing maize cultivation and changed cropping practices promote the selection of typical maize weeds that may also profit strongly from climate change. Predicting potential weed problems is of high interest for plant production. Within the project KLIFF, experiments were combined with species distribution modelling for this task in the region of Lower Saxony, Germany. For our study, we modelled ecological and damage niches of nine weed species that are significant and wide spread in maize cropping in a number of European countries. Species distribution models describe the ecological niche of a species, these are the environmental conditions under which a species can maintain a vital population. It is also possible to estimate a damage niche, i.e. the conditions under which a species causes damage in agricultural crops. For this, we combined occurrence data of European national data bases with high resolution climate, soil and land use data. Models were also projected to simulated climate conditions for the time horizon 2070 - 2100 in order to estimate climate change effects. Modelling results indicate favourable conditions for typical maize weed occurrence virtually all over the study region, but only a few species are important in maize cropping. This is in good accordance with the findings of an earlier maize weed monitoring. Reaction to changing climate conditions is species-specific, for some species neutral (E. crus-galli, other species may gain (Polygonum persicaria or loose (Viola arvensis large areas of suitable habitats. All species with damage potential under present conditions will remain important in maize cropping, some more species will gain regional importance (Calystegia sepium, Setara viridis.

  9. EXPERT MODEL OF LAND SUITABILITY ASSESSMENT FOR CROPS

    Directory of Open Access Journals (Sweden)

    Boris Đurđević

    2010-12-01

    Full Text Available A total of 17404 soil samples (2003rd-2009th year were analysed in the eastern Croatia. The largest number of soil samples belongs to the Osijek-Baranya county, which together with both Eastern sugar beet Factories (Osijek and Županja, conduct the soil fertility control (~4200 samples/yr.. Computer model suitability assessment for crops, supported by GIS, proved to be fast, efficient enough reliable in terms of the number of analyzed soil samples. It allows the visualization of the agricultural area and prediction of its production properties for the purposes of analysis, planning and rationalization of agricultural production. With more precise data about the soil (soil, climate and reliable Digital Soil Map of Croatia, the model could be an acceptable, not only to evaluate the suitability for growing different crops but also their need for fertilizer, necessary machinery, repairs (liming, and other measures of organic matter input. The abovementioned aims to eliminate or reduce effects of limiting factors in primary agricultural production. Assessment of the relative benefits of soil presented by computer model for the crops production and geostatistical method kriging in the Osijek-Baranya county showed: 1 Average soil suitability being 60.06 percent. 2 Kriging predicted that 51751 ha (17.16% are of limited resources (N1 for growing crops whereas a 86142 ha (28.57% of land is limited suitably (S3, b 132789 ha (44.04% are moderately suitable (S2 and c 30772 ha (10.28% are of excellent fertility (S1. A large number of eastern Croatian land data showed that the computer-geostatistical model for determination of soil benefits for growing crops was automated, fast and simple to use and suitable for the implementation of GIS and automatically downloading the necessary benefit indicators from the input base (land, analytical and climate as well as data from the digital soil maps able to: a visualize the suitability for soil tillage, b predict the

  10. Advances in large-scale crop modeling

    Science.gov (United States)

    Scholze, Marko; Bondeau, Alberte; Ewert, Frank; Kucharik, Chris; Priess, Jörg; Smith, Pascalle

    Intensified human activity and a growing population have changed the climate and the land biosphere. One of the most widely recognized human perturbations is the emission of carbon dioxide (C02) by fossil fuel burning and land-use change. As the terrestrial biosphere is an active player in the global carbon cycle, changes in land use feed back to the climate of the Earth through regulation of the content of atmospheric CO2, the most important greenhouse gas,and changing albedo (e.g., energy partitioning).Recently, the climate modeling community has started to develop more complex Earthsystem models that include marine and terrestrial biogeochemical processes in addition to the representation of atmospheric and oceanic circulation. However, most terrestrial biosphere models simulate only natural, or so-called potential, vegetation and do not account for managed ecosystems such as croplands and pastures, which make up nearly one-third of the Earth's land surface.

  11. Uncertainties in predicting rice yield by current crop models under a wide range of climatic conditions

    NARCIS (Netherlands)

    Li, T.; Hasegawa, T.; Yin, X.; Zhu, Y.; Boote, K.; Adam, M.; Bregaglio, S.; Buis, S.; Confalonieri, R.; Fumoto, T.; Gaydon, D.; Marcaida III, M.; Nakagawa, H.; Oriol, P.; Ruane, A.C.; Ruget, F.; Singh, B.; Singh, U.; Tang, L.; Yoshida, H.; Zhang, Z.; Bouman, B.

    2015-01-01

    Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluat

  12. Modeling the growth dynamics of four candidate crops for Controlled Ecological Life Support Systems (CELSS)

    Science.gov (United States)

    Volk, Tyler

    1987-01-01

    The production of food for human life support for advanced space missions will require the management of many different crops. The research to design these food production capabilities along with the waste management to recycle human metabolic wastes and inedible plant components are parts of Controlled Ecological Life Support Systems (CELSS). Since complete operating CELSS were not yet built, a useful adjunct to the research developing the various pieces of a CELSS are system simulation models that can examine what is currently known about the possible assembly of subsystems into a full CELSS. The growth dynamics of four crops (wheat, soybeans, potatoes, and lettuce) are examined for their general similarities and differences within the context of their important effects upon the dynamics of the gases, liquids, and solids in the CELSS. Data for the four crops currently under active research in the CELSS program using high-production hydroponics are presented. Two differential equations are developed and applied to the general characteristics of each crop growth pattern. Model parameters are determined by closely approximating each crop's data.

  13. Modeling temporal and spatial variability of crop yield

    Science.gov (United States)

    Bonetti, S.; Manoli, G.; Scudiero, E.; Morari, F.; Putti, M.; Teatini, P.

    2014-12-01

    In a world of increasing food insecurity the development of modeling tools capable of supporting on-farm decision making processes is highly needed to formulate sustainable irrigation practices in order to preserve water resources while maintaining adequate crop yield. The design of these practices starts from the accurate modeling of soil-plant-atmosphere interaction. We present an innovative 3D Soil-Plant model that couples 3D hydrological soil dynamics with a mechanistic description of plant transpiration and photosynthesis, including a crop growth module. Because of its intrinsically three dimensional nature, the model is able to capture spatial and temporal patterns of crop yield over large scales and under various climate and environmental factors. The model is applied to a 25 ha corn field in the Venice coastland, Italy, that has been continuously monitored over the years 2010 and 2012 in terms of both hydrological dynamics and yield mapping. The model results satisfactorily reproduce the large variability observed in maize yield (from 2 to 15 ton/ha). This variability is shown to be connected to the spatial heterogeneities of the farmland, which is characterized by several sandy paleo-channels crossing organic-rich silty soils. Salt contamination of soils and groundwater in a large portion of the area strongly affects the crop yield, especially outside the paleo-channels, where measured salt concentrations are lower than the surroundings. The developed model includes a simplified description of the effects of salt concentration in soil water on transpiration. The results seem to capture accurately the effects of salt concentration and the variability of the climatic conditions occurred during the three years of measurements. This innovative modeling framework paves the way to future large scale simulations of farmland dynamics.

  14. Using statistical model to simulate the impact of climate change on maize yield with climate and crop uncertainties

    Science.gov (United States)

    Zhang, Yi; Zhao, Yanxia; Wang, Chunyi; Chen, Sining

    2016-09-01

    Assessment of the impact of climate change on crop productions with considering uncertainties is essential for properly identifying and decision-making agricultural practices that are sustainable. In this study, we employed 24 climate projections consisting of the combinations of eight GCMs and three emission scenarios representing the climate projections uncertainty, and two crop statistical models with 100 sets of parameters in each model representing parameter uncertainty within the crop models. The goal of this study was to evaluate the impact of climate change on maize (Zea mays L.) yield at three locations (Benxi, Changling, and Hailun) across Northeast China (NEC) in periods 2010-2039 and 2040-2069, taking 1976-2005 as the baseline period. The multi-models ensembles method is an effective way to deal with the uncertainties. The results of ensemble simulations showed that maize yield reductions were less than 5 % in both future periods relative to the baseline. To further understand the contributions of individual sources of uncertainty, such as climate projections and crop model parameters, in ensemble yield simulations, variance decomposition was performed. The results indicated that the uncertainty from climate projections was much larger than that contributed by crop model parameters. Increased ensemble yield variance revealed the increasing uncertainty in the yield simulation in the future periods.

  15. Impacts of Future Climate Change on California Perennial Crop Yields: Model Projections with Climate and Crop Uncertainties

    Energy Technology Data Exchange (ETDEWEB)

    Lobell, D; Field, C; Cahill, K; Bonfils, C

    2006-01-10

    Most research on the agricultural impacts of climate change has focused on the major annual crops, yet perennial cropping systems are less adaptable and thus potentially more susceptible to damage. Improved assessments of yield responses to future climate are needed to prioritize adaptation strategies in the many regions where perennial crops are economically and culturally important. These impact assessments, in turn, must rely on climate and crop models that contain often poorly defined uncertainties. We evaluated the impact of climate change on six major perennial crops in California: wine grapes, almonds, table grapes, oranges, walnuts, and avocados. Outputs from multiple climate models were used to evaluate climate uncertainty, while multiple statistical crop models, derived by resampling historical databases, were used to address crop response uncertainties. We find that, despite these uncertainties, climate change in California is very likely to put downward pressure on yields of almonds, walnuts, avocados, and table grapes by 2050. Without CO{sub 2} fertilization or adaptation measures, projected losses range from 0 to >40% depending on the crop and the trajectory of climate change. Climate change uncertainty generally had a larger impact on projections than crop model uncertainty, although the latter was substantial for several crops. Opportunities for expansion into cooler regions are identified, but this adaptation would require substantial investments and may be limited by non-climatic constraints. Given the long time scales for growth and production of orchards and vineyards ({approx}30 years), climate change should be an important factor in selecting perennial varieties and deciding whether and where perennials should be planted.

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

    Science.gov (United States)

    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

  17. Modelling crop yield in Iberia under drought conditions

    Science.gov (United States)

    Ribeiro, Andreia; Páscoa, Patrícia; Russo, Ana; Gouveia, Célia

    2017-04-01

    The improved assessment of the cereal yield and crop loss under drought conditions are essential to meet the increasing economy demands. The growing frequency and severity of the extreme drought conditions in the Iberian Peninsula (IP) has been likely responsible for negative impacts on agriculture, namely on crop yield losses. Therefore, a continuous monitoring of vegetation activity and a reliable estimation of drought impacts is crucial to contribute for the agricultural drought management and development of suitable information tools. This works aims to assess the influence of drought conditions in agricultural yields over the IP, considering cereal yields from mainly rainfed agriculture for the provinces with higher productivity. The main target is to develop a strategy to model drought risk on agriculture for wheat yield at a province level. In order to achieve this goal a combined assessment was made using a drought indicator (Standardized Precipitation Evapotranspiration Index, SPEI) to evaluate drought conditions together with a widely used vegetation index (Normalized Difference Vegetation Index, NDVI) to monitor vegetation activity. A correlation analysis between detrended wheat yield and SPEI was performed in order to assess the vegetation response to each time scale of drought occurrence and also identify the moment of the vegetative cycle when the crop yields are more vulnerable to drought conditions. The time scales and months of SPEI, together with the months of NDVI, better related with wheat yield were chosen to perform a multivariate regression analysis to simulate crop yield. Model results are satisfactory and highlighted the usefulness of such analysis in the framework of developing a drought risk model for crop yields. In terms of an operational point of view, the results aim to contribute to an improved understanding of crop yield management under dry conditions, particularly adding substantial information on the advantages of combining

  18. Effect of Manure vs. Fertilizer Inputs on Productivity of Forage Crop Models

    Directory of Open Access Journals (Sweden)

    Pasquale Martiniello

    2011-06-01

    Full Text Available Manure produced by livestock activity is a dangerous product capable of causing serious environmental pollution. Agronomic management practices on the use of manure may transform the target from a waste to a resource product. Experiments performed on comparison of manure with standard chemical fertilizers (CF were studied under a double cropping per year regime (alfalfa, model I; Italian ryegrass-corn, model II; barley-seed sorghum, model III; and horse-bean-silage sorghum, model IV. The total amount of manure applied in the annual forage crops of the model II, III and IV was 158, 140 and 80 m3 ha−1, respectively. The manure applied to soil by broadcast and injection procedure provides an amount of nitrogen equal to that supplied by CF. The effect of manure applications on animal feeding production and biochemical soil characteristics was related to the models. The weather condition and manures and CF showed small interaction among treatments. The number of MFU ha−1 of biomass crop gross product produced in autumn and spring sowing models under manure applications was 11,769, 20,525, 11,342, 21,397 in models I through IV, respectively. The reduction of MFU ha−1 under CF ranges from 10.7% to 13.2% those of the manure models. The effect of manure on organic carbon and total nitrogen of topsoil, compared to model I, stressed the parameters as CF whose amount was higher in models II and III than model IV. In term of percentage the organic carbon and total nitrogen of model I and treatment with manure was reduced by about 18.5 and 21.9% in model II and model III and 8.8 and 6.3% in model IV, respectively. Manure management may substitute CF without reducing gross production and sustainability of cropping systems, thus allowing the opportunity to recycle the waste product for animal forage feeding.

  19. Effect of manure vs. fertilizer inputs on productivity of forage crop models.

    Science.gov (United States)

    Annicchiarico, Giovanni; Caternolo, Giovanni; Rossi, Emanuela; Martiniello, Pasquale

    2011-06-01

    Manure produced by livestock activity is a dangerous product capable of causing serious environmental pollution. Agronomic management practices on the use of manure may transform the target from a waste to a resource product. Experiments performed on comparison of manure with standard chemical fertilizers (CF) were studied under a double cropping per year regime (alfalfa, model I; Italian ryegrass-corn, model II; barley-seed sorghum, model III; and horse-bean-silage sorghum, model IV). The total amount of manure applied in the annual forage crops of the model II, III and IV was 158, 140 and 80 m3 ha(-1), respectively. The manure applied to soil by broadcast and injection procedure provides an amount of nitrogen equal to that supplied by CF. The effect of manure applications on animal feeding production and biochemical soil characteristics was related to the models. The weather condition and manures and CF showed small interaction among treatments. The number of MFU ha(-1) of biomass crop gross product produced in autumn and spring sowing models under manure applications was 11,769, 20,525, 11,342, 21,397 in models I through IV, respectively. The reduction of MFU ha(-1) under CF ranges from 10.7% to 13.2% those of the manure models. The effect of manure on organic carbon and total nitrogen of topsoil, compared to model I, stressed the parameters as CF whose amount was higher in models II and III than model IV. In term of percentage the organic carbon and total nitrogen of model I and treatment with manure was reduced by about 18.5 and 21.9% in model II and model III and 8.8 and 6.3% in model IV, respectively. Manure management may substitute CF without reducing gross production and sustainability of cropping systems, thus allowing the opportunity to recycle the waste product for animal forage feeding.

  20. Integrated numerical model of nitrogen transportation, absorption and transformation by two-dimension in soil-crop system

    Institute of Scientific and Technical Information of China (English)

    WANG Hong-qi; SHU Yan; QI Yong-qiang; ZHANG Jun

    2005-01-01

    A series of simulation experiments of nitrogen transportation, absorption and transformation were conducted, and the different cropping patterns of winter wheat and wastewater irrigation plans were taken into consideration. Based on the experiments, an integrated model of crop growth, roots distribution, water and nitrogen absorption by roots, water and nitrogen movement and transformation in soilcrop system by two-dimension was developed. Parameters and boundary conditions were identified and an effective computing method for optimizing watering and wastewater irrigating plans was provided.

  1. Hyperspectral Reflectance and Fluorescence Indices for Carbon Related Parameters in Corn Crops

    Science.gov (United States)

    Middleton, E. M.; Corp, L. A.; Campbell, P. E.; Daughtry, C. S.

    2006-05-01

    The relative success in monitoring physiological or stand properties related to carbon (C) assimilation using narrow band (hyperspectral) reflectance and fluorescence indices was evaluated at leaf and canopy levels for mature corn crops (Zea mays L.) in two years. The corn crops were arranged in plots, each receiving a controlled nitrogen (N) fertilization regime at one of four dosages in experiments conducted in 2004 and 2005 at the USDA facility in Beltsville, MD, USA. Leaf reflectance spectra were obtained in conjunction with leaf level photosynthesis, chlorophyll fluorescence (ChlF), and chemistry (chlorophyll and carotenoid content per leaf area; percent C and N by dry mass). Whole plant canopy spectra and leaf area index data were obtained the same week as leaf measurements, followed by determinations of yields and biomass at harvest. The spectra were acquired using a spectroradiometer (ASD-FR FieldSpec Pro, Analytical Spectral Devices, Inc., Boulder, CO, USA), either coupled with a hemisphere for leaf optical properties or to measure nadir radiances 1 m above plant canopies within a 22o field of view. In situ photosynthesis and ChlF parameters were determined simultaneously with a photosynthetic system (Li-Cor 6400, Lincoln, Nebraska, USA) fitted with a fluorimeter under controlled conditions (temperature, irradiance, carbon dioxide, and humidity). Canopy-level steady state ChlF emissions were extracted from the apparent canopy reflectance spectra at 688 and 760 nm using the Fraunhofer Line Depth (FLD) principal. Both fluorescence and reflectance indices were successful in discriminating foliar constituents (e.g., pigment ratios, C/N ratios) but only fluorescence indices were correlated with light use efficiency (LUE) and corn yields in both years. LUE was inversely correlated (r = 0.85) with the ratio of non-photochemical (Qn) to photochemical (Qp) quenching of ChlF, (Qn/Qp). LUE was not strongly influenced by pigment levels, including the chlorophyll

  2. A methodology for model-based greenhouse design: Part 3, sensitivity analysis of a combined greenhouse climate-crop yield model

    NARCIS (Netherlands)

    Vanthoor, B.H.E.; Henten, van E.J.; Stanghellini, C.; Visser, de P.H.B.

    2011-01-01

    Greenhouse design is an optimisation problem that might be solved by a model-based greenhouse design method. A sensitivity analysis of a combined greenhouse climate-crop yield model of tomato was done to identify the parameters, i.e. greenhouse design parameters, outdoor climate and climate set-poin

  3. A crop model-based approach for sunflower yields

    Directory of Open Access Journals (Sweden)

    João Guilherme Dal Belo Leite

    2014-10-01

    Full Text Available Pushed by the Brazilian biodiesel policy, sunflower (Helianthus annuus L. production is becoming increasingly regarded as an option to boost farmers' income, particularly under semi-arid conditions. Biodiesel related opportunities increase the demand for decision-making information at different levels, which could be met by simulation models. This study aimed to evaluate the performance of the crop model OILCROP-SUN to simulate sunflower development and growth under Brazilian conditions and to explore sunflower water- and nitrogen-limited, water-limited and potential yield and yield variability over an array of sowing dates in the northern region of the state of Minas Gerais, Brazil. For model calibration, an experiment was conducted in which two sunflower genotypes (H358 and E122 were cultivated in a clayey soil. Growth components (leaf area index, above ground biomass, grain yield and development stages (crop phenology were measured. A database composed of 27 sunflower experiments from five Brazilian regions was used for model evaluation. The spatial yield distribution of sunflower was mapped using ordinary kriging in ArcGIS. The model simulated sunflower grain productivity satisfactorily (Root Mean Square Error ≈ 13 %. Simulated yields were relatively high (1,750 to 4,250 kg ha-1 and the sowing window was fairly wide (Oct to Feb for northwestern locations, where sunflower could be cultivated as a second crop (double cropping at the end of the rainy season. The hybrid H358 had higher yields for all simulated sowing dates, growth conditions and selected locations.

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

    Science.gov (United States)

    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

  5. Water Stress & Biomass Monitoring and SWAP Modeling of Irrigated Crops in Saratov Region of Russia

    Science.gov (United States)

    Zeyliger, Anatoly; Ermolaeva, Olga

    2016-04-01

    Development of modern irrigation technologies are balanced between the need to maximize production and the need to minimize water use which provides harmonious interaction of irrigated systems with closely-spaced environment. Thus requires an understanding of complex interrelationships between landscape and underground of irrigated and adjacent areas in present and future conditions aiming to minimize development of negative scenarios. In this way in each irrigated areas a combination of specific factors and drivers must be recognized and evaluated. Much can be obtained by improving the efficiency use of water applied for irrigation. Modern RS monitoring technologies offers the opportunity to develop and implement an effective irrigation control program permitting today to increase efficiency of irrigation water use. These technologies provide parameters with both high temporal and adequate spatial needed to monitor agrohydrological parameters of irrigated agricultural crops. Combination of these parameters with meteorological and biophysical parameters can be used to estimate crop water stress defined as ratio between actual (ETa) and potential (ETc) evapotranspiration. Aggregation of actual values of crop water stress with biomass (yield) data predicted by agrohydrological model based on weather forecasting and scenarios of irrigation water application may be used for indication of both rational timing and amount of irrigation water allocation. This type of analysis facilitating an efficient water management can be easily extended to irrigated areas by developing maps of water efficiency application serving as an irrigation advice system for farmers at his fields and as a decision support tool for the authorities on the large perimeter irrigation management. This contribution aims to communicate an illustrative explanation about the practical application of a data combination of agrohydrological modeling and ground & space based monitoring. For this aim some

  6. Photosynthesis driven crop growth models for greenhouse cultivation; advances and bottlenecks.

    NARCIS (Netherlands)

    Challa, H.; Heuvelink, E.

    1996-01-01

    In recent years considerable progress has been made in modelling growth of green-house crops. Nevertheless, the share of research in this field compared to crop modelling in general is only a few percent. Yet, crop growth models have a great potential for greenhouse production systems, because they

  7. Modelling plant interspecific interactions from experiments of perennial crop mixtures to predict optimal combinations.

    Science.gov (United States)

    Halty, Virginia; Valdés, Matías; Tejera, Mauricio; Picasso, Valentín; Fort, Hugo

    2017-07-28

    The contribution of plant species richness to productivity and ecosystem functioning is a long standing issue in Ecology, with relevant implications for both conservation and agriculture. Both experiments and quantitative modelling are fundamental to the design of sustainable agroecosystems and the optimization of crop production. We modelled communities of perennial crop mixtures by using a generalized Lotka-Volterra model, i.e. a model such that the interspecific interactions are more general than purely competitive. We estimated model parameters -carrying capacities and interaction coefficientsfrom, respectively, the observed biomass of monocultures and bicultures measured in a large diversity experiment of seven perennial forage species in Iowa, United States. The sign and absolute value of the interaction coefficients showed that the biological interactions between species pairs included amensalism, competition, and parasitism (asymmetric positive-negative interaction), with various degrees of intensity. We tested the model fit by simulating the combinations of more than two species and comparing them with the polycultures experimental data. Overall, theoretical predictions are in good agreement with the experiments. Using this model, we also simulated species combinations that were not sown. From all possible mixtures (sown and not sown) we identified which are the most productive species combinations. Our results demonstrate that a combination of experiments and modelling can contribute to the design of sustainable agricultural systems in general and to the optimization of crop production in particular. This article is protected by copyright. All rights reserved. This article is protected by copyright. All rights reserved.

  8. Old Dog New Tricks: Use of Point-based Crop Models in Grid-based Regional Assessment of Crop Management Technologies Impact on Future Food Security

    Science.gov (United States)

    Koo, J.; Wood, S.; Cenacchi, N.; Fisher, M.; Cox, C.

    2012-12-01

    HarvestChoice (harvestchoice.org) generates knowledge products to guide strategic investments to improve the productivity and profitability of smallholder farming systems in sub-Saharan Africa (SSA). A keynote component of the HarvestChoice analytical framework is a grid-based overlay of SSA - a cropping simulation platform powered by process-based, crop models. Calibrated around the best available representation of cropping production systems in SSA, the simulation platform engages the DSSAT Crop Systems Model with the CENTURY Soil Organic Matter model (DSSAT-CENTURY) and provides a virtual experimentation module with which to explore the impact of a range of technological, managerial and environmental metrics on future crop productivity and profitability, as well as input use. For each of 5 (or 30) arc-minute grid cells in SSA, a stack of model input underlies it: datasets that cover soil properties and fertility, historic and future climate scenarios and farmers' management practices; all compiled from analyses of existing global and regional databases and consultations with other CGIAR centers. Running a simulation model is not always straightforward, especially when certain cropping systems or management practices are not even practiced by resource-poor farmers yet (e.g., precision agriculture) or they were never included in the existing simulation framework (e.g., water harvesting). In such cases, we used DSSAT-CENTURY as a function to iteratively estimate relative responses of cropping systems to technology-driven changes in water and nutrient balances compared to zero-adoption by farmers, while adjusting model input parameters to best mimic farmers' implementation of technologies in the field. We then fed the results of the simulation into to the economic and food trade model framework, IMPACT, to assess the potential implications on future food security. The outputs of the overall simulation analyses are packaged as a web-accessible database and published

  9. Economic and ecological impacts of bioenergy crop production—a modeling approach applied in Southwestern Germany

    Directory of Open Access Journals (Sweden)

    Hans-Georg Schwarz-v. Raumer

    2017-03-01

    Full Text Available This paper considers scenarios of cultivating energy crops in the German Federal State of Baden-Württemberg to identify potentials and limitations of a sustainable bioenergy production. Trade-offs are analyzed among income and production structure in agriculture, bioenergy crop production, greenhouse gas emissions, and the interests of soil, water and species habitat protection. An integrated modelling approach (IMA was implemented coupling ecological and economic models in a model chain. IMA combines the Economic Farm Emission Model (EFEM; key input: parameter sets on farm production activities, the Environmental Policy Integrated Climate model (EPIC; key input: parameter sets on environmental cropping effects and GIS geo-processing models. EFEM is a supply model that maximizes total gross margins on farm level with simultaneous calculation of greenhouse gas emission from agriculture production. Calculations by EPIC result in estimates for soil erosion by water, nitrate leaching, Soil Organic Carbon and greenhouse gas emissions from soil. GIS routines provide land suitability analyses, scenario settings concerning nature conservation and habitat models for target species and help to enable spatial explicit results. The model chain is used to calculate scenarios representing different intensities of energy crop cultivation. To design scenarios which are detailed and in step to practice, comprehensive data research as well as fact and effect analyses were carried out. The scenarios indicate that, not in general but when considering specific farm types, energy crop share extremely increases if not restricted and leads to an increase in income. If so this leads to significant increase in soil erosion by water, nitrate leaching and greenhouse gas emissions. It has to be expected that an extension of nature conservation leads to an intensification of the remaining grassland and of the arable land, which were not part of nature conservation measures

  10. Calibration of a crop model to irrigated water use using a genetic algorithm

    Directory of Open Access Journals (Sweden)

    T. Bulatewicz

    2009-03-01

    Full Text Available Near-term consumption of groundwater for irrigated agriculture in the High Plains Aquifer supports a dynamic bio-socio-economic system, all parts of which will be impacted by a future transition to sustainable usage that matches natural recharge rates. Plants are the foundation of this system and so generic plant models suitable for coupling to representations of other component processes (hydrologic, economic, etc. are key elements of needed stakeholder decision support systems. This study explores utilization of the Environmental Policy Integrated Climate (EPIC model to serve in this role. Calibration required many facilities of a fully deployed decision support system: geo-referenced databases of crop (corn, sorghum, alfalfa, and soybean, soil, weather, and water-use data (4931 well-years, interfacing heterogeneous software components, and massively parallel processing (3.8×109 model runs. Bootstrap probability distributions for ten model parameters were obtained for each crop by entropy maximization via the genetic algorithm. The relative errors in yield and water estimates based on the parameters are analyzed by crop, the level of aggregation (county- or well-level, and the degree of independence between the data set used for estimation and the data being predicted.

  11. Genotypic variation in energy efficiency in greenhouse crops: underlying physiological and morphological parameters

    NARCIS (Netherlands)

    Ploeg, van der A.

    2007-01-01

    Greenhouse horticulture in The Netherlands is a highly sophisticated form of crop production, resulting in high production levels and good product quality. However, it also requires high energy inputs, representing 15 to 20% of the production costs in most crops. It is important that energy efficien

  12. Assessment of future crop yield and agricultural sustainable water use in north china plain using multiple crop models

    Science.gov (United States)

    Huang, G.

    2016-12-01

    Currently, studying crop-water response mechanism has become an important part in the development of new irrigation technology and optimal water allocation in water-scarce regions, which is of great significance to crop growth guidance, sustainable utilization of agricultural water, as well as the sustainable development of regional agriculture. Using multiple crop models(AquaCrop,SWAP,DNDC), this paper presents the results of simulating crop growth and agricultural water consumption of the winter-wheat and maize cropping system in north china plain. These areas are short of water resources, but generates about 23% of grain production for China. By analyzing the crop yields and the water consumption of the traditional flooding irrigation, the paper demonstrates quantitative evaluation of the potential amount of water use that can be reduced by using high-efficient irrigation approaches, such as drip irrigation. To maintain food supply and conserve water resources, the research concludes sustainable irrigation methods for the three provinces for sustainable utilization of agricultural water.

  13. Evaluation of the Doraiswamy-Thompson winter wheat crop calendar model incorporating a modified spring restart sequence

    Science.gov (United States)

    Taylor, T. W.; Ravet, F. W.; Smika, D. (Principal Investigator)

    1981-01-01

    The Robertson phenology was used to provide growth stage information to a wheat stress indicator mode. A stress indicator model demands two acurate predictions from a crop calendar: date of spring growth initiation; and crop calendar stage at growth initiation. Several approaches for restarting the Robertson phenology model at spring growth initiation were studied. Although best results were obtained with a solar thermal unit method, an alternate approach which indicates soil temperature as the controlling parameter for spring growth initiation was selected and tested. The modified model (Doraiswamy-Thompson) is compared to LACIE-Robertson model predictions.

  14. Parameterization of FAO's AquaCrop Model by Integrating a Hydrological Model and Climate Indices

    Science.gov (United States)

    Langhorn, C.; Kienzle, S. W.; Doria, R.; Jiskoot, H.; Cheng, H.

    2014-12-01

    One of the greatest global challenges is to meet growing food demand under rapidly changing climate conditions. Continued global population growth increases the pressure on the agriculture sector to produce enough food to feed the world. In 2013, the province of Alberta, Canada, set a record high for principal field crop production of 34.5 million tonnes (Matejovsky, 2014). AquaCrop, a crop yield and water productivity model developed by the Land and Water Division of the Food and Agriculture Organization of the United Nations (FAO), attempts to balance the accuracy, simplicity and robustness of crop modelling (Steduto et al., 2009). The model is focused on the three components of the soil-plant-atmosphere continuum. AquaCrop is applied in this study for simulating hard red spring wheat and durum wheat yields, and simulated yields are verified against observed yields available from a crop insurer. One of the challenges of crop yield modelling is the selection of a realistic seeding date, which can vary by four to five weeks (end of March to end of April). In order to enable realistic simulation for the historical period 1950-2010 as well the future period 2041-2070, AquaCrop is coupled with the ACRU agro-hydrological modelling system to determine the soil moisture conditions after the spring snow melt, and with a WMO climate index which determines the climatological beginning of the growing season. Therefore, the selection of a realistic seeding data for individual years can be dynamically optimized, based on the combination of the beginning of the climatological growing season and soil moisture status. The results of the coupling of ACRU and calculated climate indices with AquaCrop will be presented to show how improvements of parameterization of the AquaCrop model can be used to simulate wheat yields in Southern Alberta under changing climate conditions.

  15. Regional crop modelling in Europe: The impact of climate conditions and farm characteristics on maize yields

    NARCIS (Netherlands)

    Reidsma, P.; Ewert, F.; Boogaard, H.; Diepen, van K.

    2009-01-01

    Impacts of climate variability and climate change on regional crop yields are commonly assessed using process-based crop models. These models, however, simulate potential and water limited yields, which do not always relate to observed yields. The latter are largely influenced by crop management, wh

  16. Simulating yield response to water of Teff (Eragrostis tef) with FAO's AquaCrop model

    NARCIS (Netherlands)

    Araya, A.; Keesstra, S.D.; Stroosnijder, L.

    2010-01-01

    In a semi-arid environment, the main challenge for crop production is water deficit. FAO's AquaCrop model, which simulates yield response to water, has been calibrated to explore alternative water management strategies in teff crop. To calibrate and evaluate this model, we used independent data sets

  17. Simulating yield response to water of Teff (Eragrostis tef) with FAO's AquaCrop model

    NARCIS (Netherlands)

    Araya, A.; Keesstra, S.D.; Stroosnijder, L.

    2010-01-01

    In a semi-arid environment, the main challenge for crop production is water deficit. FAO's AquaCrop model, which simulates yield response to water, has been calibrated to explore alternative water management strategies in teff crop. To calibrate and evaluate this model, we used independent data sets

  18. Irrigation management strategies for winter wheat using AquaCrop model

    National Research Council Canada - National Science Library

    M. H. Ali; I. Abustan

    2013-01-01

    .... The FAO’s newly developed crop-water model, AquaCrop, which simulates yield in response to water, has been calibrated for winter wheat and subsequently used to simulate yield under different sowing dates...

  19. Crop systems biology : an approach to connect functional genomics with crop modelling

    NARCIS (Netherlands)

    Yin, X.; Struik, P.C.

    2007-01-01

    The response of the whole crop to environmental conditions is a critical factor in agriculture. It can only be understood if the organization of the crop system is taken into account. A popular view in modern science is that genomics (and other `omics¿) will provide knowledge and tools to allow the

  20. Evaluation of the DayCent model to predict carbon fluxes in French crop sites

    Science.gov (United States)

    Fujisaki, Kenji; Martin, Manuel P.; Zhang, Yao; Bernoux, Martial; Chapuis-Lardy, Lydie

    2017-04-01

    Croplands in temperate regions are an important component of the carbon balance and can act as a sink or a source of carbon, depending on pedoclimatic conditions and management practices. Therefore the evaluation of carbon fluxes in croplands by modelling approach is relevant in the context of global change. This study was part of the Comete-Global project funded by the multi-Partner call FACCE JPI. Carbon fluxes, net ecosystem exchange (NEE), leaf area index (LAI), biomass, and grain production were simulated at the site level in three French crop experiments from the CarboEurope project. Several crops were studied, like winter wheat, rapeseed, barley, maize, and sunflower. Daily NEE was measured with eddy covariance and could be partitioned between gross primary production (GPP) and total ecosystem respiration (TER). Measurements were compared to DayCent simulations, a process-based model predicting plant production and soil organic matter turnover at daily time step. We compared two versions of the model: the original one with a simplified plant module and a newer version that simulates LAI. Input data for modelling were soil properties, climate, and management practices. Simulations of grain yields and biomass production were acceptable when using optimized crop parameters. Simulation of NEE was also acceptable. GPP predictions were improved with the newer version of the model, eliminating temporal shifts that could be observed with the original model. TER was underestimated by the model. Predicted NEE was more sensitive to soil tillage and nitrogen applications than measured NEE. DayCent was therefore a relevant tool to predict carbon fluxes in French crops at the site level. The introduction of LAI in the model improved its performance.

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

    Science.gov (United States)

    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

  2. DayCent modelling of Swiss cropping systems

    Science.gov (United States)

    Necpalova, Magdalena; Lee, Juhwan; Büchi, Lucie; Mäder, Paul; Mayer, Jochen; Charles, Raphael; van der Heijden, Marcel; Six, Johan

    2016-04-01

    There is a growing need to identify and evaluate sustainable greenhouse gas (GHG) mitigation options, their bio-economic feasibility in the agricultural sector, and support implementation of agricultural GHG mitigation activities that are an integral part of climate change strategies. In recent years, several ecosystem biogeochemical process-based models and comprehensive decision making tools integrated with these models have been developed. The DayCent model simulates all major ecosystem processes that affect soil C and N dynamics, including plant production, water flow, heat transport, SOC decomposition, N mineralization and immobilization, nitrification, denitrification, and methane oxidation. However, if the model is to be reliably used for identification of GHG mitigation options and climate change strategies across the EU agricultural regions, it requires site- and region-specific calibration and evaluation. Here, we calibrated and validated the model to Swiss climate and soil conditions and management options using available long-term experimental data. Data on crop productivity, soil organic carbon and N2O emissions were derived from four field sites located in Thervil (1977-2013), Frick (2003-2013), Changins (1971-2013), and Reckenholz (2009-2013) that have evaluated the effects of agricultural input systems (specifically, organic, biodynamic, and conventional with and without manure additions) and soil management options (various tillage practices and cover cropping). The preliminary results show that the DayCent model was able to reproduce 76% of variability in the crop productivity (n = 1 316) and 75% variability in measured soil organic carbon (n = 402) across all long-term trials. Model calibration was evaluated against independent proportions of the data. The uncertainty in model predictions induced by model structure and uncertainty in the measured data still needs to be further evaluated using the Monte Carlo approach. The calibrated model will be

  3. PARAMETER ESTIMATION OF ENGINEERING TURBULENCE MODEL

    Institute of Scientific and Technical Information of China (English)

    钱炜祺; 蔡金狮

    2001-01-01

    A parameter estimation algorithm is introduced and used to determine the parameters in the standard k-ε two equation turbulence model (SKE). It can be found from the estimation results that although the parameter estimation method is an effective method to determine model parameters, it is difficult to obtain a set of parameters for SKE to suit all kinds of separated flow and a modification of the turbulence model structure should be considered. So, a new nonlinear k-ε two-equation model (NNKE) is put forward in this paper and the corresponding parameter estimation technique is applied to determine the model parameters. By implementing the NNKE to solve some engineering turbulent flows, it is shown that NNKE is more accurate and versatile than SKE. Thus, the success of NNKE implies that the parameter estimation technique may have a bright prospect in engineering turbulence model research.

  4. Sequential use of the STICS crop model and of the MACRO pesticide fate model to simulate pesticides leaching in cropping systems.

    Science.gov (United States)

    Lammoglia, Sabine-Karen; Moeys, Julien; Barriuso, Enrique; Larsbo, Mats; Marín-Benito, Jesús-María; Justes, Eric; Alletto, Lionel; Ubertosi, Marjorie; Nicolardot, Bernard; Munier-Jolain, Nicolas; Mamy, Laure

    2017-03-01

    The current challenge in sustainable agriculture is to introduce new cropping systems to reduce pesticides use in order to reduce ground and surface water contamination. However, it is difficult to carry out in situ experiments to assess the environmental impacts of pesticide use for all possible combinations of climate, crop, and soils; therefore, in silico tools are necessary. The objective of this work was to assess pesticides leaching in cropping systems coupling the performances of a crop model (STICS) and of a pesticide fate model (MACRO). STICS-MACRO has the advantage of being able to simulate pesticides fate in complex cropping systems and to consider some agricultural practices such as fertilization, mulch, or crop residues management, which cannot be accounted for with MACRO. The performance of STICS-MACRO was tested, without calibration, from measurements done in two French experimental sites with contrasted soil and climate properties. The prediction of water percolation and pesticides concentrations with STICS-MACRO was satisfactory, but it varied with the pedoclimatic context. The performance of STICS-MACRO was shown to be similar or better than that of MACRO. The improvement of the simulation of crop growth allowed better estimate of crop transpiration therefore of water balance. It also allowed better estimate of pesticide interception by the crop which was found to be crucial for the prediction of pesticides concentrations in water. STICS-MACRO is a new promising tool to improve the assessment of the environmental risks of pesticides used in cropping systems.

  5. Quantifying the effect of crop spatial arrangement on weed suppression using functional-structural plant modelling.

    Science.gov (United States)

    Evers, Jochem B; Bastiaans, Lammert

    2016-05-01

    Suppression of weed growth in a crop canopy can be enhanced by improving crop competitiveness. One way to achieve this is by modifying the crop planting pattern. In this study, we addressed the question to what extent a uniform planting pattern increases the ability of a crop to compete with weed plants for light compared to a random and a row planting pattern, and how this ability relates to crop and weed plant density as well as the relative time of emergence of the weed. To this end, we adopted the functional-structural plant modelling approach which allowed us to explicitly include the 3D spatial configuration of the crop-weed canopy and to simulate intra- and interspecific competition between individual plants for light. Based on results of simulated leaf area development, canopy photosynthesis and biomass growth of the crop, we conclude that differences between planting pattern were small, particularly if compared to the effects of relative time of emergence of the weed, weed density and crop density. Nevertheless, analysis of simulated weed biomass demonstrated that a uniform planting of the crop improved the weed-suppression ability of the crop canopy. Differences in weed suppressiveness between planting patterns were largest with weed emergence before crop emergence, when the suppressive effect of the crop was only marginal. With simultaneous emergence a uniform planting pattern was 8 and 15 % more competitive than a row and a random planting pattern, respectively. When weed emergence occurred after crop emergence, differences between crop planting patterns further decreased as crop canopy closure was reached early on regardless of planting pattern. We furthermore conclude that our modelling approach provides promising avenues to further explore crop-weed interactions and aid in the design of crop management strategies that aim at improving crop competitiveness with weeds.

  6. Overview: early history of crop growth and photosynthesis modeling.

    Science.gov (United States)

    El-Sharkawy, Mabrouk A

    2011-02-01

    As in industrial and engineering systems, there is a need to quantitatively study and analyze the many constituents of complex natural biological systems as well as agro-ecosystems via research-based mechanistic modeling. This objective is normally addressed by developing mathematically built descriptions of multilevel biological processes to provide biologists a means to integrate quantitatively experimental research findings that might lead to a better understanding of the whole systems and their interactions with surrounding environments. Aided with the power of computational capacities associated with computer technology then available, pioneering cropping systems simulations took place in the second half of the 20th century by several research groups across continents. This overview summarizes that initial pioneering effort made to simulate plant growth and photosynthesis of crop canopies, focusing on the discovery of gaps that exist in the current scientific knowledge. Examples are given for those gaps where experimental research was needed to improve the validity and application of the constructed models, so that their benefit to mankind was enhanced. Such research necessitates close collaboration among experimentalists and model builders while adopting a multidisciplinary/inter-institutional approach.

  7. Testing the responses of four wheat crop models to heat stress at anthesis and grain filling.

    Science.gov (United States)

    Liu, Bing; Asseng, Senthold; Liu, Leilei; Tang, Liang; Cao, Weixing; Zhu, Yan

    2016-05-01

    Higher temperatures caused by future climate change will bring more frequent heat stress events and pose an increasing risk to global wheat production. Crop models have been widely used to simulate future crop productivity but are rarely tested with observed heat stress experimental datasets. Four wheat models (DSSAT-CERES-Wheat, DSSAT-Nwheat, APSIM-Wheat, and WheatGrow) were evaluated with 4 years of environment-controlled phytotron experimental datasets with two wheat cultivars under heat stress at anthesis and grain filling stages. Heat stress at anthesis reduced observed grain numbers per unit area and individual grain size, while heat stress during grain filling mainly decreased the size of the individual grains. The observed impact of heat stress on grain filling duration, total aboveground biomass, grain yield, and grain protein concentration (GPC) varied depending on cultivar and accumulated heat stress. For every unit increase of heat degree days (HDD, degree days over 30 °C), grain filling duration was reduced by 0.30-0.60%, total aboveground biomass was reduced by 0.37-0.43%, and grain yield was reduced by 1.0-1.6%, but GPC was increased by 0.50% for cv Yangmai16 and 0.80% for cv Xumai30. The tested crop simulation models could reproduce some of the observed reductions in grain filling duration, final total aboveground biomass, and grain yield, as well as the observed increase in GPC due to heat stress. Most of the crop models tended to reproduce heat stress impacts better during grain filling than at anthesis. Some of the tested models require improvements in the response to heat stress during grain filling, but all models need improvements in simulating heat stress effects on grain set during anthesis. The observed significant genetic variability in the response of wheat to heat stress needs to be considered through cultivar parameters in future simulation studies.

  8. Farmland Drought Evaluation Based on the Assimilation of Multi-Temporal Multi-Source Remote Sensing Data into AquaCrop Model

    Science.gov (United States)

    Yang, Guijun; Yang, Hao; Jin, Xiuliang; Pignatti, Stefano; Casa, Faffaele; Silverstro, Paolo Cosmo

    2016-08-01

    Drought is the most costly natural disasters in China and all over the world. It is very important to evaluate the drought-induced crop yield losses and further improve water use efficiency at regional scale. Firstly, crop biomass was estimated by the combined use of Synthetic Aperture Radar (SAR) and optical remote sensing data. Then the estimated biophysical variable was assimilated into crop growth model (FAO AquaCrop) by the Particle Swarm Optimization (PSO) method from farmland scale to regional scale.At farmland scale, the most important crop parameters of AquaCrop model were determined to reduce the used parameters in assimilation procedure. The Extended Fourier Amplitude Sensitivity Test (EFAST) method was used for assessing the contribution of different crop parameters to model output. Moreover, the AquaCrop model was calibrated using the experiment data in Xiaotangshan, Beijing.At regional scale, spatial application of our methods were carried out and validated in the rural area of Yangling, Shaanxi Province, in 2014. This study will provide guideline to make irrigation decision of balancing of water consumption and yield loss.

  9. Modeled conterminous United States Crop Cover datasets for 2000 - 2013

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The...

  10. Modeled conterminous United States Crop Cover datasets for 2001

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The...

  11. Modeled conterminous United States Crop Cover datasets for 2002

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The...

  12. Modeled conterminous United States Crop Cover datasets for 2004

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The...

  13. Modeled conterminous United States Crop Cover datasets for 2013

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The...

  14. Modeled conterminous United States Crop Cover datasets for 2012

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The...

  15. Modeled conterminous United States Crop Cover datasets for 2011

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The...

  16. Modeled conterminous United States Crop Cover datasets for 2006

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The...

  17. Modeled conterminous United States Crop Cover datasets for 2009

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — Crop cover maps have become widely used in a range of research applications. Multiple crop cover maps have been developed to suite particular research interests. The...

  18. Regional Modelling for Optimal Allocation of Agricultural Crops Considering Environmental Impacts, Housing Value and Leisure Preferences.

    OpenAIRE

    Haruvy, Nava; Shalhevet, Sarit

    2006-01-01

    Regional planning should consider the impact of agricultural crops on housing value and leisure, as well as on the local environment. We designed an optimization model for allocating agricultural crops based on farmers profits as well as the impact on these three factors. Each crop creates a different landscape, as well as a different effect on shading and noise reduction. These in turn influence the value of nearby housing and the regional leisure opportunities. Each crop also has a positive...

  19. Direct and indirect impacts of crop-livestock organization on mixed crop-livestock systems sustainability: a model-based study.

    Science.gov (United States)

    Sneessens, I; Veysset, P; Benoit, M; Lamadon, A; Brunschwig, G

    2016-11-01

    Crop-livestock production is claimed more sustainable than specialized production systems. However, the presence of controversial studies suggests that there must be conditions of mixing crop and livestock productions to allow for higher sustainable performances. Whereas previous studies focused on the impact of crop-livestock interactions on performances, we posit here that crop-livestock organization is a key determinant of farming system sustainability. Crop-livestock organization refers to the percentage of the agricultural area that is dedicated to each production. Our objective is to investigate if crop-livestock organization has both a direct and an indirect impact on mixed crop-livestock (MC-L) sustainability. In that objective, we build a whole-farm model parametrized on representative French sheep and crop farming systems in plain areas (Vienne, France). This model permits simulating contrasted MC-L systems and their subsequent sustainability through the following indicators of performance: farm income, production, N balance, greenhouse gas (GHG) emissions (/kg product) and MJ consumption (/kg product). Two MC-L systems were simulated with contrasted crop-livestock organizations (MC20-L80: 20% of crops; MC80-L20: 80% of crops). A first scenario - constraining no crop-livestock interactions in both MC-L systems - permits highlighting that crop-livestock organization has a significant direct impact on performances that implies trade-offs between objectives of sustainability. Indeed, the MC80-L20 system is showing higher performances for farm income (+44%), livestock production (+18%) and crop GHG emissions (-14%) whereas the MC20-L80 system has a better N balance (-53%) and a lower livestock MJ consumption (-9%). A second scenario - allowing for crop-livestock interactions in both MC20-L80 and MC80-L20 systems - stated that crop-livestock organization has a significant indirect impact on performances. Indeed, even if crop-livestock interactions permit

  20. Investigating Impacts of Alternative Crop Market Scenarios on Land Use Change with an Agent-Based Model

    Directory of Open Access Journals (Sweden)

    Deng Ding

    2015-11-01

    Full Text Available We developed an agent-based model (ABM to simulate farmers’ decisions on crop type and fertilizer application in response to commodity and biofuel crop prices. Farm profit maximization constrained by farmers’ profit expectations for land committed to biofuel crop production was used as the decision rule. Empirical parameters characterizing farmers’ profit expectations were derived from an agricultural landowners and operators survey and integrated in the ABM. The integration of crop production cost models and the survey information in the ABM is critical to producing simulations that can provide realistic insights into agricultural land use planning and policy making. Model simulations were run with historical market prices and alternative market scenarios for corn price, soybean to corn price ratio, switchgrass price, and switchgrass to corn stover ratio. The results of the comparison between simulated cropland percentage and crop rotations with satellite-based land cover data suggest that farmers may be underestimating the effects that continuous corn production has on yields. The simulation results for alternative market scenarios based on a survey of agricultural land owners and operators in the Clear Creek Watershed in eastern Iowa show that farmers see cellulosic biofuel feedstock production in the form of perennial grasses or corn stover as a more risky enterprise than their current crop production systems, likely because of market and production risks and lock in effects. As a result farmers do not follow a simple farm-profit maximization rule.

  1. Soil quality parameters for row-crop and grazed pasture systems with agroforestry buffers

    Science.gov (United States)

    Incorporation of trees and establishment of buffers are practices that can improve soil quality. Soil enzyme activities and water stable aggregates are sensitive indices for assessing soil quality by detecting early changes in soil management. However, studies comparing grazed pasture and row crop...

  2. Development of CROPTRIT Model: The Dynamics of Tritium in Agricultural Crops

    Energy Technology Data Exchange (ETDEWEB)

    Galeriu, Dan; Melintescu, Anca [' Horia Hulubei' National Institute for Physics and Nuclear Engineering, Department of Environmental Physics and Life, 30 Reactorului St., POB MG-6, Bucharest-Magurele, RO-077125 (Romania); Lazar, Catalin [National Agricultural Research and Development Institute Fundulea, 915200 Fundulea, Calarasi County (Romania)

    2014-07-01

    Tritium has a complex behaviour once released into the environment. Tritium can be effectively incorporated into biological systems, including the human body, as organically bound tritium (OBT) with a larger residence time than tritiated water (HTO). In the last years robust models were developed for tritium dynamics in mammals (human included), birds and fish but all of them depend on the knowledge of intake for both terrestrial or aquatic food chain. The uncertainty of the present models for tritium in crops following an accidental atmospheric release, is very high and has impacts on the engineering actions for handling and decreasing the nuclear risk. The gaps in knowledge or the local variability of key parameters were recognised as source of uncertainty. Based on an interdisciplinary approach, CROPTRIT model was gradually developed in the last decade focusing on the detecting of the uncertainty sources. Crops of interest depends on each specific case but wheat and rice cover the majority of the practical needs for radiological risk modelling (the major food in Europe and Asia). An analysis of the processes involved in the Soil-Vegetation-Atmosphere Transfer (SVAT) of tritium was done in connection with the available experimental results. The agricultural research is focused on the improving of the yield and the crop growth models were developed in relation with the genotype, weather and management of fertilisation and water. For the radiological purposes, the interest lies in the pollutant concentration at harvest and the CROPTRIT model is focused on the influence of various processes contributing to variability and uncertainty of tritium (OBT and HTO) at harvest. The current results evidentiate the role of the stomatal conductance and difficulties at the day/night transitions, as well as the complex behaviour of the maintenance respiration. A review of the experimental results demonstrates the importance of OBT formation in night conditions and difficulties

  3. Robust estimation of hydrological model parameters

    Directory of Open Access Journals (Sweden)

    A. Bárdossy

    2008-11-01

    Full Text Available The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives a unique and very best parameter vector. The parameters of fitted hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on Tukey's half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.

  4. Environmental Transport Input Parameters for the Biosphere Model

    Energy Technology Data Exchange (ETDEWEB)

    M. Wasiolek

    2004-09-10

    This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), a biosphere model supporting the total system performance assessment for the license application (TSPA-LA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA-LA, as identified in the ''Technical Work Plan for Biosphere Modeling and Expert Support'' (BSC 2004 [DIRS 169573]) (TWP). This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA). This report is one of the five reports that develop input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes the conceptual model and the mathematical model. The input parameter reports, shown to the right of the Biosphere Model Report in Figure 1-1, contain detailed description of the model input parameters. The output of this report is used as direct input in the ''Nominal Performance Biosphere Dose Conversion Factor Analysis'' and in the ''Disruptive Event Biosphere Dose Conversion Factor Analysis'' that calculate the values of biosphere dose conversion factors (BDCFs) for the groundwater and volcanic ash exposure scenarios, respectively. The purpose of this analysis was to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or in volcanic ash). The analysis

  5. Inhalation Exposure Input Parameters for the Biosphere Model

    Energy Technology Data Exchange (ETDEWEB)

    K. Rautenstrauch

    2004-09-10

    This analysis is one of 10 reports that support the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN) biosphere model. The ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents development of input parameters for the biosphere model that are related to atmospheric mass loading and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for a Yucca Mountain repository. Inhalation Exposure Input Parameters for the Biosphere Model is one of five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling, and the plan for development of the biosphere abstraction products for TSPA, as identified in the Technical Work Plan for Biosphere Modeling and Expert Support (BSC 2004 [DIRS 169573]). This analysis report defines and justifies values of mass loading for the biosphere model. Mass loading is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Mass loading values are used in the air submodel of ERMYN to calculate concentrations of radionuclides in air inhaled by a receptor and concentrations in air surrounding crops. Concentrations in air to which the receptor is exposed are then used in the inhalation submodel to calculate the dose contribution to the receptor from inhalation of contaminated airborne particles. Concentrations in air surrounding plants are used in the plant submodel to calculate the concentrations of radionuclides in foodstuffs contributed from uptake by foliar interception.

  6. PARAMETER ESTIMATION IN BREAD BAKING MODEL

    OpenAIRE

    Hadiyanto Hadiyanto; AJB van Boxtel

    2012-01-01

    Bread product quality is highly dependent to the baking process. A model for the development of product quality, which was obtained by using quantitative and qualitative relationships, was calibrated by experiments at a fixed baking temperature of 200°C alone and in combination with 100 W microwave powers. The model parameters were estimated in a stepwise procedure i.e. first, heat and mass transfer related parameters, then the parameters related to product transformations and finally pro...

  7. Parameter counting in models with global symmetries

    Energy Technology Data Exchange (ETDEWEB)

    Berger, Joshua [Institute for High Energy Phenomenology, Newman Laboratory of Elementary Particle Physics, Cornell University, Ithaca, NY 14853 (United States)], E-mail: jb454@cornell.edu; Grossman, Yuval [Institute for High Energy Phenomenology, Newman Laboratory of Elementary Particle Physics, Cornell University, Ithaca, NY 14853 (United States)], E-mail: yuvalg@lepp.cornell.edu

    2009-05-18

    We present rules for determining the number of physical parameters in models with exact flavor symmetries. In such models the total number of parameters (physical and unphysical) needed to described a matrix is less than in a model without the symmetries. Several toy examples are studied in order to demonstrate the rules. The use of global symmetries in studying the minimally supersymmetric standard model (MSSM) is examined.

  8. On parameter estimation in deformable models

    DEFF Research Database (Denmark)

    Fisker, Rune; Carstensen, Jens Michael

    1998-01-01

    Deformable templates have been intensively studied in image analysis through the last decade, but despite its significance the estimation of model parameters has received little attention. We present a method for supervised and unsupervised model parameter estimation using a general Bayesian...... method is based on a modified version of the EM algorithm. Experimental results for a deformable template used for textile inspection are presented...

  9. Cosmological models with constant deceleration parameter

    Energy Technology Data Exchange (ETDEWEB)

    Berman, M.S.; de Mello Gomide, F.

    1988-02-01

    Berman presented elsewhere a law of variation for Hubble's parameter that yields constant deceleration parameter models of the universe. By analyzing Einstein, Pryce-Hoyle and Brans-Dicke cosmologies, we derive here the necessary relations in each model, considering a perfect fluid.

  10. Scenarios of organic amendment use to increase soil carbon stocks and nitrogen availability in cropped soils at the territory scale: spatial and temporal simulations with the NCSOIL/CERES-EGC crop model

    Science.gov (United States)

    Noirot-Cosson, Paul-Emile; Vaudour, Emmanuelle; Aubry, Christine; Gilliot, Jean-Marc; Gabrielle, Benoît; Houot, Sabine

    2014-05-01

    The application of Exogenous Organic Matter (EOM) on cropped soils is a promising way to increase soil organic carbon and available nitrogen for crops while recycling organic agricultural and urban wastes. In peri-urban territories where the specialization of agriculture limits the resource in organic amendments since livestock farming is scarce, a better management of EOM land application from all origins at the territory scale could be thought to maximize their benefits. The objective was to predict the effect of various EOM types and uses on C and N fluxes and crop production for each homogeneous spatial unit of the territory, first step for the territorial optimization of EOM land application. The study area, located 30km west of Paris, covers 221km² and is mostly characterized by croplands. The effects of repeated EOM applications were studied using a mechanistic crop model: CERES-EGC accounting for soil characteristics, crop production systems, and climate. The whole territory was divided into homogeneous spatial units, each defined by soil and crop production system characteristics. Four different soil types were characterized, mapped and parameterized in the model. Kinetics of C and N mineralization during soil incubations were used to optimize soil organic matter characteristics and parameters in the sub-model NCSOIL of CERES-EGC. Crop production systems were defined and spatially inferred using the French land parcel identification system. Climatic data measured on the territory were used to make a 20 year-meteorological scenario. Based on these initial informations, crop yields and C and N fluxes were simulated for the actual crop productions and soil type combinations of the territory. Then, different scenarios of EOM uses were also simulated based on different EOM types, added quantities and frequencies of application within the crop successions respecting the 170kgN/ha/yr legal limit. All the parameters studied, crop yields, N outputs, carbon storage

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

    Science.gov (United States)

    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

  12. Simulating crop phenology in the Community Land Model and its impact on energy and carbon fluxes

    Science.gov (United States)

    A reasonable representation of crop phenology and biophysical processes in land surface models is necessary to accurately simulate energy, water and carbon budgets at the field, regional, and global scales. However, the evaluation of crop models that can be coupled to earth system models is relative...

  13. Comparison of Mathematical Models for Descibing Crop Responese to N Fertilizer

    Institute of Scientific and Technical Information of China (English)

    YANSHAOHUA; GUOJUNYAO; 等

    1999-01-01

    Four mathematical models were systematically evalusted in describing responses of four different crops at 7 rates of nitrogen application.Residual sum of squares and a total point ranking method were used to assess the model fitting for crop responses to nitrogen application.Sparrow's inverse quadratic polynomial model performed the best.

  14. A statistical analysis of three ensembles of crop model responses totemperature and CO2concentration

    DEFF Research Database (Denmark)

    Makowski, D; Asseng, S; Ewert, F.

    2015-01-01

    levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without re-running the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical......Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data...... in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration...

  15. Trait Characteristics of Diffusion Model Parameters

    Directory of Open Access Journals (Sweden)

    Anna-Lena Schubert

    2016-07-01

    Full Text Available Cognitive modeling of response time distributions has seen a huge rise in popularity in individual differences research. In particular, several studies have shown that individual differences in the drift rate parameter of the diffusion model, which reflects the speed of information uptake, are substantially related to individual differences in intelligence. However, if diffusion model parameters are to reflect trait-like properties of cognitive processes, they have to qualify as trait-like variables themselves, i.e., they have to be stable across time and consistent over different situations. To assess their trait characteristics, we conducted a latent state-trait analysis of diffusion model parameters estimated from three response time tasks that 114 participants completed at two laboratory sessions eight months apart. Drift rate, boundary separation, and non-decision time parameters showed a great temporal stability over a period of eight months. However, the coefficients of consistency and reliability were only low to moderate and highest for drift rate parameters. These results show that the consistent variance of diffusion model parameters across tasks can be regarded as temporally stable ability parameters. Moreover, they illustrate the need for using broader batteries of response time tasks in future studies on the relationship between diffusion model parameters and intelligence.

  16. Parameter identification in the logistic STAR model

    DEFF Research Database (Denmark)

    Ekner, Line Elvstrøm; Nejstgaard, Emil

    We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter is that th......We propose a new and simple parametrization of the so-called speed of transition parameter of the logistic smooth transition autoregressive (LSTAR) model. The new parametrization highlights that a consequence of the well-known identification problem of the speed of transition parameter...

  17. Sustainable crop models for fruit, vegetable and flower quality productions.

    Directory of Open Access Journals (Sweden)

    Elia Antonio

    Full Text Available Sustainable development is a paradigm that has evolved over the time, since the ideas of socially acceptable and compatible development, on which it was originally based, are now supported by the more recent notions of ecological equilibria and production process economy, both of which need to be also preserved. Environmental and health safety, rational use of the natural resources and technological tools, upkeep of high social growth rates and respect of a social equity are the basis of the sustainability for any production process, including the agriculture. The new globalization framework has penalized small farms and, at the same time, has put serious constraints to the development of stronger economic systems (medium/large farms, as well. As consequence, the EU has outlined several strategic programs to support small agricultural systems in marginal areas by: 1 strengthening all the quality- related aspects of agricultural production, including nutritional and cultural traits associated to local, typical and in some cases to neglected crops; 2 improving traditional cultural practices by adapting the cropping cycles and fomenting new partnerships between the different parts of the production chain, as for example; promotion of small horticultural chains. Specific political actions for the horticultural production sector have also been developed. Some of these policies are specifically addressed to preserve the biodiversity and to create quality labels certifying typical and/or organic products. All of these are possible strategies that may counteract and cope with the globalization process and increase the competitiveness of many production systems especially those performed by local and small entrepreneurs. New sustainable development models are required by both the market and the implicit requirements of the production system, inside a context on which Europe must face with new emerging economies with lower production costs, by increasing

  18. Sustainable crop models for fruit, vegetable and flower quality productions.

    Directory of Open Access Journals (Sweden)

    Inglese Paolo

    2011-02-01

    Full Text Available Sustainable development is a paradigm that has evolved over the time, since the ideas of socially acceptable and compatible development, on which it was originally based, are now supported by the more recent notions of ecological equilibria and production process economy, both of which need to be also preserved. Environmental and health safety, rational use of the natural resources and technological tools, upkeep of high social growth rates and respect of a social equity are the basis of the sustainability for any production process, including the agriculture. The new globalization framework has penalized small farms and, at the same time, has put serious constraints to the development of stronger economic systems (medium/large farms, as well. As consequence, the EU has outlined several strategic programs to support small agricultural systems in marginal areas by: 1 strengthening all the quality- related aspects of agricultural production, including nutritional and cultural traits associated to local, typical and in some cases to neglected crops; 2 improving traditional cultural practices by adapting the cropping cycles and fomenting new partnerships between the different parts of the production chain, as for example; promotion of small horticultural chains. Specific political actions for the horticultural production sector have also been developed. Some of these policies are specifically addressed to preserve the biodiversity and to create quality labels certifying typical and/or organic products. All of these are possible strategies that may counteract and cope with the globalization process and increase the competitiveness of many production systems especially those performed by local and small entrepreneurs. New sustainable development models are required by both the market and the implicit requirements of the production system, inside a context on which Europe must face with new emerging economies with lower production costs, by increasing

  19. Modelling the phenology and carbon budget of major crops at the field scale, supported by remote sensing data

    Science.gov (United States)

    Sus, O.; Williams, M.

    2009-04-01

    Reducing uncertainties involved in estimating the carbon balance of croplands, which are most directly, intensively and continuously affected by human intervention (i.e. land-use), is an important step towards more precisely evaluating the overall terrestrial carbon balance. Human appropriation of the land surface and its production has direct consequences on issues such as the sustainability of ecosystem services and biogeophysical as well as biogeochemical parameters of affected areas. Moreover, cropland management and phenology explains a major component of the seasonality of carbon fluxes between the terrestrial biosphere and the atmosphere of agricultural regions. To address key research questions, crop functional types (CFTs) along with land management issues, need to be considered within state-of-the-art land surface models. In this study, we embedded a crop modelling approach within the Soil-Plant-Atmosphere model (SPA) in order to build a true cropland carbon mass balance model with an explicit carbon allocation and storage pattern. SPA is a process-based model that simulates ecosystem photosynthesis and water balance at fine temporal and spatial scales and has been intensively applied to and tested against natural ecosystems and their observed carbon fluxes. Here, new carbon pools (root, leaf, stem, storage organ) have been introduced into SPA and linked with a new dynamic carbon allocation pattern, which further allows for the remobilization of carbohydrates. The crop modelling philosophy in terms of assimilates partitioning and crop development is based on Penning de Vries et al., with the simulation of crop developmental rate further having been refined using a modified Wang and Engel model. SPA now realistically simulates the carbon fluxes and stocks, evolution of LAI, phenology and evapotranspiration for three major crop types (winter/spring wheat and barley, maize). We compared modelled values of carbon fluxes against observations measured at the

  20. Parameterization of almanac crop simulation model for non-irrigated dry bean in semi-arid temperate areas in Mexico

    OpenAIRE

    Alma Delia Baez-Gonzalez; James R. Kiniry; Jose Saul Padilla Ramirez; Guillermo Medina Garcia; Jose Luis Ramos Gonzalez; Esteban Salvador Osuna Ceja

    2015-01-01

    Dry bean simulation models can be used to make management decisions when properly parameterized. This study aimed to parameterize the ALMANAC (Agricultural Land Management Alternatives with Numerical Assessment Criteria) crop simulation model for dry bean in the semi-arid temperate areas of Mexico. The parameterization process was based on data from two important non-irrigated dry bean fields in Mexico. The parameters were potential heat units (PHU), leaf area index (LAI) and harvest index (H...

  1. Species distribution models for crop pollination: a modelling framework applied to Great Britain.

    Science.gov (United States)

    Polce, Chiara; Termansen, Mette; Aguirre-Gutiérrez, Jesus; Boatman, Nigel D; Budge, Giles E; Crowe, Andrew; Garratt, Michael P; Pietravalle, Stéphane; Potts, Simon G; Ramirez, Jorge A; Somerwill, Kate E; Biesmeijer, Jacobus C

    2013-01-01

    Insect pollination benefits over three quarters of the world's major crops. There is growing concern that observed declines in pollinators may impact on production and revenues from animal pollinated crops. Knowing the distribution of pollinators is therefore crucial for estimating their availability to pollinate crops; however, in general, we have an incomplete knowledge of where these pollinators occur. We propose a method to predict geographical patterns of pollination service to crops, novel in two elements: the use of pollinator records rather than expert knowledge to predict pollinator occurrence, and the inclusion of the managed pollinator supply. We integrated a maximum entropy species distribution model (SDM) with an existing pollination service model (PSM) to derive the availability of pollinators for crop pollination. We used nation-wide records of wild and managed pollinators (honey bees) as well as agricultural data from Great Britain. We first calibrated the SDM on a representative sample of bee and hoverfly crop pollinator species, evaluating the effects of different settings on model performance and on its capacity to identify the most important predictors. The importance of the different predictors was better resolved by SDM derived from simpler functions, with consistent results for bees and hoverflies. We then used the species distributions from the calibrated model to predict pollination service of wild and managed pollinators, using field beans as a test case. The PSM allowed us to spatially characterize the contribution of wild and managed pollinators and also identify areas potentially vulnerable to low pollination service provision, which can help direct local scale interventions. This approach can be extended to investigate geographical mismatches between crop pollination demand and the availability of pollinators, resulting from environmental change or policy scenarios.

  2. Species distribution models for crop pollination: a modelling framework applied to Great Britain.

    Directory of Open Access Journals (Sweden)

    Chiara Polce

    Full Text Available Insect pollination benefits over three quarters of the world's major crops. There is growing concern that observed declines in pollinators may impact on production and revenues from animal pollinated crops. Knowing the distribution of pollinators is therefore crucial for estimating their availability to pollinate crops; however, in general, we have an incomplete knowledge of where these pollinators occur. We propose a method to predict geographical patterns of pollination service to crops, novel in two elements: the use of pollinator records rather than expert knowledge to predict pollinator occurrence, and the inclusion of the managed pollinator supply. We integrated a maximum entropy species distribution model (SDM with an existing pollination service model (PSM to derive the availability of pollinators for crop pollination. We used nation-wide records of wild and managed pollinators (honey bees as well as agricultural data from Great Britain. We first calibrated the SDM on a representative sample of bee and hoverfly crop pollinator species, evaluating the effects of different settings on model performance and on its capacity to identify the most important predictors. The importance of the different predictors was better resolved by SDM derived from simpler functions, with consistent results for bees and hoverflies. We then used the species distributions from the calibrated model to predict pollination service of wild and managed pollinators, using field beans as a test case. The PSM allowed us to spatially characterize the contribution of wild and managed pollinators and also identify areas potentially vulnerable to low pollination service provision, which can help direct local scale interventions. This approach can be extended to investigate geographical mismatches between crop pollination demand and the availability of pollinators, resulting from environmental change or policy scenarios.

  3. Parameter Estimation of Partial Differential Equation Models

    KAUST Repository

    Xun, Xiaolei

    2013-09-01

    Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown and need to be estimated from the measurements of the dynamic system in the presence of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from long-range infrared light detection and ranging data. Supplementary materials for this article are available online. © 2013 American Statistical Association.

  4. Application of lumped-parameter models

    Energy Technology Data Exchange (ETDEWEB)

    Ibsen, Lars Bo; Liingaard, M.

    2006-12-15

    This technical report concerns the lumped-parameter models for a suction caisson with a ratio between skirt length and foundation diameter equal to 1/2, embedded into an viscoelastic soil. The models are presented for three different values of the shear modulus of the subsoil. Subsequently, the assembly of the dynamic stiffness matrix for the foundation is considered, and the solution for obtaining the steady state response, when using lumped-parameter models is given. (au)

  5. Dynamic drought risk assessment using crop model and remote sensing techniques

    Science.gov (United States)

    Sun, H.; Su, Z.; Lv, J.; Li, L.; Wang, Y.

    2017-02-01

    Drought risk assessment is of great significance to reduce the loss of agricultural drought and ensure food security. The normally drought risk assessment method is to evaluate its exposure to the hazard and the vulnerability to extended periods of water shortage for a specific region, which is a static evaluation method. The Dynamic Drought Risk Assessment (DDRA) is to estimate the drought risk according to the crop growth and water stress conditions in real time. In this study, a DDRA method using crop model and remote sensing techniques was proposed. The crop model we employed is DeNitrification and DeComposition (DNDC) model. The drought risk was quantified by the yield losses predicted by the crop model in a scenario-based method. The crop model was re-calibrated to improve the performance by the Leaf Area Index (LAI) retrieved from MODerate Resolution Imaging Spectroradiometer (MODIS) data. And the in-situ station-based crop model was extended to assess the regional drought risk by integrating crop planted mapping. The crop planted area was extracted with extended CPPI method from MODIS data. This study was implemented and validated on maize crop in Liaoning province, China.

  6. PARAMETER ESTIMATION IN BREAD BAKING MODEL

    Directory of Open Access Journals (Sweden)

    Hadiyanto Hadiyanto

    2012-05-01

    Full Text Available Bread product quality is highly dependent to the baking process. A model for the development of product quality, which was obtained by using quantitative and qualitative relationships, was calibrated by experiments at a fixed baking temperature of 200°C alone and in combination with 100 W microwave powers. The model parameters were estimated in a stepwise procedure i.e. first, heat and mass transfer related parameters, then the parameters related to product transformations and finally product quality parameters. There was a fair agreement between the calibrated model results and the experimental data. The results showed that the applied simple qualitative relationships for quality performed above expectation. Furthermore, it was confirmed that the microwave input is most meaningful for the internal product properties and not for the surface properties as crispness and color. The model with adjusted parameters was applied in a quality driven food process design procedure to derive a dynamic operation pattern, which was subsequently tested experimentally to calibrate the model. Despite the limited calibration with fixed operation settings, the model predicted well on the behavior under dynamic convective operation and on combined convective and microwave operation. It was expected that the suitability between model and baking system could be improved further by performing calibration experiments at higher temperature and various microwave power levels.  Abstrak  PERKIRAAN PARAMETER DALAM MODEL UNTUK PROSES BAKING ROTI. Kualitas produk roti sangat tergantung pada proses baking yang digunakan. Suatu model yang telah dikembangkan dengan metode kualitatif dan kuantitaif telah dikalibrasi dengan percobaan pada temperatur 200oC dan dengan kombinasi dengan mikrowave pada 100 Watt. Parameter-parameter model diestimasi dengan prosedur bertahap yaitu pertama, parameter pada model perpindahan masa dan panas, parameter pada model transformasi, dan

  7. Coupling process-based models and plant architectural models: A key issue for simulating crop production

    NARCIS (Netherlands)

    Reffye, de P.; Heuvelink, E.; Guo, Y.; Hu, B.G.; Zhang, B.G.

    2009-01-01

    Process-Based Models (PBMs) can successfully predict the impact of environmental factors (temperature, light, CO2, water and nutrients) on crop growth and yield. These models are used widely for yield prediction and optimization of water and nutrient supplies. Nevertheless, PBMs do not consider plan

  8. Simulation of winter wheat yield and its uncertainty band; A comparison of two crop growth models

    Science.gov (United States)

    Javad Khordadi Varamini, Mohammad; Nassiri Mahallati, Mehdi; Alizadeh, Amin

    2016-04-01

    In this study, we used the WOFOST and AquaCrop crop growth simulation models to examine crop yield responses to a set of plausible scenarios of climate change in Mashhad region, located in Ghareghom basin, northeast of Iran up to 2040. We selected winter wheat as an indicator crop. Also six AOGCMs including GFCM21, HADCM3, INCM3, IPCM4, MPEH5 and NCCCSM under A2 and B1 emission scenarios are used. LARS-WG statistical method for downscaling is utilized. In the present research, using 7-year observed crop data, the crop models were calibrated and then validated. Evaluation of WOFOST and AquaCrop models confirmed the models are able for simulating the yield of wheat grown in the study area. The results showed that average potential yield of wheat ranged from 3.43 to 8.42 and 2.76 to 6.49 ton.ha-1, in AquaCrop and WOFOST models, respectively. Finally, the uncertainty band due to the six AOGCMs for estimating crop yield is drawn and investigated. These bands show possible changes for the yield in the future period to the past one. It can be concluded the positive effects of climate warming and elevated CO2 concentrations on the production in the studied region.

  9. Development and deployment of a water-crop-nutrient simulation model embedded in a web application

    Science.gov (United States)

    Langella, Giuliano; Basile, Angelo; Coppola, Antonio; Manna, Piero; Orefice, Nadia; Terribile, Fabio

    2016-04-01

    It is long time by now that scientific research on environmental and agricultural issues spent large effort in the development and application of models for prediction and simulation in spatial and temporal domains. This is fulfilled by studying and observing natural processes (e.g. rainfall, water and chemicals transport in soils, crop growth) whose spatiotemporal behavior can be reproduced for instance to predict irrigation and fertilizer requirements and yield quantities/qualities. In this work a mechanistic model to simulate water flow and solute transport in the soil-plant-atmosphere continuum is presented. This desktop computer program was written according to the specific requirement of developing web applications. The model is capable to solve the following issues all together: (a) water balance and (b) solute transport; (c) crop modelling; (d) GIS-interoperability; (e) embedability in web-based geospatial Decision Support Systems (DSS); (f) adaptability at different scales of application; and (g) ease of code modification. We maintained the desktop characteristic in order to further develop (e.g. integrate novel features) and run the key program modules for testing and validation purporses, but we also developed a middleware component to allow the model run the simulations directly over the web, without software to be installed. The GIS capabilities allows the web application to make simulations in a user-defined region of interest (delimited over a geographical map) without the need to specify the proper combination of model parameters. It is possible since the geospatial database collects information on pedology, climate, crop parameters and soil hydraulic characteristics. Pedological attributes include the spatial distribution of key soil data such as soil profile horizons and texture. Further, hydrological parameters are selected according to the knowledge about the spatial distribution of soils. The availability and definition in the geospatial domain

  10. Simulation of nitrogen leaching from a fertigated crop rotation in a Mediterranean climate using the EU-Rotate_N and Hydrus-2D models

    DEFF Research Database (Denmark)

    Doltra, Jordi; Nuñoz, P

    2010-01-01

    Two different modeling approaches were used to simulate the N leached during an intensively fertigated crop rotation: a recently developed crop-based simulation model (EU-Rotate_N) and a widely recognized solute transport model (Hydrus-2D). Model performance was evaluated using data from....... Accuracy of the predicted nitrate nitrogen (NO3-N) contents in the 0-90 cm soil profile was acceptable with both models, with values of the mean absolute error (MAE) below the average standard deviation of the observations. The uptake of nitrate was better simulated with EU-Rotate_N where specific crop N...... that for a successful solving of the problem studied, Hydrus-2D probably would need a more complex calibration, and that the EU-Rotate_N model can provide acceptable predictions by adjusting basic parameters for the growing conditions. Further research with other crops and soil types will allow up...

  11. Statefinder parameters in two dark energy models

    CERN Document Server

    Panotopoulos, Grigoris

    2007-01-01

    The statefinder parameters ($r,s$) in two dark energy models are studied. In the first, we discuss in four-dimensional General Relativity a two fluid model, in which dark energy and dark matter are allowed to interact with each other. In the second model, we consider the DGP brane model generalized by taking a possible energy exchange between the brane and the bulk into account. We determine the values of the statefinder parameters that correspond to the unique attractor of the system at hand. Furthermore, we produce plots in which we show $s,r$ as functions of red-shift, and the ($s-r$) plane for each model.

  12. Parameter Symmetry of the Interacting Boson Model

    CERN Document Server

    Shirokov, A M; Smirnov, Yu F; Shirokov, Andrey M.; Smirnov, Yu. F.

    1998-01-01

    We discuss the symmetry of the parameter space of the interacting boson model (IBM). It is shown that for any set of the IBM Hamiltonian parameters (with the only exception of the U(5) dynamical symmetry limit) one can always find another set that generates the equivalent spectrum. We discuss the origin of the symmetry and its relevance for physical applications.

  13. A two-dimensional simulation model of phosphorus uptake including crop growth and P-response

    NARCIS (Netherlands)

    Mollier, A.; Willigen, de P.; Heinen, M.; Morel, C.; Schneider, A.; Pellerin, S.

    2008-01-01

    Modelling nutrient uptake by crops implies considering and integrating the processes controlling the soil nutrient supply, the uptake by the root system and relationships between the crop growth response and the amount of nutrient absorbed. We developed a model that integrates both dynamics of maize

  14. The Effects of Use of Average Instead of Daily Weather Data in Crop Growth Simulation Models

    NARCIS (Netherlands)

    Nonhebel, Sanderine

    1994-01-01

    Development and use of crop growth simulation models has increased in the last decades. Most crop growth models require daily weather data as input values. These data are not easy to obtain and therefore in many studies daily data are generated, or average values are used as input data for these

  15. Assessing the sustainability of wheat-based cropping systems using APSIM: Model parameterisation and evaluation

    NARCIS (Netherlands)

    Moeller, C.; Pala, M.; Manschadi, A.M.; Meinke, H.B.; Sauerborn, J.

    2007-01-01

    Assessing the sustainability of crop and soil management practices in wheat-based rotations requires a well-tested model with the demonstrated ability to sensibly predict crop productivity and changes in the soil resource. The Agricultural Production Systems Simulator (APSIM) suite of models was

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

    Institute of Scientific and Technical Information of China (English)

    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.

  17. Use of Crop Models in Assessment of Soil Drought

    Directory of Open Access Journals (Sweden)

    Milada Stastna

    2007-03-01

    Full Text Available The aims of the study were to apply, test and to present the ability of the deterministic simulation models SIMWASER and CERES-Wheat computing soil-water balance components, percolation losses, ground water recharge and capillary rise. Two case studies for the assessment of percolation losses from irrigated carrots to deep groundwater at Obersiebenbrunn in the Marchfeld (Austria and ground water recharge and capillary rise from shallow groundwater in grass lysimeters at Berlin-Dahlem (Germany together with two test sites with similar climatic conditions and soil water storage potential but with (Grossenzesdorf, Austria and without (Zabcice, Czech Republic groundwater impact in a semi-arid agricultural area in central Europe were chosen. At Obersiebenbrunn, simulated percolation and evapotranspiration were 183 and 629 mm, while the respective measured values amounted to 198 and 635 mm. Up to 42% (194 mm of evapotranspiration was provided by groundwater at s Grossenzesdorf and only 126 mm was used for the worst case comparing to observed data. Th ese results showed both models as proper applicable tools to demonstrate crop – soil – water relations. However, the availability and management of soil water reserves will remain important, especially when extreme events such as droughts occur more frequently and annual soil and groundwater recharge decrease.

  18. Wind Farm Decentralized Dynamic Modeling With Parameters

    DEFF Research Database (Denmark)

    Soltani, Mohsen; Shakeri, Sayyed Mojtaba; Grunnet, Jacob Deleuran;

    2010-01-01

    Development of dynamic wind flow models for wind farms is part of the research in European research FP7 project AEOLUS. The objective of this report is to provide decentralized dynamic wind flow models with parameters. The report presents a structure for decentralized flow models with inputs from...

  19. Setting Parameters for Biological Models With ANIMO

    NARCIS (Netherlands)

    Schivo, Stefano; Scholma, Jetse; Karperien, Hermanus Bernardus Johannes; Post, Janine Nicole; van de Pol, Jan Cornelis; Langerak, Romanus; André, Étienne; Frehse, Goran

    2014-01-01

    ANIMO (Analysis of Networks with Interactive MOdeling) is a software for modeling biological networks, such as e.g. signaling, metabolic or gene networks. An ANIMO model is essentially the sum of a network topology and a number of interaction parameters. The topology describes the interactions

  20. Leaf Construction Cost and Related Ecophysiological Parameters of Rice Crop and Its Important Weeds

    Institute of Scientific and Technical Information of China (English)

    Vartika SINGH; Hema SINGH

    2012-01-01

    To understand the reason for the success of weeds,we investigated the energetic cost of leaf construction,and certain ecophysiological traits of rice and its dominant weeds.On physiological basis among all weeds,Caesulia axillaris Roxburgh was found to be the most serious,followed by Echinochloa cruss-galli L.Beauv and Echinochloa colonum L.Link,while Fimbristylis miliaceae L.Vahl and Cyperus iria L.were moderate weeds of the rice fields.C.axillaris had the lowest leaf construction cost (LCC) both on a mass basis (1.15 g/g) and on a unit area basis (22.93 g/m2).Comparatively higher specific leaf area,photosynthetic rate,photosynthetic nitrogen use efficiency,leaf area ratio and leaf area index provided C.axillaris with further competitive advantage.Low LCC suggests that weeds utilize carbon resource more efficiently than the crop and potentially spend the saved energy on other competitive strategies viz.seed production,biomass production and high relative growth rate,which results in low crop yield and increase in weed seed bank.

  1. Delineating Parameter Unidentifiabilities in Complex Models

    CERN Document Server

    Raman, Dhruva V; Papachristodoulou, Antonis

    2016-01-01

    Scientists use mathematical modelling to understand and predict the properties of complex physical systems. In highly parameterised models there often exist relationships between parameters over which model predictions are identical, or nearly so. These are known as structural or practical unidentifiabilities, respectively. They are hard to diagnose and make reliable parameter estimation from data impossible. They furthermore imply the existence of an underlying model simplification. We describe a scalable method for detecting unidentifiabilities, and the functional relations defining them, for generic models. This allows for model simplification, and appreciation of which parameters (or functions thereof) cannot be estimated from data. Our algorithm can identify features such as redundant mechanisms and fast timescale subsystems, as well as the regimes in which such approximations are valid. We base our algorithm on a novel quantification of regional parametric sensitivity: multiscale sloppiness. Traditional...

  2. Crop yield, genetic parameter estimation and selection of sacha inchi in central Amazon

    Directory of Open Access Journals (Sweden)

    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.

  3. Parameter Estimation, Model Reduction and Quantum Filtering

    CERN Document Server

    Chase, Bradley A

    2009-01-01

    This dissertation explores the topics of parameter estimation and model reduction in the context of quantum filtering. Chapters 2 and 3 provide a review of classical and quantum probability theory, stochastic calculus and filtering. Chapter 4 studies the problem of quantum parameter estimation and introduces the quantum particle filter as a practical computational method for parameter estimation via continuous measurement. Chapter 5 applies these techniques in magnetometry and studies the estimator's uncertainty scalings in a double-pass atomic magnetometer. Chapter 6 presents an efficient feedback controller for continuous-time quantum error correction. Chapter 7 presents an exact model of symmetric processes of collective qubit systems.

  4. Beans (Phaseolus ssp.) as a Model for Understanding Crop Evolution

    Science.gov (United States)

    Bitocchi, Elena; Rau, Domenico; Bellucci, Elisa; Rodriguez, Monica; Murgia, Maria L.; Gioia, Tania; Santo, Debora; Nanni, Laura; Attene, Giovanna; Papa, Roberto

    2017-01-01

    Here, we aim to provide a comprehensive and up-to-date overview of the most significant outcomes in the literature regarding the origin of Phaseolus genus, the geographical distribution of the wild species, the domestication process, and the wide spread out of the centers of origin. Phaseolus can be considered as a unique model for the study of crop evolution, and in particular, for an understanding of the convergent phenotypic evolution that occurred under domestication. The almost unique situation that characterizes the Phaseolus genus is that five of its ∼70 species have been domesticated (i.e., Phaseolus vulgaris, P. coccineus, P. dumosus, P. acutifolius, and P. lunatus), and in addition, for P. vulgaris and P. lunatus, the wild forms are distributed in both Mesoamerica and South America, where at least two independent and isolated episodes of domestication occurred. Thus, at least seven independent domestication events occurred, which provides the possibility to unravel the genetic basis of the domestication process not only among species of the same genus, but also between gene pools within the same species. Along with this, other interesting features makes Phaseolus crops very useful in the study of evolution, including: (i) their recent divergence, and the high level of collinearity and synteny among their genomes; (ii) their different breeding systems and life history traits, from annual and autogamous, to perennial and allogamous; and (iii) their adaptation to different environments, not only in their centers of origin, but also out of the Americas, following their introduction and wide spread through different countries. In particular for P. vulgaris this resulted in the breaking of the spatial isolation of the Mesoamerican and Andean gene pools, which allowed spontaneous hybridization, thus increasing of the possibility of novel genotypes and phenotypes. This knowledge that is associated to the genetic resources that have been conserved ex situ and in

  5. Parameter Estimation for Thurstone Choice Models

    Energy Technology Data Exchange (ETDEWEB)

    Vojnovic, Milan [London School of Economics (United Kingdom); Yun, Seyoung [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2017-04-24

    We consider the estimation accuracy of individual strength parameters of a Thurstone choice model when each input observation consists of a choice of one item from a set of two or more items (so called top-1 lists). This model accommodates the well-known choice models such as the Luce choice model for comparison sets of two or more items and the Bradley-Terry model for pair comparisons. We provide a tight characterization of the mean squared error of the maximum likelihood parameter estimator. We also provide similar characterizations for parameter estimators defined by a rank-breaking method, which amounts to deducing one or more pair comparisons from a comparison of two or more items, assuming independence of these pair comparisons, and maximizing a likelihood function derived under these assumptions. We also consider a related binary classification problem where each individual parameter takes value from a set of two possible values and the goal is to correctly classify all items within a prescribed classification error. The results of this paper shed light on how the parameter estimation accuracy depends on given Thurstone choice model and the structure of comparison sets. In particular, we found that for unbiased input comparison sets of a given cardinality, when in expectation each comparison set of given cardinality occurs the same number of times, for a broad class of Thurstone choice models, the mean squared error decreases with the cardinality of comparison sets, but only marginally according to a diminishing returns relation. On the other hand, we found that there exist Thurstone choice models for which the mean squared error of the maximum likelihood parameter estimator can decrease much faster with the cardinality of comparison sets. We report empirical evaluation of some claims and key parameters revealed by theory using both synthetic and real-world input data from some popular sport competitions and online labor platforms.

  6. Parameters-related uncertainty in modeling sugar cane yield with an agro-Land Surface Model

    Science.gov (United States)

    Valade, A.; Ciais, P.; Vuichard, N.; Viovy, N.; Ruget, F.; Gabrielle, B.

    2012-12-01

    Agro-Land Surface Models (agro-LSM) have been developed from the coupling of specific crop models and large-scale generic vegetation models. They aim at accounting for the spatial distribution and variability of energy, water and carbon fluxes within soil-vegetation-atmosphere continuum with a particular emphasis on how crop phenology and agricultural management practice influence the turbulent fluxes exchanged with the atmosphere, and the underlying water and carbon pools. A part of the uncertainty in these models is related to the many parameters included in the models' equations. In this study, we quantify the parameter-based uncertainty in the simulation of sugar cane biomass production with the agro-LSM ORCHIDEE-STICS on a multi-regional approach with data from sites in Australia, La Reunion and Brazil. First, the main source of uncertainty for the output variables NPP, GPP, and sensible heat flux (SH) is determined through a screening of the main parameters of the model on a multi-site basis leading to the selection of a subset of most sensitive parameters causing most of the uncertainty. In a second step, a sensitivity analysis is carried out on the parameters selected from the screening analysis at a regional scale. For this, a Monte-Carlo sampling method associated with the calculation of Partial Ranked Correlation Coefficients is used. First, we quantify the sensitivity of the output variables to individual input parameters on a regional scale for two regions of intensive sugar cane cultivation in Australia and Brazil. Then, we quantify the overall uncertainty in the simulation's outputs propagated from the uncertainty in the input parameters. Seven parameters are identified by the screening procedure as driving most of the uncertainty in the agro-LSM ORCHIDEE-STICS model output at all sites. These parameters control photosynthesis (optimal temperature of photosynthesis, optimal carboxylation rate), radiation interception (extinction coefficient), root

  7. Life Cycle Inventory Modelling of Land Use Induced by Crop Consumption

    DEFF Research Database (Denmark)

    Kløverpris, Jesper; Wenzel, Henrik; Nielsen, Per Henning

    2008-01-01

    for technological development have a profound influence on identification of the marginal response to crop consumption, and how the geographical location of crop consumption also influences the composition of the marginal production response in terms of cropland expansion and intensification. Crop prices have been...... with geographical information and agricultural statistics can be used to estimate long-term land use consequences of changes in crop consumption. The GTAP Model is a suitable tool although it requires implementation of land supply curves, adjustment of elasticities to reflect long-term changes, and possibly...

  8. Delineating parameter unidentifiabilities in complex models

    Science.gov (United States)

    Raman, Dhruva V.; Anderson, James; Papachristodoulou, Antonis

    2017-03-01

    Scientists use mathematical modeling as a tool for understanding and predicting the properties of complex physical systems. In highly parametrized models there often exist relationships between parameters over which model predictions are identical, or nearly identical. These are known as structural or practical unidentifiabilities, respectively. They are hard to diagnose and make reliable parameter estimation from data impossible. They furthermore imply the existence of an underlying model simplification. We describe a scalable method for detecting unidentifiabilities, as well as the functional relations defining them, for generic models. This allows for model simplification, and appreciation of which parameters (or functions thereof) cannot be estimated from data. Our algorithm can identify features such as redundant mechanisms and fast time-scale subsystems, as well as the regimes in parameter space over which such approximations are valid. We base our algorithm on a quantification of regional parametric sensitivity that we call `multiscale sloppiness'. Traditionally, the link between parametric sensitivity and the conditioning of the parameter estimation problem is made locally, through the Fisher information matrix. This is valid in the regime of infinitesimal measurement uncertainty. We demonstrate the duality between multiscale sloppiness and the geometry of confidence regions surrounding parameter estimates made where measurement uncertainty is non-negligible. Further theoretical relationships are provided linking multiscale sloppiness to the likelihood-ratio test. From this, we show that a local sensitivity analysis (as typically done) is insufficient for determining the reliability of parameter estimation, even with simple (non)linear systems. Our algorithm can provide a tractable alternative. We finally apply our methods to a large-scale, benchmark systems biology model of necrosis factor (NF)-κ B , uncovering unidentifiabilities.

  9. Systematic parameter inference in stochastic mesoscopic modeling

    Science.gov (United States)

    Lei, Huan; Yang, Xiu; Li, Zhen; Karniadakis, George Em

    2017-02-01

    We propose a method to efficiently determine the optimal coarse-grained force field in mesoscopic stochastic simulations of Newtonian fluid and polymer melt systems modeled by dissipative particle dynamics (DPD) and energy conserving dissipative particle dynamics (eDPD). The response surfaces of various target properties (viscosity, diffusivity, pressure, etc.) with respect to model parameters are constructed based on the generalized polynomial chaos (gPC) expansion using simulation results on sampling points (e.g., individual parameter sets). To alleviate the computational cost to evaluate the target properties, we employ the compressive sensing method to compute the coefficients of the dominant gPC terms given the prior knowledge that the coefficients are "sparse". The proposed method shows comparable accuracy with the standard probabilistic collocation method (PCM) while it imposes a much weaker restriction on the number of the simulation samples especially for systems with high dimensional parametric space. Fully access to the response surfaces within the confidence range enables us to infer the optimal force parameters given the desirable values of target properties at the macroscopic scale. Moreover, it enables us to investigate the intrinsic relationship between the model parameters, identify possible degeneracies in the parameter space, and optimize the model by eliminating model redundancies. The proposed method provides an efficient alternative approach for constructing mesoscopic models by inferring model parameters to recover target properties of the physics systems (e.g., from experimental measurements), where those force field parameters and formulation cannot be derived from the microscopic level in a straight forward way.

  10. Nitrous Oxide Emissions from Biofuel Crops and Parameterization in the EPIC Biogeochemical Model

    Science.gov (United States)

    This presentation describes year 1 field measurements of N2O fluxes and crop yields which are used to parameterize the EPIC biogeochemical model for the corresponding field site. Initial model simulations are also presented.

  11. Nitrous Oxide Emissions from Biofuel Crops and Parameterization in the EPIC Biogeochemical Model

    Science.gov (United States)

    This presentation describes year 1 field measurements of N2O fluxes and crop yields which are used to parameterize the EPIC biogeochemical model for the corresponding field site. Initial model simulations are also presented.

  12. Environmental Transport Input Parameters for the Biosphere Model

    Energy Technology Data Exchange (ETDEWEB)

    M. A. Wasiolek

    2003-06-27

    This analysis report is one of the technical reports documenting the Environmental Radiation Model for Yucca Mountain Nevada (ERMYN), a biosphere model supporting the total system performance assessment (TSPA) for the geologic repository at Yucca Mountain. A graphical representation of the documentation hierarchy for the ERMYN is presented in Figure 1-1. This figure shows relationships among the reports developed for biosphere modeling and biosphere abstraction products for the TSPA, as identified in the ''Technical Work Plan: for Biosphere Modeling and Expert Support'' (TWP) (BSC 2003 [163602]). Some documents in Figure 1-1 may be under development and not available when this report is issued. This figure provides an understanding of how this report contributes to biosphere modeling in support of the license application (LA), but access to the listed documents is not required to understand the contents of this report. This report is one of the reports that develops input parameter values for the biosphere model. The ''Biosphere Model Report'' (BSC 2003 [160699]) describes the conceptual model, the mathematical model, and the input parameters. The purpose of this analysis is to develop biosphere model parameter values related to radionuclide transport and accumulation in the environment. These parameters support calculations of radionuclide concentrations in the environmental media (e.g., soil, crops, animal products, and air) resulting from a given radionuclide concentration at the source of contamination (i.e., either in groundwater or volcanic ash). The analysis was performed in accordance with the TWP (BSC 2003 [163602]). This analysis develops values of parameters associated with many features, events, and processes (FEPs) applicable to the reference biosphere (DTN: M00303SEPFEPS2.000 [162452]), which are addressed in the biosphere model (BSC 2003 [160699]). The treatment of these FEPs is described in BSC (2003 [160699

  13. Application of lumped-parameter models

    DEFF Research Database (Denmark)

    Ibsen, Lars Bo; Liingaard, Morten

    This technical report concerns the lumped-parameter models for a suction caisson with a ratio between skirt length and foundation diameter equal to 1/2, embedded into an viscoelastic soil. The models are presented for three different values of the shear modulus of the subsoil (section 1.1). Subse...

  14. Models and parameters for environmental radiological assessments

    Energy Technology Data Exchange (ETDEWEB)

    Miller, C W [ed.

    1984-01-01

    This book presents a unified compilation of models and parameters appropriate for assessing the impact of radioactive discharges to the environment. Models examined include those developed for the prediction of atmospheric and hydrologic transport and deposition, for terrestrial and aquatic food-chain bioaccumulation, and for internal and external dosimetry. Chapters have been entered separately into the data base. (ACR)

  15. Multi-model uncertainty analysis in predicting grain N for crop rotations in Europe

    DEFF Research Database (Denmark)

    Yin, Xiaogang; Kersebaum, Kurt C; Kollas, Chris

    2017-01-01

    simulating different treatments (catch crops, CO2 concentrations, irrigation, N application, residues and tillage) in four multi-year rotation experiments in Europe to assess modelling accuracy. Seven grain and seed crops in four rotation systems in Europe were included in the study, namely winter wheat......Realistic estimation of grain nitrogen (N; N in grain yield) is crucial for assessing N management in crop rotations, but there is little information on the performance of commonly used crop models for simulating grain N. Therefore, the objectives of the study were to (1) test if continuous...... and spring barley compared to spring oat, winter rye, pea and winter oilseed rape. For each crop, the use of the ensemble mean significantly reduced the mean absolute percentage error (MAPE) between simulations and observations to less than 15%, thus a multi–model ensemble can more precisely predict grain N...

  16. Rice in cropping systems - Modelling transitions between flooded and non-flooded soil environments

    NARCIS (Netherlands)

    Gaydon, D.S.; Probert, M.E.; Buresh, R.J.; Meinke, H.B.; Suriadi, A.; Dobermann, A.; Bouman, B.A.M.; Timsina, J.

    2012-01-01

    Water shortages in many rice-growing regions, combined with growing global imperatives to increase food production, are driving research into increased water use efficiency and modified agricultural practices in rice-based cropping systems. Well-tested cropping systems models that capture interactio

  17. The importance of weather data in crop growth simulation models and assessment of climatic change effects

    NARCIS (Netherlands)

    Nonhebel, S.

    1993-01-01

    Yields of agricultural crops are largely determined by the weather conditions during the growing season. Weather data are therefore important input variables for crop growth simulation models. In practice, these data are accepted at their face value. This is not realistic. Like all measured

  18. Rice in cropping systems - Modelling transitions between flooded and non-flooded soil environments

    NARCIS (Netherlands)

    Gaydon, D.S.; Probert, M.E.; Buresh, R.J.; Meinke, H.B.; Suriadi, A.; Dobermann, A.; Bouman, B.A.M.; Timsina, J.

    2012-01-01

    Water shortages in many rice-growing regions, combined with growing global imperatives to increase food production, are driving research into increased water use efficiency and modified agricultural practices in rice-based cropping systems. Well-tested cropping systems models that capture interactio

  19. Inhalation Exposure Input Parameters for the Biosphere Model

    Energy Technology Data Exchange (ETDEWEB)

    M. Wasiolek

    2006-06-05

    This analysis is one of the technical reports that support the Environmental Radiation Model for Yucca Mountain, Nevada (ERMYN), referred to in this report as the biosphere model. ''Biosphere Model Report'' (BSC 2004 [DIRS 169460]) describes in detail the conceptual model as well as the mathematical model and its input parameters. This report documents development of input parameters for the biosphere model that are related to atmospheric mass loading and supports the use of the model to develop biosphere dose conversion factors (BDCFs). The biosphere model is one of a series of process models supporting the total system performance assessment (TSPA) for a Yucca Mountain repository. ''Inhalation Exposure Input Parameters for the Biosphere Model'' is one of five reports that develop input parameters for the biosphere model. A graphical representation of the documentation hierarchy for the biosphere model is presented in Figure 1-1 (based on BSC 2006 [DIRS 176938]). This figure shows the interrelationships among the products (i.e., analysis and model reports) developed for biosphere modeling and how this analysis report contributes to biosphere modeling. This analysis report defines and justifies values of atmospheric mass loading for the biosphere model. Mass loading is the total mass concentration of resuspended particles (e.g., dust, ash) in a volume of air. Mass loading values are used in the air submodel of the biosphere model to calculate concentrations of radionuclides in air inhaled by a receptor and concentrations in air surrounding crops. Concentrations in air to which the receptor is exposed are then used in the inhalation submodel to calculate the dose contribution to the receptor from inhalation of contaminated airborne particles. Concentrations in air surrounding plants are used in the plant submodel to calculate the concentrations of radionuclides in foodstuffs contributed from uptake by foliar interception. This

  20. Crop physiology calibration in the CLM

    Directory of Open Access Journals (Sweden)

    I. Bilionis

    2015-04-01

    scalable and adaptive scheme based on sequential Monte Carlo (SMC. The model showed significant improvement of crop productivity with the new calibrated parameters. We demonstrate that the calibrated parameters are applicable across alternative years and different sites.

  1. Using the CLM Crop Model to assess the impacts of changes in Climate, Atmospheric CO2, Irrigation, Fertilizer and Geographic Distribution on Historical and Future Crop Yields

    Science.gov (United States)

    Lawrence, P.

    2015-12-01

    Since the start of the green revolution global crop yields have increased linearly for most major cereal crops, so that present day global values are around twice those of the 1960s. The increase in crop yields have allowed for large increases in global agricultural production without correspondingly large increases in cropping area. Future projections under the Shared Socio-economic Pathways (SSP) framework and other assessments result in increases of global crop production of greater than 100% by the year 2050. In order to meet this increased agricultural demand within the available arable land, future production gains need to be understood in terms of the yield changes due to changes in climate, atmospheric CO2, and adaptive management such as irrigation and fertilizer application. In addition to the changes in crop yield, future agricultural demand will need to be met through increasing cropping areas into what are currently marginal lands at the cost of existing forests and other natural ecosystems. In this study we assess the utility of the crop model within the Community Land Model (CLM Crop) to provide both historical and future guidance on changes in crop yields under a range of global idealized crop modeling experiments. The idealized experiments follow the experimental design of the AgMIP Global Gridded Crop Model Intercomparison (GGCMI) in which CLM Crop is a participating model. The idealized experiments consist of global crop simulations for Cotton, Maize, Rice, Soy, Sugarcane, and Wheat under various climate, atmospheric CO2 levels, irrigation prescription, and nitrogen fertilizer application. The time periods simulated for the experiments are for the Historical period (1901 - 2005), and for the two Representative Concentration Pathways of RCP 4.5 and RCP 8.5 (2006 - 2100). Each crop is simulated on all land grid cells globally for each time period with atmospheric forcing that is a combination of: 1. transient climate and CO2; 2. transient climate

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

    Science.gov (United States)

    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.

  3. Estimation of Model Parameters for Steerable Needles

    Science.gov (United States)

    Park, Wooram; Reed, Kyle B.; Okamura, Allison M.; Chirikjian, Gregory S.

    2010-01-01

    Flexible needles with bevel tips are being developed as useful tools for minimally invasive surgery and percutaneous therapy. When such a needle is inserted into soft tissue, it bends due to the asymmetric geometry of the bevel tip. This insertion with bending is not completely repeatable. We characterize the deviations in needle tip pose (position and orientation) by performing repeated needle insertions into artificial tissue. The base of the needle is pushed at a constant speed without rotating, and the covariance of the distribution of the needle tip pose is computed from experimental data. We develop the closed-form equations to describe how the covariance varies with different model parameters. We estimate the model parameters by matching the closed-form covariance and the experimentally obtained covariance. In this work, we use a needle model modified from a previously developed model with two noise parameters. The modified needle model uses three noise parameters to better capture the stochastic behavior of the needle insertion. The modified needle model provides an improvement of the covariance error from 26.1% to 6.55%. PMID:21643451

  4. Estimation of Model Parameters for Steerable Needles.

    Science.gov (United States)

    Park, Wooram; Reed, Kyle B; Okamura, Allison M; Chirikjian, Gregory S

    2010-01-01

    Flexible needles with bevel tips are being developed as useful tools for minimally invasive surgery and percutaneous therapy. When such a needle is inserted into soft tissue, it bends due to the asymmetric geometry of the bevel tip. This insertion with bending is not completely repeatable. We characterize the deviations in needle tip pose (position and orientation) by performing repeated needle insertions into artificial tissue. The base of the needle is pushed at a constant speed without rotating, and the covariance of the distribution of the needle tip pose is computed from experimental data. We develop the closed-form equations to describe how the covariance varies with different model parameters. We estimate the model parameters by matching the closed-form covariance and the experimentally obtained covariance. In this work, we use a needle model modified from a previously developed model with two noise parameters. The modified needle model uses three noise parameters to better capture the stochastic behavior of the needle insertion. The modified needle model provides an improvement of the covariance error from 26.1% to 6.55%.

  5. An Optimization Model of Tunnel Support Parameters

    Directory of Open Access Journals (Sweden)

    Su Lijuan

    2015-05-01

    Full Text Available An optimization model was developed to obtain the ideal values of the primary support parameters of tunnels, which are wide-ranging in high-speed railway design codes when the surrounding rocks are at the III, IV, and V levels. First, several sets of experiments were designed and simulated using the FLAC3D software under an orthogonal experimental design. Six factors, namely, level of surrounding rock, buried depth of tunnel, lateral pressure coefficient, anchor spacing, anchor length, and shotcrete thickness, were considered. Second, a regression equation was generated by conducting a multiple linear regression analysis following the analysis of the simulation results. Finally, the optimization model of support parameters was obtained by solving the regression equation using the least squares method. In practical projects, the optimized values of support parameters could be obtained by integrating known parameters into the proposed model. In this work, the proposed model was verified on the basis of the Liuyang River Tunnel Project. Results show that the optimization model significantly reduces related costs. The proposed model can also be used as a reliable reference for other high-speed railway tunnels.

  6. Crop plants as models for understanding plant adaptation and diversification

    Directory of Open Access Journals (Sweden)

    Kenneth M Olsen

    2013-08-01

    Full Text Available Since the time of Darwin, biologists have understood the promise of crop plants and their wild relatives for providing insight into the mechanisms of phenotypic evolution. The intense selection imposed by our ancestors during plant domestication and subsequent crop improvement has generated remarkable transformations of plant phenotypes. Unlike evolution in natural settings, descendent and antecedent conditions for crop plants are often both extant, providing opportunities for direct comparisons through crossing and other experimental approaches. Moreover, since domestication has repeatedly generated a suite of domestication syndrome traits that are shared among crops, opportunities exist for gaining insight into the genetic and developmental mechanisms that underlie parallel adaptive evolution. Advances in our understanding of the genetic architecture of domestication-related traits have emerged from combining powerful molecular technologies with advanced experimental designs, including nested association mapping, genome-wide association studies, population genetic screens for signatures of selection, and candidate gene approaches. These studies may be combined with high-throughput evaluations of the various omics involved in trait transformation, revealing a diversity of underlying causative mutations affecting phenotypes and their downstream propagation through biological networks. We summarize the state of our knowledge of the mutational spectrum that generates phenotypic novelty in domesticated plant species, and our current understanding of how domestication can reshape gene expression networks and emergent phenotypes. An exploration of traits that have been subject to similar selective pressures across crops (e.g., flowering time suggests that a diversity of targeted genes and causative mutational changes can underlie parallel adaptation in the context of crop evolution.

  7. Consequential life cycle inventory modelling of land use induced by crop consumption

    DEFF Research Database (Denmark)

    Kløverpris, Jesper Hedal

    The purpose of the present PhD project was to identify the mechanisms governing global land use consequences of increased crop demand in a given location and, based on this conceptual analysis, to present and demonstrate a method proposal for construction of land use data that can be used in life...... cycle assessments involving crop consumption. Increased demand for a given crop can be met by intensification, expansion, and/or by displacement of other crops or pastures. The last option will reduce the supply of other agricultural products, which may then be replaced elsewhere. Such displacement......-replacement mechanisms are governed by the availability of suitable agricultural land and several economic conditions, such as transport and trade costs. To estimate the land use response to an increase in crop demand, economic modelling can be used. In this project, the economic equilibrium model GTAP (Global Trade...

  8. Analysis of Modeling Parameters on Threaded Screws.

    Energy Technology Data Exchange (ETDEWEB)

    Vigil, Miquela S. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Brake, Matthew Robert [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Vangoethem, Douglas [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2015-06-01

    Assembled mechanical systems often contain a large number of bolted connections. These bolted connections (joints) are integral aspects of the load path for structural dynamics, and, consequently, are paramount for calculating a structure's stiffness and energy dissipation prop- erties. However, analysts have not found the optimal method to model appropriately these bolted joints. The complexity of the screw geometry cause issues when generating a mesh of the model. This paper will explore different approaches to model a screw-substrate connec- tion. Model parameters such as mesh continuity, node alignment, wedge angles, and thread to body element size ratios are examined. The results of this study will give analysts a better understanding of the influences of these parameters and will aide in finding the optimal method to model bolted connections.

  9. Impact of climate change on crop yield and role of model for achieving food security.

    Science.gov (United States)

    Kumar, Manoj

    2016-08-01

    In recent times, several studies around the globe indicate that climatic changes are likely to impact the food production and poses serious challenge to food security. In the face of climate change, agricultural systems need to adapt measures for not only increasing food supply catering to the growing population worldwide with changing dietary patterns but also to negate the negative environmental impacts on the earth. Crop simulation models are the primary tools available to assess the potential consequences of climate change on crop production and informative adaptive strategies in agriculture risk management. In consideration with the important issue, this is an attempt to provide a review on the relationship between climate change impacts and crop production. It also emphasizes the role of crop simulation models in achieving food security. Significant progress has been made in understanding the potential consequences of environment-related temperature and precipitation effect on agricultural production during the last half century. Increased CO2 fertilization has enhanced the potential impacts of climate change, but its feasibility is still in doubt and debates among researchers. To assess the potential consequences of climate change on agriculture, different crop simulation models have been developed, to provide informative strategies to avoid risks and understand the physical and biological processes. Furthermore, they can help in crop improvement programmes by identifying appropriate future crop management practises and recognizing the traits having the greatest impact on yield. Nonetheless, climate change assessment through model is subjected to a range of uncertainties. The prediction uncertainty can be reduced by using multimodel, incorporating crop modelling with plant physiology, biochemistry and gene-based modelling. For devloping new model, there is a need to generate and compile high-quality field data for model testing. Therefore, assessment of

  10. Accounting for crop rotations in acreage choice modeling: a tractable modeling framework

    OpenAIRE

    Carpentier, Alain; Gohin, Alexandre

    2014-01-01

    Crop rotation effects and constraints are major determinants of farmers’ crop choices. Crop rotations are also keystone elements of most environmentally friendly cropping systems. The aim of this paper is twofold. First, it proposes simple tools for investigating optimal dynamic crop acreage choices accounting for crop rotation effects and constraints in an uncertain context. Second, it illustrates the impacts of crop rotation effects and constraints on farmers’ acreage choices through simple...

  11. Crop monitoring & yield forecasting system based on Synthetic Aperture Radar (SAR) and process-based crop growth model: Development and validation in South and South East Asian Countries

    Science.gov (United States)

    Setiyono, T. D.

    2014-12-01

    Accurate and timely information on rice crop growth and yield helps governments and other stakeholders adapting their economic policies and enables relief organizations to better anticipate and coordinate relief efforts in the wake of a natural catastrophe. Such delivery of rice growth and yield information is made possible by regular earth observation using space-born Synthetic Aperture Radar (SAR) technology combined with crop modeling approach to estimate yield. Radar-based remote sensing is capable of observing rice vegetation growth irrespective of cloud coverage, an important feature given that in incidences of flooding the sky is often cloud-covered. The system allows rapid damage assessment over the area of interest. Rice yield monitoring is based on a crop growth simulation and SAR-derived key information, particularly start of season and leaf growth rate. Results from pilot study sites in South and South East Asian countries suggest that incorporation of SAR data into crop model improves yield estimation for actual yields. Remote-sensing data assimilation into crop model effectively capture responses of rice crops to environmental conditions over large spatial coverage, which otherwise is practically impossible to achieve. Such improvement of actual yield estimates offers practical application such as in a crop insurance program. Process-based crop simulation model is used in the system to ensure climate information is adequately captured and to enable mid-season yield forecast.

  12. Hyperspectral versus multispectral crop-productivity modeling and type discrimination for the HyspIRI mission

    Science.gov (United States)

    Mariotto, Isabella; Thenkabail, Prasad S.; Huete, Alfredo; Slonecker, E. Terrence; Platonov, Alexander

    2013-01-01

    Precise monitoring of agricultural crop biomass and yield quantities is critical for crop production management and prediction. The goal of this study was to compare hyperspectral narrowband (HNB) versus multispectral broadband (MBB) reflectance data in studying irrigated cropland characteristics of five leading world crops (cotton, wheat, maize, rice, and alfalfa) with the objectives of: 1. Modeling crop productivity, and 2. Discriminating crop types. HNB data were obtained from Hyperion hyperspectral imager and field ASD spectroradiometer, and MBB data were obtained from five broadband sensors: Landsat-7 Enhanced Thematic Mapper Plus (ETM +), Advanced Land Imager (ALI), Indian Remote Sensing (IRS), IKONOS, and QuickBird. A large collection of field spectral and biophysical variables were gathered for the 5 crops in Central Asia throughout the growing seasons of 2006 and 2007. Overall, the HNB and hyperspectral vegetation index (HVI) crop biophysical models explained about 25% greater variability when compared with corresponding MBB models. Typically, 3 to 7 HNBs, in multiple linear regression models of a given crop variable, explained more than 93% of variability in crop models. The evaluation of λ1 (400–2500 nm) versus λ2 (400–2500 nm) plots of various crop biophysical variables showed that the best two-band normalized difference HVIs involved HNBs centered at: (i) 742 nm and 1175 nm (HVI742-1175), (ii) 1296 nm and 1054 nm (HVI1296-1054), (iii) 1225 nm and 697 nm (HVI1225-697), and (iv) 702 nm and 1104 nm (HVI702-1104). Among the most frequently occurring HNBs in various crop biophysical models, 74% were located in the 1051–2331 nm spectral range, followed by 10% in the moisture sensitive 970 nm, 6% in the red and red-edge (630–752 nm), and the remaining 10% distributed between blue (400–500 nm), green (501–600 nm), and NIR (760–900 nm). Discriminant models, used for discriminating 3 or 4 or 5 crop types, showed significantly higher accuracies

  13. The AgMIP Coordinated Climate-Crop Modeling Project (C3MP): Methods and Protocols

    Science.gov (United States)

    Shukla, Sonali P.; Ruane, Alexander Clark

    2014-01-01

    Climate change is expected to alter a multitude of factors important to agricultural systems, including pests, diseases, weeds, extreme climate events, water resources, soil degradation, and socio-economic pressures. Changes to carbon dioxide concentration ([CO2]), temperature, and water (CTW) will be the primary drivers of change in crop growth and agricultural systems. Therefore, establishing the CTW-change sensitivity of crop yields is an urgent research need and warrants diverse methods of investigation. Crop models provide a biophysical, process-based tool to investigate crop responses across varying environmental conditions and farm management techniques, and have been applied in climate impact assessment by using a variety of methods (White et al., 2011, and references therein). However, there is a significant amount of divergence between various crop models' responses to CTW changes (Rotter et al., 2011). While the application of a site-based crop model is relatively simple, the coordination of such agricultural impact assessments on larger scales requires consistent and timely contributions from a large number of crop modelers, each time a new global climate model (GCM) scenario or downscaling technique is created. A coordinated, global effort to rapidly examine CTW sensitivity across multiple crops, crop models, and sites is needed to aid model development and enhance the assessment of climate impacts (Deser et al., 2012). To fulfill this need, the Coordinated Climate-Crop Modeling Project (C3MP) (Ruane et al., 2014) was initiated within the Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweig et al., 2013). The submitted results from C3MP Phase 1 (February 15, 2013-December 31, 2013) are currently being analyzed. This chapter serves to present and update the C3MP protocols, discuss the initial participation and general findings, comment on needed adjustments, and describe continued and future development. AgMIP aims to improve

  14. Evaluation of land surface model simulations of evapotranspiration over a 12 year crop succession: impact of the soil hydraulic properties

    Directory of Open Access Journals (Sweden)

    S. Garrigues

    2014-10-01

    Full Text Available Evapotranspiration has been recognized as one of the most uncertain term in the surface water balance simulated by land surface models. In this study, the SURFEX/ISBA-A-gs simulations of evapotranspiration are assessed at local scale over a 12 year Mediterranean crop succession. The model is evaluated in its standard implementation which relies on the use of the ISBA pedotransfer estimates of the soil properties. The originality of this work consists in explicitly representing the succession of crop cycles and inter-crop bare soil periods in the simulations and assessing its impact on the dynamic of simulated and measured evapotranspiration over a long period of time. The analysis focuses on key soil parameters which drive the simulation of evapotranspiration, namely the rooting depth, the soil moisture at saturation, the soil moisture at field capacity and the soil moisture at wilting point. The simulations achieved with the standard values of these parameters are compared to those achieved with the in situ values. The portability of the ISBA pedotransfer functions is evaluated over a typical Mediterranean crop site. Various in situ estimates of the soil parameters are considered and distinct parametrization strategies are tested to represent the evapotranspiration dynamic over the crop succession. This work shows that evapotranspiration mainly results from the soil evaporation when it is continuously simulated over a Mediterranean crop succession. The evapotranspiration simulated with the standard surface and soil parameters of the model is largely underestimated. The deficit in cumulative evapotranspiration amounts to 24% over 12 years. The bias in daily daytime evapotranspiration is −0.24 mm day−1. The ISBA pedotransfer estimates of the soil moisture at saturation and at wilting point are overestimated which explains most of the evapotranspiration underestimation. The overestimation of the soil moisture at wilting point causes the

  15. Evaluation of land surface model simulations of evapotranspiration over a 12 year crop succession: impact of the soil hydraulic properties

    Science.gov (United States)

    Garrigues, S.; Olioso, A.; Calvet, J.-C.; Martin, E.; Lafont, S.; Moulin, S.; Chanzy, A.; Marloie, O.; Desfonds, V.; Bertrand, N.; Renard, D.

    2014-10-01

    Evapotranspiration has been recognized as one of the most uncertain term in the surface water balance simulated by land surface models. In this study, the SURFEX/ISBA-A-gs simulations of evapotranspiration are assessed at local scale over a 12 year Mediterranean crop succession. The model is evaluated in its standard implementation which relies on the use of the ISBA pedotransfer estimates of the soil properties. The originality of this work consists in explicitly representing the succession of crop cycles and inter-crop bare soil periods in the simulations and assessing its impact on the dynamic of simulated and measured evapotranspiration over a long period of time. The analysis focuses on key soil parameters which drive the simulation of evapotranspiration, namely the rooting depth, the soil moisture at saturation, the soil moisture at field capacity and the soil moisture at wilting point. The simulations achieved with the standard values of these parameters are compared to those achieved with the in situ values. The portability of the ISBA pedotransfer functions is evaluated over a typical Mediterranean crop site. Various in situ estimates of the soil parameters are considered and distinct parametrization strategies are tested to represent the evapotranspiration dynamic over the crop succession. This work shows that evapotranspiration mainly results from the soil evaporation when it is continuously simulated over a Mediterranean crop succession. The evapotranspiration simulated with the standard surface and soil parameters of the model is largely underestimated. The deficit in cumulative evapotranspiration amounts to 24% over 12 years. The bias in daily daytime evapotranspiration is -0.24 mm day-1. The ISBA pedotransfer estimates of the soil moisture at saturation and at wilting point are overestimated which explains most of the evapotranspiration underestimation. The overestimation of the soil moisture at wilting point causes the underestimation of

  16. Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis.

    Directory of Open Access Journals (Sweden)

    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.

  17. Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis.

    Science.gov (United States)

    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.

  18. A generic probability based model to derive regional patterns of crops in time and space

    Science.gov (United States)

    Wattenbach, Martin; Luedtke, Stefan; Redweik, Richard; van Oijen, Marcel; Balkovic, Juraj; Reinds, Gert Jan

    2015-04-01

    Croplands are not only the key to human food supply, they also change the biophysical and biogeochemical properties of the land surface leading to changes in the water cycle, energy portioning, they influence soil erosion and substantially contribute to the amount of greenhouse gases entering the atmosphere. The effects of croplands on the environment depend on the type of crop and the associated management which both are related to the site conditions, economic boundary settings as well as preferences of individual farmers. The method described here is designed to predict the most probable crop to appear at a given location and time. The method uses statistical crop area information on NUTS2 level from EUROSTAT and the Common Agricultural Policy Regionalized Impact Model (CAPRI) as observation. These crops are then spatially disaggregated to the 1 x 1 km grid scale within the region, using the assumption that the probability of a crop appearing at a given location and a given year depends on a) the suitability of the land for the cultivation of the crop derived from the MARS Crop Yield Forecast System (MCYFS) and b) expert knowledge of agricultural practices. The latter includes knowledge concerning the feasibility of one crop following another (e.g. a late-maturing crop might leave too little time for the establishment of a winter cereal crop) and the need to combat weed infestations or crop diseases. The model is implemented in R and PostGIS. The quality of the generated crop sequences per grid cell is evaluated on the basis of the given statistics reported by the joint EU/CAPRI database. The assessment is given on NUTS2 level using per cent bias as a measure with a threshold of 15% as minimum quality. The results clearly indicates that crops with a large relative share within the administrative unit are not as error prone as crops that allocate only minor parts of the unit. However, still roughly 40% show an absolute per cent bias above the 15% threshold. This

  19. An integrated soil-crop system model for water and nitrogen management in North China

    Science.gov (United States)

    Liang, Hao; Hu, Kelin; Batchelor, William D.; Qi, Zhiming; Li, Baoguo

    2016-05-01

    An integrated model WHCNS (soil Water Heat Carbon Nitrogen Simulator) was developed to assess water and nitrogen (N) management in North China. It included five main modules: soil water, soil temperature, soil carbon (C), soil N, and crop growth. The model integrated some features of several widely used crop and soil models, and some modifications were made in order to apply the WHCNS model under the complex conditions of intensive cropping systems in North China. The WHCNS model was evaluated using an open access dataset from the European International Conference on Modeling Soil Water and N Dynamics. WHCNS gave better estimations of soil water and N dynamics, dry matter accumulation and N uptake than 14 other models. The model was tested against data from four experimental sites in North China under various soil, crop, climate, and management practices. Simulated soil water content, soil nitrate concentrations, crop dry matter, leaf area index and grain yields all agreed well with measured values. This study indicates that the WHCNS model can be used to analyze and evaluate the effects of various field management practices on crop yield, fate of N, and water and N use efficiencies in North China.

  20. Effects of Monoculture, Crop Rotation, and Soil Moisture Content on Selected Soil Physicochemical and Microbial Parameters in Wheat Fields

    Directory of Open Access Journals (Sweden)

    A. Marais

    2012-01-01

    Full Text Available Different plants are known to have different soil microbial communities associated with them. Agricultural management practices such as fertiliser and pesticide addition, crop rotation, and grazing animals can lead to different microbial communities in the associated agricultural soils. Soil dilution plates, most-probable-number (MPN, community level physiological profiling (CLPP, and buried slide technique as well as some measured soil physicochemical parameters were used to determine changes during the growing season in the ecosystem profile in wheat fields subjected to wheat monoculture or wheat in annual rotation with medic/clover pasture. Statistical analyses showed that soil moisture had an over-riding effect on seasonal fluctuations in soil physicochemical and microbial populations. While within season soil microbial activity could be differentiated between wheat fields under rotational and monoculture management, these differences were not significant.

  1. A Spatial-Dynamic Agent-based Model of Energy Crop Introduction in Jiangsu province, China

    Science.gov (United States)

    Shu, K.; Schneider, U. A.; Scheffran, J.

    2012-12-01

    Bioenergy, as one promising option to replace a fraction of conventional fossil fuels and lower net greenhouse gas emissions, has gained many countries', in particular developing ones' attention. Their focus is mainly on the design of efficient bioenergy utilization pathways which adapt to both local geographic features and economic conditions. The establishment of a biomass production sector would be the first and pivotal component in the whole industrial chain. Several existing studies have estimated the global biomass for energy potential but arrived at very different results. One reason for the large uncertainty of biomass potential may be ascribed to the diverse nature of biomass leading to different estimates in different circumstances. Therefore, specific research at the local level is essential. Following this thought, our research conducted in the Jiangsu province, a representative region in China, will explore the spatial distribution of biomass production. The employed methodology can also be applied to other locations both in China and similar developing countries if model parameters are adequately adjusted. In this study, we analyze the local situation in the Jiangsu province focusing on the selection of new energy crops, since the cultivation of dedicated crop for energy use is still in experimental phase. We also examine the land use conflict which is especially relevant to China with more than 1.3 billion people and a severe burden on food supply. We develop an agent-based model to find the optimal spatial distribution of biomass (SDA-SDB) in Jiangsu province. Compromising data accessibility and heterogeneity of environmental factors across the province, we resolve our model at county level and consider the aggregated farming community in one county as a single agent. The aim of SDA-SDB is to simulate farmers' decision process of allocating land to either food or energy crops facing limited resources and political targets for bioenergy development

  2. The Lund Model at Nonzero Impact Parameter

    CERN Document Server

    Janik, R A; Janik, Romuald A.; Peschanski, Robi

    2003-01-01

    We extend the formulation of the longitudinal 1+1 dimensional Lund model to nonzero impact parameter using the minimal area assumption. Complete formulae for the string breaking probability and the momenta of the produced mesons are derived using the string worldsheet Minkowskian helicoid geometry. For strings stretched into the transverse dimension, we find probability distribution with slope linear in m_T similar to the statistical models but without any thermalization assumptions.

  3. IMPROVEMENT OF FLUID PIPE LUMPED PARAMETER MODEL

    Institute of Scientific and Technical Information of China (English)

    Kong Xiaowu; Wei Jianhua; Qiu Minxiu; Wu Genmao

    2004-01-01

    The traditional lumped parameter model of fluid pipe is introduced and its drawbacks are pointed out.Furthermore, two suggestions are put forward to remove these drawbacks.Firstly, the structure of equivalent circuit is modified, and then the evaluation of equivalent fluid resistance is change to take the frequency-dependent friction into account.Both simulation and experiment prove that this model is precise to characterize the dynamic behaviors of fluid in pipe.

  4. Using the GENESYS model quantifying the effect of cropping systems on gene escape from GM rape varieties to evaluate and design cropping systems

    Directory of Open Access Journals (Sweden)

    Colbach Nathalie

    2004-01-01

    Full Text Available Gene flow in rapeseed is a process taking place both in space and over the years and cannot be studied exclusively by field trials. Consequently, the GENESYS model was developed to quantify the effects of cropping systems on transgene escape from rapeseed crops to rapeseed volunteers in neighbour plots and in the subsequent crops. In the present work, this model was used to evaluate the risk of rape harvest contamination by extraneous genes in various farming systems in case of co-existing GM, conventional and organic crops. When 50 % of the rape varieties in the region were transgenic, the rate of GM seeds in non-GM crop harvests on farms with large fields was lower than the 0.9 % purity threshold proposed by the EC for rape crop production (food and feed harvests, but on farms with smaller fields, the threshold was exceeded. Harvest impurity increased in organic farms, mainly because of their small field size. The model was then used to evaluate the consequences of changes in farming practices and to identify those changes reducing harvest contamination. The effects of these changes depended on the field pattern and farming system. The most efficient practices in limiting harvest impurity comprised improved set-aside management by sowing a cover crop in spring on all set-aside fields in the region, permanently banning rape crops and set-aside around seed production fields and (for non-GM farmers clustering farm fields to reduce gene inflow from neighbour fields.

  5. Consistent Stochastic Modelling of Meteocean Design Parameters

    DEFF Research Database (Denmark)

    Sørensen, John Dalsgaard; Sterndorff, M. J.

    2000-01-01

    Consistent stochastic models of metocean design parameters and their directional dependencies are essential for reliability assessment of offshore structures. In this paper a stochastic model for the annual maximum values of the significant wave height, and the associated wind velocity, current...... velocity, and water level is presented. The stochastic model includes statistical uncertainty and dependency between the four stochastic variables. Further, a new stochastic model for annual maximum directional significant wave heights is presented. The model includes dependency between the maximum wave...... height from neighboring directional sectors. Numerical examples are presented where the models are calibrated using the Maximum Likelihood method to data from the central part of the North Sea. The calibration of the directional distributions is made such that the stochastic model for the omnidirectional...

  6. Evapotranspiration simulated by CRITERIA and AquaCrop models in stony soils

    National Research Council Canada - National Science Library

    Pasquale Campi; Francesca Modugno; Alejandra Navarro; Fausto Tomei; Giulia Villani; Marcello Mastrorilli

    2015-01-01

    .... The objective of this paper is to test CRITERIA and AquaCrop models in order to evaluate their suitability in estimating evapotranspiration at the field scale in two types of soil in the Mediterranean region...

  7. Assessing winter cover crop nutrient uptake efficiency using a water quality simulation model

    Science.gov (United States)

    Yeo, In-Young; Lee, Sangchui; Sadeghi, Ali M.; Beeson, Peter C.; Hively, W. Dean; McCarty, Greg W.; Lang, Megan W.

    2013-01-01

    Winter cover crops are an effective conservation management practice with potential to improve water quality. Throughout the Chesapeake Bay Watershed (CBW), which is located in the Mid-Atlantic US, winter cover crop use has been emphasized and federal and state cost-share programs are available to farmers to subsidize the cost of winter cover crop establishment. The objective of this study was to assess the long-term effect of planting winter cover crops at the watershed scale and to identify critical source areas of high nitrate export. A physically-based watershed simulation model, Soil and Water Assessment Tool (SWAT), was calibrated and validated using water quality monitoring data and satellite-based estimates of winter cover crop species performance to simulate hydrological processes and nutrient cycling over the period of 1991–2000. Multiple scenarios were developed to obtain baseline information on nitrate loading without winter cover crops planted and to investigate how nitrate loading could change with different winter cover crop planting scenarios, including different species, planting times, and implementation areas. The results indicate that winter cover crops had a negligible impact on water budget, but significantly reduced nitrate leaching to groundwater and delivery to the waterways. Without winter cover crops, annual nitrate loading was approximately 14 kg ha−1, but it decreased to 4.6–10.1 kg ha−1 with winter cover crops resulting in a reduction rate of 27–67% at the watershed scale. Rye was most effective, with a potential to reduce nitrate leaching by up to 93% with early planting at the field scale. Early planting of winter cover crops (~30 days of additional growing days) was crucial, as it lowered nitrate export by an additional ~2 kg ha−1 when compared to late planting scenarios. The effectiveness of cover cropping increased with increasing extent of winter cover crop implementation. Agricultural fields with well-drained soils

  8. Substrate Cultivation of Chrysanthemum: Plant performance in 6 cropping systems and the effect of parameters associated with root environment

    NARCIS (Netherlands)

    Guo, X.; Blok, C.

    2010-01-01

    Summary Chrysanthemum is an important greenhouse crop in Holland and is still cultivated in soil. To prevent the emission of nutrients and crop protecting agents, an emission:free cropping system should be developed. This experiment was conducted to that purpose. The objectives of this experiment we

  9. Substrate Cultivation of Chrysanthemum: Plant performance in 6 cropping systems and the effect of parameters associated with root environment

    NARCIS (Netherlands)

    Guo, X.; Blok, C.

    2010-01-01

    Summary Chrysanthemum is an important greenhouse crop in Holland and is still cultivated in soil. To prevent the emission of nutrients and crop protecting agents, an emission:free cropping system should be developed. This experiment was conducted to that purpose. The objectives of this experiment we

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

    Institute of Scientific and Technical Information of China (English)

    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.

  11. Improving ecophysiological simulation models to predict the impact of elevated atmospheric CO2 concentration on crop productivity

    NARCIS (Netherlands)

    Yin, X.

    2013-01-01

    Background - Process-based ecophysiological crop models are pivotal in assessing responses of crop productivity and designing strategies of adaptation to climate change. Most existing crop models generally over-estimate the effect of elevated atmospheric [CO2], despite decades of experimental resear

  12. Uncertainties in Predicting Rice Yield by Current Crop Models Under a Wide Range of Climatic Conditions

    Science.gov (United States)

    Li, Tao; Hasegawa, Toshihiro; Yin, Xinyou; Zhu, Yan; Boote, Kenneth; Adam, Myriam; Bregaglio, Simone; Buis, Samuel; Confalonieri, Roberto; Fumoto, Tamon; Gaydon, Donald; Marcaida, Manuel, III; Nakagawa, Hiroshi; Oriol, Philippe; Ruane, Alex C.; Ruget, Francoise; Singh, Balwinder; Singh, Upendra; Tang, Liang; Tao, Fulu; Wilkens, Paul; Yoshida, Hiroe; Zhang, Zhao; Bouman, Bas

    2014-01-01

    Predicting rice (Oryza sativa) productivity under future climates is important for global food security. Ecophysiological crop models in combination with climate model outputs are commonly used in yield prediction, but uncertainties associated with crop models remain largely unquantified. We evaluated 13 rice models against multi-year experimental yield data at four sites with diverse climatic conditions in Asia and examined whether different modeling approaches on major physiological processes attribute to the uncertainties of prediction to field measured yields and to the uncertainties of sensitivity to changes in temperature and CO2 concentration [CO2]. We also examined whether a use of an ensemble of crop models can reduce the uncertainties. Individual models did not consistently reproduce both experimental and regional yields well, and uncertainty was larger at the warmest and coolest sites. The variation in yield projections was larger among crop models than variation resulting from 16 global climate model-based scenarios. However, the mean of predictions of all crop models reproduced experimental data, with an uncertainty of less than 10 percent of measured yields. Using an ensemble of eight models calibrated only for phenology or five models calibrated in detail resulted in the uncertainty equivalent to that of the measured yield in well-controlled agronomic field experiments. Sensitivity analysis indicates the necessity to improve the accuracy in predicting both biomass and harvest index in response to increasing [CO2] and temperature.

  13. Order Parameters of the Dilute A Models

    CERN Document Server

    Warnaar, S O; Seaton, K A; Nienhuis, B

    1993-01-01

    The free energy and local height probabilities of the dilute A models with broken $\\Integer_2$ symmetry are calculated analytically using inversion and corner transfer matrix methods. These models possess four critical branches. The first two branches provide new realisations of the unitary minimal series and the other two branches give a direct product of this series with an Ising model. We identify the integrable perturbations which move the dilute A models away from the critical limit. Generalised order parameters are defined and their critical exponents extracted. The associated conformal weights are found to occur on the diagonal of the relevant Kac table. In an appropriate regime the dilute A$_3$ model lies in the universality class of the Ising model in a magnetic field. In this case we obtain the magnetic exponent $\\delta=15$ directly, without the use of scaling relations.

  14. Testing Linear Models for Ability Parameters in Item Response Models

    NARCIS (Netherlands)

    Glas, Cees A.W.; Hendrawan, Irene

    2005-01-01

    Methods for testing hypotheses concerning the regression parameters in linear models for the latent person parameters in item response models are presented. Three tests are outlined: A likelihood ratio test, a Lagrange multiplier test and a Wald test. The tests are derived in a marginal maximum like

  15. Assessing changes to South African maize production areas in 2055 using empirical and process-based crop models

    Science.gov (United States)

    Estes, L.; Bradley, B.; Oppenheimer, M.; Beukes, H.; Schulze, R. E.; Tadross, M.

    2010-12-01

    Rising temperatures and altered precipitation patterns associated with climate change pose a significant threat to crop production, particularly in developing countries. In South Africa, a semi-arid country with a diverse agricultural sector, anthropogenic climate change is likely to affect staple crops and decrease food security. Here, we focus on maize production, South Africa’s most widely grown crop and one with high socio-economic value. We build on previous coarser-scaled studies by working at a finer spatial resolution and by employing two different modeling approaches: the process-based DSSAT Cropping System Model (CSM, version 4.5), and an empirical distribution model (Maxent). For climate projections, we use an ensemble of 10 general circulation models (GCMs) run under both high and low CO2 emissions scenarios (SRES A2 and B1). The models were down-scaled to historical climate records for 5838 quinary-scale catchments covering South Africa (mean area = 164.8 km2), using a technique based on self-organizing maps (SOMs) that generates precipitation patterns more consistent with observed gradients than those produced by the parent GCMs. Soil hydrological and mechanical properties were derived from textural and compositional data linked to a map of 26422 land forms (mean area = 46 km2), while organic carbon from 3377 soil profiles was mapped using regression kriging with 8 spatial predictors. CSM was run using typical management parameters for the several major dryland maize production regions, and with projected CO2 values. The Maxent distribution model was trained using maize locations identified using annual phenology derived from satellite images coupled with airborne crop sampling observations. Temperature and precipitation projections were based on GCM output, with an additional 10% increase in precipitation to simulate higher water-use efficiency under future CO2 concentrations. The two modeling approaches provide spatially explicit projections of

  16. Evaluation of six potential evapotranspiration models for estimating crop potential and actual evapotranspiration in arid regions

    Science.gov (United States)

    Li, Sien; Kang, Shaozhong; Zhang, Lu; Zhang, Jianhua; Du, Taisheng; Tong, Ling; Ding, Risheng

    2016-12-01

    Using potential evapotranspiration (PET) to estimate crop actual evapotranspiration (AET) is a critical approach in hydrological models. However, which PET model performs best and can be used to predict crop AET over the entire growth season in arid regions still remains unclear. The six frequently-used PET models, i.e. Blaney-Criddle (BC), Hargreaves (HA), Priestley-Taylor (PT), Dalton (DA), Penman (PE) and Shuttleworth (SW) models were considered and evaluated in the study. Five-year eddy covariance data over the maize field and vineyard in arid northwest China were used to examine the accuracy of PET models in estimating daily crop AET. Results indicate that the PE, SW and PT models underestimated daily ET by less than 6% with RMSE lower than 35 W m-2 during the four years, while the BC, HA and DA models under-predicted daily ET approximately by 10% with RMSE higher than 40 W m-2. Compared to BC, HA and DA models, PE, SW and PT models were more reliable and accurate for estimating crop PET and AET in arid regions. Thus the PE, SW and PT models were recommended for predicting crop evapotranspiration in hydrological models in arid regions.

  17. General description and operation of the agro-environmental system: Crop management modeling. [Virginia

    Science.gov (United States)

    Gross, E.; Scott, J. H., Jr.

    1981-01-01

    Input for a data management system to provide farmers with information to improve crop management practices in Virginia requires monitoring of control crops at field stations, crop surveys derived from remotely sensed aircraft data, meteorological data from synchronous satellites, and details of local agricultural conditions. Presently models are under development for determining pest problems, water balance in the soil, stages of plant maturity, and optimum planting date. The status of the Cerospora leafspot model for peanut crop management is considered. Other models under development planned relate to Cylindtocladium Blackrot and Sclerotinia blight of peanuts, cyst nematode (Globerdena solanacearum) of tobacco, and red crown rot of soybeans. A software for program for estimating precipitation and solar radiation on a statewise basis is also being developed.

  18. Modelling spin Hamiltonian parameters of molecular nanomagnets.

    Science.gov (United States)

    Gupta, Tulika; Rajaraman, Gopalan

    2016-07-12

    Molecular nanomagnets encompass a wide range of coordination complexes possessing several potential applications. A formidable challenge in realizing these potential applications lies in controlling the magnetic properties of these clusters. Microscopic spin Hamiltonian (SH) parameters describe the magnetic properties of these clusters, and viable ways to control these SH parameters are highly desirable. Computational tools play a proactive role in this area, where SH parameters such as isotropic exchange interaction (J), anisotropic exchange interaction (Jx, Jy, Jz), double exchange interaction (B), zero-field splitting parameters (D, E) and g-tensors can be computed reliably using X-ray structures. In this feature article, we have attempted to provide a holistic view of the modelling of these SH parameters of molecular magnets. The determination of J includes various class of molecules, from di- and polynuclear Mn complexes to the {3d-Gd}, {Gd-Gd} and {Gd-2p} class of complexes. The estimation of anisotropic exchange coupling includes the exchange between an isotropic metal ion and an orbitally degenerate 3d/4d/5d metal ion. The double-exchange section contains some illustrative examples of mixed valance systems, and the section on the estimation of zfs parameters covers some mononuclear transition metal complexes possessing very large axial zfs parameters. The section on the computation of g-anisotropy exclusively covers studies on mononuclear Dy(III) and Er(III) single-ion magnets. The examples depicted in this article clearly illustrate that computational tools not only aid in interpreting and rationalizing the observed magnetic properties but possess the potential to predict new generation MNMs.

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

    Science.gov (United States)

    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-models

  20. Modelling soil properties in a crop field located in Croatia

    Science.gov (United States)

    Bogunovic, Igor; Pereira, Paulo; Millan, Mesic; Percin, Aleksandra; Zgorelec, Zeljka

    2016-04-01

    Development of tillage activities had negative effects on soil quality as destruction of soil horizons, compacting and aggregates destruction, increasing soil erosion and loss of organic matter. For a better management in order to mitigate the effects of intensive soil management in land degradation it is fundamental to map the spatial distribution of soil properties (Brevik et al., 2016). The understanding the distribution of the variables in space is very important for a sustainable management, in order to identify areas that need a potential intervention and decrease the economic losses (Galiati et al., 2016). The objective of this work is study the spatial distribution of some topsoil properties as clay, fine silt, coarse silt, fine sand, coarse sand, penetration resistance, moisture and organic matter in a crop field located in Croatia. A grid with 275x25 (625 m2) was designed and a total of 48 samples were collected. Previous to data modelling, data normality was checked using the Shapiro wilk-test. As in previous cases (Pereira et al., 2015), data did not followed the normal distribution, even after a logarithmic (Log), square-root, and box cox transformation. Thus, for modeling proposes, we used the log transformed data, since was the closest to the normality. In order to identify groups among the variables we applied a principal component analysis (PCA), based on the correlation matrix. On average clay content was 15.47% (±3.23), fine silt 24.24% (±4.08), coarse silt 35.34% (±3.12), fine sand 20.93% (±4.68), coarse sand 4.02% (±1.69), penetration resistance 0.66 MPa (±0.28), organic matter 1.51% (±0.25) and soil moisture 32.04% (±3.27). The results showed that the PCA identified three factors explained at least one of the variables. The first factor had high positive loadings in soil clay, fine silt and organic matter and a high negative loading in fine sand. The second factor had high positive loadings in coarse sand and moisture and a high

  1. Development of a growth model-based decision support system for crop management

    Institute of Scientific and Technical Information of China (English)

    ZHU Yan; TANG Liang; LIU Xiaojun; TIAN Yongchao; YAO Xia; CAO Weixing

    2007-01-01

    A growth model-based decision support system for crop management (GMDSSCM) was developed,which integrates process-based models of four different crops-wheat,rice,rape and cotton-and realized decision support function,thus facilitating the simulation and application of the crop models for different purposes.The individual models include six sub models for simulating phase development,organ formation,biomass production,yield and quality formation,soil-crop water relations and nutrient (N,P,K)balance.The implemented system can be used for evaluating individual and comprehensive management strategies based on the results of crop growth simulation under various environments and different genotypes.A stand-alone edition (GMDSSCMA) was established on VC++ and VB platforms by adopting the characteristics of object-oriented and component-based software and with the effective integration and coupling of the growth prediction and decision-making functions.A web-based system (GMDSSCMw) was then further developed on the .net platform using C# language.These GMDSSCM systems have realized dynamic prediction of crop growth and decision making on cultural management,and thus should be helpful for the construction and application of informational and digital fanning system.

  2. Systematic parameter inference in stochastic mesoscopic modeling

    CERN Document Server

    Lei, Huan; Li, Zhen; Karniadakis, George

    2016-01-01

    We propose a method to efficiently determine the optimal coarse-grained force field in mesoscopic stochastic simulations of Newtonian fluid and polymer melt systems modeled by dissipative particle dynamics (DPD) and energy conserving dissipative particle dynamics (eDPD). The response surfaces of various target properties (viscosity, diffusivity, pressure, etc.) with respect to model parameters are constructed based on the generalized polynomial chaos (gPC) expansion using simulation results on sampling points (e.g., individual parameter sets). To alleviate the computational cost to evaluate the target properties, we employ the compressive sensing method to compute the coefficients of the dominant gPC terms given the prior knowledge that the coefficients are sparse. The proposed method shows comparable accuracy with the standard probabilistic collocation method (PCM) while it imposes a much weaker restriction on the number of the simulation samples especially for systems with high dimensional parametric space....

  3. Assessing Optimal Flight Parameters for Generating Accurate Multispectral Orthomosaicks by UAV to Support Site-Specific Crop Management

    Directory of Open Access Journals (Sweden)

    Francisco-Javier Mesas-Carrascosa

    2015-09-01

    Full Text Available This article describes the technical specifications and configuration of a multirotor unmanned aerial vehicle (UAV to acquire remote images using a six-band multispectral sensor. Several flight missions were programmed as follows: three flight altitudes (60, 80 and 100 m, two flight modes (stop and cruising modes and two ground control point (GCP settings were considered to analyze the influence of these parameters on the spatial resolution and spectral discrimination of multispectral orthomosaicked images obtained using Pix4Dmapper. Moreover, it is also necessary to consider the area to be covered or the flight duration according to any flight mission programmed. The effect of the combination of all these parameters on the spatial resolution and spectral discrimination of the orthomosaicks is presented. Spectral discrimination has been evaluated for a specific agronomical purpose: to use the UAV remote images for the detection of bare soil and vegetation (crop and weeds for in-season site-specific weed management. These results show that a balance between spatial resolution and spectral discrimination is needed to optimize the mission planning and image processing to achieve   every agronomic objective. In this way, users do not have to sacrifice flying at low altitudes to cover the whole area of interest completely.

  4. Disaggregated N2O emission factors in China based on cropping parameters create a robust approach to the IPCC Tier 2 methodology.

    Science.gov (United States)

    Shepherd, Anita; Yan, Xiaoyuan; Nayak, Dali; Newbold, Jamie; Moran, Dominic; Dhanoa, Mewa Singh; Goulding, Keith; Smith, Pete; Cardenas, Laura M

    2015-12-01

    China accounts for a third of global nitrogen fertilizer consumption. Under an International Panel on Climate Change (IPCC) Tier 2 assessment, emission factors (EFs) are developed for the major crop types using country-specific data. IPCC advises a separate calculation for the direct nitrous oxide (N2O) emissions of rice cultivation from that of cropland and the consideration of the water regime used for irrigation. In this paper we combine these requirements in two independent analyses, using different data quality acceptance thresholds, to determine the influential parameters on emissions with which to disaggregate and create N2O EFs. Across China, the N2O EF for lowland horticulture was slightly higher (between 0.74% and 1.26% of fertilizer applied) than that for upland crops (values ranging between 0.40% and 1.54%), and significantly higher than for rice (values ranging between 0.29% and 0.66% on temporarily drained soils, and between 0.15% and 0.37% on un-drained soils). Higher EFs for rice were associated with longer periods of drained soil and the use of compound fertilizer; lower emissions were associated with the use of urea or acid soils. Higher EFs for upland crops were associated with clay soil, compound fertilizer or maize crops; lower EFs were associated with sandy soil and the use of urea. Variation in emissions for lowland vegetable crops was closely associated with crop type. The two independent analyses in this study produced consistent disaggregated N2O EFs for rice and mixed crops, showing that the use of influential cropping parameters can produce robust EFs for China.

  5. Validation of Crop Weather Models for'Crop Assessment arid Yield ...

    African Journals Online (AJOL)

    over predict grain yields of maize, sorghum and wheat, a fact that could be attributed to the inadequacy of the model .... Of particular interest in this category is the cesses (Hanks and Hill, 1980). ... ter and basic infiltration rate for soii data. The.

  6. Spatial modeling of contemporary crop yields in China under sustainable and unsustainable water use scenarios

    Science.gov (United States)

    Grogan, D. S.; Zhang, F.; Li, C.; Frolking, S.

    2011-12-01

    Irrigated agriculture is an important part of China's population and economic growth. Currently, water needed to irrigate crops can be drawn from surface runoff, streams, reservoirs, renewable groundwater, or fossil groundwater. Fossil groundwater is not sustainable over long time periods, and therefore regions that rely on this source for irrigation water could face water shortages in the future, and may already be experiencing water stress today. This study uses two models, one to calculate water balance and the other to simulate crop yields, to address the question: how much unsustainable water is currently used for irrigation in China, and by how much would the use of only sustainable water reduce crop yields? The amount of sustainable water available for irrigation is determined using the WBMplus model. This model uses precipitation and temperature drivers, along with gridded data of soils, cropping, and irrigation, to simulate soil moisture, potential evapotranspiration, surface runoff, stream flow, and reservoir storage, in 30 min grid cells. The model also computes demand for irrigation water, and the capacity of various sources to supply that demand in each grid cell. The DNDC model, which has been evaluated against crop yield in a number of studies in China, is used to predict crop yield for ~50 crop types involved in ~100 cropping systems across China, under zero and full irrigation for each grid cell. Yields using only the sustainable irrigation water capacity will be calculated by weighing the zero and full irrigation yields based on the water availability results of WBMplus for each grid cell. With this methodology, we estimate how national-scale food production would be changed by limiting agricultural water use.

  7. Modelling tourists arrival using time varying parameter

    Science.gov (United States)

    Suciptawati, P.; Sukarsa, K. G.; Kencana, Eka N.

    2017-06-01

    The importance of tourism and its related sectors to support economic development and poverty reduction in many countries increase researchers’ attentions to study and model tourists’ arrival. This work is aimed to demonstrate time varying parameter (TVP) technique to model the arrival of Korean’s tourists to Bali. The number of Korean tourists whom visiting Bali for period January 2010 to December 2015 were used to model the number of Korean’s tourists to Bali (KOR) as dependent variable. The predictors are the exchange rate of Won to IDR (WON), the inflation rate in Korea (INFKR), and the inflation rate in Indonesia (INFID). Observing tourists visit to Bali tend to fluctuate by their nationality, then the model was built by applying TVP and its parameters were approximated using Kalman Filter algorithm. The results showed all of predictor variables (WON, INFKR, INFID) significantly affect KOR. For in-sample and out-of-sample forecast with ARIMA’s forecasted values for the predictors, TVP model gave mean absolute percentage error (MAPE) as much as 11.24 percent and 12.86 percent, respectively.

  8. [Stability analysis of allelopathic effects of Panax notoginseng on main crops by AMMI model].

    Science.gov (United States)

    Zhang, Zi-long; Hou, Jun-ling; Wang, Wen-quan

    2015-01-01

    This paper is aimed to study the differences of allelopathic effects of Panax notoginseng under different allelopathic chemicals resources and selection of appropriate rotation crops. The additive main effects and multiplicative interaction ( AMMI) model had been used to evaluate the stability of allelopathic effects of P. notoginseng on the varieties of corn, wheat and rice properly. The model could use not only to evaluate the stability of non-regional trial data but also explore the interaction between the rotation crop genotypes and donor substances more efficiently. Meanwhile, correspondence analysis can be used in the AMMI to evaluate genotype stability and donor substances. Ejingza No. 1 (g6) had stronger allelopathic effects with high stability, but Yunrui No. 1 (g9) which was appropriate rotation crop genotype, had weaker allelopathic effects with high stability. These findings will aid in choosing appropriate rotation crops and establishing proper rotation system.

  9. Injury Profile SIMulator, a Qualitative Aggregative Modelling Framework to Predict Crop Injury Profile as a Function of Cropping Practices, and the Abiotic and Biotic Environment. I. Conceptual Bases

    Science.gov (United States)

    Aubertot, Jean-Noël; Robin, Marie-Hélène

    2013-01-01

    The limitation of damage caused by pests (plant pathogens, weeds, and animal pests) in any agricultural crop requires integrated management strategies. Although significant efforts have been made to i) develop, and to a lesser extent ii) combine genetic, biological, cultural, physical and chemical control methods in Integrated Pest Management (IPM) strategies (vertical integration), there is a need for tools to help manage Injury Profiles (horizontal integration). Farmers design cropping systems according to their goals, knowledge, cognition and perception of socio-economic and technological drivers as well as their physical, biological, and chemical environment. In return, a given cropping system, in a given production situation will exhibit a unique injury profile, defined as a dynamic vector of the main injuries affecting the crop. This simple description of agroecosystems has been used to develop IPSIM (Injury Profile SIMulator), a modelling framework to predict injury profiles as a function of cropping practices, abiotic and biotic environment. Due to the tremendous complexity of agroecosystems, a simple holistic aggregative approach was chosen instead of attempting to couple detailed models. This paper describes the conceptual bases of IPSIM, an aggregative hierarchical framework and a method to help specify IPSIM for a given crop. A companion paper presents a proof of concept of the proposed approach for a single disease of a major crop (eyespot on wheat). In the future, IPSIM could be used as a tool to help design ex-ante IPM strategies at the field scale if coupled with a damage sub-model, and a multicriteria sub-model that assesses the social, environmental, and economic performances of simulated agroecosystems. In addition, IPSIM could also be used to help make diagnoses on commercial fields. It is important to point out that the presented concepts are not crop- or pest-specific and that IPSIM can be used on any crop. PMID:24019908

  10. Principles of crop modelling and simulation: II. the implications of the objective in model development

    Directory of Open Access Journals (Sweden)

    Dourado-Neto D.

    1998-01-01

    Full Text Available With the purpose of presenting to scientists the implications of the objective in model development and a basic vision of modeling, with its potential applications and limitations in agriculture, an integration of crop modeling professionals with agricultural professionals is suggested. Models mean modernization of the information, of the measurement process and of an efficient way to learn more about complex systems. They are one of the best mechanisms of transforming information in useful knowledge and of transferring this knowledge to others. One of the problems that impede a larger progress in modeling is the lack of communication between modelers and a frequent appearance of modelers without a global vision of reality.

  11. A Comparative Study on Satellite- and Model-Based Crop Phenology in West Africa

    Directory of Open Access Journals (Sweden)

    Elodie Vintrou

    2014-02-01

    Full Text Available Crop phenology is essential for evaluating crop production in the food insecure regions of West Africa. The aim of the paper is to study whether satellite observation of plant phenology are consistent with ground knowledge of crop cycles as expressed in agro-simulations. We used phenological variables from a MODIS Land Cover Dynamics (MCD12Q2 product and examined whether they reproduced the spatio-temporal variability of crop phenological stages in Southern Mali. Furthermore, a validated cereal crop growth model for this region, SARRA-H (System for Regional Analysis of Agro-Climatic Risks, provided precise agronomic information. Remotely-sensed green-up, maturity, senescence and dormancy MODIS dates were extracted for areas previously identified as crops and were compared with simulated leaf area indices (LAI temporal profiles generated using the SARRA-H crop model, which considered the main cropping practices. We studied both spatial (eight sites throughout South Mali during 2007 and temporal (two sites from 2002 to 2008 differences between simulated crop cycles and determined how the differences were indicated in satellite-derived phenometrics. The spatial comparison of the phenological indicator observations and simulations showed mainly that (i the satellite-derived start-of-season (SOS was detected approximately 30 days before the model-derived SOS; and (ii the satellite-derived end-of-season (EOS was typically detected 40 days after the model-derived EOS. Studying the inter-annual difference, we verified that the mean bias was globally consistent for different climatic conditions. Therefore, the land cover dynamics derived from the MODIS time series can reproduce the spatial and temporal variability of different start-of-season and end-of-season crop species. In particular, we recommend simultaneously using start-of-season phenometrics with crop models for yield forecasting to complement commonly used climate data and provide a better

  12. Production versus environmental impact trade-offs for Swiss cropping systems: a model-based approach

    Science.gov (United States)

    Necpalova, Magdalena; Lee, Juhwan; Six, Johan

    2017-04-01

    There is a growing need to improve sustainability of agricultural systems. The key focus remains on optimizing current production systems in order to deliver food security at low environmental costs. It is therefore essential to identify and evaluate agricultural management practices for their potential to maintain or increase productivity and mitigate climate change and N pollution. Previous research on Swiss cropping systems has been concentrated on increasing crop productivity and soil fertility. Thus, relatively little is known about management effects on net soil greenhouse gas (GHG) emissions and environmental N losses in the long-term. The aim of this study was to extrapolate findings from Swiss long-term field experiments and to evaluate the system-level sustainability of a wide range of cropping systems under conditions beyond field experimentation by comparing their crop productivity and impacts on soil carbon, net soil GHG emissions, NO3 leaching and soil N balance over 30 years. The DayCent model was previously parameterized for common Swiss crops and crop-specific management practices and evaluated for productivity, soil carbon dynamics and N2O emissions from Swiss cropping systems. Based on a prediction uncertainty criterion for crop productivity and soil carbon (rRMSEorganic (ORG), integrated (IN) and mineral (MIN); (b) tillage: conventional (CT), reduced (RT) and no-till (NT); (c) cover cropping: no cover cropping (NCC), winter cover cropping (CC) and winter green manuring (GM). The productivity of Swiss cropping systems was mainly driven by total N inputs to the systems. The GWP of systems ranged from -450 to 1309 kg CO2 eq ha-1 yr-1. All studied systems, except for ORG-RT-GM systems, acted as a source of net soil GHG emissions with the relative contribution of soil N2O emissions to GWP of more than 60%. The GWP of systems with CT decreased consistently with increasing use of organic manures (MIN>IN>ORG). NT relative to RT management showed to be

  13. Parameter estimation, model reduction and quantum filtering

    Science.gov (United States)

    Chase, Bradley A.

    This thesis explores the topics of parameter estimation and model reduction in the context of quantum filtering. The last is a mathematically rigorous formulation of continuous quantum measurement, in which a stream of auxiliary quantum systems is used to infer the state of a target quantum system. Fundamental quantum uncertainties appear as noise which corrupts the probe observations and therefore must be filtered in order to extract information about the target system. This is analogous to the classical filtering problem in which techniques of inference are used to process noisy observations of a system in order to estimate its state. Given the clear similarities between the two filtering problems, I devote the beginning of this thesis to a review of classical and quantum probability theory, stochastic calculus and filtering. This allows for a mathematically rigorous and technically adroit presentation of the quantum filtering problem and solution. Given this foundation, I next consider the related problem of quantum parameter estimation, in which one seeks to infer the strength of a parameter that drives the evolution of a probe quantum system. By embedding this problem in the state estimation problem solved by the quantum filter, I present the optimal Bayesian estimator for a parameter when given continuous measurements of the probe system to which it couples. For cases when the probe takes on a finite number of values, I review a set of sufficient conditions for asymptotic convergence of the estimator. For a continuous-valued parameter, I present a computational method called quantum particle filtering for practical estimation of the parameter. Using these methods, I then study the particular problem of atomic magnetometry and review an experimental method for potentially reducing the uncertainty in the estimate of the magnetic field beyond the standard quantum limit. The technique involves double-passing a probe laser field through the atomic system, giving

  14. Supporting Crop Loss Insurance Policy of Indonesia through Rice Yield Modelling and Forecasting

    Science.gov (United States)

    van Verseveld, Willem; Weerts, Albrecht; Trambauer, Patricia; de Vries, Sander; Conijn, Sjaak; van Valkengoed, Eric; Hoekman, Dirk; Grondard, Nicolas; Hengsdijk, Huib; Schrevel, Aart; Vlasbloem, Pieter; Klauser, Dominik

    2017-04-01

    The Government of Indonesia has decided on a crop insurance policy to assist Indonesia's farmers and to boost food security. To support the Indonesian government, the G4INDO project (www.g4indo.org) is developing/constructing an integrated platform implemented in the Delft-FEWS forecasting system (Werner et al., 2013). The integrated platform brings together remote sensed data (both visible and radar) and hydrologic, crop and reservoir modelling and forecasting to improve the modelling and forecasting of rice yield. The hydrological model (wflow_sbm), crop model (wflow_lintul) and reservoir models (RTC-Tools) are coupled on time stepping basis in the OpenStreams framework (see https://github.com/openstreams/wflow) and deployed in the integrated platform to support seasonal forecasting of water availability and crop yield. First we will show the general idea about the G4INDO project, the integrated platform (including Sentinel 1 & 2 data) followed by first (reforecast) results of the coupled models for predicting water availability and crop yield in the Brantas catchment in Java, Indonesia. Werner, M., Schellekens, J., Gijsbers, P., Van Dijk, M., Van den Akker, O. and Heynert K, 2013. The Delft-FEWS flow forecasting system, Environmental Modelling & Software; 40:65-77. DOI: 10.1016/j.envsoft.2012.07.010.

  15. Assessing Uncertainties of Water Footprints Using an Ensemble of Crop Growth Models on Winter Wheat

    Directory of Open Access Journals (Sweden)

    Kurt Christian Kersebaum

    2016-12-01

    Full Text Available Crop productivity and water consumption form the basis to calculate the water footprint (WF of a specific crop. Under current climate conditions, calculated evapotranspiration is related to observed crop yields to calculate WF. The assessment of WF under future climate conditions requires the simulation of crop yields adding further uncertainty. To assess the uncertainty of model based assessments of WF, an ensemble of crop models was applied to data from five field experiments across Europe. Only limited data were provided for a rough calibration, which corresponds to a typical situation for regional assessments, where data availability is limited. Up to eight models were applied for wheat. The coefficient of variation for the simulated actual evapotranspiration between models was in the range of 13%–19%, which was higher than the inter-annual variability. Simulated yields showed a higher variability between models in the range of 17%–39%. Models responded differently to elevated CO2 in a FACE (Free-Air Carbon Dioxide Enrichment experiment, especially regarding the reduction of water consumption. The variability of calculated WF between models was in the range of 15%–49%. Yield predictions contributed more to this variance than the estimation of water consumption. Transpiration accounts on average for 51%–68% of the total actual evapotranspiration.

  16. Can Agrometeorological Indices of Adverse Weather Conditions Help to Improve Yield Prediction by Crop Models?

    Directory of Open Access Journals (Sweden)

    Branislava Lalić

    2014-12-01

    Full Text Available The impact of adverse weather conditions (AWCs on crop production is random in both time and space and depends on factors such as severity, previous agrometeorological conditions, and plant vulnerability at a specific crop development stage. Any exclusion or improper treatment of any of these factors can cause crop models to produce significant under- or overestimates of yield. The analysis presented in this paper focuses on a range of agrometeorological indices (AMI related to AWCs that might affect real yield as well as simulated yield. For this purpose, the analysis addressed four indicators of extreme temperatures and three indicators of dry conditions during the growth period of maize and winter wheat in Austria, Croatia, Serbia, Slovakia, and Sweden. It is shown that increases in the number and intensity of AWCs cannot be unambiguously associated with increased deviations in simulated yields. The identified correlations indicate an increase in modeling uncertainty. This finding represents important information for the crop modeling community. Additionally, it opens a window of opportunity for a statistical (“event scenario” approach based on correlations between agrometeorological indices of AWCs and crop yield data series. This approach can provide scenarios for certain locations, crop types, and AWC patterns and, therefore, improve yield forecasting in the presence of AWCs.

  17. Simulating Crop Net Primary Production in China from 2000 to 2050 by Linking the Crop-C model with a FGOALS's Model Climate Change Scenario

    Institute of Scientific and Technical Information of China (English)

    ZHANG Wen; HUANG Yao; SUN Wenjuan; YU Yongqiang

    2007-01-01

    Net primary production (NPP) of crop represents the capacity of sequestrating atmospheric CO2 in agro-ecosystem, and it plays an important role in terrestrial carbon cycling. By linking the Crop-C model with climate change scenario projected by a coupled GCM FGOALS via geographical information system(GIS) techniques, crop NPP in China was simulated from 2000 to 2050. The national averaged surface air temperature from FGOALS is projected to increase by 1.0°C over this period and the corresponding atmospheric CO2 concentration is 535 ppm by 2050 under the IPCC A1B scenario. With a spatial resolution of 10 × 10 km2, model simulation indicated that an annual average increase of 0.6 Tg C yr-1 (Tg=1012g)would be possible under the A1B scenario. The NPP in the late 2040s would increase by 5% (30 Tg C)within the 98×106 hm2 cropland area in contrast with that in the early 2000s. A further investigation suggested that changes in the NPP would not be evenly distributed in China. A higher increase would occur in a majority of regions located in eastern and northwestern China, while a slight reduction would appear in Hebei and Tianjin in northern China. The spatial characteristics of the crop NPP change are attributed primarily to the uneven distribution of temperature change.

  18. Using FAO-56 model to estimate soil and crop water status: Application to a citrus orchard under regulated deficit irrigation

    Science.gov (United States)

    Provenzano, Giuseppe; Gonzàles-Altozano, Pablo; Manzano-Juàrez, Juan; Rallo, Giovanni

    2015-04-01

    Agro-hydrological models allow schematizing exchange processes in the soil-plant-atmosphere continuum (SPAC) on a wide range of spatial and temporal scales. Each section of the SPAC system is characterized by complex behaviours arising, for instance, the adaptive plant strategies in response to soil water deficit conditions. Regulated deficit irrigation (RDI) has been considered as one of the potential strategies for sustainable crop production in regions characterized by water scarcity. Moreover, reducing water supply at certain growth stages can improve water use efficiency (WUE) and quality of productions, without affecting significantly crop yield. Environmental policy requires to improve WUE in crops with high water requirements, so that it is necessary to identify easy-to-use tools aimed at irrigation water saving strategies, without the need of tedious and time consuming experiments. Accurate evaluation of crop water status and actual transpiration plays a key role in irrigation scheduling under RDI, in order to avoid that water stress becomes too severe and detrimental to yield and fruit quality. Objective of the research was to assess the suitability of FAO56 agro-hydrological model (Allen et al., 1998) on citrus orchards under different water deficit conditions, to estimate soil and crop water status. The ability of the model to predict actual crop water stress was evaluated based on the temporal dynamic of simulated relative transpirations and on the similarities with the corresponding dynamic of measured midday stem water potentials, MSWP. During dry periods, simulated relative crop transpiration was correlated to MSWP with the aim to assess the model ability to predict crop water stress and to identify "plant-based" irrigation scheduling parameters. Experiments were carried out during three years from 2009 and 2011 in Senyera (39° 3' 35.4" N, 0° 30' 28.2" W), Spain, in a commercial orchard planted with Navelina/Cleopatra citrus trees. Three RDI

  19. Modelling of Cadmium Transport in Soil-Crop System

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    A model for simulating cadmium transport in a soil-plant system was built using a commercial simu lating program named Powersim on the basis of input-output processes happening in the soil-plant system.Convective and dispersive transport processes of cadmium in soil profile are embedded. Simulations on a daily base have been done up to a total simulating time of 250 years. Results show that applications of sewage sludge and fertilizer at the simulated rates would only cause slight cadmium accumulations in each layer of the soil, and cadmium accumulation would be levelling off, reaching an equilibrium concentrations layer by layer downward after certain time. The time scale to reach an equilibrium concentration varies from 10 years for the top three layers to over 250 years for the bottom layers. Plant cadmium uptake would increase from 52 ug m-2 under initial soil cadmium concentrations to 65 μg m-2 under equilibrium soil cadmium concentrations, which would not exceed the maximum allowable cadmium concentration in wheat grains. Main parameters which influence cadmium accumulation and transport in soil are total cadmium input, rainfall, evaporation, plant uptake and soil properties.

  20. Modeling nitrogen and water management effects in a wheat-maize double-cropping system.

    Science.gov (United States)

    Fang, Q; Ma, L; Yu, Q; Malone, R W; Saseendran, S A; Ahuja, L R

    2008-01-01

    Excessive N and water use in agriculture causes environmental degradation and can potentially jeopardize the sustainability of the system. A field study was conducted from 2000 to 2002 to study the effects of four N treatments (0, 100, 200, and 300 kg N ha(-1) per crop) on a wheat (Triticum aestivum L.) and maize (Zea mays L.) double cropping system under 70 +/- 15% field capacity in the North China Plain (NCP). The root zone water quality model (RZWQM), with the crop estimation through resource and environment synthesis (CERES) plant growth modules incorporated, was evaluated for its simulation of crop production, soil water, and N leaching in the double cropping system. Soil water content, biomass, and grain yield were better simulated with normalized root mean square errors (NRMSE, RMSE divided by mean observed value) from 0.11 to 0.15 than soil NO(3)-N and plant N uptake that had NRMSE from 0.19 to 0.43 across these treatments. The long-term simulation with historical weather data showed that, at 200 kg N ha(-1) per crop application rate, auto-irrigation triggered at 50% of the field capacity and recharged to 60% field capacity in the 0- to 50-cm soil profile were adequate for obtaining acceptable yield levels in this intensified double cropping system. Results also showed potential savings of more than 30% of the current N application rates per crop from 300 to 200 kg N ha(-1), which could reduce about 60% of the N leaching without compromising crop yields.

  1. A Systematic Review of Perennial Staple Crops Literature Using Topic Modeling and Bibliometric Analysis.

    Science.gov (United States)

    Kane, Daniel A; Rogé, Paul; Snapp, Sieglinde S

    2016-01-01

    Research on perennial staple crops has increased in the past ten years due to their potential to improve ecosystem services in agricultural systems. However, multiple past breeding efforts as well as research on traditional ratoon systems mean there is already a broad body of literature on perennial crops. In this review, we compare the development of research on perennial staple crops, including wheat, rice, rye, sorghum, and pigeon pea. We utilized the advanced search capabilities of Web of Science, Scopus, ScienceDirect, and Agricola to gather a library of 914 articles published from 1930 to the present. We analyzed the metadata in the entire library and in collections of literature on each crop to understand trends in research and publishing. In addition, we applied topic modeling to the article abstracts, a type of text analysis that identifies frequently co-occurring terms and latent topics. We found: 1.) Research on perennials is increasing overall, but individual crops have each seen periods of heightened interest and research activity; 2.) Specialist journals play an important role in supporting early research efforts. Research often begins within communities of specialists or breeders for the individual crop before transitioning to a more general scientific audience; 3.) Existing perennial agricultural systems and their domesticated crop material, such as ratoon rice systems, can provide a useful foundation for breeding efforts, accelerating the development of truly perennial crops and farming systems; 4.) Primary research is lacking for crops that are produced on a smaller scale globally, such as pigeon pea and sorghum, and on the ecosystem service benefits of perennial agricultural systems.

  2. A Systematic Review of Perennial Staple Crops Literature Using Topic Modeling and Bibliometric Analysis

    Science.gov (United States)

    2016-01-01

    Research on perennial staple crops has increased in the past ten years due to their potential to improve ecosystem services in agricultural systems. However, multiple past breeding efforts as well as research on traditional ratoon systems mean there is already a broad body of literature on perennial crops. In this review, we compare the development of research on perennial staple crops, including wheat, rice, rye, sorghum, and pigeon pea. We utilized the advanced search capabilities of Web of Science, Scopus, ScienceDirect, and Agricola to gather a library of 914 articles published from 1930 to the present. We analyzed the metadata in the entire library and in collections of literature on each crop to understand trends in research and publishing. In addition, we applied topic modeling to the article abstracts, a type of text analysis that identifies frequently co-occurring terms and latent topics. We found: 1.) Research on perennials is increasing overall, but individual crops have each seen periods of heightened interest and research activity; 2.) Specialist journals play an important role in supporting early research efforts. Research often begins within communities of specialists or breeders for the individual crop before transitioning to a more general scientific audience; 3.) Existing perennial agricultural systems and their domesticated crop material, such as ratoon rice systems, can provide a useful foundation for breeding efforts, accelerating the development of truly perennial crops and farming systems; 4.) Primary research is lacking for crops that are produced on a smaller scale globally, such as pigeon pea and sorghum, and on the ecosystem service benefits of perennial agricultural systems. PMID:27213283

  3. A simulation model assisted study on water and nitrogen dynamics and their effects on crop performance in the wheat-maize system: (II) model calibration, evaluation and simulated experimentation

    Institute of Scientific and Technical Information of China (English)

    Hongzhan L(U); Weili LIANG; Guiyan WANG; David J.CONNOR; Glyn M. RIMMINGTON

    2009-01-01

    The test on the model with data collected from two years' field experiments revealed an ability to satisfactorily simulate crop parameters such as LAI, biomass accumulation and partitioning, yield, and variables influencing crop growth and development as nitrogen uptake by crops and partitioning in different organs, and dynamics of soil water and nitrogen including infiltration and leaching. With the model, crop yield, water use efficiency (WUE), nitrogen use efficiency (NYE) and water-nitrogen leaching at specific soil layers under various water and nitrogen management practices were simulated to provide data used as references for designing sustainable nitrogen and water management practices. The outputs of the simulated experiment with various treatments of irrigation and nitrogen application indicated that crop yield was closely related to water and nitrogen application, crop water use was positively related to irrigation amount, and nitrogen fertilization could improve the crop water use and WUE within certain limits. This is a valuable evidence to be considered in water-saving farming. Nitrogen uptake had a positive relation to nitrogen application, while irrigation to some extent improved its uptake by crops and hence increased NYE. Additionally, irrigation and fertilization had great effects on nitrogen leaching. Thus, in order to improve WUE and NYE, the model showed how nitrogen application and irrigation should be well coordinated.

  4. A guide to generalized additive models in crop science using SAS and R

    Directory of Open Access Journals (Sweden)

    Josefine Liew

    2015-06-01

    Full Text Available Linear models and generalized linear models are well known and are used extensively in crop science. Generalized additive models (GAMs are less well known. GAMs extend generalized linear models through inclusion of smoothing functions of explanatory variables, e.g., spline functions, allowing the curves to bend to better describe the observed data. This article provides an introduction to GAMs in the context of crop science experiments. This is exemplified using a dataset consisting of four populations of perennial sow-thistle (Sonchus arvensis L., originating from two regions, for which emergence of shoots over time was compared.

  5. Integrating Growth Stage Deficit Irrigation into a Process Based Crop Model

    Science.gov (United States)

    Lopez, Jose R.; Winter, Jonathan M.; Elliott, Joshua; Ruane, Alex C.; Porter, Cheryl; Hoogenboom, Gerrit

    2017-01-01

    Current rates of agricultural water use are unsustainable in many regions, creating an urgent need to identify improved irrigation strategies for water limited areas. Crop models can be used to quantify plant water requirements, predict the impact of water shortages on yield, and calculate water productivity (WP) to link water availability and crop yields for economic analyses. Many simulations of crop growth and development, especially in regional and global assessments, rely on automatic irrigation algorithms to estimate irrigation dates and amounts. However, these algorithms are not well suited for water limited regions because they have simplistic irrigation rules, such as a single soil-moisture based threshold, and assume unlimited water. To address this constraint, a new modeling framework to simulate agricultural production in water limited areas was developed. The framework consists of a new automatic irrigation algorithm for the simulation of growth stage based deficit irrigation under limited seasonal water availability; and optimization of growth stage specific parameters. The new automatic irrigation algorithm was used to simulate maize and soybean in Gainesville, Florida, and first used to evaluate the sensitivity of maize and soybean simulations to irrigation at different growth stages and then to test the hypothesis that water productivity calculated using simplistic irrigation rules underestimates WP. In the first experiment, the effect of irrigating at specific growth stages on yield and irrigation water use efficiency (IWUE) in maize and soybean was evaluated. In the reproductive stages, IWUE tended to be higher than in the vegetative stages (e.g. IWUE was 18% higher than the well watered treatment when irrigating only during R3 in soybean), and when rainfall events were less frequent. In the second experiment, water productivity (WP) was significantly greater with optimized irrigation schedules compared to non-optimized irrigation schedules in

  6. A bi-directional gap model for simulating the directional thermal radiance of row crops

    Institute of Scientific and Technical Information of China (English)

    CHEN; Liangfu; (陈良富); LIU; Qinhuo; (柳钦火); FAN; Wenjie; (范闻捷); LI; Xiaowen; (李小文); XIAO; Qing; (肖青); YAN; Guangjian; (闫广建); TIAN; Guoliang; (田国良)

    2002-01-01

    Row crops are a kind of typical vegetation canopy between discrete canopy and continuous canopy. Kimes et al. studied the directional thermal radiation of row crops using the geometrical optical model, which simplified row structure as "box" and neglected the gap among foliage and did not consider the emissivity effects. In this work we take account of the gaps along illumination and viewing directions and propose a bi-direction gap model on the basis of the idea of gap probability of discrete vegetation canopy introduced by "Li-Strahler" and inter-correlation of continuous vegetation developed by Kuusk. It can be used to explain "hot spot" effects in thermal infrared region. The gap model has been validated by field experiment on winter wheat planted in shape of rows and results show that the gap model is better than Kimes' model in describing the directionality of thermal infrared emission for row crops.

  7. 'Fingerprints' of four crop models as affected by soil input data aggregation

    DEFF Research Database (Denmark)

    Angulo, Carlos; Gaiser, Thomas; Rötter, Reimund P;

    2014-01-01

    . In this study we used four crop models (SIMPLACE, DSSAT-CSM, EPIC and DAISY) differing in the detail of modeling above-ground biomass and yield as well as of modeling soil water dynamics, water uptake and drought effects on plants to simulate winter wheat in two (agro-climatologically and geo...

  8. Crop coefficients parametrization using remote sensing in basin-scale hydrological modelling

    Science.gov (United States)

    Hunink, Johannes E.; Eekhout, Joris P. C.; de Vente, Joris; Contreras, Sergio; Droogers, Peter

    2016-04-01

    Satellite-based vegetation indices as Normalized Difference Vegetation Index (NDVI) are increasingly used to derive crop coefficients (kc) for field-scale soil water balance modelling, and for operational monitoring of evapotranspiration (ET). However, for basin-scale hydrological modelling, kc values are traditionally based on literature values, crop and management specific (e.g. FAO-56). For basin-scale analysis, these tabular kc-values are prone to misinterpretations, such as, site specific crop seasons and climate variability within the catchment. Compared to the traditional approach, the advantage of using an NDVI-based method is that observed information on current vegetative status is captured, from which "real" crop coefficients may be derived. However, for future scenario analysis, no satellite-based data are available, hence, crop coefficients need to be estimated either from literature values that are not site-specific, or based on historic NDVI observations. The aim of this study is to evaluate the impacts of various crop coefficient parameterization methods on the performance of a basin-scale hydrological model. We assume actual NDVI as the best available proxy for the crop coefficient and calibrate a hydrological model (SPHY) with monthly reservoir inflows: the reference model. Then, we change the crop coefficient parameterizations of this model with three different parameterizations and compare outputs for a validation period. The study is performed in the sub-humid to semi-arid Upper Segura basin (2592 km2) in SE Spain. The three parameterization methods we evaluate are: (1) land-cover specific kc values using traditional approach from reference tables (FAO-56), (2) land-cover specific kc values obtained from seasonal trajectories of NDVI, (3) pixel-specific seasonal kc values from NDVI trajectories of each pixel. To evaluate the performance of the three methods, spatial and temporal patterns of simulated streamflow, evapotranspiration, and soil

  9. Parameter optimization in S-system models

    Directory of Open Access Journals (Sweden)

    Vasconcelos Ana

    2008-04-01

    Full Text Available Abstract Background The inverse problem of identifying the topology of biological networks from their time series responses is a cornerstone challenge in systems biology. We tackle this challenge here through the parameterization of S-system models. It was previously shown that parameter identification can be performed as an optimization based on the decoupling of the differential S-system equations, which results in a set of algebraic equations. Results A novel parameterization solution is proposed for the identification of S-system models from time series when no information about the network topology is known. The method is based on eigenvector optimization of a matrix formed from multiple regression equations of the linearized decoupled S-system. Furthermore, the algorithm is extended to the optimization of network topologies with constraints on metabolites and fluxes. These constraints rejoin the system in cases where it had been fragmented by decoupling. We demonstrate with synthetic time series why the algorithm can be expected to converge in most cases. Conclusion A procedure was developed that facilitates automated reverse engineering tasks for biological networks using S-systems. The proposed method of eigenvector optimization constitutes an advancement over S-system parameter identification from time series using a recent method called Alternating Regression. The proposed method overcomes convergence issues encountered in alternate regression by identifying nonlinear constraints that restrict the search space to computationally feasible solutions. Because the parameter identification is still performed for each metabolite separately, the modularity and linear time characteristics of the alternating regression method are preserved. Simulation studies illustrate how the proposed algorithm identifies the correct network topology out of a collection of models which all fit the dynamical time series essentially equally well.

  10. iPot: Improved potato monitoring in Belgium using remote sensing and crop growth modelling

    Science.gov (United States)

    Piccard, Isabelle; Gobin, Anne; Curnel, Yannick; Goffart, Jean-Pierre; Planchon, Viviane; Wellens, Joost; Tychon, Bernard; Cattoor, Nele; Cools, Romain

    2016-04-01

    Potato processors, traders and packers largely work with potato contracts. The close follow up of contracted parcels is important to improve the quantity and quality of the crop and reduce risks related to storage, packaging or processing. The use of geo-information by the sector is limited, notwithstanding the great benefits that this type of information may offer. At the same time, new sensor-based technologies continue to gain importance and farmers increasingly invest in these. The combination of geo-information and crop modelling might strengthen the competitiveness of the Belgian potato chain in a global market. The iPot project, financed by the Belgian Science Policy Office (Belspo), aims at providing the Belgian potato processing sector, represented by Belgapom, with near real time information on field condition (weather-soil), crop development and yield estimates, derived from a combination of satellite images and crop growth models. During the cropping season regular UAV flights (RGB, 3x3 cm) and high resolution satellite images (DMC/Deimos, 22m pixel size) were combined to elucidate crop phenology and performance at variety trials. UAV images were processed using a K-means clustering algorithm to classify the crop according to its greenness at 5m resolution. Vegetation indices such as %Cover and LAI were calculated with the Cyclopes algorithm (INRA-EMMAH) on the DMC images. Both DMC and UAV-based cover maps showed similar patterns, and helped detect different crop stages during the season. A wide spread field monitoring campaign with crop observations and measurements allowed for further calibration of the satellite image derived vegetation indices. Curve fitting techniques and phenological models were developed and compared with the vegetation indices during the season, both at trials and farmers' fields. Understanding and predicting crop phenology and canopy development is important for timely crop management and ultimately for yield estimates. An

  11. Modeling of Parameters of Subcritical Assembly SAD

    CERN Document Server

    Petrochenkov, S; Puzynin, I

    2005-01-01

    The accepted conceptual design of the experimental Subcritical Assembly in Dubna (SAD) is based on the MOX core with a nominal unit capacity of 25 kW (thermal). This corresponds to the multiplication coefficient $k_{\\rm eff} =0.95$ and accelerator beam power 1 kW. A subcritical assembly driven with the existing 660 MeV proton accelerator at the Joint Institute for Nuclear Research has been modelled in order to make choice of the optimal parameters for the future experiments. The Monte Carlo method was used to simulate neutron spectra, energy deposition and doses calculations. Some of the calculation results are presented in the paper.

  12. Modeling Agricultural Crop Production in China using AVHRR-based Vegetation Health Indices

    Science.gov (United States)

    Yang, B.; Kogan, F.; Guo, W.; Zhiyuan, P.; Xianfeng, J.

    Weather related crop losses have always been a concern for farmers On a wider scale it has always influenced decision of Governments traders and other policy makers for the purpose of balanced food supplies trade and distribution of aid to the nations in need Therefore national policy and decision makers are giving increasing importance to early assessment of crop losses in response to weather fluctuations This presentation emphasizes utility of AVHRR-based Vegetation health index VHI for early warning of drought-related losses of agricultural production in China The VHI is a three-channel index characterizing greenness vigor and temperature of land surface which can be used as proxy for estimation of how healthy and potentially productive could be vegetation China is the largest in the world producer of grain including wheat and rice and cotton In the major agricultural areas China s crop production is very dependent on weather The VHI being a proxy indicator of weather impact on vegetation showed some correlation with productivity of agricultural crops during the critical period of their development The periods of the strongest correlation were investigated and used to build regression models where crop yield deviation from technological trend was accepted as a dependent and VHI as independent variables The models were developed for several major crops including wheat corn and soybeans

  13. Mapping crop evapotranspiration by integrating vegetation indices into a soil water balance model

    Science.gov (United States)

    Consoli, Simona; Vanella, Daniela

    2015-04-01

    The approach combines the basal crop coefficient (Kcb) derived from vegetation indices (VIs) with the daily soil water balance, as proposed in the FAO-56 paper, to estimate daily crop evapotranspiration (ETc) rates of orange trees. The reliability of the approach to detect water stress was also assessed. VIs were simultaneously retrieved from WorldView-2 imagery and hyper-spectral data collected in the field for comparison. ETc estimated were analysed at the light of independent measurements of the same fluxes by an eddy covariance (EC) system located in the study area. The soil water depletion in the root zone of the crop simulated by the model was also validated by using an in situ soil water monitoring. Average overestimate of daily ETc of 6% was obtained from the proposed approach with respect to EC measurements, evidencing a quite satisfactory agreement between data. The model also detected several periods of light stress for the crop under study, corresponding to an increase of the root zone water deficit matching quite well the in situ soil water monitoring. The overall outcomes of this study showed that the FAO-56 approach with remote sensing-derived basal crop coefficient can have the potential to be applied for estimating crop water requirements and enhancing water management strategies in agricultural contexts.

  14. Crops in silico: A community wide multi-scale computational modeling framework of plant canopies

    Science.gov (United States)

    Srinivasan, V.; Christensen, A.; Borkiewic, K.; Yiwen, X.; Ellis, A.; Panneerselvam, B.; Kannan, K.; Shrivastava, S.; Cox, D.; Hart, J.; Marshall-Colon, A.; Long, S.

    2016-12-01

    Current crop models predict a looming gap between supply and demand for primary foodstuffs over the next 100 years. While significant yield increases were achieved in major food crops during the early years of the green revolution, the current rates of yield increases are insufficient to meet future projected food demand. Furthermore, with projected reduction in arable land, decrease in water availability, and increasing impacts of climate change on future food production, innovative technologies are required to sustainably improve crop yield. To meet these challenges, we are developing Crops in silico (Cis), a biologically informed, multi-scale, computational modeling framework that can facilitate whole plant simulations of crop systems. The Cis framework is capable of linking models of gene networks, protein synthesis, metabolic pathways, physiology, growth, and development in order to investigate crop response to different climate scenarios and resource constraints. This modeling framework will provide the mechanistic details to generate testable hypotheses toward accelerating directed breeding and engineering efforts to increase future food security. A primary objective for building such a framework is to create synergy among an inter-connected community of biologists and modelers to create a realistic virtual plant. This framework advantageously casts the detailed mechanistic understanding of individual plant processes across various scales in a common scalable framework that makes use of current advances in high performance and parallel computing. We are currently designing a user friendly interface that will make this tool equally accessible to biologists and computer scientists. Critically, this framework will provide the community with much needed tools for guiding future crop breeding and engineering, understanding the emergent implications of discoveries at the molecular level for whole plant behavior, and improved prediction of plant and ecosystem

  15. Evaluation of the Aqua‎Crop Model to Simulate Maize Yiled Response under Salinity Stress

    Directory of Open Access Journals (Sweden)

    Aida Mehrazar

    2017-01-01

    Full Text Available Introduction: Limited water resources and its salinity uptrend has caused reducing water and soil quality and consequently reducing the crop production. Thus, use of saline water is the management strategies to decrease drought and water crisis. Furthermore, simulation models are valuable tools for improving on-farm water management and study about the effects of water quality and quantity on crop yield.. The AquaCrop model has recently been developed by the FAO which has the ability to check the production process under different propositions. The initial version of the model was introduced for simulation of crop yield and soil water movement in 2007, that the effect of salinity on crop yield was not considered. Version 4 of the model was released in 2012 in which also considered the effects of salinity on crop yield and simulation of solute Transmission in soil profile. Material and methods: In this project, evaluation of the AquaCrop model and its accuracy was studied in the simulating yield of maize under salt stress. This experiment was conducted in Karaj, on maize hybrid (Zea ma ys L in a sandy soil for investigation of salinity stress on maize yield in 2011-2012. This experiment was conducted in form of randomized complete block design in four replications and five levels of salinity treatments including 0, 4.53, 9.06, 13.59 and 18.13 dS/m at the two times sampling. To evaluate the effect of different levels of salinity on the yield of maize was used Version 4 AquaCrop model and SAS ver 9.1 software .The model calibration was performed by comparing the results of the field studies and the results of simulations in the model. In calculating the yield under different scenarios of salt stress by using AquaCrop, the model needs climate data, soil data, vegetation data and information related to farm management. The effects of salinity on yield and some agronomic and physiological traits of hybrid maize (Shoot length, root length, dry weight

  16. Unscented Kalman filter with parameter identifiability analysis for the estimation of multiple parameters in kinetic models

    OpenAIRE

    Baker Syed; Poskar C; Junker Björn

    2011-01-01

    Abstract In systems biology, experimentally measured parameters are not always available, necessitating the use of computationally based parameter estimation. In order to rely on estimated parameters, it is critical to first determine which parameters can be estimated for a given model and measurement set. This is done with parameter identifiability analysis. A kinetic model of the sucrose accumulation in the sugar cane culm tissue developed by Rohwer et al. was taken as a test case model. Wh...

  17. Modelling and Forecasting of Rice Yield in support of Crop Insurance

    Science.gov (United States)

    Weerts, A.; van Verseveld, W.; Trambauer, P.; de Vries, S.; Conijn, S.; van Valkengoed, E.; Hoekman, D.; Hengsdijk, H.; Schrevel, A.

    2016-12-01

    The Government of Indonesia has embarked on a policy to bring crop insurance to all of Indonesia's farmers. To support the Indonesian government, the G4INDO project (www.g4indo.org) is developing/constructing an integrated platform for judging and handling insurance claims. The platform consists of bringing together remote sensed data (both visible and radar) and hydrologic and crop modelling and forecasting to improve predictions in one forecasting platform (i.e. Delft-FEWS, Werner et al., 2013). The hydrological model and crop model (LINTUL) are coupled on time stepping basis in the OpenStreams framework (see https://github.com/openstreams/wflow) and deployed in a Delft-FEWS forecasting platform to support seasonal forecasting of water availability and crop yield. First we will show the general idea about the project, the integrated platform (including Sentinel 1 & 2 data) followed by first (reforecast) results of the coupled models for predicting water availability and crop yield in the Brantas catchment in Java, Indonesia. Werner, M., Schellekens, J., Gijsbers, P., Van Dijk, M., Van den Akker, O. and Heynert K, 2013. The Delft-FEWS flow forecasting system, Environmental Modelling & Software; 40:65-77. DOI: 10.1016/j.envsoft.2012.07.010 .

  18. Evapotranspiration simulated by CRITERIA and AquaCrop models in stony soils

    Directory of Open Access Journals (Sweden)

    Pasquale Campi

    2015-06-01

    Full Text Available The performance of a water balance model is also based on the ability to correctly perform simulations in heterogeneous soils. The objective of this paper is to test CRITERIA and AquaCrop models in order to evaluate their suitability in estimating evapotranspiration at the field scale in two types of soil in the Mediterranean region: non-stony and stony soil. The first step of the work was to calibrate both models under the non-stony conditions. The models were calibrated by using observations on wheat crop (leaf area index or canopy cover, and phenological stages as a function of degree days and pedo-climatic measurements. The second step consisted in the analysing the impact of the soil type on the models performances by comparing simulated and measured values. The outputs retained in the analysis were soil water content (at the daily scale and crop evapotranspiration (at two time scales: daily and crop season. The model performances were evaluated through four statistical tests: normalised difference (D% at the seasonal time scale; and relative root mean square error (RRMSE, efficiency index (EF, coefficient of determination (r2 at the daily scale. At the seasonal scale, values of D% were less than 15% in stony and on-stony soils, indicating a good performance attained by both models. At the daily scale, the RRMSE values (<30% indicate that the evapotranspiration simulated by CRITERIA is acceptable in both soil types. In the stony soil conditions, 3 out 4 statistical tests (RRMSE, EF, r2 indicate the inadequacy of AquaCrop to simulate correctly daily evapotranspiration. The higher performance of CRITERIA model to simulate daily evapotranspiration in stony soils, is due to the soil submodel, which requires the percentage skeleton as an input, while AquaCrop model takes into account the presence of skeleton by reducing the soil volume.

  19. Coupling land surface and crop growth models for predicting evapotranspiration and carbon exchange in wheat-maize rotation croplands

    Directory of Open Access Journals (Sweden)

    H. Lei

    2010-10-01

    Full Text Available The North China Plain is one of the key crop-producing regions in China. However, water resources in the area are limited. Accurate modeling of water consumption and crop production in response to the changing environment is important. To describe the two-way interactions among climate, irrigation, and crop growth better, the modified crop phenology and physiology scheme from the SiBcrop model was coupled with the second version of the Simple Biosphere model (SiB2 to simulate crop phenology, crop production, and evapotranspiration of winter wheat and summer maize, which are two of the main crops in the region. In the coupled model, the leaf area index (LAI produced by the crop phenology and physiology scheme was used in estimating sub-hourly energy and carbon fluxes. Observations obtained from two typical eddy covariance sites located in this region were used to validate the model. The coupled model was able to accurately simulate carbon and energy fluxes, soil water content, biomass carbon, and crop yield, especially for latent heat flux and carbon flux. The LAI was also well simulated by the model. Therefore, the coupled model is capable of assessing the responses of water resources and crop production to the changes of future climate and irrigation schedules of this region.

  20. Crop Model Improvement Reduces the Uncertainty of the Response to Temperature of Multi-Model Ensembles

    Science.gov (United States)

    Maiorano, Andrea; Martre, Pierre; Asseng, Senthold; Ewert, Frank; Mueller, Christoph; Roetter, Reimund P.; Ruane, Alex C.; Semenov, Mikhail A.; Wallach, Daniel; Wang, Enli

    2016-01-01

    To improve climate change impact estimates and to quantify their uncertainty, multi-model ensembles (MMEs) have been suggested. Model improvements can improve the accuracy of simulations and reduce the uncertainty of climate change impact assessments. Furthermore, they can reduce the number of models needed in a MME. Herein, 15 wheat growth models of a larger MME were improved through re-parameterization and/or incorporating or modifying heat stress effects on phenology, leaf growth and senescence, biomass growth, and grain number and size using detailed field experimental data from the USDA Hot Serial Cereal experiment (calibration data set). Simulation results from before and after model improvement were then evaluated with independent field experiments from a CIMMYT worldwide field trial network (evaluation data set). Model improvements decreased the variation (10th to 90th model ensemble percentile range) of grain yields simulated by the MME on average by 39% in the calibration data set and by 26% in the independent evaluation data set for crops grown in mean seasonal temperatures greater than 24 C. MME mean squared error in simulating grain yield decreased by 37%. A reduction in MME uncertainty range by 27% increased MME prediction skills by 47%. Results suggest that the mean level of variation observed in field experiments and used as a benchmark can be reached with half the number of models in the MME. Improving crop models is therefore important to increase the certainty of model-based impact assessments and allow more practical, i.e. smaller MMEs to be used effectively.

  1. Moose models with vanishing $S$ parameter

    CERN Document Server

    Casalbuoni, R; Dominici, Daniele

    2004-01-01

    In the linear moose framework, which naturally emerges in deconstruction models, we show that there is a unique solution for the vanishing of the $S$ parameter at the lowest order in the weak interactions. We consider an effective gauge theory based on $K$ SU(2) gauge groups, $K+1$ chiral fields and electroweak groups $SU(2)_L$ and $U(1)_Y$ at the ends of the chain of the moose. $S$ vanishes when a link in the moose chain is cut. As a consequence one has to introduce a dynamical non local field connecting the two ends of the moose. Then the model acquires an additional custodial symmetry which protects this result. We examine also the possibility of a strong suppression of $S$ through an exponential behavior of the link couplings as suggested by Randall Sundrum metric.

  2. Model parameters for simulation of physiological lipids

    Science.gov (United States)

    McGlinchey, Nicholas

    2016-01-01

    Coarse grain simulation of proteins in their physiological membrane environment can offer insight across timescales, but requires a comprehensive force field. Parameters are explored for multicomponent bilayers composed of unsaturated lipids DOPC and DOPE, mixed‐chain saturation POPC and POPE, and anionic lipids found in bacteria: POPG and cardiolipin. A nonbond representation obtained from multiscale force matching is adapted for these lipids and combined with an improved bonding description of cholesterol. Equilibrating the area per lipid yields robust bilayer simulations and properties for common lipid mixtures with the exception of pure DOPE, which has a known tendency to form nonlamellar phase. The models maintain consistency with an existing lipid–protein interaction model, making the force field of general utility for studying membrane proteins in physiologically representative bilayers. © 2016 The Authors. Journal of Computational Chemistry Published by Wiley Periodicals, Inc. PMID:26864972

  3. VIC–CropSyst-v2: A regional-scale modeling platform to simulate the nexus of climate, hydrology, cropping systems, and human decisions

    Directory of Open Access Journals (Sweden)

    K. Malek

    2017-08-01

    Full Text Available Food supply is affected by a complex nexus of land, atmosphere, and human processes, including short- and long-term stressors (e.g., drought and climate change, respectively. A simulation platform that captures these complex elements can be used to inform policy and best management practices to promote sustainable agriculture. We have developed a tightly coupled framework using the macroscale variable infiltration capacity (VIC hydrologic model and the CropSyst agricultural model. A mechanistic irrigation module was also developed for inclusion in this framework. Because VIC–CropSyst combines two widely used and mechanistic models (for crop phenology, growth, management, and macroscale hydrology, it can provide realistic and hydrologically consistent simulations of water availability, crop water requirements for irrigation, and agricultural productivity for both irrigated and dryland systems. This allows VIC–CropSyst to provide managers and decision makers with reliable information on regional water stresses and their impacts on food production. Additionally, VIC–CropSyst is being used in conjunction with socioeconomic models, river system models, and atmospheric models to simulate feedback processes between regional water availability, agricultural water management decisions, and land–atmosphere interactions. The performance of VIC–CropSyst was evaluated on both regional (over the US Pacific Northwest and point scales. Point-scale evaluation involved using two flux tower sites located in agricultural fields in the US (Nebraska and Illinois. The agreement between recorded and simulated evapotranspiration (ET, applied irrigation water, soil moisture, leaf area index (LAI, and yield indicated that, although the model is intended to work on regional scales, it also captures field-scale processes in agricultural areas.

  4. VIC-CropSyst-v2: A regional-scale modeling platform to simulate the nexus of climate, hydrology, cropping systems, and human decisions

    Science.gov (United States)

    Malek, Keyvan; Stöckle, Claudio; Chinnayakanahalli, Kiran; Nelson, Roger; Liu, Mingliang; Rajagopalan, Kirti; Barik, Muhammad; Adam, Jennifer C.

    2017-08-01

    Food supply is affected by a complex nexus of land, atmosphere, and human processes, including short- and long-term stressors (e.g., drought and climate change, respectively). A simulation platform that captures these complex elements can be used to inform policy and best management practices to promote sustainable agriculture. We have developed a tightly coupled framework using the macroscale variable infiltration capacity (VIC) hydrologic model and the CropSyst agricultural model. A mechanistic irrigation module was also developed for inclusion in this framework. Because VIC-CropSyst combines two widely used and mechanistic models (for crop phenology, growth, management, and macroscale hydrology), it can provide realistic and hydrologically consistent simulations of water availability, crop water requirements for irrigation, and agricultural productivity for both irrigated and dryland systems. This allows VIC-CropSyst to provide managers and decision makers with reliable information on regional water stresses and their impacts on food production. Additionally, VIC-CropSyst is being used in conjunction with socioeconomic models, river system models, and atmospheric models to simulate feedback processes between regional water availability, agricultural water management decisions, and land-atmosphere interactions. The performance of VIC-CropSyst was evaluated on both regional (over the US Pacific Northwest) and point scales. Point-scale evaluation involved using two flux tower sites located in agricultural fields in the US (Nebraska and Illinois). The agreement between recorded and simulated evapotranspiration (ET), applied irrigation water, soil moisture, leaf area index (LAI), and yield indicated that, although the model is intended to work on regional scales, it also captures field-scale processes in agricultural areas.

  5. Preparatory steps for a robust dynamic model for organically bound tritium dynamics in agricultural crops

    Energy Technology Data Exchange (ETDEWEB)

    Melintescu, A.; Galeriu, D. [' Horia Hulubei' National Institute for Physics and Nuclear Engineering, Bucharest-Magurele (Romania); Diabate, S.; Strack, S. [Institute of Toxicology and Genetics, Karlsruhe Institute of Technology - KIT, Eggenstein-Leopoldshafen (Germany)

    2015-03-15

    The processes involved in tritium transfer in crops are complex and regulated by many feedback mechanisms. A full mechanistic model is difficult to develop due to the complexity of the processes involved in tritium transfer and environmental conditions. First, a review of existing models (ORYZA2000, CROPTRIT and WOFOST) presenting their features and limits, is made. Secondly, the preparatory steps for a robust model are discussed, considering the role of dry matter and photosynthesis contribution to the OBT (Organically Bound Tritium) dynamics in crops.

  6. Modelling rainfall interception by vegetation of variable density using an adapted analytical model. Part 2. Model validation for a tropical upland mixed cropping system

    Science.gov (United States)

    van Dijk, A. I. J. M.; Bruijnzeel, L. A.

    2001-07-01

    To improve the description of rainfall partitioning by a vegetation canopy that changes in time a number of adaptations to the revised analytical model for rainfall interception by sparse canopies [J. Hydrol., 170 (1995) 79] was proposed in the first of two papers. The current paper presents an application of this adapted analytical model to simulate throughfall, stemflow and interception as measured in a mixed agricultural cropping system involving cassava, maize and rice during two seasons of growth and serial harvesting in upland West Java, Indonesia. Measured interception losses were 18 and 8% during the two measuring periods, while stemflow fractions were estimated at 2 and 4%, respectively. The main reasons for these discrepancies were differences in vegetation density and composition, as well as differences in the exposure of the two sites used in the two respective years. Functions describing the development of the leaf area index of each of the component crops in time were developed. Leaf area index (ranging between 0.7 and 3.8) was related to canopy cover fraction (0.41-0.94). Using average values and time series of the respective parameters, interception losses were modelled using both the revised analytical model and the presently adapted version. The results indicate that the proposed model adaptations substantially improve the performance of the analytical model and provide a more solid base for parameterisation of the analytical model in vegetation of variable density.

  7. Improving plot- and regional-scale crop models for simulating impacts of climate variability and extremes

    Science.gov (United States)

    Tao, F.; Rötter, R.

    2013-12-01

    Many studies on global climate report that climate variability is increasing with more frequent and intense extreme events1. There are quite large uncertainties from both the plot- and regional-scale models in simulating impacts of climate variability and extremes on crop development, growth and productivity2,3. One key to reducing the uncertainties is better exploitation of experimental data to eliminate crop model deficiencies and develop better algorithms that more adequately capture the impacts of extreme events, such as high temperature and drought, on crop performance4,5. In the present study, in a first step, the inter-annual variability in wheat yield and climate from 1971 to 2012 in Finland was investigated. Using statistical approaches the impacts of climate variability and extremes on wheat growth and productivity were quantified. In a second step, a plot-scale model, WOFOST6, and a regional-scale crop model, MCWLA7, were calibrated and validated, and applied to simulate wheat growth and yield variability from 1971-2012. Next, the estimated impacts of high temperature stress, cold damage, and drought stress on crop growth and productivity based on the statistical approaches, and on crop simulation models WOFOST and MCWLA were compared. Then, the impact mechanisms of climate extremes on crop growth and productivity in the WOFOST model and MCWLA model were identified, and subsequently, the various algorithm and impact functions were fitted against the long-term crop trial data. Finally, the impact mechanisms, algorithms and functions in WOFOST model and MCWLA model were improved to better simulate the impacts of climate variability and extremes, particularly high temperature stress, cold damage and drought stress for location-specific and large area climate impact assessments. Our studies provide a good example of how to improve, in parallel, the plot- and regional-scale models for simulating impacts of climate variability and extremes, as needed for

  8. DATA SHEET MODEL FOR DEVELOPING A RED LIST REGARDING CROP LANDRACES IN ROMANIA

    Directory of Open Access Journals (Sweden)

    Maria-Mihaela Antofie

    2010-01-01

    Full Text Available A data sheet model this paper is proposing that will be required for developing a future Red List for crop landraces in Romania. Such a Red List is not yet published in our country and the genetic erosion for crops is increasing especially because of the pressure of commercial crops entering the market-place and also because of the climate change and desertification as major threats. As a consequence for safeguarding food and feed it is compulsory to preserving genetic resources and a special attention should be devoted to on farm conservation. Developing a red list for crop landraces in Romania will support further on farm conservation of such crops and will more emphasize the role of gene banks in our country. Furthermore such a red list will ground the development of a new agriculture vision and policy regarding the implementation of appropriate incentive measures for supporting on farm conservation of crop landraces in specific area in Romania such is the protected area system.

  9. Developing a global crop model for maize, wheat, and soybean production

    Science.gov (United States)

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

    2008-12-01

    Recently, the world food supply has faced a crisis due to increasing food prices driven by rising food demand, increasing fuel prices, poor harvests due to climate factors, and the use of crops such as maize and soybean to produce biofuel. In order to assess the future of global food availability, there is a need for understanding the factors underlying food production. Farmer management practices along with climatic conditions are the main elements directly influencing crop yield. As a consequence, estimations of future world food production require the use of a global crop model that simulates reasonably the effect of both climate and management practices on yield. Only a few global crop models have been developed to date, and currently none of them represent management factors adequately, principally due to the lack of spatially explicit datasets at the global scale. In this study, we present a global crop model designed for maize, wheat, and soybean production that incorporates planting and harvest decisions, along with irrigation options based on newly available data. The crop model is built on a simple water-balance algorithm based on the Penman- Monteith equation combined with a light use efficiency approach that calculates biomass production under non-nutrient-limiting conditions. We used a world crop calendar dataset to develop statistical relationships between climate variables and planting dates for different regions of the world. Development stages are defined based on total growing degree days required to reach the beginning of each phase. Irrigation options are considered in regions where water stress occurs and irrigation infrastructures exist. We use a global dataset on irrigated areas for each crop type. The quantity of water applied is then calculated in order to avoid water stress but with an upper threshold derived from total irrigation withdrawal quantity estimated by the global water use model WaterGAP 2. Our analysis will present the model

  10. Regional modelling of nitrate leaching from Swiss organic and conventional cropping systems under climate change

    Science.gov (United States)

    Calitri, Francesca; Necpalova, Magdalena; Lee, Juhwan; Zaccone, Claudio; Spiess, Ernst; Herrera, Juan; Six, Johan

    2016-04-01

    Organic cropping systems have been promoted as a sustainable alternative to minimize the environmental impacts of conventional practices. Relatively little is known about the potential to reduce NO3-N leaching through the large-scale adoption of organic practices. Moreover, the potential to mitigate NO3-N leaching and thus the N pollution under future climate change through organic farming remain unknown and highly uncertain. Here, we compared regional NO3-N leaching from organic and conventional cropping systems in Switzerland using a terrestrial biogeochemical process-based model DayCent. The objectives of this study are 1) to calibrate and evaluate the model for NO3-N leaching measured under various management practices from three experiments at two sites in Switzerland; 2) to estimate regional NO3-N leaching patterns and their spatial uncertainty in conventional and organic cropping systems (with and without cover crops) for future climate change scenario A1B; 3) to explore the sensitivity of NO3-N leaching to changes in soil and climate variables; and 4) to assess the nitrogen use efficiency for conventional and organic cropping systems with and without cover crops under climate change. The data for model calibration/evaluation were derived from field experiments conducted in Liebefeld (canton Bern) and Eschikon (canton Zürich). These experiments evaluated effects of various cover crops and N fertilizer inputs on NO3-N leaching. The preliminary results suggest that the model was able to explain 50 to 83% of the inter-annual variability in the measured soil drainage (RMSE from 12.32 to 16.89 cm y-1). The annual NO3-N leaching was also simulated satisfactory (RMSE = 3.94 to 6.38 g N m-2 y-1), although the model had difficulty to reproduce the inter-annual variability in the NO3-N leaching losses correctly (R2 = 0.11 to 0.35). Future climate datasets (2010-2099) from the 10 regional climate models (RCM) were used in the simulations. Regional NO3-N leaching

  11. Comprehensive Suitability Evaluation of Tea Crops Using GIS and a Modified Land Ecological Suitability Evaluation Model

    Institute of Scientific and Technical Information of China (English)

    LI Bo; ZHANG Feng; ZHANG Li-Wen; HUANG Jing-Feng; JIN Zhi-Feng; D. K. GUPTA

    2012-01-01

    Tea (Camellia sinensis) is one of the most valuable cash crops in southern China; however,the planting distribution of tea crops is not optimal and the production and cultivation regions of tea crops are restricted by law and custom.In order to evaluate the suitability of tea crops in Zhejiang Province,the annual mean temperature,the annual accumulated temperature above 10 ℃,the frequency of extremely low temperature below -13 ℃,the mean humidity from April to October,slope,aspect,altitude,soil type,and soil texture were selected from climate,topography,and soil factors as factors for land ecological evaluation by the Delphi method based on the ecological characteristics of tea crops.These nine factors were quantitatively analyzed using a geographic information system (GIS).The grey relational analysis (GRA) was combined with the analytic hierarchy process (AHP) to address the uncertainties during the process of evaluating the traditional land ecological suitability,and a modified land ecological suitability evaluation (LESE) model was built.Based on the land-use map of Zhejiang Province,the regions that were completely unsuitable for tea cultivation in the province were eliminated and then the spatial distribution of the ecological suitability of tea crops was generated using the modified LESE model and GIS.The results demonstrated that the highly,moderately,and non-suitable regions for the cultivation of tea crops in Zhejiang Province were 27552.66,42724.64,and 26507.97 km2,and accounted for 28.47%,44.14%,and 27.39% of the total evaluation area,respectively.Validation of the method showed a high degree of coincidence with the current planting distribution of tea crops in Zhejiang Province.The modified LESE model combined with GIS could be useful in quickly and accurately evaluating the land ecological suitability of tea crops,providing a scientific basis for the rational distribution of tea crops and acting as a reference to land policy makers and land

  12. Development model for energy crop plantations in the Czech Republic for the years 2008-2030

    Energy Technology Data Exchange (ETDEWEB)

    Havlickova, Kamila; Suchy, Jiri [Department of Phytoenergy, Silva Tarouca Research Institute for Landscape and Ornamental Gardening, Publ. Res. Inst., Kvetnove nam. 391, 252 43 Pruhonice (Czech Republic)

    2010-09-15

    This paper deals with modelling the development of plantations for intentional biomass production. The model of plots for the areas of interest consider the following biomass sources: intentionally produced biomass from SRC of fast-growing trees and non-woody energy crops (sorrel, reed grass and triticale). Statistical data for the entire area of interest (NUTS1 size) and data for a part of this area (NUTS3 size - 18% of total area of interest) were used to determine data on the area of arable land and permanent grasslands in the initial year. This paper presents a model of the development of production plots for the period 2008-2030. Yields are calculated of selected energy crops with regard to their growing cycle using so-called triangular method. The core of the algorithm for calculation of growing area of energy crop is an optimalization of processes regarding economic and technical demands for long-term and sustainable production of biomass. (author)

  13. Differences in Crenate Broomrape Parasitism Dynamics on Three Legume Crops Using a Thermal Time Model

    Science.gov (United States)

    Pérez-de-Luque, Alejandro; Flores, Fernando; Rubiales, Diego

    2016-01-01

    Root parasitic weeds are a major limiting production factor in a number of crops, and control is difficult. Genetic resistance and chemical control lead the fight, but without unequivocal success. Models that help to describe and even predict the evolution of parasitism underground are a valuable tool for herbicide applications, and even could help in breeding programs. Legumes are heavily affected by Orobanche crenata (crenate broomrape) in the Mediterranean basin. This work presents a descriptive model based on thermal time and correlating growing degree days (GDD) with the different developmental stages of the parasite. The model was developed in three different legume crops (faba bean, grass pea and lentil) attacked by crenate broomrape. The developmental stages of the parasite strongly correlated with the GDD and differences were found depending on the host crop. PMID:28018421

  14. Volatilization of parathion and chlorothalonil from a potato crop simulated by the PEARL model.

    Science.gov (United States)

    Leistra, Minze; van den Berg, Frederik

    2007-04-01

    The volatilization of pesticides from crop canopies in the field should be modeled within the context of evaluating environmental exposure. A model concept based on diffusion through a laminar air-boundary layer was incorporated into the PEARL model (pesticide emission assessment at regional and local scales) and used to simulate volatilization of the pesticides parathion and chlorothalonil from a potato crop in a field experiment. Rate coefficients for the competing processes of plant penetration, wash off, and phototransformation in the canopy had to be derived from a diversity of literature data. Cumulative volatilization of the moderately volatile parathion (31% of the dosage in 7.6 days) could be simulated after calibrating two input data derived for the related compound parathion-methyl. The less volatile and more slowly transformed chlorothalonil showed 5% volatilization in 7.6 days, which could be explained by the simulation. Simulated behavior of the pesticides in the crop canopy roughly corresponded to published data.

  15. Effects of the partitioning of diffuse and direct solar radiation on satellite-based modeling of crop gross primary production

    Science.gov (United States)

    Xin, Qinchuan; Gong, Peng; Suyker, Andrew E.; Si, Yali

    2016-08-01

    Modeling crop gross primary production (GPP) is critical to understanding the carbon dynamics of agro-ecosystems. Satellite-based studies have widely used production efficiency models (PEM) to estimate cropland GPP, wherein light use efficiency (LUE) is a key model parameter. One factor that has not been well considered in many PEMs is that canopy LUE could vary with illumination conditions. This study investigates how the partitioning of diffuse and direct solar radiation influences cropland GPP using both flux tower and satellite data. The field-measured hourly LUE under cloudy conditions was 1.50 and 1.70 times higher than that under near clear-sky conditions for irrigated corn and soybean, respectively. We applied a two-leaf model to simulate the canopy radiative transfer process, where modeled photosynthetically active radiation (PAR) absorbed by canopy agreed with tower measurements (R2 = 0.959 and 0.914 for corn and soybean, respectively). Derived canopy LUE became similar after accounting for the impact of light saturation on leaf photosynthetic capacity under varied illumination conditions. The impacts of solar radiation partitioning on satellite-based modeling of crop GPP was examined using vegetation indices (VI) derived from MODIS data. Consistent with the field modeling results, the relationship between daily GPP and PAR × VI under varied illumination conditions showed different patterns in terms of regression slope and intercept. We proposed a function to correct the influences of direct and diffuse radiation partitioning and the explained variance of flux tower GPP increased in all experiments. Our results suggest that the non-linear response of leaf photosynthesis to light absorption contributes to higher canopy LUE on cloudy days than on clear days. We conclude that accounting for the impacts of solar radiation partitioning is necessary for modeling crop GPP on a daily or shorter basis.

  16. Modeling rice metabolism: from elucidating environmental effects on cellular phenotype to guiding crop improvement

    Directory of Open Access Journals (Sweden)

    Meiyappan Lakshmanan

    2016-11-01

    Full Text Available Crop productivity is severely limited by various biotic and abiotic stresses. Thus, it is highly needed to understand the underlying mechanisms of environmental stress response and tolerance in plants, which could be addressed by systems biology approach. To this end, high-throughput omics profiling and in silico modeling can be considered to explore the environmental effects on phenotypic states and metabolic behaviors of rice crops at the systems level. Especially, the advent of constraint-based metabolic reconstruction and analysis paves a way to characterize the plant cellular physiology under various stresses by combining the mathematical network models with multi-omics data. Rice metabolic networks have been reconstructed since 2013 and currently 6 such networks are available, where 5 are at genome-scale. Since their publication, these models have been utilized to systematically elucidate the rice abiotic stress responses and identify agronomic traits for crop improvement. In this review, we summarize the current status of the existing rice metabolic networks and models with their applications. Furthermore, we also highlight future directions of rice modeling studies, particularly stressing how these models can be used to contextualize the affluent multi-omics data that are readily available in the public domain. Overall, we envisage a number of studies in the future, exploiting the available metabolic models to enhance the yield and quality of rice and other food crops.

  17. Consequential life cycle inventory modelling of land use induced by crop consumption

    DEFF Research Database (Denmark)

    Kløverpris, Jesper Hedal

    -replacement mechanisms are governed by the availability of suitable agricultural land and several economic conditions, such as transport and trade costs. To estimate the land use response to an increase in crop demand, economic modelling can be used. In this project, the economic equilibrium model GTAP (Global Trade......The purpose of the present PhD project was to identify the mechanisms governing global land use consequences of increased crop demand in a given location and, based on this conceptual analysis, to present and demonstrate a method proposal for construction of land use data that can be used in life...

  18. SVAT modeling of crop physiological response to drought in potatoes under different types of deficit irrigation

    DEFF Research Database (Denmark)

    Plauborg, Finn; Mollerup, Mikkel; Abrahamsen, Per

      Further understanding of the crop physiologic responses to drought caused by deficit irrigation (DI), regular or partial root drying (PRD), have been obtained in several studies in tomatoes and potatoes under controlled environment. The improved quantitative description of the production...... of abscisic acid in the root system and as well as its influence on stomatal regulation of gas exhange has been implemented in the Daisy model, a comprehensive work partly financed by the SAFIR project ( http://www.safir4eu.org/ ). Hence, the improved Daisy model now calculates crop production based on gas...

  19. Uncertainty Quantification for Optical Model Parameters

    CERN Document Server

    Lovell, A E; Sarich, J; Wild, S M

    2016-01-01

    Although uncertainty quantification has been making its way into nuclear theory, these methods have yet to be explored in the context of reaction theory. For example, it is well known that different parameterizations of the optical potential can result in different cross sections, but these differences have not been systematically studied and quantified. The purpose of this work is to investigate the uncertainties in nuclear reactions that result from fitting a given model to elastic-scattering data, as well as to study how these uncertainties propagate to the inelastic and transfer channels. We use statistical methods to determine a best fit and create corresponding 95\\% confidence bands. A simple model of the process is fit to elastic-scattering data and used to predict either inelastic or transfer cross sections. In this initial work, we assume that our model is correct, and the only uncertainties come from the variation of the fit parameters. We study a number of reactions involving neutron and deuteron p...

  20. Numerical modeling of partial discharges parameters

    Directory of Open Access Journals (Sweden)

    Kartalović Nenad M.

    2016-01-01

    Full Text Available In recent testing of the partial discharges or the use for the diagnosis of insulation condition of high voltage generators, transformers, cables and high voltage equipment develops rapidly. It is a result of the development of electronics, as well as, the development of knowledge about the processes of partial discharges. The aim of this paper is to contribute the better understanding of this phenomenon of partial discharges by consideration of the relevant physical processes in isolation materials and isolation systems. Prebreakdown considers specific processes, and development processes at the local level and their impact on specific isolation material. This approach to the phenomenon of partial discharges needed to allow better take into account relevant discharge parameters as well as better numerical model of partial discharges.

  1. Advancing the climate data driven crop-modeling studies in the dry areas of Northern Syria and Lebanon: an important first step for assessing impact of future climate.

    Science.gov (United States)

    Dixit, Prakash N; Telleria, Roberto

    2015-04-01

    Inter-annual and seasonal variability in climatic parameters, most importantly rainfall, have potential to cause climate-induced risk in long-term crop production. Short-term field studies do not capture the full nature of such risk and the extent to which modifications to crop, soil and water management recommendations may be made to mitigate the extent of such risk. Crop modeling studies driven by long-term daily weather data can predict the impact of climate-induced risk on crop growth and yield however, the availability of long-term daily weather data can present serious constraints to the use of crop models. To tackle this constraint, two weather generators namely, LARS-WG and MarkSim, were evaluated in order to assess their capabilities of reproducing frequency distributions, means, variances, dry spell and wet chains of observed daily precipitation, maximum and minimum temperature, and solar radiation for the eight locations across cropping areas of Northern Syria and Lebanon. Further, the application of generated long-term daily weather data, with both weather generators, in simulating barley growth and yield was also evaluated. We found that overall LARS-WG performed better than MarkSim in generating daily weather parameters and in 50 years continuous simulation of barley growth and yield. Our findings suggest that LARS-WG does not necessarily require long-term e.g., >30 years observed weather data for calibration as generated results proved to be satisfactory with >10 years of observed data except in area with higher altitude. Evaluating these weather generators and the ability of generated weather data to perform long-term simulation of crop growth and yield is an important first step to assess the impact of future climate on yields, and to identify promising technologies to make agricultural systems more resilient in the given region. Copyright © 2015 Elsevier B.V. All rights reserved.

  2. Irrigation management strategies for winter wheat using AquaCrop model

    Directory of Open Access Journals (Sweden)

    M. H. Ali

    2013-09-01

    Full Text Available Many regions of the world face the challenge to ensure high yield with limited water supply. This calls for utilization of available water in an efficient and sustainable manner. Quantitative models can assist in management decision and planning purposes. The FAO’s newly developed crop-water model, AquaCrop, which simulates yield in response to water, has been calibrated for winter wheat and subsequently used to simulate yield under different sowing dates, irrigation frequencies, and irrigation sequences using 10 years daily weather data. The simulation results suggest that “2 irrigation frequency” is the most water-efficient schedule for wheat under the prevailing climatic and soil conditions. The results also indicate decreasing yield trend under late sowing. The normal/recommended sequence of irrigation performed better than the seven-days shifting from the normal. The results will help to formulate irrigation management plan based on the resource availability (water, and land availability from previous crop.

  3. Modeling technical change in climate analysis: evidence from agricultural crop damages.

    Science.gov (United States)

    Ahmed, Adeel; Devadason, Evelyn S; Al-Amin, Abul Quasem

    2017-05-01

    This study accounts for the Hicks neutral technical change in a calibrated model of climate analysis, to identify the optimum level of technical change for addressing climate changes. It demonstrates the reduction to crop damages, the costs to technical change, and the net gains for the adoption of technical change for a climate-sensitive Pakistan economy. The calibrated model assesses the net gains of technical change for the overall economy and at the agriculture-specific level. The study finds that the gains of technical change are overwhelmingly higher than the costs across the agriculture subsectors. The gains and costs following technical change differ substantially for different crops. More importantly, the study finds a cost-effective optimal level of technical change that potentially reduces crop damages to a minimum possible level. The study therefore contends that the climate policy for Pakistan should consider the role of technical change in addressing climate impacts on the agriculture sector.

  4. Multi-Attribute Modelling of Economic and Ecological Impacts of Agricultural Innovations on Cropping Systems

    Directory of Open Access Journals (Sweden)

    Sara Scatasta

    2006-04-01

    Full Text Available Modeling of economic and ecological impacts of genetically modified crops is a demanding task. We present some models made for the purpose of the ECOGEN project "Soil ecological and economic evaluation of genetically modified crops". One of the goals of the project is to develop a computer-based decision support system for the assessment of economic and ecological impacts of using genetically modified crops, with special emphasis on soil biology and ecology. The decision support system is based on a rule-based model incorporating both economic and ecological criteria. In this paper we present an extension to previous results specifying further two sub-models assessing economic impacts of cropping systems at farm and regional level. Following a real option approach we show how both social and private costs and benefits, both at farm and regional level, can be classified in reversible and irreversible, and what irreversibility means for the size of the uncertainty associated to the adoption of agricultural innovations. All the qualitative models are developed using a qualitative multi-attribute modeling methodology, supported by the software tool DEXi.

  5. Population Modeling Approach to Optimize Crop Harvest Strategy. The Case of Field Tomato

    Directory of Open Access Journals (Sweden)

    Maarten L. A. T. M. Hertog

    2017-04-01

    Full Text Available In this study, the aim is to develop a population model based approach to optimize fruit harvesting strategies with regard to fruit quality and its derived economic value. This approach was applied to the case of tomato fruit harvesting under Vietnamese conditions. Fruit growth and development of tomato (cv. “Savior” was monitored in terms of fruit size and color during both the Vietnamese winter and summer growing seasons. A kinetic tomato fruit growth model was applied to quantify biological fruit-to-fruit variation in terms of their physiological maturation. This model was successfully calibrated. Finally, the model was extended to translate the fruit-to-fruit variation at harvest into the economic value of the harvested crop. It can be concluded that a model based approach to the optimization of harvest date and harvest frequency with regard to economic value of the crop as such is feasible. This approach allows growers to optimize their harvesting strategy by harvesting the crop at more uniform maturity stages meeting the stringent retail demands for homogeneous high quality product. The total farm profit would still depend on the impact a change in harvesting strategy might have on related expenditures. This model based harvest optimisation approach can be easily transferred to other fruit and vegetable crops improving homogeneity of the postharvest product streams.

  6. An operational model to estimate hourly and daily crop evapotranspiration in hilly terrain: validation on wheat and oat crops

    Science.gov (United States)

    Rana, Gianfranco; Katerji, Nader; Ferrara, Rossana M.; Martinelli, Nicola

    2011-03-01

    In this paper, we present an operational model to estimate the actual evapotranspiration (ET) of crops cultivated on hilly terrains. This new model has the following three characteristics: (1) ET modelling is based on a Penman-Monteith (PM) type equation (Monteith 1965) where canopy resistance is simulated by following an approach already illustrated by Katerji and Perrier (Agronomie 3(6):513-521, 1983); (2) the estimation of ET, by means of the PM equation, is made by using meteorological variables simulated on sloped sites as input; (3) these variables are simulated by using simple relationships linking the variables measured at a reference site on plane to the topographic characteristics of the site (slope, orientation, altitude as difference between reference, and sloped sites). This approach presents two advantages if compared with previously proposed models: Not only computation steps are greatly simplified but also error sources due to the simulation of climatic variables in sloped sites and the ET estimation are well distinguished. This model was validated at hourly and daily scales at four sites cultivated with wheat and oats offering a wide range of slope and orientation values: a reference site on plane, site 1 (9° sloping, NW orientation, 7 m from the reference site in plane), site 2 (6°, SE, 12 m) and site 3 (1°, SE, 18 m). At hourly scale, the new model performed well at all sites studied. The observed slope of the linear relationships between estimated and measured ET values ranged between 0.93 and 1.03, with coefficients of determination, r 2, between 0.80 and 0.98. At daily scale, the slopes of the linear relationships between measured and estimated ET for the sites on plane and the sloped sites were practically the same (0.98 ± 0.01); however, the coefficient of determination r 2 observed in the site on plane was clearly greater (0.98) than that observed in the sloped sites (0.83). The presented analysis does not show any significant

  7. Farm Household Economic Model of The Integrated Crop Livestock System: Conceptual and Empirical Study

    Directory of Open Access Journals (Sweden)

    Atien Priyanti

    2007-06-01

    Full Text Available An integrated approach to enhance rice production in Indonesia is very prospectus throughout the implementation of adapted and liable integrated program. One of the challenges in rice crop sub sector is the stagnation of its production due to the limitation of organic matter availability. This provides an opportunity for livestock development to overcome the problems on land fertility through the use of manure as the source of organic fertilizer. Ministry of Agriculture had implemented a program on Increasing Integrated Rice Productivity with an Integrated Crop Livestock System as one of the potential components since 2002. Integrated crop livestock system program with special reference to rice field and beef cattle is an alternative to enhance the potential development of agriculture sector in Indonesia. The implementation on this integrated program is to enhance rice production and productivity through a system involving beef cattle with its goal on increasing farmers’ income. Household economic model can be used as one of the analysis to evaluate the success of the implemented crop livestock system program. The specificity of the farmers is that rationality behavior of the role as production and consumption decision making. In this case, farmers perform the production to meet home consumption based on the resources that used directly for its production. The economic analysis of farmers household can be described to anticipate policy options through this model. Factors influencing farmers’ decisions and direct interrelations to production and consumption aspects that have complex implications for the farmers’ welfare of the integrated crop livestock system program.

  8. Integrated modelling of crop production and nitrate leaching with the Daisy model

    DEFF Research Database (Denmark)

    Manevski, Kiril; Børgesen, Christen Duus; Li, Xiaoxin

    2016-01-01

    parameters set. On daily time scale, validation for highly dynamic variables such as soil water transport remains challenging. iii) Model application is demonstrated with relevance for scientists and regional managers. The integrated modelling strategy is applicable for other process-based models similar...

  9. Harmonization and translation of crop modeling data to ensure interoperability

    NARCIS (Netherlands)

    Porter, C.; Villalobos, C.; Holzworth, D.; Nelson, R.; White, J.W.; Athanasiadis, I.N.; Janssen, S.J.C.; Ripoche, D.; Cufi, J.; Raes, D.; Zhang, M.; Knapen, M.J.R.; Sahajpal, R.; Boote, K.; Jones, J.W.

    2014-01-01

    The Agricultural Model Intercomparison and Improvement Project (AgMIP) seeks to improve the capability of ecophysiological and economic models to describe the potential impacts of climate change on agricultural systems. AgMIP protocols emphasize the use of multiple models; consequently, data harmoni

  10. Harmonization and translation of crop modeling data to ensure interoperability

    NARCIS (Netherlands)

    Porter, C.; Villalobos, C.; Holzworth, D.; Nelson, R.; White, J.W.; Athanasiadis, I.N.; Janssen, S.J.C.; Ripoche, D.; Cufi, J.; Raes, D.; Zhang, M.; Knapen, M.J.R.; Sahajpal, R.; Boote, K.; Jones, J.W.

    2014-01-01

    The Agricultural Model Intercomparison and Improvement Project (AgMIP) seeks to improve the capability of ecophysiological and economic models to describe the potential impacts of climate change on agricultural systems. AgMIP protocols emphasize the use of multiple models; consequently, data

  11. Simple and robust determination of the activity signature of key carbohydrate metabolism enzymes for physiological phenotyping in model and crop plants

    DEFF Research Database (Denmark)

    Jammer, Alexandra; Gasperl, Anna; Luschin-Ebengreuth, Nora;

    2015-01-01

    The analysis of physiological parameters is important to understand the link between plant phenotypes and their genetic bases, and therefore is needed as an important element in the analysis of model and crop plants. The activities of enzymes involved in primary carbohydrate metabolism have been...... shown to be strongly associated with growth performance, crop yield, and quality, as well as stress responses. A simple, fast, and cost-effective method to determine activities for 13 key enzymes involved in carbohydrate metabolism has been established, mainly based on coupled spectrophotometric kinetic...

  12. Parameter Optimisation for the Behaviour of Elastic Models over Time

    DEFF Research Database (Denmark)

    Mosegaard, Jesper

    2004-01-01

    Optimisation of parameters for elastic models is essential for comparison or finding equivalent behaviour of elastic models when parameters cannot simply be transferred or converted. This is the case with a large range of commonly used elastic models. In this paper we present a general method...... that will optimise parameters based on the behaviour of the elastic models over time....

  13. Model Identification of Linear Parameter Varying Aircraft Systems

    OpenAIRE

    Fujimore, Atsushi; Ljung, Lennart

    2007-01-01

    This article presents a parameter estimation of continuous-time polytopic models for a linear parameter varying (LPV) system. The prediction error method of linear time invariant (LTI) models is modified for polytopic models. The modified prediction error method is applied to an LPV aircraft system whose varying parameter is the flight velocity and model parameters are the stability and control derivatives (SCDs). In an identification simulation, the polytopic model is more suitable for expre...

  14. Modeling Crop Water Productivity Using a Coupled SWAT–MODSIM Model

    Directory of Open Access Journals (Sweden)

    Saeid Ashraf Vaghefi

    2017-02-01

    Full Text Available This study examines the water productivity of irrigated wheat and maize yields in Karkheh River Basin (KRB in the semi-arid region of Iran using a coupled modeling approach consisting of the hydrological model (SWAT and the river basin water allocation model (MODSIM. Dynamic irrigation requirements instead of constant time series of demand were considered. As the cereal production of KRB plays a major role in supplying the food market of Iran, it is necessary to understand the crop yield-water relations for irrigated wheat and maize in the lower part of KRB (LKRB where most of the irrigated agricultural plains are located. Irrigated wheat and maize yields (Y and consumptive water use (AET were modeled with uncertainty analysis at a subbasin level for 1990–2010. Simulated Y and AET were used to calculate crop water productivity (CWP. The coupled SWAT–MODSIM approach improved the accuracy of SWAT outputs by considering the water allocation derived from MODSIM. The results indicated that the highest CWP across this region was 1.31 kg·m−3 and 1.13 kg·m−3 for wheat and maize, respectively; and the lowest was less than 0.62 kg·m−3 and 0.58 kg·m−3. A close linear relationship was found for CWP and yield. The results showed a continuing increase for AET over the years while CWP peaks and then declines. This is evidence of the existence of a plateau in CWP as AET continues to increase and evidence of the fact that higher AET does not necessarily result in a higher yield.

  15. An algorithmic calibration approach to identify globally optimal parameters for constraining the DayCent model

    Energy Technology Data Exchange (ETDEWEB)

    Rafique, Rashid; Kumar, Sandeep; Luo, Yiqi; Kiely, Gerard; Asrar, Ghassem R.

    2015-02-01

    he accurate calibration of complex biogeochemical models is essential for the robust estimation of soil greenhouse gases (GHG) as well as other environmental conditions and parameters that are used in research and policy decisions. DayCent is a popular biogeochemical model used both nationally and internationally for this purpose. Despite DayCent’s popularity, its complex parameter estimation is often based on experts’ knowledge which is somewhat subjective. In this study we used the inverse modelling parameter estimation software (PEST), to calibrate the DayCent model based on sensitivity and identifi- ability analysis. Using previously published N2 O and crop yield data as a basis of our calibration approach, we found that half of the 140 parameters used in this study were the primary drivers of calibration dif- ferences (i.e. the most sensitive) and the remaining parameters could not be identified given the data set and parameter ranges we used in this study. The post calibration results showed improvement over the pre-calibration parameter set based on, a decrease in residual differences 79% for N2O fluxes and 84% for crop yield, and an increase in coefficient of determination 63% for N2O fluxes and 72% for corn yield. The results of our study suggest that future studies need to better characterize germination tem- perature, number of degree-days and temperature dependency of plant growth; these processes were highly sensitive and could not be adequately constrained by the data used in our study. Furthermore, the sensitivity and identifiability analysis was helpful in providing deeper insight for important processes and associated parameters that can lead to further improvement in calibration of DayCent model.

  16. Comparing estimates of climate change impacts from process-based and statistical crop models

    Science.gov (United States)

    Lobell, David B.; Asseng, Senthold

    2017-01-01

    The potential impacts of climate change on crop productivity are of widespread interest to those concerned with addressing climate change and improving global food security. Two common approaches to assess these impacts are process-based simulation models, which attempt to represent key dynamic processes affecting crop yields, and statistical models, which estimate functional relationships between historical observations of weather and yields. Examples of both approaches are increasingly found in the scientific literature, although often published in different disciplinary journals. Here we compare published sensitivities to changes in temperature, precipitation, carbon dioxide (CO2), and ozone from each approach for the subset of crops, locations, and climate scenarios for which both have been applied. Despite a common perception that statistical models are more pessimistic, we find no systematic differences between the predicted sensitivities to warming from process-based and statistical models up to +2 °C, with limited evidence at higher levels of warming. For precipitation, there are many reasons why estimates could be expected to differ, but few estimates exist to develop robust comparisons, and precipitation changes are rarely the dominant factor for predicting impacts given the prominent role of temperature, CO2, and ozone changes. A common difference between process-based and statistical studies is that the former tend to include the effects of CO2 increases that accompany warming, whereas statistical models typically do not. Major needs moving forward include incorporating CO2 effects into statistical studies, improving both approaches’ treatment of ozone, and increasing the use of both methods within the same study. At the same time, those who fund or use crop model projections should understand that in the short-term, both approaches when done well are likely to provide similar estimates of warming impacts, with statistical models generally

  17. Thermal bidirectional gap probability model for row crop canopies and validation

    Institute of Scientific and Technical Information of China (English)

    YAN; Guangjian(阎广建); IANG; Lingmei(蒋玲梅); WANG; Jindi(王锦地); CHEN; Liangfu(陈良富); LI; Xiaowen(李小文)

    2003-01-01

    Based on the row structure model of Kimes and the mean gap probability model in single direction, we develop a bidirectional gap probability model for row crop canopies. A concept of overlap index is introduced in this model to consider the gaps and their correlation between the sun and view directions. Multiangular thermal emission data sets were measured in Shunyi, Beijing, and these data are used in model validation in this paper. By comparison with the Kimes model that does not consider the gap probability, and the model considering the gap in view direction only, it is found that our bidirectional gap probability model fits the field measurements over winter wheat much better.

  18. Assessing the agricultural costs of climate change: Combining results from crop and economic models

    Science.gov (United States)

    Howitt, R. E.

    2016-12-01

    Any perturbation to a resource system used by humans elicits both technical and behavioral changes. For agricultural production, economic criteria and their associated models are usually good predictors of human behavior in agricultural production. Estimation of the agricultural costs of climate change requires careful downscaling of global climate models to the level of agricultural regions. Plant growth models for the dominant crops are required to accurately show the full range of trade-offs and adaptation mechanisms needed to minimize the cost of climate change. Faced with the shifts in the fundamental resource base of agriculture, human behavior can either exacerbate or offset the impact of climate change on agriculture. In addition, agriculture can be an important source of increased carbon sequestration. However the effectiveness and timing of this sequestration depends on agricultural practices and farmer behavior. Plant growth models and economic models have been shown to interact in two broad fashions. First there is the direct embedding of a parametric representation plant growth simulations in the economic model production function. A second and more general approach is to have plant growth and crop process models interact with economic models as they are simulated. The development of more general wrapper programs that transfer information between models rapidly and efficiently will encourage this approach. However, this method does introduce complications in terms of matching up disparate scales both in time and space between models. Another characteristic behavioral response of agricultural production is the distinction between the intensive margin which considers the quantity of resource, for example fertilizer, used for a given crop, and the extensive margin of adjustment that measures how farmers will adjust their crop proportions in response to climate change. Ideally economic models will measure the response to both these margins of adjustment

  19. Two chaotic global models for cereal crops cycles observed from satellite in northern Morocco.

    Science.gov (United States)

    Mangiarotti, Sylvain; Drapeau, Laurent; Letellier, Christophe

    2014-06-01

    The dynamics underlying cereal crops in the northern region of Morocco is investigated using a global modelling technique applied to a vegetation index time series derived from satellite measurements, namely, the normalized difference vegetation index from 1982 to 2008. Two three-dimensional chaotic global models of reduced size (14-term and 15-term models) are obtained. The model validation is performed by comparing their horizons of predictability with those provided in previous studies. The attractors produced by the two global models have a complex foliated structure-evidenced in a Poincaré section-rending a topological characterization difficult to perform. Thus, the Kaplan-Yorke dimension is estimated from the synthetic data produced by our global models. Our results suggest that cereal crops in the northern Morocco are governed by a weakly dissipative three-dimensional chaotic dynamics.

  20. Quality of modelling for Integrated Crop Management : Issues for discussion

    NARCIS (Netherlands)

    Werf, van der W.; Leeuwis, C.; Rossing, W.A.H.

    1999-01-01

    Models are used in research and extension, to draw together information and to suggest actions. In combination with computers, models constitute frequently used means of information transfer within and among groups of people in agricultural knowledge systems: researchers, farmers, policy makers, and

  1. A spectral directional reflectance model of row crops

    NARCIS (Netherlands)

    Zhao, F.J.; Gu, X.F.; Verhoef, W.; Wang, Q.; Yu, T.; Liu, Q.; Huang, H.A.; Qin, W.; Chen, Liangfu; Zhao, H.

    2010-01-01

    A computationally efficient reflectance model for row planted canopies is developed in this paper through separating the contributions of incident direct and diffuse radiation scattered by row canopies. The row model allows calculating the reflectance spectrum in any given direction for the optical

  2. A spectral directional reflectance model of row crops

    NARCIS (Netherlands)

    Zhao, F.J.; Gu, X.F.; Verhoef, W.; Wang, Q.; Yu, T.; Liu, Q.; Huang, H.A.; Qin, W.; Chen, Liangfu; Zhao, H.

    2010-01-01

    A computationally efficient reflectance model for row planted canopies is developed in this paper through separating the contributions of incident direct and diffuse radiation scattered by row canopies. The row model allows calculating the reflectance spectrum in any given direction for the optical

  3. ORCHIDEE-CROP (v0, a new process based Agro-Land Surface Model: model description and evaluation over Europe

    Directory of Open Access Journals (Sweden)

    X. Wu

    2015-06-01

    Full Text Available The responses of crop functioning to changing climate and atmospheric CO2 concentration ([CO2] could have large effects on food production, and impact carbon, water and energy fluxes, causing feedbacks to climate. To simulate the responses of temperate crops to changing climate and [CO2], accounting for the specific phenology of crops mediated by management practice, we present here the development of a process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0, which integrates a generic crop phenology and harvest module and a very simple parameterization of nitrogen fertilization, into the land surface model (LSM ORCHIDEEv196, in order to simulate biophysical and biochemical interactions in croplands, as well as plant productivity and harvested yield. The model is applicable for a range of temperate crops, but it is tested here for maize and winter wheat, with the phenological parameterizations of two European varieties originating from the STICS agronomical model. We evaluate the ORCHIDEE-CROP (v0 model against eddy covariance and biometric measurements at 7 winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are CO2 fluxes (NEE, latent heat and sensible heat fluxes. Additional measurements of leaf area index (LAI, aboveground biomass and yield are used as well. Evaluation results reveal that ORCHIDEE-CROP (v0 reproduces the observed timing of crop development stages and the amplitude of pertaining LAI changes in contrast to ORCHIDEEv196 in which by default crops have the same phenology than grass. A near-halving of the root mean square error of LAI from 2.38 ± 0.77 to 1.08 ± 0.34 m2 m−2 is obtained between ORCHIDEEv196 and ORCHIDEE-CROP (v0 across the 7 study sites. Improved crop phenology and carbon allocation lead to a general good match between modelled and observed aboveground biomass (with a normalized root mean squared error (NRMSE of 11.0–54.2 %, crop yield, as well as

  4. ORCHIDEE-CROP (v0), a new process based Agro-Land Surface Model: model description and evaluation over Europe

    Science.gov (United States)

    Wu, X.; Vuichard, N.; Ciais, P.; Viovy, N.; de Noblet-Ducoudré, N.; Wang, X.; Magliulo, V.; Wattenbach, M.; Vitale, L.; Di Tommasi, P.; Moors, E. J.; Jans, W.; Elbers, J.; Ceschia, E.; Tallec, T.; Bernhofer, C.; Grünwald, T.; Moureaux, C.; Manise, T.; Ligne, A.; Cellier, P.; Loubet, B.; Larmanou, E.; Ripoche, D.

    2015-06-01

    The responses of crop functioning to changing climate and atmospheric CO2 concentration ([CO2]) could have large effects on food production, and impact carbon, water and energy fluxes, causing feedbacks to climate. To simulate the responses of temperate crops to changing climate and [CO2], accounting for the specific phenology of crops mediated by management practice, we present here the development of a process-oriented terrestrial biogeochemical model named ORCHIDEE-CROP (v0), which integrates a generic crop phenology and harvest module and a very simple parameterization of nitrogen fertilization, into the land surface model (LSM) ORCHIDEEv196, in order to simulate biophysical and biochemical interactions in croplands, as well as plant productivity and harvested yield. The model is applicable for a range of temperate crops, but it is tested here for maize and winter wheat, with the phenological parameterizations of two European varieties originating from the STICS agronomical model. We evaluate the ORCHIDEE-CROP (v0) model against eddy covariance and biometric measurements at 7 winter wheat and maize sites in Europe. The specific ecosystem variables used in the evaluation are CO2 fluxes (NEE), latent heat and sensible heat fluxes. Additional measurements of leaf area index (LAI), aboveground biomass and yield are used as well. Evaluation results reveal that ORCHIDEE-CROP (v0) reproduces the observed timing of crop development stages and the amplitude of pertaining LAI changes in contrast to ORCHIDEEv196 in which by default crops have the same phenology than grass. A near-halving of the root mean square error of LAI from 2.38 ± 0.77 to 1.08 ± 0.34 m2 m-2 is obtained between ORCHIDEEv196 and ORCHIDEE-CROP (v0) across the 7 study sites. Improved crop phenology and carbon allocation lead to a general good match between modelled and observed aboveground biomass (with a normalized root mean squared error (NRMSE) of 11.0-54.2 %), crop yield, as well as of the daily

  5. Modeling Soil Organic Matter Dynamics Under Intensive Cropping Systems on the Huang-Huai-Hai Plain of China

    Institute of Scientific and Technical Information of China (English)

    LEI Hong-Jun; LI Bao-Guo; BAI You-Lu; HUANG Yuan-Fang; L(U) Yi-Zhong; LI Gui-Tong

    2006-01-01

    A modified CQESTR model, a simple yet useful model frequently used for estimating carbon sequestration in agricultural soils, was developed and applied to evaluate the effects of intensive cropping on soil organic matter (SOM)dynamics and mineralization as well as to estimate carbon dioxide emission from agricultural soils at seven sites on the Huang-Huai-Hai Plain of China. The model was modified using site-specific parameters from short- and mid-term buried organic material experiments at four stages of biomass decomposition. The predicted SOM results were validated using independent data from seven long-term (10- to 20-year) soil fertility experiments in this region. Regression analysis on1151 pairs of predicted and measured SOM data had an r2 of 0.91 (P ≤ 0.01). Therefore, the modified model was able to predict the mineralization of crop residues, organic amendments, and native SOM. Linear regression also showed that SOM mineralization rate (MR) in the plow layer increased by 0.22% when annual crop yield increased by 1 t ha-1 (P ≤ 0.01),suggesting an improvement in SOM quality. Apparently, not only did the annual soil respiration efflux merely reflect the intensity of soil organism and plant metabolism, but also the SOM MR in the plow layer. These results suggested that the modified model was simple yet valuable in predicting SOM trends at a single agricultural field and could be a powerful tool for estimating C-storage potential and reconstructing C storage on the Huang-Huai-Hai Plain of China.

  6. Comparison of three weather generators for crop modeling: a case study for subtropical environments

    NARCIS (Netherlands)

    Hartkamp, A.D.; White, J.W.; Hoogenboom, G.

    2003-01-01

    The use and application of decision support systems (DDS) that consider variation in climate and soil conditions has expanded in recent years. Most of these DSS are based on crop simulation models that require daily weather data, so access to weather data, at single sites as well as large amount of

  7. Coupling AVHRR imagery with biogeochemical models of methane emission from rice crops

    Science.gov (United States)

    Paliouras, Eleni Joyce

    2000-10-01

    Rice is a staple food source for much of the world and most of it is grown in paddies which remain flooded for a large part of the growing season. This anaerobic environment is ideal for the activities of methanogenic bacteria, that are responsible for the production of methane gas, some of which is released into the atmosphere. In order to better understand the role that rice cropping plays in the levels of atmospheric methane, several models have been developed to predict the methane flux from the paddies. These models generally utilize some type of nominal plant growth curve based on one or two pieces of ground truth data. Ideally, satellite data could be used instead to provide these models with an estimate of biomass change over the growing season, eliminating the need for related ground truth. A technique proposed to accomplish this is presented here, and results that demonstrate its success when applied to rice cropping areas of Texas are discussed. Also presented is a method for utilizing satellite data to map rice cropping areas that could eventually aid in a scheme for populating a GIS-type database with information on exact rice cropping areas. Such a database could then be directly tied to the methane emission models to obtain flux estimates for extensive regional areas.

  8. Principles of crop modeling and simulation: I. uses of mathematical models in agricultural science

    Directory of Open Access Journals (Sweden)

    Dourado-Neto D.

    1998-01-01

    Full Text Available Modeling techniques applied to agriculture can be useful to define research priorities and understanding the basic interactions of the soil-plant-atmosphere system. Using a model to estimate the importance and the effect of certain parameters, a researcher can notice which factors can be most useful. The modeler should define his objectives before beginning his work and construct a model that fulfills the proposed objectives.

  9. Modeling Root Growth, Crop Growth and N Uptake of Winter Wheat Based on SWMS_2D: Model and Validation

    Directory of Open Access Journals (Sweden)

    Dejun Yang

    Full Text Available ABSTRACT Simulations for root growth, crop growth, and N uptake in agro-hydrological models are of significant concern to researchers. SWMS_2D is one of the most widely used physical hydrologically related models. This model solves equations that govern soil-water movement by the finite element method, and has a public access source code. Incorporating key agricultural components into the SWMS_2D model is of practical importance, especially for modeling some critical cereal crops such as winter wheat. We added root growth, crop growth, and N uptake modules into SWMS_2D. The root growth model had two sub-models, one for root penetration and the other for root length distribution. The crop growth model used was adapted from EU-ROTATE_N, linked to the N uptake model. Soil-water limitation, nitrogen limitation, and temperature effects were all considered in dry-weight modeling. Field experiments for winter wheat in Bouwing, the Netherlands, in 1983-1984 were selected for validation. Good agreements were achieved between simulations and measurements, including soil water content at different depths, normalized root length distribution, dry weight and nitrogen uptake. This indicated that the proposed new modules used in the SWMS_2D model are robust and reliable. In the future, more rigorous validation should be carried out, ideally under 2D situations, and attention should be paid to improve some modules, including the module simulating soil N mineralization.

  10. Assessment of AquaCrop model in the simulation of durum wheat (Triticum aestivum L. growth and yield under different water regimes in Tadla- Morocco

    Directory of Open Access Journals (Sweden)

    Bassou BOUAZZAM

    2017-09-01

    Full Text Available Simulation models that clarify the effects of water on crop yield are useful tools for improving farm level water management and optimizing water use efficiency. In this study, AquaCrop was evaluated for Karim genotype which is the main durum winter wheat (Triticum aestivum L. practiced in Tadla. AquaCrop is based on the water-driven growth module, in that transpiration is converted into biomass through a water productivity parameter. The model was calibrated on data from a full irrigation treatment in 2014/15 and validated on other stressed and unstressed treatments including rain-fed conditions in 2014/15 and 2015/16. Results showed that the model provided excellent simulations of canopy cover, biomass and grain yield. Overall, the relationship between observed and modeled wheat grain yield for all treatments combined produced an R2 of 0.79, a mean squared error of 1.01 t ha-1 and an efficiency coefficient of 0.68. The model satisfactory predicted the trend of soil water reserve. Consequently, AquaCrop can be a valuable tool for simulating wheat grain yield in Tadla plain, particularly considering the fact that the model requires a relatively small number of input data. However, the performance of the model has to be fine-tuned under a wider range of conditions.

  11. [Calculation of parameters in forest evapotranspiration model].

    Science.gov (United States)

    Wang, Anzhi; Pei, Tiefan

    2003-12-01

    Forest evapotranspiration is an important component not only in water balance, but also in energy balance. It is a great demand for the development of forest hydrology and forest meteorology to simulate the forest evapotranspiration accurately, which is also a theoretical basis for the management and utilization of water resources and forest ecosystem. Taking the broadleaved Korean pine forest on Changbai Mountain as an example, this paper constructed a mechanism model for estimating forest evapotranspiration, based on the aerodynamic principle and energy balance equation. Using the data measured by the Routine Meteorological Measurement System and Open-Path Eddy Covariance Measurement System mounted on the tower in the broadleaved Korean pine forest, the parameters displacement height d, stability functions for momentum phi m, and stability functions for heat phi h were ascertained. The displacement height of the study site was equal to 17.8 m, near to the mean canopy height, and the functions of phi m and phi h changing with gradient Richarson number R i were constructed.

  12. Parameterization of the InVEST Crop Pollination Model to spatially predict abundance of wild blueberry (Vaccinium angustifolium Aiton) native bee pollinators in Maine, USA

    Science.gov (United States)

    Groff, Shannon C.; Loftin, Cynthia S.; Drummond, Frank; Bushmann, Sara; McGill, Brian J.

    2016-01-01

    Non-native honeybees historically have been managed for crop pollination, however, recent population declines draw attention to pollination services provided by native bees. We applied the InVEST Crop Pollination model, developed to predict native bee abundance from habitat resources, in Maine's wild blueberry crop landscape. We evaluated model performance with parameters informed by four approaches: 1) expert opinion; 2) sensitivity analysis; 3) sensitivity analysis informed model optimization; and, 4) simulated annealing (uninformed) model optimization. Uninformed optimization improved model performance by 29% compared to expert opinion-informed model, while sensitivity-analysis informed optimization improved model performance by 54%. This suggests that expert opinion may not result in the best parameter values for the InVEST model. The proportion of deciduous/mixed forest within 2000 m of a blueberry field also reliably predicted native bee abundance in blueberry fields, however, the InVEST model provides an efficient tool to estimate bee abundance beyond the field perimeter.

  13. Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users.

    Science.gov (United States)

    Calera, Alfonso; Campos, Isidro; Osann, Anna; D'Urso, Guido; Menenti, Massimo

    2017-05-11

    The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e., professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies (Geographic Information Systems based on web technologies). This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools, which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.

  14. Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users

    Science.gov (United States)

    Calera, Alfonso; Campos, Isidro; Osann, Anna; D’Urso, Guido; Menenti, Massimo

    2017-01-01

    The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e., professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies (Geographic Information Systems based on web technologies). This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools, which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools. PMID:28492515

  15. Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users

    Directory of Open Access Journals (Sweden)

    Alfonso Calera

    2017-05-01

    Full Text Available The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e., professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies (Geographic Information Systems based on web technologies. This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools, which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.

  16. A review on statistical models for identifying climate contributions to crop yields

    Institute of Scientific and Technical Information of China (English)

    SHI Wenjiao; TAO Fulu; ZHANG Zhao

    2013-01-01

    Statistical models using historical data on crop yields and weather to calibrate relatively simple regression equations have been widely and extensively applied in previous studies,and have provided a common alternative to process-based models,which require extensive input data on cultivar,management,and soil conditions.However,very few studies had been conducted to review systematically the previous statistical models for indentifying climate contributions to crop yields.This paper introduces three main statistical methods,i.e.,time-series model,cross-section model and panel model,which have been used to identify such issues in the field of agrometeorology.Generally,research spatial scale could be categorized into two types using statistical models,including site scale and regional scale (e.g.global scale,national scale,provincial scale and county scale).Four issues exist in identifying response sensitivity of crop yields to climate change by statistical models.The issues include the extent of spatial and temporal scale,non-climatic trend removal,colinearity existing in climate variables and non-consideration of adaptations.Respective resolutions for the above four issues have been put forward in the section of perspective on the future of statistical models finally.

  17. Bayesian inference of baseline fertility and treatment effects via a crop yield-fertility model.

    Directory of Open Access Journals (Sweden)

    Hungyen Chen

    Full Text Available To effectively manage soil fertility, knowledge is needed of how a crop uses nutrients from fertilizer applied to the soil. Soil quality is a combination of biological, chemical and physical properties and is hard to assess directly because of collective and multiple functional effects. In this paper, we focus on the application of these concepts to agriculture. We define the baseline fertility of soil as the level of fertility that a crop can acquire for growth from the soil. With this strict definition, we propose a new crop yield-fertility model that enables quantification of the process of improving baseline fertility and the effects of treatments solely from the time series of crop yields. The model was modified from Michaelis-Menten kinetics and measured the additional effects of the treatments given the baseline fertility. Using more than 30 years of experimental data, we used the Bayesian framework to estimate the improvements in baseline fertility and the effects of fertilizer and farmyard manure (FYM on maize (Zea mays, barley (Hordeum vulgare, and soybean (Glycine max yields. Fertilizer contributed the most to the barley yield and FYM contributed the most to the soybean yield among the three crops. The baseline fertility of the subsurface soil was very low for maize and barley prior to fertilization. In contrast, the baseline fertility in this soil approximated half-saturated fertility for the soybean crop. The long-term soil fertility was increased by adding FYM, but the effect of FYM addition was reduced by the addition of fertilizer. Our results provide evidence that long-term soil fertility under continuous farming was maintained, or increased, by the application of natural nutrients compared with the application of synthetic fertilizer.

  18. Modelling the dissipation and leaching of two herbicides in decomposing mulch of crop residues

    Science.gov (United States)

    Aslam, Sohaib; Iqbal, Akhtar; Lafolie, François; Recous, Sylvie; Benoit, Pierre; Garnier, Patricia

    2013-04-01

    Conservation agricultural practices are increasingly adopted because of ecosystem services such as conservation of soil and water resources. These farming systems are characterized mainly by the presence of mulch made of residues of harvested or cover crops on soil surface. The mulch can intercept and retain applied pesticides depending on pesticide molecule and rainfall timing. The pesticide wash-off from mulch is considered a key process in pesticide fate and can have effects on degradation and transport processes. This work highlights a modelling approach to study the pesticide wash-off from mulch residues and their further transport in soil under two rainfall regimes. Transformation and leaching of two herbicides, s-metolachlor and glyphosate, was studied and simulated by Pastis-mulch model. A pesticide module describing pesticide degradation in mulch and soil was coupled to a transport model including a mulch module. The model was tested to simulate the pesticide dissipation, wash-off from mulch and further leaching in soil. Pesticide degradation parameters in mulch were estimated from incubation experiments with 14C-labelled molecules in small cylinders. The model was then tested using the data obtained through a soil column experiment (reconstructed soil cores :15 cm diameter x 35 cm depth), a mulch of Zea mais + Doliquos lablab and with two treatments varied by water regimes: i) frequent rain (temperate, twice a week) with week intensity (6 mm/hr); and ii) occasional rain (tropical, twice a month) with stronger intensity (20 mm/hr). Columns were incubated at 20 °C for 84 days to monitor soil water, C, N and pesticide dynamics. Model successfully simulated the experimental data of pesticide dissipation in mulch residues. Results showed that the rain regime affected more S-metolachlor than glyphosate behavior. The simulated results indicated also that the dynamics in mulch of the two molecules differed according to the rain treatment. Glyphosate showed a

  19. Developing High-resolution Soil Database for Regional Crop Modeling in East Africa

    Science.gov (United States)

    Han, E.; Ines, A. V. M.

    2014-12-01

    The most readily available soil data for regional crop modeling in Africa is the World Inventory of Soil Emission potentials (WISE) dataset, which has 1125 soil profiles for the world, but does not extensively cover countries Ethiopia, Kenya, Uganda and Tanzania in East Africa. Another dataset available is the HC27 (Harvest Choice by IFPRI) in a gridded format (10km) but composed of generic soil profiles based on only three criteria (texture, rooting depth, and organic carbon content). In this paper, we present a development and application of a high-resolution (1km), gridded soil database for regional crop modeling in East Africa. Basic soil information is extracted from Africa Soil Information Service (AfSIS), which provides essential soil properties (bulk density, soil organic carbon, soil PH and percentages of sand, silt and clay) for 6 different standardized soil layers (5, 15, 30, 60, 100 and 200 cm) in 1km resolution. Soil hydraulic properties (e.g., field capacity and wilting point) are derived from the AfSIS soil dataset using well-proven pedo-transfer functions and are customized for DSSAT-CSM soil data requirements. The crop model is used to evaluate crop yield forecasts using the new high resolution soil database and compared with WISE and HC27. In this paper we will present also the results of DSSAT loosely coupled with a hydrologic model (VIC) to assimilate root-zone soil moisture. Creating a grid-based soil database, which provides a consistent soil input for two different models (DSSAT and VIC) is a critical part of this work. The created soil database is expected to contribute to future applications of DSSAT crop simulation in East Africa where food security is highly vulnerable.

  20. Modeling the Impacts of Soil Organic Carbon Content of Croplands on Crop Yields in China

    Institute of Scientific and Technical Information of China (English)

    QIU Jian-jun; WANG Li-gang; LI Hu; TANG Hua-jun; LI Chang-sheng; Eric Van Ranst

    2009-01-01

    This study quantified the impacts of soil organic carbon(SOC)content on the grain yield of crops using a biogeochemical model(DNDC,denitrification-decomposition).Data on climate,soil properties,and farming management regimes of cropping systems were collected from six typical agricultural zones(northeast,north,northwest,mid-south,east and southwest regions of China,respectively)and integrated into a GIS database to support the model runs.According to the model,if the initial SOC content in the cropland was increased by 1g C kg-1,the crop yield may be increased by 176 kg ha-1 for maize in the northeast region,454 kg ha-1 for a maize-wheat rotation in the north region,328 kg ha-1 for maize in the northwest region,185 kg ha-1 for single-rice in the mid-south region,266 kg ha-1 for double-rice in east region,and 229 kg ha-1 for rice and wheat rotation in southwest region.There is a great potential for enhancing the crop yield by improving the SOC content in each region of China.

  1. Evapotranspiration Modeling by Linear, Nonlinear Regression and Artificial Neural Network in Greenhouse (Case study Reference Crop, Cucumber and Tomato

    Directory of Open Access Journals (Sweden)

    vahid Rezaverdinejad

    2017-01-01

    important models to estimate ETc in greenhouse. The inputs of these models are net radiation, temperature, day after planting and air vapour pressure deficit (or relative humidity. Materials and Methods: In this study, daily ETc of reference crop, greenhouse tomato and cucumber crops were measured using lysimeter method in Urmia region. Several linear, nonlinear regressions and artificial neural networks were considered for ETc modelling in greenhouse. For this purpose, the effective meteorological parameters on ETc process includes: air temperature (T, air humidity (RH, air pressure (P, air vapour pressure deficit (VPD, day after planting (N and greenhouse net radiation (SR were considered and measured. According to the goodness of fit, different models of artificial neural networks and regression were compared and evaluated. Furthermore, based on partial derivatives of regression models, sensitivity analysis was conducted. The accuracy and performance of the employed models was judged by ten statistical indices namely root mean square error (RMSE, normalized root mean square error (NRMSE and coefficient of determination (R2. Results and Discussion: Based on the results, the most accurate regression model to reference ETc prediction was obtained three variables exponential function of VPD, RH and SR with RMSE=0.378 mm day-1. The RMSE of optimal artificial neural network to reference ET prediction for train and test data sets were obtained 0.089 and 0.365 mm day-1, respectively. The performance of logarithmic and exponential functions to prediction of cucumber ETc were proper, with high dependent variables especially, and the most accurate regression model to cucumber ET prediction was obtained for exponential function of five variables: VPD, N, T, RH and SR with RMSE=0.353 mm day-1. In addition, for tomato ET prediction, the most accurate regression model was obtained for exponential function of four variables: VPD, N, RH and SR with RMSE= 0.329 mm day-1. The best

  2. Can increased leaf photosynthesis be converted into higher crop mass production? A simulation study for rice using the crop model GECROS.

    Science.gov (United States)

    Yin, Xinyou; Struik, Paul C

    2017-04-01

    Various genetic engineering routes to enhance C3 leaf photosynthesis have been proposed to improve crop productivity. However, their potential contribution to crop productivity needs to be assessed under realistic field conditions. Using 31 year weather data, we ran the crop model GECROS for rice in tropical, subtropical, and temperate environments, to evaluate the following routes: (1) improving mesophyll conductance (gm); (2) improving Rubisco specificity (Sc/o); (3) improving both gm and Sc/o; (4) introducing C4 biochemistry; (5) introducing C4 Kranz anatomy that effectively minimizes CO2 leakage; (6) engineering the complete C4 mechanism; (7) engineering cyanobacterial bicarbonate transporters; (8) engineering a more elaborate cyanobacterial CO2-concentrating mechanism (CCM) with the carboxysome in the chloroplast; and (9) a mechanism that combines the low ATP cost of the cyanobacterial CCM and the high photosynthetic capacity per unit leaf nitrogen. All routes improved crop mass production, but benefits from Routes 1, 2, and 7 were ≤10%. Benefits were higher in the presence than in the absence of drought, and under the present climate than for the climate predicted for 2050. Simulated crop mass differences resulted not only from the increased canopy photosynthesis competence but also from changes in traits such as light interception and crop senescence. The route combinations gave larger effects than the sum of the effects of the single routes, but only Route 9 could bring an advantage of ≥50% under any environmental conditions. To supercharge crop productivity, exploring a combination of routes in improving the CCM, photosynthetic capacity, and quantum efficiency is required. © The Author 2017. Published by Oxford University Press on behalf of the Society for Experimental Biology.

  3. Can increased leaf photosynthesis be converted into higher crop mass production? A simulation study for rice using the crop model GECROS

    Science.gov (United States)

    Struik, Paul C.

    2017-01-01

    Abstract Various genetic engineering routes to enhance C3 leaf photosynthesis have been proposed to improve crop productivity. However, their potential contribution to crop productivity needs to be assessed under realistic field conditions. Using 31 year weather data, we ran the crop model GECROS for rice in tropical, subtropical, and temperate environments, to evaluate the following routes: (1) improving mesophyll conductance (gm); (2) improving Rubisco specificity (Sc/o); (3) improving both gm and Sc/o; (4) introducing C4 biochemistry; (5) introducing C4 Kranz anatomy that effectively minimizes CO2 leakage; (6) engineering the complete C4 mechanism; (7) engineering cyanobacterial bicarbonate transporters; (8) engineering a more elaborate cyanobacterial CO2-concentrating mechanism (CCM) with the carboxysome in the chloroplast; and (9) a mechanism that combines the low ATP cost of the cyanobacterial CCM and the high photosynthetic capacity per unit leaf nitrogen. All routes improved crop mass production, but benefits from Routes 1, 2, and 7 were ≤10%. Benefits were higher in the presence than in the absence of drought, and under the present climate than for the climate predicted for 2050. Simulated crop mass differences resulted not only from the increased canopy photosynthesis competence but also from changes in traits such as light interception and crop senescence. The route combinations gave larger effects than the sum of the effects of the single routes, but only Route 9 could bring an advantage of ≥50% under any environmental conditions. To supercharge crop productivity, exploring a combination of routes in improving the CCM, photosynthetic capacity, and quantum efficiency is required. PMID:28379522

  4. Environmental risk assessment of blight-resistant potato: use of a crop model to quantify nitrogen cycling at scales of the field and cropping system.

    Science.gov (United States)

    Young, Mark W; Mullins, Ewen; Squire, Geoffrey R

    2017-07-25

    Environmental risk assessment of GM crops in Europe proceeds by step-wise estimation of effect, first in the plant, then the field plot (e.g. 10-100 m(-2)), field (1000-10,000 m(-2)) and lastly in the environment in which the crop would be grown (100-10,000 km(2)). Processes that operate at large scales, such as cycling of carbon (C) and nitrogen (N), are difficult to predict from plot scales. Here, a procedure is illustrated in which plot scale data on yield (offtake) and N inputs for blight resistant (both GM and non-GM) and blight-susceptible potato are upscaled by a model of crop resource use to give a set of indicators and metrics defining N uptake and release in realistic crop sequences. The greatest potential damage to environment is due to loss of N from the field after potato harvest, mainly because of the large quantity of mineral and plant matter, high in N, that may die or be left in the field. Blight infection intensifies this loss, since less fertiliser N is taken up by plants and more (as a proportion of plant mass) is returned to the soil. In a simulation based on actual crop sequences, N returns at harvest of potato were raised from 100 kg ha(-1) in resistant to 150 kg ha(-1) in susceptible varieties subject to a 40% yield loss. Based on estimates that blight-resistant types would require ~20% of the fungicide applied to susceptible types, introduction of resistant types into a realistic 6-year cropping sequence would reduce overall fungicide use to between 72 and 54% depending on the inputs to other crops in the sequence.

  5. Energy technology impacts on agriculture with a bibliography of models for impact assessment on crop ecosystems

    Energy Technology Data Exchange (ETDEWEB)

    Rupp, E.M.; Luxmoore, R.J.; Parzyck, D.C.

    1979-09-01

    Possible impacts of energy technologies on agriculture are evaluated, and some of the available simulation models that can be used for predictive purposes are identified. An overview of energy technologies and impacts on the environment is presented to provide a framework for the commentary on the models. Coal combustion is shown to have major impacts on the environment and these will continue into the next century according to current Department of Energy projections. Air pollution effects will thus remain as the major impacts on crop ecosystems. Two hundred reports were evaluated, representing a wide range of models increasing in complexity from mathematical functions (fitted to data) through parametric models (which represent phenomena without describing the mechanisms) to mechanistic models (based on physical, chemical, and physiological principles). Many models were viewed as suitable for adaptation to technology assessment through the incorporation of representative dose-response relationships. It is clear that in many cases available models cannot be taken and directly applied in technology assessment. Very few models of air pollutant-crop interactions were identified, even though there is a considerable data base of pollutant effects on crops.

  6. Transfer function modeling of damping mechanisms in distributed parameter models

    Science.gov (United States)

    Slater, J. C.; Inman, D. J.

    1994-01-01

    This work formulates a method for the modeling of material damping characteristics in distributed parameter models which may be easily applied to models such as rod, plate, and beam equations. The general linear boundary value vibration equation is modified to incorporate hysteresis effects represented by complex stiffness using the transfer function approach proposed by Golla and Hughes. The governing characteristic equations are decoupled through separation of variables yielding solutions similar to those of undamped classical theory, allowing solution of the steady state as well as transient response. Example problems and solutions are provided demonstrating the similarity of the solutions to those of the classical theories and transient responses of nonviscous systems.

  7. Editorial: Plant organ abscission: from models to crops

    Science.gov (United States)

    The shedding of plant organs is a highly coordinated process essential for both vegetative and reproductive development (Addicott, 1982; Sexton and Roberts, 1982; Roberts et al., 2002; Leslie et al., 2007; Roberts and Gonzalez-Carranza, 2007; Estornell et al., 2013). Research with model plants, name...

  8. A comprehensively quantitative method of evaluating the impact of drought on crop yield using daily multi-scale SPEI and crop growth process model

    Science.gov (United States)

    Wang, Qianfeng; Wu, Jianjun; Li, Xiaohan; Zhou, Hongkui; Yang, Jianhua; Geng, Guangpo; An, Xueli; Liu, Leizhen; Tang, Zhenghong

    2017-04-01

    The quantitative evaluation of the impact of drought on crop yield is one of the most important aspects in agricultural water resource management. To assess the impact of drought on wheat yield, the Environmental Policy Integrated Climate (EPIC) crop growth model and daily Standardized Precipitation Evapotranspiration Index (SPEI), which is based on daily meteorological data, are adopted in the Huang Huai Hai Plain. The winter wheat crop yields are estimated at 28 stations, after calibrating the cultivar coefficients based on the experimental site data, and SPEI data was taken 11 times across the growth season from 1981 to 2010. The relationship between estimated yield and multi-scale SPEI were analyzed. The optimum time scale SPEI to monitor drought during the crop growth period was determined. The reference yield was determined by averaging the yields from numerous non-drought years. From this data, we propose a comprehensive quantitative method which can be used to predict the impact of drought on wheat yields by combining the daily multi-scale SPEI and crop growth process model. This method was tested in the Huang Huai Hai Plain. The results suggested that estimation of calibrated EPIC was a good predictor of crop yield in the Huang Huai Hai Plain, with lower RMSE (15.4 %) between estimated yield and observed yield at six agrometeorological stations. The soil moisture at planting time was affected by the precipitation and evapotranspiration during the previous 90 days (about 3 months) in the Huang Huai Hai Plain. SPEIG90 was adopted as the optimum time scale SPEI to identify the drought and non-drought years, and identified a drought year in 2000. The water deficit in the year 2000 was significant, and the rate of crop yield reduction did not completely correspond with the volume of water deficit. Our proposed comprehensive method which quantitatively evaluates the impact of drought on crop yield is reliable. The results of this study further our understanding

  9. A comprehensively quantitative method of evaluating the impact of drought on crop yield using daily multi-scale SPEI and crop growth process model

    Science.gov (United States)

    Wang, Qianfeng; Wu, Jianjun; Li, Xiaohan; Zhou, Hongkui; Yang, Jianhua; Geng, Guangpo; An, Xueli; Liu, Leizhen; Tang, Zhenghong

    2016-11-01

    The quantitative evaluation of the impact of drought on crop yield is one of the most important aspects in agricultural water resource management. To assess the impact of drought on wheat yield, the Environmental Policy Integrated Climate (EPIC) crop growth model and daily Standardized Precipitation Evapotranspiration Index (SPEI), which is based on daily meteorological data, are adopted in the Huang Huai Hai Plain. The winter wheat crop yields are estimated at 28 stations, after calibrating the cultivar coefficients based on the experimental site data, and SPEI data was taken 11 times across the growth season from 1981 to 2010. The relationship between estimated yield and multi-scale SPEI were analyzed. The optimum time scale SPEI to monitor drought during the crop growth period was determined. The reference yield was determined by averaging the yields from numerous non-drought years. From this data, we propose a comprehensive quantitative method which can be used to predict the impact of drought on wheat yields by combining the daily multi-scale SPEI and crop growth process model. This method was tested in the Huang Huai Hai Plain. The results suggested that estimation of calibrated EPIC was a good predictor of crop yield in the Huang Huai Hai Plain, with lower RMSE (15.4 %) between estimated yield and observed yield at six agrometeorological stations. The soil moisture at planting time was affected by the precipitation and evapotranspiration during the previous 90 days (about 3 months) in the Huang Huai Hai Plain. SPEIG90 was adopted as the optimum time scale SPEI to identify the drought and non-drought years, and identified a drought year in 2000. The water deficit in the year 2000 was significant, and the rate of crop yield reduction did not completely correspond with the volume of water deficit. Our proposed comprehensive method which quantitatively evaluates the impact of drought on crop yield is reliable. The results of this study further our understanding

  10. Reconciling the Mitscherlich's law of diminishing returns with Liebig's law of the minimum. Some results on crop modeling.

    Science.gov (United States)

    Ferreira, Iuri E P; Zocchi, Silvio S; Baron, Daniel

    2017-08-26

    Reliable fertilizer recommendations depend on the correctness of the crop production models fitted to the data, but generally the crop models are built empirically, neglecting important physiological aspects related with response to fertilizers, or they are based in laws of plant mineral nutrition seen by many authors as conflicting theories: the Liebig's Law of the Minimum and Mitscherlich's Law of Diminishing Returns. We developed a new approach to modelling the crop response to fertilizers that reconcile these laws. In this study, the Liebig's Law is applied at the cellular level to explain plant production and, as a result, crop models compatible with the Law of Diminishing Returns are derived. Some classical crop models appear here as special cases of our methodology, and a new interpretation for Mitscherlich's Law is also provided. Copyright © 2017 Elsevier Inc. All rights reserved.

  11. Simulation of Rainfed Wheat Yield using AquaCrop Model, Case Study: Sisab Rainfed Researches Station, Northern Khorasan

    Directory of Open Access Journals (Sweden)

    N. Khalili

    2015-06-01

    Full Text Available Modeling of crop growth plays an important role in evaluation of drought impacts on rainfed yield, choosing an optimum sowing date, and managerial decision-makings. Aquacrop model is a new crop model that developed by Food and Agriculture Organization (FAO, that is a model for simulation of crop yield based on “yield response to water“ with meteorological, crop, soli and management practices data as inputs. This model has to be calibrated and validated for each crop species and each location. In this paper, the Aquacrop has been calibrated and evaluated for rainfed wheat in Sisab station (Northern Khorasan. For this purpose, daily meteorological data and historical yield data from two cropping season (2007-2008 and 2008-2009 in the Sisab station have been used to calibrate this model. Next, meteorological data and historical yield data of five cropping season (2002-2003 to 2006-2007 are used to validate the model. The result shows that the Aqucrop can accurately predict crop yield as R2, RMSE, NRMSE, ME, and D-Index are achieved 0.86, 0.062, 5.235, 0.917 and 0.877, respectively.

  12. On the modeling of internal parameters in hyperelastic biological materials

    CERN Document Server

    Giantesio, Giulia

    2016-01-01

    This paper concerns the behavior of hyperelastic energies depending on an internal parameter. First, the situation in which the internal parameter is a function of the gradient of the deformation is presented. Second, two models where the parameter describes the activation of skeletal muscle tissue are analyzed. In those models, the activation parameter depends on the strain and it is important to consider the derivative of the parameter with respect to the strain in order to capture the proper behavior of the stress.

  13. Determining extreme parameter correlation in ground water models

    DEFF Research Database (Denmark)

    Hill, Mary Cole; Østerby, Ole

    2003-01-01

    In ground water flow system models with hydraulic-head observations but without significant imposed or observed flows, extreme parameter correlation generally exists. As a result, hydraulic conductivity and recharge parameters cannot be uniquely estimated. In complicated problems, such correlation...... correlation coefficients, but it required sensitivities that were one to two significant digits less accurate than those that required using parameter correlation coefficients; and (3) both the SVD and parameter correlation coefficients identified extremely correlated parameters better when the parameters...

  14. Effect of the time of application of phosphorus fertilizer on yield and quality parameters of melon crop amended with winery waste compost.

    Science.gov (United States)

    Requejo Mariscal, María Isabel; Cartagena, María Carmen; Villena Gordo, Raquel; Arce Martínez, Augusto; Ribas Elcorobarrutia, Francisco; Jesús Cabello Cabello, María; Castellanos Serrano, María Teresa

    2016-04-01

    In Spain, drip irrigation systems are widely used for horticultural crop production. In drip irrigation systems, emitter clogging has been identified as one of the most important concerns. Clogging is closely related to the quality of the irrigation water and the structure of the emitter flow path, and occurs as a result of multiple physical, biological and chemical factors. So, the use of acid fertilizers (e.g. phosphoric acid) in these systems is common to avoid the emitter clogging. Moreover, in this country the use of exhausted grape marc compost as source of nutrients and organic matter has been identified as a good management option of soil fertility, especially in grape-growing areas with a large generation of wastes from the wine and distillery industries. The purpose of this work was to study the effect of the time of application of phosphorus fertilizer with fertirrigation in a melon crop amended with winery waste compost on yield and quality parameters. During two years, the melon crop was grown under field conditions and beside the control treatment, three doses of compost were applied: 6.7, 13.3 and 20.0 t ha-1. All the compost treatments received 120 kg ha-1 of phosphorus fertilizer (phosphoric acid) for the season varying the time of application: The first year phosphorus application started after male and female flowering, and the second year the application started before flowering. Yield and quality parameters were evaluated to assess the suitability of these practices. Acknowledgements: This project has been supported by INIA-RTA2010-00110-C03. Keywords: Phosphorus fertilizer, exhausted grape marc compost, melon crop, yield and quality parameters.

  15. Modelling the perennial energy crop market: the role of spatial diffusion.

    Science.gov (United States)

    Alexander, Peter; Moran, Dominic; Rounsevell, Mark D A; Smith, Pete

    2013-11-06

    Biomass produced from energy crops, such as Miscanthus and short rotation coppice is expected to contribute to renewable energy targets, but the slower than anticipated development of the UK market implies the need for greater understanding of the factors that govern adoption. Here, we apply an agent-based model of the UK perennial energy crop market, including the contingent interaction of supply and demand, to understand the spatial and temporal dynamics of energy crop adoption. Results indicate that perennial energy crop supply will be between six and nine times lower than previously published, because of time lags in adoption arising from a spatial diffusion process. The model simulates time lags of at least 20 years, which is supported empirically by the analogue of oilseed rape adoption in the UK from the 1970s. This implies the need to account for time lags arising from spatial diffusion in evaluating land-use change, climate change (mitigation or adaptation) or the adoption of novel technologies.

  16. Crop physiology calibration in CLM

    Directory of Open Access Journals (Sweden)

    I. Bilionis

    2014-10-01

    Full Text Available Farming is using more terrestrial ground, as population increases and agriculture is increasingly used for non-nutritional purposes such as biofuel production. This agricultural expansion exerts an increasing impact on the terrestrial carbon cycle. In order to understand the impact of such processes, the Community Land Model (CLM has been augmented with a CLM-Crop extension that simulates the development of three crop types: maize, soybean, and spring wheat. The CLM-Crop model is a complex system that relies on a suite of parametric inputs that govern plant growth under a given atmospheric forcing and available resources. CLM-Crop development used measurements of gross primary productivity and net ecosystem exchange from AmeriFlux sites to choose parameter values that optimize crop productivity in the model. In this paper we calibrate these parameters for one crop type, soybean, in order to provide a faithful projection in terms of both plant development and net carbon exchange. Calibration is performed in a Bayesian framework by developing a scalable and adaptive scheme based on sequential Monte Carlo (SMC.

  17. Retrieval of spinach crop parameters by microwave remote sensing with back propagation artificial neural networks: A comparison of different transfer functions

    Science.gov (United States)

    Prasad, Rajendra; Pandey, A.; Singh, K. P.; Singh, V. P.; Mishra, R. K.; Singh, D.

    2012-08-01

    Back propagation artificial natural network (BPANN) is a well known and widely used machine learning methodology in the field of remote sensing. In this paper an attempt is made to retrieve the spinach crop parameters like biomass, leaf area index, average plant height and soil moisture content by using the X-band scattering coefficients with BPANN at different growth stages of this crop. The maturity age of this crop was found to be 45 days from the date of sowing. After 45 days from the date of sowing, this crop was cut at a certain height for production. Then, it is a point of interest to investigate the microwave response of variation in production. Significant variations in all the crop parameters were observed after cutting the crop and consequently made the problem more critical. Our work confirms the utility of BPANN in handling such a non-linear data set. The BPANN is essentially a network of simple processing nodes arranged into different layers as input, hidden and the output. The input layer propagates components of a particular input vector after weighting these with synaptic weights to each node in the hidden layer. At each node, these weighted input vector components are added. Each hidden layer computes output corresponding to these weighted sum through a non-linear/linear function (e.g. LOGSIG, TANSIG and PURLIN). These functions are known as transfer functions. Thus, each of the hidden layer nodes compute output values, which become inputs to the nodes of the output layer. At nodes of output layer also a weighted sum of outputs of previous layer (hidden layer) are obtained and processed through a transfer function. Thus, the output layer nodes compute the network output for the particular input vector. In this paper, output nodes use linear transfer function. Different transfer functions e.g. TANSIG, LOGSIG and PURELIN were used and the performance of the ANN was optimized by changing the number of neurons in the hidden layers. The present

  18. Model comparisons and genetic and environmental parameter ...

    African Journals Online (AJOL)

    arc

    South African Journal of Animal Science 2005, 35 (1) ... Genetic and environmental parameters were estimated for pre- and post-weaning average daily gain ..... and BWT (and medium maternal genetic correlations) indicates that these traits ...

  19. Sensitivity analysis of CLIMEX parameters in modeling potential distribution of Phoenix dactylifera L.

    Directory of Open Access Journals (Sweden)

    Farzin Shabani

    Full Text Available Using CLIMEX and the Taguchi Method, a process-based niche model was developed to estimate potential distributions of Phoenix dactylifera L. (date palm, an economically important crop in many counties. Development of the model was based on both its native and invasive distribution and validation was carried out in terms of its extensive distribution in Iran. To identify model parameters having greatest influence on distribution of date palm, a sensitivity analysis was carried out. Changes in suitability were established by mapping of regions where the estimated distribution changed with parameter alterations. This facilitated the assessment of certain areas in Iran where parameter modifications impacted the most, particularly in relation to suitable and highly suitable locations. Parameter sensitivities were also evaluated by the calculation of area changes within the suitable and highly suitable categories. The low temperature limit (DV2, high temperature limit (DV3, upper optimal temperature (SM2 and high soil moisture limit (SM3 had the greatest impact on sensitivity, while other parameters showed relatively less sensitivity or were insensitive to change. For an accurate fit in species distribution models, highly sensitive parameters require more extensive research and data collection methods. Results of this study demonstrate a more cost effective method for developing date palm distribution models, an integral element in species management, and may prove useful for streamlining requirements for data collection in potential distribution modeling for other species as well.

  20. NEW DOCTORAL DEGREE Parameter estimation problem in the Weibull model

    OpenAIRE

    Marković, Darija

    2009-01-01

    In this dissertation we consider the problem of the existence of best parameters in the Weibull model, one of the most widely used statistical models in reliability theory and life data theory. Particular attention is given to a 3-parameter Weibull model. We have listed some of the many applications of this model. We have described some of the classical methods for estimating parameters of the Weibull model, two graphical methods (Weibull probability plot and hazard plot), and two analyt...

  1. Applicability of logistic model and integrated satellite data for rice crop phenology detection

    Science.gov (United States)

    Chen, Chi-Farn; Son, Nguyen-Thanh; Chen, Cheng-Ru; Chang, Ly-Yu; Chiang, Shou-Hao

    2016-04-01

    Changes in climate condition through global warming locally altered climatic and hydrological conditions and likely trigger the increase of insect populations and diseases, causing the potential loss of rice yields. Because the rice fields damaged by diseases or insects may affect neighbouring fields, monitoring the cropping progress was important to provide agronomic planners with valuable information that could be used to timely devise strategies to mitigate possible impacts on the potential yield. This study aimed to develop an approach to monitor rice sowing and harvesting progress from the integrated Moderate Resolution Imaging Spectroradiometer (MODIS)-Landsat satellite data. We processed for the 2007 winter-spring and summer-autumn cropping seasons in 2007, following four main steps: (1) constructing a set of MODIS-Landsat fusion data using the spatiotemporal adaptive reflectance fusion model (STARFM), (2) creating smooth time-series enhanced vegetation 2 (EVI2) data using the commonly-used empirical mode decomposition (EMD), (3) detecting key phenological stages of rice crops the double logistic algorithm, and (4) error verification of the detected sowing and harvesting dates using field data. The comparison results between the EVI2 data derived from the fusion data and that from the Landsat yielded close agreement between these two datasets (R2 > 0.9). The double logistic algorithm applied to the filtered time-series EVI2 data to estimate phenological events of rice crops indicated the validity of our approach for monitoring the progress of sowing and harvesting activities in the region. The results obtained by comparisons between the estimated sowing/ harvesting dates and the field survey data indicated that the root mean squared error (RMSE) values archived for the winter-spring crop were respectively 8.4 and 5.5 days, while those for the summer-autumn crop were 9.4 and 12.8 days, respectively. The results obtained from this study could provide decision

  2. Estimating crop-specific evapotranspiration using remote-sensing imagery at various spatial resolutions for improving crop growth modelling

    NARCIS (Netherlands)

    Sepulcre-Canto, G.; Gellens-Meulenberghs, F.; Arboleda, A.; Duveiller, G.; Wit, de A.J.W.; Eerens, H.; Djaby, B.; Defourny, P.

    2013-01-01

    By governing water transfer between vegetation and atmosphere, evapotranspiration (ET) can have a strong influence on crop yields. An estimation of ET from remote sensing is proposed by the EUMETSAT ‘Satellite Application Facility’ (SAF) on Land Surface Analysis (LSA). This ET product is obtained op

  3. 'Waterstreams': A model for estimation of crop water demand, water supply, salt accumulation and discharge for soilless crops

    NARCIS (Netherlands)

    Voogt, W.; Swinkels, G.L.A.M.; Os, van E.A.

    2012-01-01

    Abstract: Closed growing systems are obligatory for soilless grown greenhouse crops in The Netherlands. It requires water sources of high quality as sodium (Na) accumulation is a potential risk and necessitates frequent discharge, which causes undesirable emission of nutrients and plant protection p

  4. How Do Various Maize Crop Models Vary in Their Responses to Climate Change Factors?

    Science.gov (United States)

    Bassu, Simona; Brisson, Nadine; Grassini, Patricio; Durand, Jean-Louis; Boote, Kenneth; Lizaso, Jon; Jones, James W.; Rosenzweig, Cynthia; Ruane, Alex C.; Adam, Myriam; Baron, Christian; Basso, Bruno; Biernath, Christian; Boogaard, Hendrik; Conijn, Sjaak; Corbeels, Marc; Deryng, Delphine; DeSanctis, Giacomo; Gayler, Sebastian; Grassini, Patricio; Hatfield, Jerry; Hoek, Steven; Izaurralde, Cesar; Jongschaap, Raymond; Kemanian, Armen R.; Kersebaum, K. Christian

    2014-01-01

    Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(sup 1) per degC. Doubling [CO2] from 360 to 720 lmol mol 1 increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2] among models. Model responses to temperature and [CO2] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

  5. DYNAMIC MODEL OF CROP GROWTH SYSTEM AND NUMERICAL SIMULATION OF CROP GROWTH PROCESS UNDER THE MULTI-ENVIRONMENT EXTERNAL FORCE ACTION

    Institute of Scientific and Technical Information of China (English)

    李自珍; 王万雄; 徐彩琳

    2003-01-01

    According to the biomechanic theory and method, the dynamic mechanism of crop growth under the external force action of multi-environment factors (light, temperature, soil and nutrients etc.) was comprehensively explored. Continuous-time Markov (CTM) approach was adopted to build the dynamic model of the crop growth system and the simulated numerical method. The growth rate responses to the variation of the external force and the change of biomass saturation value were studied. The crop grew in the semiarid area was taken as an example to carry out the numerical simulation analysis, therefore the results provide the quantity basis for the field management. Comparing the dynamic model with the other plant growth model, the superiority of the former is that it displays multi-dimension of resource utilization by means of combining macroscopic with microcosmic and reveals the process of resource transition. The simulation method of crop growth system is advanced and manipulated. A real simulation result is well identical with field observational results.

  6. Parameter optimization model in electrical discharge machining process

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Electrical discharge machining (EDM) process, at present is still an experience process, wherein selected parameters are often far from the optimum, and at the same time selecting optimization parameters is costly and time consuming. In this paper,artificial neural network (ANN) and genetic algorithm (GA) are used together to establish the parameter optimization model. An ANN model which adapts Levenberg-Marquardt algorithm has been set up to represent the relationship between material removal rate (MRR) and input parameters, and GA is used to optimize parameters, so that optimization results are obtained. The model is shown to be effective, and MRR is improved using optimized machining parameters.

  7. SVAT modeling of crop physiological response to drought in potatoes under different types of deficit irrigation

    DEFF Research Database (Denmark)

    Plauborg, Finn; Mollerup, Mikkel; Abrahamsen, Per

      Further understanding of the crop physiologic responses to drought caused by deficit irrigation (DI), regular or partial root drying (PRD), have been obtained in several studies in tomatoes and potatoes under controlled environment. The improved quantitative description of the production...... of abscisic acid in the root system and as well as its influence on stomatal regulation of gas exhange has been implemented in the Daisy model, a comprehensive work partly financed by the SAFIR project ( http://www.safir4eu.org/ ). Hence, the improved Daisy model now calculates crop production based on gas...... to include 2D root development, water and nitrogen uptake to enable studies of the effect of PRD/DI on improving water-use- efficiency. The present paper presents the new processes implemented in Daisy, and a comprehensive test of the model against data obtained under field conditions. Preliminary results...

  8. Crop growth and two dimensional modeling of soil water transport in drip irrigated potatoes

    DEFF Research Database (Denmark)

    Plauborg, Finn; Iversen, Bo Vangsø; Mollerup, Mikkel

    2009-01-01

    Drip irrigation can be an effective way to improve water and nitrogen use efficiency in soil and hence to reduce the environmental pollution. In the EU project SAFIR ( http://www.safir4eu.org/ ) a potato experiment was carried out in lysimeters on three different soil types: coarse sand, loamy sand...... of abscisic acid (ABA). Model outputs from the mechanistic simulation model Daisy, in SAFIR developed to include 2D soil processes and gas exchange processes based on Ball et al. and Farquhar were compared with measured crop dynamics, final DM yield and volumetric water content in the soil measured by TDR...... probes. The probes were installed parallel to the tillage direction at different positions in the potato ridge. The new Daisy 2D model showed to be able to simulate crop growth, water use and soil water distribution fairly well...

  9. Species Distribution Models for Crop Pollination: A Modelling Framework Applied to Great Britain

    NARCIS (Netherlands)

    Polce, C.; Termansen, M.; Aguirre-Gutiérrez, J.; Boatman, N.D.; Budge, G.E.; Crowe, A.; Garratt, M.P.; Pietravalle, S.; Potts, S.G.; Ramirez, J. A.; Somerwill, K.E.; Biesmeijer, J.C.

    2013-01-01

    Insect pollination benefits over three quarters of the world's major crops. There is growing concern that observed declines in pollinators may impact on production and revenues from animal pollinated crops. Knowing the distribution of pollinators is therefore crucial for estimating their

  10. Species Distribution Models for Crop Pollination: A Modelling Framework Applied to Great Britain

    NARCIS (Netherlands)

    C. Polce; M. Termansen; J. Aguirre-Gutiérrez; N.D. Boatman; G.E. Budge; A. Crowe; M.P. Garratt; S. Pietravalle; S.G. Potts; J. A. Ramirez; K.E. Somerwill; J.C. Biesmeijer

    2013-01-01

    Insect pollination benefits over three quarters of the world's major crops. There is growing concern that observed declines in pollinators may impact on production and revenues from animal pollinated crops. Knowing the distribution of pollinators is therefore crucial for estimating their availabilit

  11. Determining the Appropriate Crop Rotation Plan in a Farm Scale Using Fuzzy Goal Programming Model

    Directory of Open Access Journals (Sweden)

    A. Alizadeh Zoeram

    2016-03-01

    Full Text Available Introduction One of the important subject in the field of agricultural programming is reaching to a pattern or appropriate crop rotation to plant. Existing constraints, including the amount of available resources, and different goals, makes the decision to optimize the use of resources and production factors a complicated task. Therefore, applying mathematical models can be a grate help in this field. The goal of this study is to determine the appropriate patterns of crop cultivation in a farm in the North Khorasan province. Materials and Methods Implem enting fuzzy goal programming (FGP model based on different scenarios was employed to achieve our goals. According to results ,represented process , constraints and problem goals, four plant patterns are offered based on eight proposed scenarios for crop products in this farm or this study. These proposed cultivation pattern can help to make better decision for determination the appropriate rotation of crops in different conditions and different goals by decision makers. Results Discussion Finally, proposed cultivation patterns were prioritized according to maximum amount of reaching the desired level of total goals. Based on maximum level of reaching goals, different scenarios consisted of income, cost, production resources, income-cost, income-production resources, cost-production resources, income-cost-production resources with equal weights, and income-cost-production resources with different weights have been prioritized and four cropping pattern have been detected. In first pattern, three scenario consisted of scenario 1 (income, scenario 4 (income-cost and scenario 5 (income-production resources have combined. The second pattern have made scenario 2 (cost. In third pattern, scenario 3 (production resources, scenario 6 (cost-production resources and scenario 7 (income-cost-production resources with equal weights have combined. The scenario 8 (income-cost-production resources with different

  12. Sensitivity of a Shallow-Water Model to Parameters

    CERN Document Server

    Kazantsev, Eugene

    2011-01-01

    An adjoint based technique is applied to a shallow water model in order to estimate the influence of the model's parameters on the solution. Among parameters the bottom topography, initial conditions, boundary conditions on rigid boundaries, viscosity coefficients Coriolis parameter and the amplitude of the wind stress tension are considered. Their influence is analyzed from three points of view: 1. flexibility of the model with respect to a parameter that is related to the lowest value of the cost function that can be obtained in the data assimilation experiment that controls this parameter; 2. possibility to improve the model by the parameter's control, i.e. whether the solution with the optimal parameter remains close to observations after the end of control; 3. sensitivity of the model solution to the parameter in a classical sense. That implies the analysis of the sensitivity estimates and their comparison with each other and with the local Lyapunov exponents that characterize the sensitivity of the mode...

  13. Statistical rice yield modeling using blended MODIS-Landsat based crop phenology metrics in Taiwan

    Science.gov (United States)

    Chen, C. R.; Chen, C. F.; Nguyen, S. T.; Lau, K. V.

    2015-12-01

    Taiwan is a populated island with a majority of residents settled in the western plains where soils are suitable for rice cultivation. Rice is not only the most important commodity, but also plays a critical role for agricultural and food marketing. Information of rice production is thus important for policymakers to devise timely plans for ensuring sustainably socioeconomic development. Because rice fields in Taiwan are generally small and yet crop monitoring requires information of crop phenology associating with the spatiotemporal resolution of satellite data, this study used Landsat-MODIS fusion data for rice yield modeling in Taiwan. We processed the data for the first crop (Feb-Mar to Jun-Jul) and the second (Aug-Sep to Nov-Dec) in 2014 through five main steps: (1) data pre-processing to account for geometric and radiometric errors of Landsat data, (2) Landsat-MODIS data fusion using using the spatial-temporal adaptive reflectance fusion model, (3) construction of the smooth time-series enhanced vegetation index 2 (EVI2), (4) rice yield modeling using EVI2-based crop phenology metrics, and (5) error verification. The fusion results by a comparison bewteen EVI2 derived from the fusion image and that from the reference Landsat image indicated close agreement between the two datasets (R2 > 0.8). We analysed smooth EVI2 curves to extract phenology metrics or phenological variables for establishment of rice yield models. The results indicated that the established yield models significantly explained more than 70% variability in the data (p-value 0.8), in both cases. The root mean square error (RMSE) and mean absolute error (MAE) used to measure the model accuracy revealed the consistency between the estimated yields and the government's yield statistics. This study demonstrates advantages of using EVI2-based phenology metrics (derived from Landsat-MODIS fusion data) for rice yield estimation in Taiwan prior to the harvest period.

  14. Simulating Stochastic Crop Management in Cropping Systems

    Science.gov (United States)

    Introduction -- Crop simulation models are uniquely suitable for examining long term crop responses to environmental variability due to changes in climate or other factors. Long-term studies typically emphasize variability related to weather conditions; certain weather-dependent cropping practices m...

  15. RESEARCH AND DEVELOPMENT MULTICRITERIAL ECONOMIC AND MATHEMATICAL MODELS COMPREHENSIVE ASSESSMENT TECHNOLOGY GROWING CROPS

    Directory of Open Access Journals (Sweden)

    Loyko V. I.

    2016-10-01

    Full Text Available Production and processing of grains formed in the national economic system of the country a number of cereals-governmental sectors, such as grain production, grain elevator industry, flour, cereals and mixed fodder production, which constitute the grain complex country. The significance and role of the grain as a commodity in the state economy can not be overestimated. This product, is totally liquid, which has a constant, steady demand at any time of the year, in any region. Ongoing measures to increase grain production and improve its implementation did not have a complex character, therefore, insignificant effect on the efficiency of the industry and the competitiveness of grain production. The shortagecovered by imports.According to the characteristics of management in agriculture, it should be emphasized that the absence of objective and timely information at all stages of production of the plant-breeding, and as a result, non-optimal choice of technology of cultivation of agricultural crops, might result in the fact that the cost of labor and material resources increases significantly, the company does not receive profits, and sometimes suffers losses. When selecting cultivation technology for agricultural crops, an agronomist has a database of more than a hundred times-personal of alternative technologies for each crop. It is up to the decision-maker (DMP to find specific criteria to select the most suitable (for the owners and the climatic zone technology of cultivating for the culture. These circumstances explain the relevance of in-depth research of economic and mathematical models and methods of analysis and evaluation of the economic efficiency of technologies of cultivation agricultural crops. The article deals with the process of developing multicriteria economic-mathematical model of a comprehensive assessment of technology of cultivation of agricultural crops.

  16. Estimation of shape model parameters for 3D surfaces

    DEFF Research Database (Denmark)

    Erbou, Søren Gylling Hemmingsen; Darkner, Sune; Fripp, Jurgen;

    2008-01-01

    Statistical shape models are widely used as a compact way of representing shape variation. Fitting a shape model to unseen data enables characterizing the data in terms of the model parameters. In this paper a Gauss-Newton optimization scheme is proposed to estimate shape model parameters of 3D s...

  17. A STELLA model to estimate water and nitrogen dynamics in a short-rotation woody crop plantation

    Science.gov (United States)

    Ying Ouyang; Jiaen Zhang; Theodor D. Leininger; Brent R. Frey

    2015-01-01

    Although short-rotation woody crop biomass production technology has demonstrated a promising potential to supply feedstocks for bioenergy production, the water and nutrient processes in the woody crop planation ecosystem are poorly understood. In this study, a computer model was developed to estimate the dynamics of water and nitrogen (N) species (e.g., NH4...

  18. Temperature and precipitation effects on wheat yield across a European transect: a crop model ensemble analysis using impact response surfaces

    NARCIS (Netherlands)

    Pirttioja, N.; Carter, T.R.; Fronzek, S.; Bindi, M.; Hoffmann, H.; Palosuo, T.; Ruiz-Ramos, M.; Tao, F.; Trnka, M.; Acutis, M.; Supit, I.

    2015-01-01

    This study explored the utility of the impact response surface (IRS) approach for investigating model ensemble crop yield responses under a large range of changes in climate. IRSs of spring and winter wheat Triticum aestivum yields were constructed from a 26-member ensemble of process-based crop sim

  19. Compositional modelling of distributed-parameter systems

    NARCIS (Netherlands)

    Maschke, Bernhard; Schaft, van der Arjan; Lamnabhi-Lagarrigue, F.; Loría, A.; Panteley, E.

    2005-01-01

    The Hamiltonian formulation of distributed-parameter systems has been a challenging reserach area for quite some time. (A nice introduction, especially with respect to systems stemming from fluid dynamics, can be found in [26], where also a historical account is provided.) The identification of the

  20. Saline Water Irrigation Scheduling Through a Crop-Water-Salinity Production Function and a Soil-Water-Salinity Dynamic Model

    Institute of Scientific and Technical Information of China (English)

    WANG Yang-Ren; KANG Shao-Zhong; LI Fu-heng; ZHANG Lu; ZHANG Jian-Hua

    2007-01-01

    Using a crop-water-salinity production function and a soil-water-salinity dynamic model, optimal irrigation scheduling was developed to maximize net return per irrigated area. Plot and field experiments were used to obtain the crop water sensitivity index, the salinity sensitivity index, and other parameters. Using data collected during 35 years to calculate the 10-day mean precipitation and evaporation, the variation in soil salinity concentrations and in the yields of winter wheat and cotton were simulated for 49 irrigation scheduling that were combined from 7 irrigation schemes over 3 irrigation dates and 7 salinity concentrations of saline irrigation water (fresh water and 6 levels of saline water). Comparison of predicted results with irrigation data obtained from a large area of the field showed that the model was valid and reliable. Based on the analysis of the investment cost of the irrigation that employed deep tube wells or shallow tube wells, a saline water irrigation schedule and a corresponding strategy for groundwater development and utilization were proposed. For wheat or cotton, if the salinity concentration was higher than 7.0 g L-1 in groundwater, irrigation was needed with only fresh water; if about 5.0 g L-1, irrigation was required twice with fresh water and once with saline water; and if not higher than 3.0 g L-1, irrigation could be solely with saline water.

  1. A meteorological forcing data set for global crop modeling: Development, evaluation, and intercomparison

    Science.gov (United States)

    Iizumi, Toshichika; Okada, Masashi; Yokozawza, Masayuki

    2014-01-01

    The Global Risk Assessment toward Stable Production of Food (GRASP) project uses global crop models to evaluate the impacts on global food security by changes in climate extremes, water resources, and land use. Such models require meteorological forcing data. This study presents the development of the GRASP forcing data that is a hybrid of the reanalyses (ERA-40 and JRA-25) and observations. The GRASP data offer daily mean, maximum, and minimum 2 m air temperatures as well as precipitation, solar radiation, vapor pressure, and 10 m wind speed over global land areas, excluding Antarctica, for the period 1961-2010 at a grid size of 1.125°. The monthly climatologies of the variables of the GRASP data were forced to be close to those of the observations for the baseline period (1961-1990 or 1983-2005) through bias corrections. The GRASP data are intercompared with other forcing data for land surface modeling (the S06, WATCH Forcing Data, and WATCH Forcing Data Methodology Applied to ERA-Interim data). The results demonstrate that the daily minimum temperature, diurnal temperature range, vapor pressure, solar radiation, and wind speed from the GRASP data are more valuable for crop modeling than the reanalyses and other forcing data. For remaining variables, the reliability of the GRASP data is higher than that of the reanalyses and on a similar level with that of the other forcing data. The GRASP data offer accurate estimates of daily weather as the inputs for crop models, providing unique opportunities to link historical changes in climate with crop production over the last half century.

  2. A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice v. 1) - Part 1: Model description

    Science.gov (United States)

    Masutomi, Yuji; Ono, Keisuke; Mano, Masayoshi; Maruyama, Atsushi; Miyata, Akira

    2016-11-01

    Crop growth and agricultural management can affect climate at various spatial and temporal scales through the exchange of heat, water, and gases between land and atmosphere. Therefore, simulation of fluxes for heat, water, and gases from agricultural land is important for climate simulations. A land surface model (LSM) combined with a crop growth model (CGM), called an LSM-CGM combined model, is a useful tool for simulating these fluxes from agricultural land. Therefore, we developed a new LSM-CGM combined model for paddy rice fields, the MATCRO-Rice model. The main objective of this paper is to present the full description of MATCRO-Rice. The most important feature of MATCRO-Rice is that it can consistently simulate latent and sensible heat fluxes, net carbon uptake by crop, and crop yield by exchanging variables between the LSM and CGM. This feature enables us to apply the model to a wide range of integrated issues.

  3. Parameter Estimation and Experimental Design in Groundwater Modeling

    Institute of Scientific and Technical Information of China (English)

    SUN Ne-zheng

    2004-01-01

    This paper reviews the latest developments on parameter estimation and experimental design in the field of groundwater modeling. Special considerations are given when the structure of the identified parameter is complex and unknown. A new methodology for constructing useful groundwater models is described, which is based on the quantitative relationships among the complexity of model structure, the identifiability of parameter, the sufficiency of data, and the reliability of model application.

  4. Modelling and prediction of crop losses from NOAA polar-orbiting operational satellites

    Directory of Open Access Journals (Sweden)

    Felix Kogan

    2016-05-01

    Full Text Available Weather-related crop losses have always been a concern for farmers, governments, traders, and policy-makers for the purpose of balanced food supply/demands, trade, and distribution of aid to the nations in need. Among weather disasters, drought plays a major role in large-scale crop losses. This paper discusses utility of operational satellite-based vegetation health (VH indices for modelling cereal yield and for early warning of drought-related crop losses. The indices were tested in Saratov oblast (SO, one of the principal grain growing regions of Russia. Correlation and regression analysis were applied to model cereal yield from VH indices during 1982–2001. A strong correlation between mean SO's cereal yield and VH indices were found during the critical period of cereals, which starts two–three weeks before and ends two–three weeks after the heading stage. Several models were constructed where VH indices served as independent variables (predictors. The models were validated independently based on SO cereal yield during 1982–2012. Drought-related cereal yield losses can be predicted three months in advance of harvest and six–eight months in advance of official grain production statistic is released. The error of production losses prediction is 7%–10%. The error of prediction drops to 3%–5% in the years of intensive droughts.

  5. Bayesian approach to decompression sickness model parameter estimation.

    Science.gov (United States)

    Howle, L E; Weber, P W; Nichols, J M

    2017-03-01

    We examine both maximum likelihood and Bayesian approaches for estimating probabilistic decompression sickness model parameters. Maximum likelihood estimation treats parameters as fixed values and determines the best estimate through repeated trials, whereas the Bayesian approach treats parameters as random variables and determines the parameter probability distributions. We would ultimately like to know the probability that a parameter lies in a certain range rather than simply make statements about the repeatability of our estimator. Although both represent powerful methods of inference, for models with complex or multi-peaked likelihoods, maximum likelihood parameter estimates can prove more difficult to interpret than the estimates of the parameter distributions provided by the Bayesian approach. For models of decompression sickness, we show that while these two estimation methods are complementary, the credible intervals generated by the Bayesian approach are more naturally suited to quantifying uncertainty in the model parameters.

  6. A stochastic ensemble-based model to predict crop water requirements from numerical weather forecasts and VIS-NIR high resolution satellite images in Southern Italy

    Science.gov (United States)

    Pelosi, Anna; Falanga Bolognesi, Salvatore; De Michele, Carlo; Medina Gonzalez, Hanoi; Villani, Paolo; D'Urso, Guido; Battista Chirico, Giovanni

    2015-04-01

    Irrigation agriculture is one the biggest consumer of water in Europe, especially in southern regions, where it accounts for up to 70% of the total water consumption. The EU Common Agricultural Policy, combined with the Water Framework Directive, imposes to farmers and irrigation managers a substantial increase of the efficiency in the use of water in agriculture for the next decade. Ensemble numerical weather predictions can be valuable data for developing operational advisory irrigation services. We propose a stochastic ensemble-based model providing spatial and temporal estimates of crop water requirements, implemented within an advisory service offering detailed maps of irrigation water requirements and crop water consumption estimates, to be used by water irrigation managers and farmers. The stochastic model combines estimates of crop potential evapotranspiration retrieved from ensemble numerical weather forecasts (COSMO-LEPS, 16 members, 7 km resolution) and canopy parameters (LAI, albedo, fractional vegetation cover) derived from high resolution satellite images in the visible and near infrared wavelengths. The service provides users with daily estimates of crop water requirements for lead times up to five days. The temporal evolution of the crop potential evapotranspiration is simulated with autoregressive models. An ensemble Kalman filter is employed for updating model states by assimilating both ground based meteorological variables (where available) and numerical weather forecasts. The model has been applied in Campania region (Southern Italy), where a satellite assisted irrigation advisory service has been operating since 2006. This work presents the results of the system performance for one year of experimental service. The results suggest that the proposed model can be an effective support for a sustainable use and management of irrigation water, under conditions of water scarcity and drought. Since the evapotranspiration term represents a staple

  7. 基于水分驱动的 AquaCrop 模型及其研究进展%AquaCrop model based on water-driven principle and its research progress

    Institute of Scientific and Technical Information of China (English)

    张万红; 刘文兆; 王芸

    2014-01-01

    介绍了AquaCrop模型的原理及基本参数,从模型的校验与应用两方面阐述了该模型的研究进展。指出目前仍缺乏实测数据验证AquaCrop模型对蒸发及蒸腾的模拟效果;AquaCrop模型在严重水分及盐分胁迫下模拟结果精度较差;已开展的模拟研究地域范围窄;由于缺少更复杂的生理子模块,AquaCrop模型不能很好解释水分胁迫对光合产物向籽粒运输分配过程的影响。为了提高模型的模拟精度并进一步延伸模型的应用范围,应完善模型水分及盐胁迫模块,并在较广范围内获取丰富的实测数据对模型开展进一步的校验研究。%The theory ,parameters and characteristics of AquaCrop model were introduced and its research progress was reviewed from the viewpoints of validation and application of the model .It was pointed that :the measured data of e-vaporation and transpiration were still lacked for the validation of simulating results of AquaCrop model ;the performance of AquaCrop model was poor under severe water and salt stress conditions ;The locations for model study were not di-verse;Because of lacking of the move complicated plant physiological submodel ,AquaCrop model was not able to account for water stress impact on biomass partitioning into yield .In order to increase the accuracy degree of AquaCrop model and extend its application range ,it is necessary to get abundant data measured for diverse locations and perfect the module of water and salt stress .

  8. Modeling Long Term Corn Yield Response to Nitrogen Rate and Crop Rotation

    Directory of Open Access Journals (Sweden)

    Laila Alejandra Puntel

    2016-11-01

    Full Text Available Improved prediction of optimal N fertilizer rates for corn (Zea mays L. can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM to simulate corn and soybean (Glycine max L. yields, the economic optimum N rate (EONR using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha-1 applied to corn. Our objectives were to: a quantify model prediction accuracy before and after calibration, and report calibration steps; b compare crop model-based techniques in estimating optimal N rate for corn; and c utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simultaneously simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration, which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration. For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-yr mean differences in EONR’s were within the historical N rate error range (40 to 50 kg N ha-1. However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching with precipitation. We concluded that long term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add

  9. Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation.

    Science.gov (United States)

    Puntel, Laila A; Sawyer, John E; Barker, Daniel W; Dietzel, Ranae; Poffenbarger, Hanna; Castellano, Michael J; Moore, Kenneth J; Thorburn, Peter; Archontoulis, Sotirios V

    2016-01-01

    Improved prediction of optimal N fertilizer rates for corn (Zea mays L.) can reduce N losses and increase profits. We tested the ability of the Agricultural Production Systems sIMulator (APSIM) to simulate corn and soybean (Glycine max L.) yields, the economic optimum N rate (EONR) using a 16-year field-experiment dataset from central Iowa, USA that included two crop sequences (continuous corn and soybean-corn) and five N fertilizer rates (0, 67, 134, 201, and 268 kg N ha(-1)) applied to corn. Our objectives were to: (a) quantify model prediction accuracy before and after calibration, and report calibration steps; (b) compare crop model-based techniques in estimating optimal N rate for corn; and (c) utilize the calibrated model to explain factors causing year to year variability in yield and optimal N. Results indicated that the model simulated well long-term crop yields response to N (relative root mean square error, RRMSE of 19.6% before and 12.3% after calibration), which provided strong evidence that important soil and crop processes were accounted for in the model. The prediction of EONR was more complex and had greater uncertainty than the prediction of crop yield (RRMSE of 44.5% before and 36.6% after calibration). For long-term site mean EONR predictions, both calibrated and uncalibrated versions can be used as the 16-year mean differences in EONR's were within the historical N rate error range (40-50 kg N ha(-1)). However, for accurate year-by-year simulation of EONR the calibrated version should be used. Model analysis revealed that higher EONR values in years with above normal spring precipitation were caused by an exponential increase in N loss (denitrification and leaching) with precipitation. We concluded that long-term experimental data were valuable in testing and refining APSIM predictions. The model can be used as a tool to assist N management guidelines in the US Midwest and we identified five avenues on how the model can add value toward

  10. Atmospheric CO2 concentration impacts on maize yield performance under dry conditions: do crop model simulate it right ?

    Science.gov (United States)

    Durand, Jean-Louis; Delusca, Kénel; Boote, Ken; Lizaso, Jon; Manderscheid, Remy; Jochaim Weigel, Hans; Ruane, Alex C.; Rosenzweig, Cynthia; Jones, Jim; Ahuja, Laj; Anapalli, Saseendran; Basso, Bruno; Baron, Christian; Bertuzzi, Patrick; Biernath, Christian; Deryng, Delphine; Ewert, Frank; Gaiser, Thomas; Gayler, Sebastian; Heinlein, Florian; Kersebaum, Kurt Christian; Kim, Soo-Hyung; Müller, Christoph; Nendel, Claas; Olioso, Albert; Priesack, Eckhart; Ramirez-Villegas, Julian; Ripoche, Dominique; Rötter, Reimund; Seidel, Sabine; Srivastava, Amit; Tao, Fulu; Timlin, Dennis; Twine, Tracy; Wang, Enli; Webber, Heidi; Zhao, Shigan

    2017-04-01

    In most regions of the world, maize yields are at risk of be reduced due to rising temperatures and reduced water availability. Rising temperature tends to reduce the length of the growth cycle and the amount of intercepted solar energy. Water deficits reduce the leaf area expansion, photosynthesis and sometimes, with an even more pronounced impact, severely reduce the efficiency of kernel set. In maize, the major consequence of atmospheric CO2 concentration ([CO2]) is the stomatal closure-induced reduction of leaf transpiration rate, which tends to mitigate those negative impacts. Indeed FACE studies report significant positive responses to CO2 of maize yields (and other C4 crops) under dry conditions only. Given the projections by climatologists (typically doubling of [CO2] by the end of this century) projected impacts must take that climate variable into account. However, several studies show a large incertitude in estimating the impact of increasing [CO2] on maize remains using the main crop models. The aim of this work was to compare the simulations of different models using input data from a FACE experiment conducted in Braunschweig during 2 years under limiting and non-limiting water conditions. Twenty modelling groups using different maize models were given the same instructions and input data. Following calibration of cultivar parameters under non-limiting water conditions and under ambient [CO2] treatments of both years, simulations were undertaken for the other treatments: High [ CO2 ] (550 ppm) 2007 and 2008 in both irrigation regimes, and DRY AMBIENT 2007 and 2008. Only under severe water deficits did models simulate an increase in yield for CO2 enrichment, which was associated with higher harvest index and, for those models which simulated it, higher grain number. However, the CO2 enhancement under water deficit simulated by the 20 models was 20 % at most and 10 % on average only, i.e. twice less than observed in that experiment. As in the experiment

  11. Agave as a model CAM crop system for a warming and drying world

    Science.gov (United States)

    Stewart, J. Ryan

    2015-01-01

    As climate change leads to drier and warmer conditions in semi-arid regions, growing resource-intensive C3 and C4 crops will become more challenging. Such crops will be subjected to increased frequency and intensity of drought and heat stress. However, agaves, even more than pineapple (Ananas comosus) and prickly pear (Opuntia ficus-indica and related species), typify highly productive plants that will respond favorably to global warming, both in natural and cultivated settings. With nearly 200 species spread throughout the U.S., Mexico, and Central America, agaves have evolved traits, including crassulacean acid metabolism (CAM), that allow them to survive extreme heat and drought. Agaves have been used as sources of food, beverage, and fiber by societies for hundreds of years. The varied uses of Agave, combined with its unique adaptations to environmental stress, warrant its consideration as a model CAM crop. Besides the damaging cycles of surplus and shortage that have long beset the tequila industry, the relatively long maturation cycle of Agave, its monocarpic flowering habit, and unique morphology comprise the biggest barriers to its widespread use as a crop suitable for mechanized production. Despite these challenges, agaves exhibit potential as crops since they can be grown on marginal lands, but with more resource input than is widely assumed. If these constraints can be reconciled, Agave shows considerable promise as an alternative source for food, alternative sweeteners, and even bioenergy. And despite the many unknowns regarding agaves, they provide a means to resolve disparities in resource availability and needs between natural and human systems in semi-arid regions. PMID:26442005

  12. Application of a CROPWAT Model to Analyze Crop Yields in Nicaragua

    Science.gov (United States)

    Doria, R.; Byrne, J. M.

    2013-12-01

    ABSTRACT Changes in climate are likely to influence crop yields due to varying evapotranspiration and precipitation over agricultural regions. In Nicaragua, agriculture is extensive, with new areas of land brought into production as the population increases. Nicaraguan staple food items (maize and beans) are produced mostly by small scale farmers with less than 10 hectares, but they are critical for income generation and food security for rural communities. Given that the majority of these farmers are dependent on rain for crop irrigation, and that maize and beans are sensitive to variations in temperature and rainfall patterns, the present study was undertaken to assess the impact of climate change on these crop yields. Climate data were generated per municipio representing the three major climatic zones of the country: the wet Pacific lowland, the cooler Central highland, and the Caribbean lowland. Historical normal climate data from 1970-2000 (baseline period) were used as input to CROPWAT model to analyze the potential and actual evapotranspiration (ETo and ETa, respectively) that affects crop yields. Further, generated local climatic data of future years (2030-2099) under various scenarios were inputted to the CROPWAT to determine changes in ETo and ETa from the baseline period. Spatial variability maps of both ETo and ETa as well as crop yields were created. Results indicated significant variation in seasonal rainfall depth during the baseline period and predicted decreasing trend in the future years that eventually affects yields. These maps enable us to generate appropriate adaptation measures and best management practices for small scale farmers under future climate change scenarios. KEY WORDS: Climate change, evapotranspiration, CROPWAT, yield, Nicaragua

  13. Agave as a model CAM crop system for a warming and drying world.

    Science.gov (United States)

    Stewart, J Ryan

    2015-01-01

    As climate change leads to drier and warmer conditions in semi-arid regions, growing resource-intensive C3 and C4 crops will become more challenging. Such crops will be subjected to increased frequency and intensity of drought and heat stress. However, agaves, even more than pineapple (Ananas comosus) and prickly pear (Opuntia ficus-indica and related species), typify highly productive plants that will respond favorably to global warming, both in natural and cultivated settings. With nearly 200 species spread throughout the U.S., Mexico, and Central America, agaves have evolved traits, including crassulacean acid metabolism (CAM), that allow them to survive extreme heat and drought. Agaves have been used as sources of food, beverage, and fiber by societies for hundreds of years. The varied uses of Agave, combined with its unique adaptations to environmental stress, warrant its consideration as a model CAM crop. Besides the damaging cycles of surplus and shortage that have long beset the tequila industry, the relatively long maturation cycle of Agave, its monocarpic flowering habit, and unique morphology comprise the biggest barriers to its widespread use as a crop suitable for mechanized production. Despite these challenges, agaves exhibit potential as crops since they can be grown on marginal lands, but with more resource input than is widely assumed. If these constraints can be reconciled, Agave shows considerable promise as an alternative source for food, alternative sweeteners, and even bioenergy. And despite the many unknowns regarding agaves, they provide a means to resolve disparities in resource availability and needs between natural and human systems in semi-arid regions.

  14. Agave as a model CAM crop system for a warming and drying world

    Directory of Open Access Journals (Sweden)

    J. Ryan eStewart

    2015-09-01

    Full Text Available As climate change leads to drier and warmer conditions in semi-arid regions, growing resource-intensive C3 and C4 crops will become more challenging. Such crops will be subjected to increased frequency and intensity of drought and heat stress. However, agaves, even more than pineapple (Ananas comosus and prickly pear (Opuntia ficus-indica and related species, typify highly productive plants that will respond favorably to global warming, both in natural and cultivated settings. With nearly 200 species spread throughout the U.S., Mexico, and Central America, agaves have evolved traits, including crassulacean acid metabolism (CAM, that allow them to survive extreme heat and drought. Agaves have been used as sources of food, beverage, and fiber by societies for hundreds of years. The varied uses of Agave, combined with its unique adaptations to environmental stress, warrant its consideration as a model CAM crop. Besides the damaging cycles of surplus and shortage that have long beset the tequila industry, the relatively long maturation cycle of Agave, its monocarpic flowering habit, and unique morphology comprise the biggest barriers to its widespread use as a crop suitable for mechanized production. Despite these challenges, agaves exhibit potential as crops since they can be grown on marginal lands, but with more resource input than is widely assumed. If these constraints can be reconciled, Agave shows considerable promise as an alternative source for food, alternative sweeteners, and even bioenergy. And despite the many unknowns regarding agaves, they provide a means to resolve disparities between natural and human systems in semi-arid regions.

  15. A double-hurdle model estimation of cocoa farmers' willingness to pay for crop insurance in Ghana.

    Science.gov (United States)

    Okoffo, Elvis Dartey; Denkyirah, Elisha Kwaku; Adu, Derick Taylor; Fosu-Mensah, Benedicta Yayra

    2016-01-01

    Agriculture is an important sector in Ghana's economy, however, with high risk due to natural factors like climate change, pests and diseases and bush fires among others. Farmers in the Brong-Ahafo region of Ghana which is known as one of the major cocoa producing regions, face these risks which sometimes results in crop failure. The need for farmers to therefore insure their farms against crop loss is crucial. Insurance has been a measure to guard against risk. The aim of this study was to assess cocoa farmers' willingness to access crop insurance, the factors affecting willingness to pay (WTP) for crop insurance scheme and insurance companies' willingness to provide crop insurance to cocoa farmers. Multi-stage sampling technique was used to sample 240 farmers from four communities in the Dormaa West District in Brong-Ahafo Region. The double-hurdle model shows that age, marital status and education significantly and positively influenced cocoa farmer's willingness to insure their farms whiles household size and cropped area negatively influenced farmers' willingness to insure their farms. Similarly, age, household size and cropped area significantly and positively influenced the premium cocoa farmers were willing to pay whiles marital status and cocoa income negatively influenced the premium farmers were willing to pay. The contingent valuation method shows that the maximum, minimum and average amounts cocoa farmers are willing to pay for crop insurance per production cost per acre was GH¢128.40, GH¢32.10 and GH¢49.32 respectively. Insurance companies do not have crop insurance policy but willing to provide crop insurance policy to cocoa farmers on a condition that farmers adopt modern cultivation practices to reduce the level of risk. The study recommends that cocoa farmers should be well educated on crop insurance and should be involved in planning the crop insurance scheme in order to conclude on the premium to be paid by them.

  16. Irrigation water consumption modelling of a soilless cucumber crop under specific greenhouse conditions in a humid tropical climate

    Directory of Open Access Journals (Sweden)

    Galo Alberto Salcedo

    Full Text Available ABSTRACT: The irrigation water consumption of a soilless cucumber crop under greenhouse conditions in a humid tropical climate has been evaluated in this paper in order to improve the irrigation water and fertilizers management in these specific conditions. For this purpose, a field experiment was conducted. Two trials were carried out during the years 2011 and 2014 in an experimental farm located in Vinces (Ecuador. In each trial, the complete growing cycle of a cucumber crop grown under a greenhouse was evaluated. Crop development was monitored and a good fit to a sigmoidal Gompertz type growth function was reported. The daily water uptake of the crop was measured and related to the most relevant indoor climate variables. Two different combination methods, namely the Penman-Monteith equation and the Baille equation, were applied. However, the results obtained with these combination methods were not satisfactory due to the poor correlation between the climatic variables, especially the incoming radiation, and the crop's water uptake (WU. On contrary, a good correlation was reported between the crop's water uptake and the leaf area index (LAI, especially in the initial crop stages. However, when the crop is fully developed, the WU stabilizes and becomes independent from the LAI. A preliminary model to simulate the water uptake of the crop was adjusted using the data obtained in the first experiment and then validated with the data of the second experiment.

  17. Impact of Uncertainty in SWAT Model Simulations on Consequent Decisions on Optimal Crop Management Practices

    Science.gov (United States)

    Krishnan, N.; Sudheer, K. P.; Raj, C.; Chaubey, I.

    2015-12-01

    The diminishing quantities of non-renewable forms of energy have caused an increasing interest in the renewable sources of energy, such as biofuel, in the recent years. However, the demand for biofuel has created a concern for allocating grain between the fuel and food industry. Consequently, appropriate regulations that limit grain based ethanol production have been developed and are put to practice, which resulted in cultivating perennial grasses like Switch grass and Miscanthus to meet the additional cellulose demand. A change in cropping and management practice, therefore, is essential to cater the conflicting requirement for food and biofuel, which has a long-term impact on the downstream water quality. Therefore it is essential to implement optimal cropping practices to reduce the pollutant loadings. Simulation models in conjunction with optimization procedures are useful in developing efficient cropping practices in such situations. One such model is the Soil and Water Assessment Tool (SWAT), which can simulate both the water and the nutrient cycle, as well as quantify long-term impacts of changes in management practice in the watershed. It is envisaged that the SWAT model, along with an optimization algorithm, can be used to identify the optimal cropping pattern that achieves the minimum guaranteed grain production with less downstream pollution, while maximizing the biomass production for biofuel generation. However, the SWAT simulations do have a certain level of uncertainty that needs to be accounted for before making decisions. Therefore, the objectives of this study are twofold: (i) to understand how model uncertainties influence decision-making, and (ii) to develop appropriate management scenarios that account the uncertainty. The simulation uncertainty of the SWAT model is assessed using Shuffled Complex Evolutionary Metropolis Algorithm (SCEM). With the data collected from St. Joseph basin, IN, USA, the preliminary results indicate that model

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

    Directory of Open Access Journals (Sweden)

    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.

  19. Parameter and Uncertainty Estimation in Groundwater Modelling

    DEFF Research Database (Denmark)

    Jensen, Jacob Birk

    The data basis on which groundwater models are constructed is in general very incomplete, and this leads to uncertainty in model outcome. Groundwater models form the basis for many, often costly decisions and if these are to be made on solid grounds, the uncertainty attached to model results must...... be quantified. This study was motivated by the need to estimate the uncertainty involved in groundwater models.Chapter 2 presents an integrated surface/subsurface unstructured finite difference model that was developed and applied to a synthetic case study.The following two chapters concern calibration...... and uncertainty estimation. Essential issues relating to calibration are discussed. The classical regression methods are described; however, the main focus is on the Generalized Likelihood Uncertainty Estimation (GLUE) methodology. The next two chapters describe case studies in which the GLUE methodology...

  20. Parameter redundancy in discrete state‐space and integrated models

    Science.gov (United States)

    McCrea, Rachel S.

    2016-01-01

    Discrete state‐space models are used in ecology to describe the dynamics of wild animal populations, with parameters, such as the probability of survival, being of ecological interest. For a particular parametrization of a model it is not always clear which parameters can be estimated. This inability to estimate all parameters is known as parameter redundancy or a model is described as nonidentifiable. In this paper we develop methods that can be used to detect parameter redundancy in discrete state‐space models. An exhaustive summary is a combination of parameters that fully specify a model. To use general methods for detecting parameter redundancy a suitable exhaustive summary is required. This paper proposes two methods for the derivation of an exhaustive summary for discrete state‐space models using discrete analogues of methods for continuous state‐space models. We also demonstrate that combining multiple data sets, through the use of an integrated population model, may result in a model in which all parameters are estimable, even though models fitted to the separate data sets may be parameter redundant. PMID:27362826

  1. Parameter redundancy in discrete state-space and integrated models.

    Science.gov (United States)

    Cole, Diana J; McCrea, Rachel S

    2016-09-01

    Discrete state-space models are used in ecology to describe the dynamics of wild animal populations, with parameters, such as the probability of survival, being of ecological interest. For a particular parametrization of a model it is not always clear which parameters can be estimated. This inability to estimate all parameters is known as parameter redundancy or a model is described as nonidentifiable. In this paper we develop methods that can be used to detect parameter redundancy in discrete state-space models. An exhaustive summary is a combination of parameters that fully specify a model. To use general methods for detecting parameter redundancy a suitable exhaustive summary is required. This paper proposes two methods for the derivation of an exhaustive summary for discrete state-space models using discrete analogues of methods for continuous state-space models. We also demonstrate that combining multiple data sets, through the use of an integrated population model, may result in a model in which all parameters are estimable, even though models fitted to the separate data sets may be parameter redundant. © 2016 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  2. Cover crops as a gateway to greater conservation in Iowa?: Integrating crop models, field trials, economics and farmer perspectives regarding soil resilience in light of climate change

    Science.gov (United States)

    Roesch-McNally, G. E.; Basche, A.; Tyndall, J.; Arbuckle, J. G.; Miguez, F.; Bowman, T.

    2014-12-01

    Scientists predict a number of climate changes for the US Midwest with expected declines in crop productivity as well as eco-hydrological impacts. More frequent extreme rain events particularly in the spring may well increase saturated soils thus complicating agronomic interests and also exacerbate watershed scale impairments (e.g., sediment, nutrient loss). In order to build more resilient production systems in light of climate change, farmers will increasingly need to implement conservation practices (singularly or more likely in combination) that enable farmers to manage profitable businesses yet mitigate consequential environmental impacts that have both in-field and off-farm implications. Cover crops are empirically known to promote many aspects of soil and water health yet even the most aggressive recent estimates show that only 1-2% of the total acreage in Iowa have been planted to cover crops. In order to better understand why farmers are reluctant to adopt cover crops across Iowa we combined agronomic and financial data from long-term field trials, working farm trials and model simulations so as to present comprehensive data-driven information to farmers in focus group discussions in order to understand existing barriers, perceived benefits and responses to the information presented. Four focus groups (n=29) were conducted across Iowa in four geographic regions. Focus group discussions help explore the nuance of farmers' responses to modeling outputs and their real-life agronomic realities, thus shedding light on the social and psychological barriers with cover crop utilization. Among the key insights gained, comprehensive data-driven research can influence farmer perspectives on potential cover crop impacts to cash crop yields, experienced costs are potentially quite variable, and having field/farm benefits articulated in economic terms are extremely important when farmers weigh the opportunity costs associated with adopting new practices. Our work

  3. An automatic and effective parameter optimization method for model tuning

    Directory of Open Access Journals (Sweden)

    T. Zhang

    2015-11-01

    simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9 %. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameter tuning during the model development stage.

  4. Ternary interaction parameters in calphad solution models

    Energy Technology Data Exchange (ETDEWEB)

    Eleno, Luiz T.F., E-mail: luizeleno@usp.br [Universidade de Sao Paulo (USP), SP (Brazil). Instituto de Fisica; Schön, Claudio G., E-mail: schoen@usp.br [Universidade de Sao Paulo (USP), SP (Brazil). Computational Materials Science Laboratory. Department of Metallurgical and Materials Engineering

    2014-07-01

    For random, diluted, multicomponent solutions, the excess chemical potentials can be expanded in power series of the composition, with coefficients that are pressure- and temperature-dependent. For a binary system, this approach is equivalent to using polynomial truncated expansions, such as the Redlich-Kister series for describing integral thermodynamic quantities. For ternary systems, an equivalent expansion of the excess chemical potentials clearly justifies the inclusion of ternary interaction parameters, which arise naturally in the form of correction terms in higher-order power expansions. To demonstrate this, we carry out truncated polynomial expansions of the excess chemical potential up to the sixth power of the composition variables. (author)

  5. Assessment of structural model and parameter uncertainty with a multi-model system for soil water balance models

    Science.gov (United States)

    Michalik, Thomas; Multsch, Sebastian; Frede, Hans-Georg; Breuer, Lutz

    2016-04-01

    Water for agriculture is strongly limited in arid and semi-arid regions and often of low quality in terms of salinity. The application of saline waters for irrigation increases the salt load in the rooting zone and has to be managed by leaching to maintain a healthy soil, i.e. to wash out salts by additional irrigation. Dynamic simulation models are helpful tools to calculate the root zone water fluxes and soil salinity content in order to investigate best management practices. However, there is little information on structural and parameter uncertainty for simulations regarding the water and salt balance of saline irrigation. Hence, we established a multi-model system with four different models (AquaCrop, RZWQM, SWAP, Hydrus1D/UNSATCHEM) to analyze the structural and parameter uncertainty by using the Global Likelihood and Uncertainty Estimation (GLUE) method. Hydrus1D/UNSATCHEM and SWAP were set up with multiple sets of different implemented functions (e.g. matric and osmotic stress for root water uptake) which results in a broad range of different model structures. The simulations were evaluated against soil water and salinity content observations. The posterior distribution of the GLUE analysis gives behavioral parameters sets and reveals uncertainty intervals for parameter uncertainty. Throughout all of the model sets, most parameters accounting for the soil water balance show a low uncertainty, only one or two out of five to six parameters in each model set displays a high uncertainty (e.g. pore-size distribution index in SWAP and Hydrus1D/UNSATCHEM). The differences between the models and model setups reveal the structural uncertainty. The highest structural uncertainty is observed for deep percolation fluxes between the model sets of Hydrus1D/UNSATCHEM (~200 mm) and RZWQM (~500 mm) that are more than twice as high for the latter. The model sets show a high variation in uncertainty intervals for deep percolation as well, with an interquartile range (IQR) of

  6. Simulation of crop evapotranspiration and crop coefficient in weighing lysimeters

    Science.gov (United States)

    Accurate quantification of crop evapotranspiration (ET) is critical in optimizing irrigation water productivity, especially, in the semiarid regions of the world where limited rainfall is supplemented by irrigation for profitable crop production. In this context, cropping system models are potential...

  7. Agroclimatic modeling for the simulation of phenology, yield and quality of crop production

    Science.gov (United States)

    Mechlia, Netij Ben; Carroll, John J.

    1989-03-01

    This paper describes the development of basic simulation concepts that can be used in models aimed at forecasting. The objective is to demonstrate the utility of considering the crop's basic environmental requirements or “climatic normals” in producing a self-contained comprehensive model. We seek to develop a model in which regularly measured weather data can be used to provide information on the crop performance. Following an abbreviated overview of modeling alternatives, the model design is described. The results of this study are a set of criteria and functions needed to predict the temporal evolution of phenological stages, fruit growth, fruit maturation, and fruit coloration for two varieties of oranges (Navel and Valencia). The major factors considered are the effect of temperature and solar radiation on flowering time, and flowering duration and the number of flowers; the effect of past stress, temperature, evaporation, wind, and rain, planting density, and tree age on fruit set. Given the number of fruits set, growth, maturation and coloration are modeled as responding primarily to temperature and water balance. Possible damage due to freezes is also modeled. These form the basis of a time dependent model (reported elsewhere) which uses daily air temperature and wind data for the prediction of these quantities. These criteria and functions are derived from an extensive body of published observations from many parts of the world, and are selected to be variety specific and independent of local climatology or other site-specific effects.

  8. Parameter estimation and error analysis in environmental modeling and computation

    Science.gov (United States)

    Kalmaz, E. E.

    1986-01-01

    A method for the estimation of parameters and error analysis in the development of nonlinear modeling for environmental impact assessment studies is presented. The modular computer program can interactively fit different nonlinear models to the same set of data, dynamically changing the error structure associated with observed values. Parameter estimation techniques and sequential estimation algorithms employed in parameter identification and model selection are first discussed. Then, least-square parameter estimation procedures are formulated, utilizing differential or integrated equations, and are used to define a model for association of error with experimentally observed data.

  9. Summary of the DREAM8 Parameter Estimation Challenge: Toward Parameter Identification for Whole-Cell Models.

    Directory of Open Access Journals (Sweden)

    Jonathan R Karr

    2015-05-01

    Full Text Available Whole-cell models that explicitly represent all cellular components at the molecular level have the potential to predict phenotype from genotype. However, even for simple bacteria, whole-cell models will contain thousands of parameters, many of which are poorly characterized or unknown. New algorithms are needed to estimate these parameters and enable researchers to build increasingly comprehensive models. We organized the Dialogue for Reverse Engineering Assessments and Methods (DREAM 8 Whole-Cell Parameter Estimation Challenge to develop new parameter estimation algorithms for whole-cell models. We asked participants to identify a subset of parameters of a whole-cell model given the model's structure and in silico "experimental" data. Here we describe the challenge, the best performing methods, and new insights into the identifiability of whole-cell models. We also describe several valuable lessons we learned toward improving future challenges. Going forward, we believe that collaborative efforts supported by inexpensive cloud computing have the potential to solve whole-cell model parameter estimation.

  10. Parameter estimation of hydrologic models using data assimilation

    Science.gov (United States)

    Kaheil, Y. H.

    2005-12-01

    The uncertainties associated with the modeling of hydrologic systems sometimes demand that data should be incorporated in an on-line fashion in order to understand the behavior of the system. This paper represents a Bayesian strategy to estimate parameters for hydrologic models in an iterative mode. The paper presents a modified technique called localized Bayesian recursive estimation (LoBaRE) that efficiently identifies the optimum parameter region, avoiding convergence to a single best parameter set. The LoBaRE methodology is tested for parameter estimation for two different types of models: a support vector machine (SVM) model for predicting soil moisture, and the Sacramento Soil Moisture Accounting (SAC-SMA) model for estimating streamflow. The SAC-SMA model has 13 parameters that must be determined. The SVM model has three parameters. Bayesian inference is used to estimate the best parameter set in an iterative fashion. This is done by narrowing the sampling space by imposing uncertainty bounds on the posterior best parameter set and/or updating the "parent" bounds based on their fitness. The new approach results in fast convergence towards the optimal parameter set using minimum training/calibration data and evaluation of fewer parameter sets. The efficacy of the localized methodology is also compared with the previously used Bayesian recursive estimation (BaRE) algorithm.

  11. Advancing the climate data driven crop-modeling studies in the dry areas of Northern Syria and Lebanon: An important first step for assessing impact of future climate

    Energy Technology Data Exchange (ETDEWEB)

    Dixit, Prakash N., E-mail: p.dixit@cgiar.org; Telleria, Roberto

    2015-04-01

    Inter-annual and seasonal variability in climatic parameters, most importantly rainfall, have potential to cause climate-induced risk in long-term crop production. Short-term field studies do not capture the full nature of such risk and the extent to which modifications to crop, soil and water management recommendations may be made to mitigate the extent of such risk. Crop modeling studies driven by long-term daily weather data can predict the impact of climate-induced risk on crop growth and yield however, the availability of long-term daily weather data can present serious constraints to the use of crop models. To tackle this constraint, two weather generators namely, LARS-WG and MarkSim, were evaluated in order to assess their capabilities of reproducing frequency distributions, means, variances, dry spell and wet chains of observed daily precipitation, maximum and minimum temperature, and solar radiation for the eight locations across cropping areas of Northern Syria and Lebanon. Further, the application of generated long-term daily weather data, with both weather generators, in simulating barley growth and yield was also evaluated. We found that overall LARS-WG performed better than MarkSim in generating daily weather parameters and in 50 years continuous simulation of barley growth and yield. Our findings suggest that LARS-WG does not necessarily require long-term e.g., > 30 years observed weather data for calibration as generated results proved to be satisfactory with > 10 years of observed data except in area with higher altitude. Evaluating these weather generators and the ability of generated weather data to perform long-term simulation of crop growth and yield is an important first step to assess the impact of future climate on yields, and to identify promising technologies to make agricultural systems more resilient in the given region. - Highlights: • LARS-WG performed better than MarkSim in generating daily weather parameters. • LARS-WG can serve

  12. Assessments of Maize Yield Potential in the Korean Peninsula Using Multiple Crop Models

    Science.gov (United States)

    Kim, S. H.; Myoung, B.; Lim, C. H.; Lee, S. G.; Lee, W. K.; Kafatos, M.

    2015-12-01

    The Korean Peninsular has unique agricultural environments due to the differences in the political and socio-economical systems between the Republic of Korea (SK, hereafter) and the Democratic Peoples' Republic of Korea (NK, hereafter). NK has been suffering from the lack of food supplies caused by natural disasters, land degradation and failed political system. The neighboring developed country SK has a better agricultural system but very low food self-sufficiency rate (around 1% of maize). Maize is an important crop in both countries since it is staple food for NK and SK is No. 2 maize importing country in the world after Japan. Therefore evaluating maize yield potential (Yp) in the two distinct regions is essential to assess food security under climate change and variability. In this study, we have utilized multiple process-based crop models capable of regional-scale assessments to evaluate maize Yp over the Korean Peninsula - the GIS version of EPIC model (GEPIC) and APSIM model that can be expanded to regional scales (APSIM regions). First we evaluated model performance and skill for 20 years from 1991 to 2010 using reanalysis data (Local Data Assimilation and Prediction System (LDAPS); 1.5km resolution) and observed data. Each model's performances were compared over different regions within the Korean Peninsula of different regional climate characteristics. To quantify the major influence of individual climate variables, we also conducted a sensitivity test using 20 years of climatology. Lastly, a multi-model ensemble analysis was performed to reduce crop model uncertainties. The results will provide valuable information for estimating the climate change or variability impacts on Yp over the Korean Peninsula.

  13. GIS-Based Hydrogeological-Parameter Modeling

    Institute of Scientific and Technical Information of China (English)

    2000-01-01

    A regression model is proposed to relate the variation of water well depth with topographic properties (area and slope), the variation of hydraulic conductivity and vertical decay factor. The implementation of this model in GIS environment (ARC/TNFO) based on known water data and DEM is used to estimate the variation of hydraulic conductivity and decay factor of different lithoiogy units in watershed context.

  14. Estimation of Kinetic Parameters in an Automotive SCR Catalyst Model

    DEFF Research Database (Denmark)

    Åberg, Andreas; Widd, Anders; Abildskov, Jens;

    2016-01-01

    A challenge during the development of models for simulation of the automotive Selective Catalytic Reduction catalyst is the parameter estimation of the kinetic parameters, which can be time consuming and problematic. The parameter estimation is often carried out on small-scale reactor tests, or p...

  15. Mirror symmetry for two parameter models, 2

    CERN Document Server

    Candelas, Philip; Katz, S; Morrison, Douglas Robert Ogston; Philip Candelas; Anamaria Font; Sheldon Katz; David R Morrison

    1994-01-01

    We describe in detail the space of the two K\\"ahler parameters of the Calabi--Yau manifold \\P_4^{(1,1,1,6,9)}[18] by exploiting mirror symmetry. The large complex structure limit of the mirror, which corresponds to the classical large radius limit, is found by studying the monodromy of the periods about the discriminant locus, the boundary of the moduli space corresponding to singular Calabi--Yau manifolds. A symplectic basis of periods is found and the action of the Sp(6,\\Z) generators of the modular group is determined. From the mirror map we compute the instanton expansion of the Yukawa couplings and the generalized N=2 index, arriving at the numbers of instantons of genus zero and genus one of each degree. We also investigate an SL(2,\\Z) symmetry that acts on a boundary of the moduli space.

  16. Insights into plant size-density relationships from models and agricultural crops.

    Science.gov (United States)

    Deng, Jianming; Zuo, Wenyun; Wang, Zhiqiang; Fan, Zhexuan; Ji, Mingfei; Wang, Genxuan; Ran, Jinzhi; Zhao, Changming; Liu, Jianquan; Niklas, Karl J; Hammond, Sean T; Brown, James H

    2012-05-29

    There is general agreement that competition for resources results in a tradeoff between plant mass, M, and density, but the mathematical form of the resulting thinning relationship and the mechanisms that generate it are debated. Here, we evaluate two complementary models, one based on the space-filling properties of canopy geometry and the other on the metabolic basis of resource use. For densely packed stands, both models predict that density scales as M(-3/4), energy use as M(0), and total biomass as M(1/4). Compilation and analysis of data from 183 populations of herbaceous crop species, 473 stands of managed tree plantations, and 13 populations of bamboo gave four major results: (i) At low initial planting densities, crops grew at similar rates, did not come into contact, and attained similar mature sizes; (ii) at higher initial densities, crops grew until neighboring plants came into contact, growth ceased as a result of competition for limited resources, and a tradeoff between density and size resulted in critical density scaling as M(-0.78), total resource use as M(-0.02), and total biomass as M(0.22); (iii) these scaling exponents are very close to the predicted values of M(-3/4), M(0), and M(1/4), respectively, and significantly different from the exponents suggested by some earlier studies; and (iv) our data extend previously documented scaling relationships for trees in natural forests to small herbaceous annual crops. These results provide a quantitative, predictive framework with important implications for the basic and applied plant sciences.

  17. Accuracy of Parameter Estimation in Gibbs Sampling under the Two-Parameter Logistic Model.

    Science.gov (United States)

    Kim, Seock-Ho; Cohen, Allan S.

    The accuracy of Gibbs sampling, a Markov chain Monte Carlo procedure, was considered for estimation of item and ability parameters under the two-parameter logistic model. Memory test data were analyzed to illustrate the Gibbs sampling procedure. Simulated data sets were analyzed using Gibbs sampling and the marginal Bayesian method. The marginal…

  18. The review of dynamic monitoring technology for crop growth

    Science.gov (United States)

    Zhang, Hong-wei; Chen, Huai-liang; Zou, Chun-hui; Yu, Wei-dong

    2010-10-01

    In this paper, crop growth monitoring methods are described elaborately. The crop growth models, Netherlands-Wageningen model system, the United States-GOSSYM model and CERES models, Australia APSIM model and CCSODS model system in China, are introduced here more focus on the theories of mechanism, applications, etc. The methods and application of remote sensing monitoring methods, which based on leaf area index (LAI) and biomass were proposed by different scholars at home and abroad, are highly stressed in the paper. The monitoring methods of remote sensing coupling with crop growth models are talked out at large, including the method of "forced law" which using remote sensing retrieval state parameters as the crop growth model parameters input, and then to enhance the dynamic simulation accuracy of crop growth model and the method of "assimilation of Law" which by reducing the gap difference between the value of remote sensing retrieval and the simulated values of crop growth model and thus to estimate the initial value or parameter values to increasing the simulation accuracy. At last, the developing trend of monitoring methods are proposed based on the advantages and shortcomings in previous studies, it is assured that the combination of remote sensing with moderate resolution data of FY-3A, MODIS, etc., crop growth model, "3S" system and observation in situ are the main methods in refinement of dynamic monitoring and quantitative assessment techniques for crop growth in future.

  19. On linear models and parameter identifiability in experimental biological systems.

    Science.gov (United States)

    Lamberton, Timothy O; Condon, Nicholas D; Stow, Jennifer L; Hamilton, Nicholas A

    2014-10-07

    A key problem in the biological sciences is to be able to reliably estimate model parameters from experimental data. This is the well-known problem of parameter identifiability. Here, methods are developed for biologists and other modelers to design optimal experiments to ensure parameter identifiability at a structural level. The main results of the paper are to provide a general methodology for extracting parameters of linear models from an experimentally measured scalar function - the transfer function - and a framework for the identifiability analysis of complex model structures using linked models. Linked models are composed by letting the output of one model become the input to another model which is then experimentally measured. The linked model framework is shown to be applicable to designing experiments to identify the measured sub-model and recover the input from the unmeasured sub-model, even in cases that the unmeasured sub-model is not identifiable. Applications for a set of common model features are demonstrated, and the results combined in an example application to a real-world experimental system. These applications emphasize the insight into answering "where to measure" and "which experimental scheme" questions provided by both the parameter extraction methodology and the linked model framework. The aim is to demonstrate the tools' usefulness in guiding experimental design to maximize parameter information obtained, based on the model structure.

  20. Effects of crop species richness on pest-natural enemy systems based on an experimental model system using a microlandscape.

    Science.gov (United States)

    Zhao, ZiHua; Shi, PeiJian; Men, XingYuan; Ouyang, Fang; Ge, Feng

    2013-08-01

    The relationship between crop richness and predator-prey interactions as they relate to pest-natural enemy systems is a very important topic in ecology and greatly affects biological control services. The effects of crop arrangement on predator-prey interactions have received much attention as the basis for pest population management. To explore the internal mechanisms and factors driving the relationship between crop richness and pest population management, we designed an experimental model system of a microlandscape that included 50 plots and five treatments. Each treatment had 10 repetitions in each year from 2007 to 2010. The results showed that the biomass of pests and their natural enemies increased with increasing crop biomass and decreased with decreasing crop biomass; however, the effects of plant biomass on the pest and natural enemy biomass were not significant. The relationship between adjacent trophic levels was significant (such as pests and their natural enemies or crops and pests), whereas non-adjacent trophic levels (crops and natural enemies) did not significantly interact with each other. The ratio of natural enemy/pest biomass was the highest in the areas of four crop species that had the best biological control service. Having either low or high crop species richness did not enhance the pest population management service and lead to loss of biological control. Although the resource concentration hypothesis was not well supported by our results, high crop species richness could suppress the pest population, indicating that crop species richness could enhance biological control services. These results could be applied in habitat management aimed at biological control, provide the theoretical basis for agricultural landscape design, and also suggest new methods for integrated pest management.

  1. Crop model sensitivity to the estimated daily global solar radiation data

    Directory of Open Access Journals (Sweden)

    Pavel Kapler

    2006-01-01

    Full Text Available The results of the previous studies have suggested that the estimated RG values are loaded with an error, which might compromise the precision of the subsequent crop model applications. Therefore a detailed analysis of the error propagation was made using two crop models i.e. CERES-Barley and CERES-Wheat. Database of meteorological data originating from 8 stations in Austria and Czech Republic was used in order to carry out the analysis. It has been found that even application of the method based on sunshine duration that yield the lowest bias in RG estimates significantly influences number of key crop model outputs. It has been also noted that in 5–6 seasons out of 100 cases the deviation greater than ±10 % is to be expected whilst the occurrence of ±25% could not be also ruled out. The precision of the yield estimates and other crop model outputs is lower then expected but still acceptable for most application with mean bias error in range of 2.0–4.1% when estimates based on the diurnal temperature range and cloud cover are used. In this case yield deviations over ±10% occurred in about 20% cases (depending on the crop whilst the probability of significant yield departure (±25% doubled of that found for the previous method. The methods based on the diurnal temperature range and daily precipitation sum showed an increase of the systematic bias of yield of winter wheat and considerably higher number of seasons with yield departures over ±25%. Utilisation of the methods based on the diurnal temperature range only for the purposes of seasonal yield forecasting or climate change impact assessment is questionable as the probability of significant yield departure is very high (as well as the systematic error. These findings should act as an incentive to the further research aimed at development of more precise and widely applicable methods of estimating daily RG based more on the underlying physical principles and/or remote sensing approach

  2. Calibration of Daycent biogeochemical model for rice paddies in three agro-ecological zones in Peninsular India to optimize cropping practices and predict GHG emissions

    Science.gov (United States)

    Rajan, S.; Kritee, K.; Keough, C.; Parton, W. J.; Ogle, S. M.

    2014-12-01

    Rice is a staple for nearly half of the world population with irrigated and rainfed lowland rice accounting for about 80% of the worldwide harvested rice area. Increased atmospheric CO2 and rising temperatures are expected to adversely affect rice yields by the end of the 21st century. In addition, different crop management practices affect methane and nitrous oxide emissions from rice paddies antagonistically warranting a review of crop management practices such that farmers can adapt to the changing climate and also help mitigate climate change. The Daily DayCent is a biogeochemical model that operates on a daily time step, driven by four ecological drivers, i.e. climate, soil, vegetation, and management practices. The model is widely used to simulate daily fluxes of various gases, plant productivity, nutrient availability, and other ecosystem parameters in response to changes in land management and climate. We employed the DayCent model as a tool to optimize rice cropping practices in Peninsular India so as to develop a set of farming recommendations to ensure a triple win (i.e. higher yield, higher profit and lower GHG emissions). We applied the model to simulate both N2O and CH4 emissions, and crop yields from four rice paddies in three different agro-ecological zones under different management practices, and compared them with measured GHG and yield data from these plots. We found that, like all process based models, the biggest constraint in using the model was input data acquisition. Lack of accurate documentation of historic land use and management practices, missing historical daily weather data, and difficulty in obtaining digital records of soil and crop/vegetation parameters related to our experimental plots came in the way of our execution of this model. We will discuss utilization of estimates based on available literature, or knowledge-based values in lieu of missing measured parameters in our simulations with DayCent which could prove to be a

  3. Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System

    Directory of Open Access Journals (Sweden)

    Jakob Geipel

    2014-10-01

    Full Text Available Precision Farming (PF management strategies are commonly based on estimations of within-field yield potential, often derived from remotely-sensed products, e.g., Vegetation Index (VI maps. These well-established means, however, lack important information, like crop height. Combinations of VI-maps and detailed 3D Crop Surface Models (CSMs enable advanced methods for crop yield prediction. This work utilizes an Unmanned Aircraft System (UAS to capture standard RGB imagery datasets for corn grain yield prediction at three early- to mid-season growth stages. The imagery is processed into simple VI-orthoimages for crop/non-crop classification and 3D CSMs for crop height determination at different spatial resolutions. Three linear regression models are tested on their prediction ability using site-specific (i unclassified mean heights, (ii crop-classified mean heights and (iii a combination of crop-classified mean heights with according crop coverages. The models show determination coefficients \\({R}^{2}\\ of up to 0.74, whereas model (iii performs best with imagery captured at the end of stem elongation and intermediate spatial resolution (0.04m\\(\\cdot\\px\\(^{-1}\\.Following these results, combined spectral and spatial modeling, based on aerial images and CSMs, proves to be a suitable method for mid-season corn yield prediction.

  4. CHAMP: Changepoint Detection Using Approximate Model Parameters

    Science.gov (United States)

    2014-06-01

    positions as a Markov chain in which the transition probabilities are defined by the time since the last changepoint: p(τi+1 = t|τi = s) = g(t− s), (1...experimentally verified using artifi- cially generated data and are compared to those of Fearnhead and Liu [5]. 2 Related work Hidden Markov Models (HMMs) are...length α, and maximum number of particles M . Output: Viterbi path of changepoint times and models // Initialize data structures 1: max path, prev queue

  5. A Hidden Markov Models Approach for Crop Classification: Linking Crop Phenology to Time Series of Multi-Sensor Remote Sensing Data

    Directory of Open Access Journals (Sweden)

    Sofia Siachalou

    2015-03-01

    Full Text Available Vegetation monitoring and mapping based on multi-temporal imagery has recently received much attention due to the plethora of medium-high spatial resolution satellites and the improved classification accuracies attained compared to uni-temporal approaches. Efficient image processing strategies are needed to exploit the phenological information present in temporal image sequences and to limit data redundancy and computational complexity. Within this framework, we implement the theory of Hidden Markov Models in crop classification, based on the time-series analysis of phenological states, inferred by a sequence of remote sensing observations. More specifically, we model the dynamics of vegetation over an agricultural area of Greece, characterized by spatio-temporal heterogeneity and small-sized fields, using RapidEye and Landsat ETM+ imagery. In addition, the classification performance of image sequences with variable spatial and temporal characteristics is evaluated and compared. The classification model considering one RapidEye and four pan-sharpened Landsat ETM+ images was found superior, resulting in a conditional kappa from 0.77 to 0.94 per class and an overall accuracy of 89.7%. The results highlight the potential of the method for operational crop mapping in Euro-Mediterranean areas and provide some hints for optimal image acquisition windows regarding major crop types in Greece.

  6. Rainfall and crop modeling-based water stress assessment for rainfed maize cultivation in peninsular India

    Science.gov (United States)

    Manivasagam, V. S.; Nagarajan, R.

    2017-03-01

    Water stress due to uneven rainfall distribution causes a significant impact on the agricultural production of monsoon-dependent peninsular India. In the present study, water stress assessment for rainfed maize crop is carried out for kharif (June-October) and rabi (October-February) cropping seasons which coincide with two major Indian monsoons. Rainfall analysis (1976-2010) shows that the kharif season receives sufficient weekly rainfall (28 ± 32 mm) during 26th-39th standard meteorological weeks (SMWs) from southwest monsoon, whereas the rabi season experiences a major portion of its weekly rainfall due to northeast monsoon between the 42nd and 51st SMW (31 ± 42 mm). The later weeks experience minimal rainfall (5.5 ± 15 mm) and thus expose the late sown maize crops to a severe water stress during its maturity stage. Wet and dry spell analyses reveal a substantial increase in the rainfall intensity over the last few decades. However, the distribution of rainfall shows a striking decrease in the number of wet spells, with prolonged dry spells in both seasons. Weekly rainfall classification shows that the flowering and maturity stages of kharif maize (33rd-39th SMWs) can suffer around 30-40% of the total water stress. In the case of rabi maize, the analysis reveals that a shift in the sowing time from the existing 42nd SMW (16-22 October) to the 40th SMW (1-7 October) can avoid terminal water stress. Further, AquaCrop modeling results show that one or two minimal irrigations during the flowering and maturity stages (33rd-39th SMWs) of kharif maize positively avoid the mild water stress exposure. Similarly, rabi maize requires an additional two or three lifesaving irrigations during its flowering and maturity stages (48th-53rd SMWs) to improve productivity. Effective crop planning with appropriate sowing time, short duration crop, and high yielding drought-resistant varieties will allow for better utilization of the monsoon rain, thus reducing water stress with

  7. Assessing the Use of Remote Sensing and a Crop Growth Model to Improve Modeled Streamflow in Central Asia

    Science.gov (United States)

    Richey, A. S.; Richey, J. E.; Tan, A.; Liu, M.; Adam, J. C.; Sokolov, V.

    2015-12-01

    Central Asia presents a perfect case study to understand the dynamic, and often conflicting, linkages between food, energy, and water in natural systems. The destruction of the Aral Sea is a well-known environmental disaster, largely driven by increased irrigation demand on the rivers that feed the endorheic sea. Continued reliance on these rivers, the Amu Darya and Syr Darya, often place available water resources at odds between hydropower demands upstream and irrigation requirements downstream. A combination of tools is required to understand these linkages and how they may change in the future as a function of climate change and population growth. In addition, the region is geopolitically complex as the former Soviet basin states develop management strategies to sustainably manage shared resources. This complexity increases the importance of relying upon publically available information sources and tools. Preliminary work has shown potential for the Variable Infiltration Capacity (VIC) model to recreate the natural water balance in the Amu Darya and Syr Darya basins by comparing results to total terrestrial water storage changes observed from NASA's Gravity Recovery and Climate Experiment (GRACE) satellite mission. Modeled streamflow is well correlated to observed streamflow at upstream gauges prior to the large-scale expansion of irrigation and hydropower. However, current modeled results are unable to capture the human influence of water use on downstream flow. This study examines the utility of a crop simulation model, CropSyst, to represent irrigation demand and GRACE to improve modeled streamflow estimates in the Amu Darya and Syr Darya basins. Specifically we determine crop water demand with CropSyst utilizing available data on irrigation schemes and cropping patterns. We determine how this demand can be met either by surface water, modeled by VIC with a reservoir operation scheme, and/or by groundwater derived from GRACE. Finally, we assess how the

  8. Weekly Rainfall Analysis for Crop Planning Using Markov’ s Chain Model for Kandhamal District of Odisha, India

    Directory of Open Access Journals (Sweden)

    S. K. Kar

    2014-09-01

    Full Text Available Weekly rainfall analysis of Kandhamal district during the period of 1965 to 2010 were taken for analysis purpose, the analysis is very much important for crop planning and analyzing the probability of occurrence of dry and wet periods. This will act as bench mark for crop planning as well as sustainable agricultural management of Kandhamal district which comes under the agro-climatic zone of East-coast hill region. Markov chain model has been utilized to derive the probability of dry or wet weeks and also forward and backward accumulation of rain water suitable for crop production. This analysis can be helpful to find out different cropping system including intercropping and sequence cropping suitable during that period.

  9. WINKLER'S SINGLE-PARAMETER SUBGRADE MODEL FROM ...

    African Journals Online (AJOL)

    Preferred Customer

    [3, 9]. However, mainly due to the simplicity of Winkler's model in practical applications and .... this case, the coefficient B takes the dimension of a ... In plane-strain problems, the assumption of ... loaded circular region; s is the radial coordinate.

  10. Quantifying the effect of crop spatial arrangement on weed suppression using functional-structural plant modelling

    NARCIS (Netherlands)

    Evers, Jochem B.; Bastiaans, Lammert

    2016-01-01

    Suppression of weed growth in a crop canopy can be enhanced by improving crop competitiveness. One way to achieve this is by modifying the crop planting pattern. In this study, we addressed the question to what extent a uniform planting pattern increases the ability of a crop to compete with weed

  11. OPTIMIZING SYSTEM OF RICE INTENSIFICATION PARAMETERS USING AQUACROP MODEL FOR INCREASING WATER PRODUCTIVITY AND WATER USE EFFICIENCY IN RICE PRODUCTION

    Directory of Open Access Journals (Sweden)

    Z. Katambara

    2014-01-01

    Full Text Available Producing more rice while using less water is among the calls in water scarce regions so as to feed the growing population and cope with the changing climate. Among the suitable techniques towards this achievement is the use of system of rice intensification (SRI, which has been reported as an approach that uses less water and has high water productivity and water use efficiency. Despite its promising results, the use of SRI practice in Tanzania is limited due to less knowledge with regard to the transplanting age, plant spacing, and minimum soil moisture to be allowed for irrigation, and alternate wetting and drying interval for various geographical locations. The AquaCrop crop water productivity model, which is capable of simulating crop water requirements and yield for a given parameter set, was used to identify suitable SRI parameters for Mkindo area in Morogoro region, Tanzania. Using no stress in soil fertility, plant spacings ranging from 5 cm to 50 cm were evaluated. Results suggest that the yield and biomass produced per ha increase with decreasing spacing from 50 cm to 20 cm. Preliminary field results suggest that the optimum spacing is round 25 cm. However, the model structure does not take into consideration number of tillers produced. As such, the study calls for incorporation of the tillering processes into AquaCrop model.

  12. Improved Methodology for Parameter Inference in Nonlinear, Hydrologic Regression Models

    Science.gov (United States)

    Bates, Bryson C.

    1992-01-01

    A new method is developed for the construction of reliable marginal confidence intervals and joint confidence regions for the parameters of nonlinear, hydrologic regression models. A parameter power transformation is combined with measures of the asymptotic bias and asymptotic skewness of maximum likelihood estimators to determine the transformation constants which cause the bias or skewness to vanish. These optimized constants are used to construct confidence intervals and regions for the transformed model parameters using linear regression theory. The resulting confidence intervals and regions can be easily mapped into the original parameter space to give close approximations to likelihood method confidence intervals and regions for the model parameters. Unlike many other approaches to parameter transformation, the procedure does not use a grid search to find the optimal transformation constants. An example involving the fitting of the Michaelis-Menten model to velocity-discharge data from an Australian gauging station is used to illustrate the usefulness of the methodology.

  13. A simulation of water pollution model parameter estimation

    Science.gov (United States)

    Kibler, J. F.

    1976-01-01

    A parameter estimation procedure for a water pollution transport model is elaborated. A two-dimensional instantaneous-release shear-diffusion model serves as representative of a simple transport process. Pollution concentration levels are arrived at via modeling of a remote-sensing system. The remote-sensed data are simulated by adding Gaussian noise to the concentration level values generated via the transport model. Model parameters are estimated from the simulated data using a least-squares batch processor. Resolution, sensor array size, and number and location of sensor readings can be found from the accuracies of the parameter estimates.

  14. On retrial queueing model with fuzzy parameters

    Science.gov (United States)

    Ke, Jau-Chuan; Huang, Hsin-I.; Lin, Chuen-Horng

    2007-01-01

    This work constructs the membership functions of the system characteristics of a retrial queueing model with fuzzy customer arrival, retrial and service rates. The α-cut approach is used to transform a fuzzy retrial-queue into a family of conventional crisp retrial queues in this context. By means of the membership functions of the system characteristics, a set of parametric non-linear programs is developed to describe the family of crisp retrial queues. A numerical example is solved successfully to illustrate the validity of the proposed approach. Because the system characteristics are expressed and governed by the membership functions, more information is provided for use by management. By extending this model to the fuzzy environment, fuzzy retrial-queue is represented more accurately and analytic results are more useful for system designers and practitioners.

  15. Solar parameters for modeling interplanetary background

    CERN Document Server

    Bzowski, M; Tokumaru, M; Fujiki, K; Quemerais, E; Lallement, R; Ferron, S; Bochsler, P; McComas, D J

    2011-01-01

    The goal of the Fully Online Datacenter of Ultraviolet Emissions (FONDUE) Working Team of the International Space Science Institute in Bern, Switzerland, was to establish a common calibration of various UV and EUV heliospheric observations, both spectroscopic and photometric. Realization of this goal required an up-to-date model of spatial distribution of neutral interstellar hydrogen in the heliosphere, and to that end, a credible model of the radiation pressure and ionization processes was needed. This chapter describes the solar factors shaping the distribution of neutral interstellar H in the heliosphere. Presented are the solar Lyman-alpha flux and the solar Lyman-alpha resonant radiation pressure force acting on neutral H atoms in the heliosphere, solar EUV radiation and the photoionization of heliospheric hydrogen, and their evolution in time and the still hypothetical variation with heliolatitude. Further, solar wind and its evolution with solar activity is presented in the context of the charge excha...

  16. A REGIONAL ANALYSIS OF THE USE OF TRACTORS ON MODEL FARMS PRODUCING ENERGY CROPS

    Directory of Open Access Journals (Sweden)

    Benedykt Pepliński

    2015-06-01

    Full Text Available The potential area of energy crops in Poland is estimated at 1.0–4.5 million ha. The decrease in the prices of energy reduces the high pressure to cut the costs of biomass production. The aim of this study is an analysis of the use of tractors on model farms producing energy crops, which have different areas, intensity of production and quality of soils from different regions of Poland. The use of tractors increased along with the farm area, the soil quality and production intensity. The use of tractors on the smallest farms is low, so they should buy old tractors. A large share of crops for biogas leads to the situation where it takes 20–30 years of work for tractors to achieve full wear of 12,000 hours on farms with 130 ha of farmland, whereas it takes only 8–14 years on farms with 600 and 1500 ha of farmland. Regional differences in the use of tractors increased along with the farm area from 4.7–5.7% on the smallest farms to 10.1–14.8% on the largest farms.

  17. Energy sorghum--a genetic model for the design of C4 grass bioenergy crops.

    Science.gov (United States)

    Mullet, John; Morishige, Daryl; McCormick, Ryan; Truong, Sandra; Hilley, Josie; McKinley, Brian; Anderson, Robert; Olson, Sara N; Rooney, William

    2014-07-01

    Sorghum is emerging as an excellent genetic model for the design of C4 grass bioenergy crops. Annual energy Sorghum hybrids also serve as a source of biomass for bioenergy production. Elucidation of Sorghum's flowering time gene regulatory network, and identification of complementary alleles for photoperiod sensitivity, enabled large-scale generation of energy Sorghum hybrids for testing and commercial use. Energy Sorghum hybrids with long vegetative growth phases were found to accumulate more than twice as much biomass as grain Sorghum, owing to extended growing seasons, greater light interception, and higher radiation use efficiency. High biomass yield, efficient nitrogen recycling, and preferential accumulation of stem biomass with low nitrogen content contributed to energy Sorghum's elevated nitrogen use efficiency. Sorghum's integrated genetics-genomics-breeding platform, diverse germplasm, and the opportunity for annual testing of new genetic designs in controlled environments and in multiple field locations is aiding fundamental discovery, and accelerating the improvement of biomass yield and optimization of composition for biofuels production. Recent advances in wide hybridization between Sorghum and other C4 grasses could allow the deployment of improved genetic designs of annual energy Sorghums in the form of wide-hybrid perennial crops. The current trajectory of energy Sorghum genetic improvement indicates that it will be possible to sustainably produce biofuels from C4 grass bioenergy crops that are cost competitive with petroleum-based transportation fuels.

  18. Rice proteomics: a model system for crop improvement and food security.

    Science.gov (United States)

    Kim, Sun Tae; Kim, Sang Gon; Agrawal, Ganesh Kumar; Kikuchi, Shoshi; Rakwal, Randeep

    2014-03-01

    Rice proteomics has progressed at a tremendous pace since the year 2000, and that has resulted in establishing and understanding the proteomes of tissues, organs, and organelles under both normal and abnormal (adverse) environmental conditions. Established proteomes have also helped in re-annotating the rice genome and revealing the new role of previously known proteins. The progress of rice proteomics had recognized it as the corner/stepping stone for at least cereal crops. Rice proteomics remains a model system for crops as per its exemplary proteomics research. Proteomics-based discoveries in rice are likely to be translated in improving crop plants and vice versa against ever-changing environmental factors. This review comprehensively covers rice proteomics studies from August 2010 to July 2013, with major focus on rice responses to diverse abiotic (drought, salt, oxidative, temperature, nutrient, hormone, metal ions, UV radiation, and ozone) as well as various biotic stresses, especially rice-pathogen interactions. The differentially regulated proteins in response to various abiotic stresses in different tissues have also been summarized, indicating key metabolic and regulatory pathways. We envision a significant role of rice proteomics in addressing the global ground level problem of food security, to meet the demands of the human population which is expected to reach six to nine billion by 2040.

  19. Linear Sigma Models With Strongly Coupled Phases -- One Parameter Models

    CERN Document Server

    Hori, Kentaro

    2013-01-01

    We systematically construct a class of two-dimensional $(2,2)$ supersymmetric gauged linear sigma models with phases in which a continuous subgroup of the gauge group is totally unbroken. We study some of their properties by employing a recently developed technique. The focus of the present work is on models with one K\\"ahler parameter. The models include those corresponding to Calabi-Yau threefolds, extending three examples found earlier by a few more, as well as Calabi-Yau manifolds of other dimensions and non-Calabi-Yau manifolds. The construction leads to predictions of equivalences of D-brane categories, systematically extending earlier examples. There is another type of surprise. Two distinct superconformal field theories corresponding to Calabi-Yau threefolds with different Hodge numbers, $h^{2,1}=23$ versus $h^{2,1}=59$, have exactly the same quantum K\\"ahler moduli space. The strong-weak duality plays a crucial r\\^ole in confirming this, and also is useful in the actual computation of the metric on t...

  20. Sensitivity analysis of six soil organic matter models applied to the decomposition of animal manures and crop residues

    Directory of Open Access Journals (Sweden)

    Daniele Cavalli

    2016-09-01

    Full Text Available Two features distinguishing soil organic matter simulation models are the type of kinetics used to calculate pool decomposition rates, and the algorithm used to handle the effects of nitrogen (N shortage on carbon (C decomposition. Compared to widely used first-order kinetics, Monod kinetics more realistically represent organic matter decomposition, because they relate decomposition to both substrate and decomposer size. Most models impose a fixed C to N ratio for microbial biomass. When N required by microbial biomass to decompose a given amount of substrate-C is larger than soil available N, carbon decomposition rates are limited proportionally to N deficit (N inhibition hypothesis. Alternatively, C-overflow was proposed as a way of getting rid of excess C, by allocating it to a storage pool of polysaccharides. We built six models to compare the combinations of three decomposition kinetics (first-order, Monod, and reverse Monod, and two ways to simulate the effect of N shortage on C decomposition (N inhibition and C-overflow. We conducted sensitivity analysis to identify model parameters that mostly affected CO2 emissions and soil mineral N during a simulated 189-day laboratory incubation assuming constant water content and temperature. We evaluated model outputs sensitivity at different stages of organic matter decomposition in a soil amended with three inputs of increasing C to N ratio: liquid manure, solid manure, and low-N crop residue. Only few model parameters and their interactions were responsible for consistent variations of CO2 and soil mineral N. These parameters were mostly related to microbial biomass and to the partitioning of applied C among input pools, as well as their decomposition constants. In addition, in models with Monod kinetics, CO2 was also sensitive to a variation of the half-saturation constants. C-overflow enhanced pool decomposition compared to N inhibition hypothesis when N shortage occurred. Accumulated C in the

  1. Parameter identification in tidal models with uncertain boundaries

    NARCIS (Netherlands)

    Bagchi, Arunabha; ten Brummelhuis, P.G.J.; ten Brummelhuis, Paul

    1994-01-01

    In this paper we consider a simultaneous state and parameter estimation procedure for tidal models with random inputs, which is formulated as a minimization problem. It is assumed that some model parameters are unknown and that the random noise inputs only act upon the open boundaries. The

  2. Exploring the interdependencies between parameters in a material model.

    Energy Technology Data Exchange (ETDEWEB)

    Silling, Stewart Andrew; Fermen-Coker, Muge

    2014-01-01

    A method is investigated to reduce the number of numerical parameters in a material model for a solid. The basis of the method is to detect interdependencies between parameters within a class of materials of interest. The method is demonstrated for a set of material property data for iron and steel using the Johnson-Cook plasticity model.

  3. An Alternative Three-Parameter Logistic Item Response Model.

    Science.gov (United States)

    Pashley, Peter J.

    Birnbaum's three-parameter logistic function has become a common basis for item response theory modeling, especially within situations where significant guessing behavior is evident. This model is formed through a linear transformation of the two-parameter logistic function in order to facilitate a lower asymptote. This paper discusses an…

  4. Parameter identification in tidal models with uncertain boundaries

    NARCIS (Netherlands)

    Bagchi, Arunabha; Brummelhuis, ten Paul

    1994-01-01

    In this paper we consider a simultaneous state and parameter estimation procedure for tidal models with random inputs, which is formulated as a minimization problem. It is assumed that some model parameters are unknown and that the random noise inputs only act upon the open boundaries. The hyperboli

  5. A compact cyclic plasticity model with parameter evolution

    DEFF Research Database (Denmark)

    Krenk, Steen; Tidemann, L.

    2017-01-01

    , and it is demonstrated that this simple formulation enables very accurate representation of experimental results. An extension of the theory to account for model parameter evolution effects, e.g. in the form of changing yield level, is included in the form of extended evolution equations for the model parameters...

  6. Regionalization of SWAT Model Parameters for Use in Ungauged Watersheds

    Directory of Open Access Journals (Sweden)

    Indrajeet Chaubey

    2010-11-01

    Full Text Available There has been a steady shift towards modeling and model-based approaches as primary methods of assessing watershed response to hydrologic inputs and land management, and of quantifying watershed-wide best management practice (BMP effectiveness. Watershed models often require some degree of calibration and validation to achieve adequate watershed and therefore BMP representation. This is, however, only possible for gauged watersheds. There are many watersheds for which there are very little or no monitoring data available, thus the question as to whether it would be possible to extend and/or generalize model parameters obtained through calibration of gauged watersheds to ungauged watersheds within the same region. This study explored the possibility of developing regionalized model parameter sets for use in ungauged watersheds. The study evaluated two regionalization methods: global averaging, and regression-based parameters, on the SWAT model using data from priority watersheds in Arkansas. Resulting parameters were tested and model performance determined on three gauged watersheds. Nash-Sutcliffe efficiencies (NS for stream flow obtained using regression-based parameters (0.53–0.83 compared well with corresponding values obtained through model calibration (0.45–0.90. Model performance obtained using global averaged parameter values was also generally acceptable (0.4 ≤ NS ≤ 0.75. Results from this study indicate that regionalized parameter sets for the SWAT model can be obtained and used for making satisfactory hydrologic response predictions in ungauged watersheds.

  7. NWP model forecast skill optimization via closure parameter variations

    Science.gov (United States)

    Järvinen, H.; Ollinaho, P.; Laine, M.; Solonen, A.; Haario, H.

    2012-04-01

    We present results of a novel approach to tune predictive skill of numerical weather prediction (NWP) models. These models contain tunable parameters which appear in parameterizations schemes of sub-grid scale physical processes. The current practice is to specify manually the numerical parameter values, based on expert knowledge. We developed recently a concept and method (QJRMS 2011) for on-line estimation of the NWP model parameters via closure parameter variations. The method called EPPES ("Ensemble prediction and parameter estimation system") utilizes ensemble prediction infra-structure for parameter estimation in a very cost-effective way: practically no new computations are introduced. The approach provides an algorithmic decision making tool for model parameter optimization in operational NWP. In EPPES, statistical inference about the NWP model tunable parameters is made by (i) generating an ensemble of predictions so that each member uses different model parameter values, drawn from a proposal distribution, and (ii) feeding-back the relative merits of the parameter values to the proposal distribution, based on evaluation of a suitable likelihood function against verifying observations. In this presentation, the method is first illustrated in low-order numerical tests using a stochastic version of the Lorenz-95 model which effectively emulates the principal features of ensemble prediction systems. The EPPES method correctly detects the unknown and wrongly specified parameters values, and leads to an improved forecast skill. Second, results with an ensemble prediction system emulator, based on the ECHAM5 atmospheric GCM show that the model tuning capability of EPPES scales up to realistic models and ensemble prediction systems. Finally, preliminary results of EPPES in the context of ECMWF forecasting system are presented.

  8. Bayesian estimation of parameters in a regional hydrological model

    Directory of Open Access Journals (Sweden)

    K. Engeland

    2002-01-01

    Full Text Available This study evaluates the applicability of the distributed, process-oriented Ecomag model for prediction of daily streamflow in ungauged basins. The Ecomag model is applied as a regional model to nine catchments in the NOPEX area, using Bayesian statistics to estimate the posterior distribution of the model parameters conditioned on the observed streamflow. The distribution is calculated by Markov Chain Monte Carlo (MCMC analysis. T