Amplitude Models for Discrimination and Yield Estimation
Energy Technology Data Exchange (ETDEWEB)
Phillips, William Scott [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2016-09-01
This seminar presentation describes amplitude models and yield estimations that look at the data in order to inform legislation. The following points were brought forth in the summary: global models that will predict three-component amplitudes (R-T-Z) were produced; Q models match regional geology; corrected source spectra can be used for discrimination and yield estimation; three-component data increase coverage and reduce scatter in source spectral estimates; three-component efforts must include distance-dependent effects; a community effort on instrument calibration is needed.
The Impact of Statistical Leakage Models on Design Yield Estimation
Directory of Open Access Journals (Sweden)
Rouwaida Kanj
2011-01-01
Full Text Available Device mismatch and process variation models play a key role in determining the functionality and yield of sub-100 nm design. Average characteristics are often of interest, such as the average leakage current or the average read delay. However, detecting rare functional fails is critical for memory design and designers often seek techniques that enable accurately modeling such events. Extremely leaky devices can inflict functionality fails. The plurality of leaky devices on a bitline increase the dimensionality of the yield estimation problem. Simplified models are possible by adopting approximations to the underlying sum of lognormals. The implications of such approximations on tail probabilities may in turn bias the yield estimate. We review different closed form approximations and compare against the CDF matching method, which is shown to be most effective method for accurate statistical leakage modeling.
Estimating Corporate Yield Curves
Antionio Diaz; Frank Skinner
2001-01-01
This paper represents the first study of retail deposit spreads of UK financial institutions using stochastic interest rate modelling and the market comparable approach. By replicating quoted fixed deposit rates using the Black Derman and Toy (1990) stochastic interest rate model, we find that the spread between fixed and variable rates of interest can be modeled (and priced) using an interest rate swap analogy. We also find that we can estimate an individual bank deposit yield curve as a spr...
Evaluating accuracy of DSSAT model for soybean yield estimation using satellite weather data
Ovando, Gustavo; Sayago, Silvina; Bocco, Mónica
2018-04-01
Crop models allow simulating the development and yield of the crops, to represent and to evaluate the influence of multiple factors. The DSSAT cropping system model is one of the most widely used and contains CROPGRO module for soybean. This crop has a great importance for many southern countries of Latin America and for Argentina. Solar radiation and rainfall are necessary variables as inputs for crop models; however these data are not as readily available. The satellital products from Clouds and Earth's Radiant Energy System (CERES) and Tropic Rainfall Measurement Mission (TRMM) provide continuous spatial and temporal information of solar radiation and precipitation, respectively. This study evaluates and quantifies the uncertainty in estimating soybean yield using a DSSAT model, when recorded weather data are replaced with CERES and TRMM ones. Different percentages of data replacements, soybean maturity groups and planting dates are considered, for 2006-2016 period in Oliveros (Argentina). Results show that CERES and TRMM products can be used for soybean yield estimation with DSSAT considering that: percentage of data replacement, campaign, planting date and maturity group, determine the amounts and trends of yield errors. Replacements with CERES data up to 30% result in %RMSE lower than 10% in 87% of the cases; while the replacement with TRMM data presents the best statisticals in campaigns with high yields. Simulations based entirely on CERES solar radiation give better results than those with TRMM. In general, similar percentages of replacement show better performance in the estimation of soybean yield for solar radiation than the replacement of precipitation values.
Muslim, Mohammad; Romshoo, Shakil Ahmad; Rather, A Q
2015-06-01
The Kashmir Himalayan region of India is expected to be highly prone to the change in agricultural land use because of its geo-ecological fragility, strategic location vis-à-vis the Himalayan landscape, its trans-boundary river basins, and inherent socio-economic instabilities. Food security and sustainability of the region are thus greatly challenged by these impacts. The effect of future climate change, increased competition for land and water, labor from non-agricultural sectors, and increasing population adds to this complex problem. In current study, paddy rice yield at regional level was estimated using GIS-based environment policy integrated climate (GEPIC) model. The general approach of current study involved combining regional level crop database, regional soil data base, farm management data, and climatic data outputs with GEPIC model. The simulated yield showed that estimated production to be 4305.55 kg/ha (43.05 q h(-1)). The crop varieties like Jhelum, K-39, Chenab, China 1039, China-1007, and Shalimar rice-1 grown in plains recorded average yield of 4783.3 kg/ha (47.83 q ha(-1)). Meanwhile, high altitude areas with varieties like Kohsaar, K-78 (Barkat), and K-332 recorded yield of 4102.2 kg/ha (41.02 q ha(-1)). The observed and simulated yield showed a good match with R (2) = 0.95, RMSE = 132.24 kg/ha, respectively.
Kolotii, Andrii; Kussul, Nataliia; Skakun, Sergii; Shelestov, Andrii; Ostapenko, Vadim; Oliinyk, Tamara
2015-04-01
Efficient and timely crop monitoring and yield forecasting are important tasks for ensuring of stability and sustainable economic development [1]. As winter crops pay prominent role in agriculture of Ukraine - the main focus of this study is concentrated on winter wheat. In our previous research [2, 3] it was shown that usage of biophysical parameters of crops such as FAPAR (derived from Geoland-2 portal as for SPOT Vegetation data) is far more efficient for crop yield forecasting to NDVI derived from MODIS data - for available data. In our current work efficiency of usage such biophysical parameters as LAI, FAPAR, FCOVER (derived from SPOT Vegetation and PROBA-V data at resolution of 1 km and simulated within WOFOST model) and NDVI product (derived from MODIS) for winter wheat monitoring and yield forecasting is estimated. As the part of crop monitoring workflow (vegetation anomaly detection, vegetation indexes and products analysis) and yield forecasting SPIRITS tool developed by JRC is used. Statistics extraction is done for landcover maps created in SRI within FP-7 SIGMA project. Efficiency of usage satellite based and modelled with WOFOST model biophysical products is estimated. [1] N. Kussul, S. Skakun, A. Shelestov, O. Kussul, "Sensor Web approach to Flood Monitoring and Risk Assessment", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 815-818. [2] F. Kogan, N. Kussul, T. Adamenko, S. Skakun, O. Kravchenko, O. Kryvobok, A. Shelestov, A. Kolotii, O. Kussul, and A. Lavrenyuk, "Winter wheat yield forecasting in Ukraine based on Earth observation, meteorological data and biophysical models," International Journal of Applied Earth Observation and Geoinformation, vol. 23, pp. 192-203, 2013. [3] Kussul O., Kussul N., Skakun S., Kravchenko O., Shelestov A., Kolotii A, "Assessment of relative efficiency of using MODIS data to winter wheat yield forecasting in Ukraine", in: IGARSS 2013, 21-26 July 2013, Melbourne, Australia, pp. 3235 - 3238.
Refinement and evaluation of the Massachusetts firm-yield estimator model version 2.0
Levin, Sara B.; Archfield, Stacey A.; Massey, Andrew J.
2011-01-01
The firm yield is the maximum average daily withdrawal that can be extracted from a reservoir without risk of failure during an extended drought period. Previously developed procedures for determining the firm yield of a reservoir were refined and applied to 38 reservoir systems in Massachusetts, including 25 single- and multiple-reservoir systems that were examined during previous studies and 13 additional reservoir systems. Changes to the firm-yield model include refinements to the simulation methods and input data, as well as the addition of several scenario-testing capabilities. The simulation procedure was adapted to run at a daily time step over a 44-year simulation period, and daily streamflow and meteorological data were compiled for all the reservoirs for input to the model. Another change to the model-simulation methods is the adjustment of the scaling factor used in estimating groundwater contributions to the reservoir. The scaling factor is used to convert the daily groundwater-flow rate into a volume by multiplying the rate by the length of reservoir shoreline that is hydrologically connected to the aquifer. Previous firm-yield analyses used a constant scaling factor that was estimated from the reservoir surface area at full pool. The use of a constant scaling factor caused groundwater flows during periods when the reservoir stage was very low to be overestimated. The constant groundwater scaling factor used in previous analyses was replaced with a variable scaling factor that is based on daily reservoir stage. This change reduced instability in the groundwater-flow algorithms and produced more realistic groundwater-flow contributions during periods of low storage. Uncertainty in the firm-yield model arises from many sources, including errors in input data. The sensitivity of the model to uncertainty in streamflow input data and uncertainty in the stage-storage relation was examined. A series of Monte Carlo simulations were performed on 22 reservoirs
A Theoretical Model for Estimation of Yield Strength of Fiber Metal Laminate
Bhat, Sunil; Nagesh, Suresh; Umesh, C. K.; Narayanan, S.
2017-08-01
The paper presents a theoretical model for estimation of yield strength of fiber metal laminate. Principles of elasticity and formulation of residual stress are employed to determine the stress state in metal layer of the laminate that is found to be higher than the stress applied over the laminate resulting in reduced yield strength of the laminate in comparison with that of the metal layer. The model is tested over 4A-3/2 Glare laminate comprising three thin aerospace 2014-T6 aluminum alloy layers alternately bonded adhesively with two prepregs, each prepreg built up of three uni-directional glass fiber layers laid in longitudinal and transverse directions. Laminates with prepregs of E-Glass and S-Glass fibers are investigated separately under uni-axial tension. Yield strengths of both the Glare variants are found to be less than that of aluminum alloy with use of S-Glass fiber resulting in higher laminate yield strength than with the use of E-Glass fiber. Results from finite element analysis and tensile tests conducted over the laminates substantiate the theoretical model.
Effects of Source RDP Models and Near-source Propagation: Implication for Seismic Yield Estimation
Saikia, C. K.; Helmberger, D. V.; Stead, R. J.; Woods, B. B.
- It has proven difficult to uniquely untangle the source and propagation effects on the observed seismic data from underground nuclear explosions, even when large quantities of near-source, broadband data are available for analysis. This leads to uncertainties in our ability to quantify the nuclear seismic source function and, consequently the accuracy of seismic yield estimates for underground explosions. Extensive deterministic modeling analyses of the seismic data recorded from underground explosions at a variety of test sites have been conducted over the years and the results of these studies suggest that variations in the seismic source characteristics between test sites may be contributing to the observed differences in the magnitude/yield relations applicable at those sites. This contributes to our uncertainty in the determination of seismic yield estimates for explosions at previously uncalibrated test sites. In this paper we review issues involving the relationship of Nevada Test Site (NTS) source scaling laws to those at other sites. The Joint Verification Experiment (JVE) indicates that a magnitude (mb) bias (δmb) exists between the Semipalatinsk test site (STS) in the former Soviet Union (FSU) and the Nevada test site (NTS) in the United States. Generally this δmb is attributed to differential attenuation in the upper-mantle beneath the two test sites. This assumption results in rather large estimates of yield for large mb tunnel shots at Novaya Zemlya. A re-examination of the US testing experiments suggests that this δmb bias can partly be explained by anomalous NTS (Pahute) source characteristics. This interpretation is based on the modeling of US events at a number of test sites. Using a modified Haskell source description, we investigated the influence of the source Reduced Displacement Potential (RDP) parameters ψ ∞ , K and B by fitting short- and long-period data simultaneously, including the near-field body and surface waves. In general
MODIS Data Assimilation in the CROPGRO model for improving soybean yield estimations
Richetti, J.; Monsivais-Huertero, A.; Ahmad, I.; Judge, J.
2017-12-01
Soybean is one of the main agricultural commodities in the world. Thus, having better estimates of its agricultural production is important. Improving the soybean crop models in Brazil is crucial for better understanding of the soybean market and enhancing decision making, because Brazil is the second largest soybean producer in the world, Parana state is responsible for almost 20% of it, and by itself would be the fourth greatest soybean producer in the world. Data assimilation techniques provide a method to improve spatio-temporal continuity of crops through integration of remotely sensed observations and crop growth models. This study aims to use MODIS EVI to improve DSSAT-CROPGRO soybean yield estimations in the Parana state, southern Brazil. The method uses the Ensemble Kalman filter which assimilates MODIS Terra and Aqua combined products (MOD13Q1 and MYD13Q1) into the CROPGRO model to improve the agricultural production estimates through update of light interception data over time. Expected results will be validated with monitored commercial farms during the period of 2013-2014.
Mishra, V.; Cruise, J.; Mecikalski, J. R.
2017-12-01
Much effort has been expended recently on the assimilation of remotely sensed soil moisture into operational land surface models (LSM). These efforts have normally been focused on the use of data derived from the microwave bands and results have often shown that improvements to model simulations have been limited due to the fact that microwave signals only penetrate the top 2-5 cm of the soil surface. It is possible that model simulations could be further improved through the introduction of geostationary satellite thermal infrared (TIR) based root zone soil moisture in addition to the microwave deduced surface estimates. In this study, root zone soil moisture estimates from the TIR based Atmospheric Land Exchange Inverse (ALEXI) model were merged with NASA Soil Moisture Active Passive (SMAP) based surface estimates through the application of informational entropy. Entropy can be used to characterize the movement of moisture within the vadose zone and accounts for both advection and diffusion processes. The Principle of Maximum Entropy (POME) can be used to derive complete soil moisture profiles and, fortuitously, only requires a surface boundary condition as well as the overall mean moisture content of the soil column. A lower boundary can be considered a soil parameter or obtained from the LSM itself. In this study, SMAP provided the surface boundary while ALEXI supplied the mean and the entropy integral was used to tie the two together and produce the vertical profile. However, prior to the merging, the coarse resolution (9 km) SMAP data were downscaled to the finer resolution (4.7 km) ALEXI grid. The disaggregation scheme followed the Soil Evaporative Efficiency approach and again, all necessary inputs were available from the TIR model. The profiles were then assimilated into a standard agricultural crop model (Decision Support System for Agrotechnology, DSSAT) via the ensemble Kalman Filter. The study was conducted over the Southeastern United States for the
Berry, D.P.; Buckley, F.; Dillon, P.; Evans, R.D.; Rath, M.; Veerkamp, R.F.
2003-01-01
Genetic (co)variances between body condition score (BCS), body weight (BW), milk yield, and fertility were estimated using a random regression animal model extended to multivariate analysis. The data analyzed included 81,313 BCS observations, 91,937 BW observations, and 100,458 milk test-day yields
Costa, Anna; Molnar, Peter; Anghileri, Daniela
2017-04-01
Suspended sediment is associated with nutrient and contaminant transport in water courses. Estimating suspended sediment load is relevant for water-quality assessment, recreational activities, reservoir sedimentation issues, and ecological habitat assessment. Suspended sediment concentration (SSC) along channels is usually reproduced by suspended sediment rating curves, which relate SSC to discharge with a power law equation. Large uncertainty characterizes rating curves based only on discharge, because sediment supply is not explicitly accounted for. The aim of this work is to develop a source-oriented formulation of suspended sediment dynamics and to estimate suspended sediment yield at the outlet of a large Alpine catchment (upper Rhône basin, Switzerland). We propose a novel modelling approach for suspended sediment which accounts for sediment supply by taking into account the variety of sediment sources in an Alpine environment, i.e. the spatial location of sediment sources (e.g. distance from the outlet and lithology) and the different processes of sediment production and transport (e.g. by rainfall, overland flow, snowmelt). Four main sediment sources, typical of Alpine environments, are included in our model: glacial erosion, hillslope erosion, channel erosion and erosion by mass wasting processes. The predictive model is based on gridded datasets of precipitation and air temperature which drive spatially distributed degree-day models to simulate snowmelt and ice-melt, and determine erosive rainfall. A mass balance at the grid scale determines daily runoff. Each cell belongs to a different sediment source (e.g. hillslope, channel, glacier cell). The amount of sediment entrained and transported in suspension is simulated through non-linear functions of runoff, specific for sediment production and transport processes occurring at the grid scale (e.g. rainfall erosion, snowmelt-driven overland flow). Erodibility factors identify different lithological units
Integrating remote sensing, geographic information system and modeling for estimating crop yield
Salazar, Luis Alonso
This thesis explores various aspects of the use of remote sensing, geographic information system and digital signal processing technologies for broad-scale estimation of crop yield in Kansas. Recent dry and drought years in the Great Plains have emphasized the need for new sources of timely, objective and quantitative information on crop conditions. Crop growth monitoring and yield estimation can provide important information for government agencies, commodity traders and producers in planning harvest, storage, transportation and marketing activities. The sooner this information is available the lower the economic risk translating into greater efficiency and increased return on investments. Weather data is normally used when crop yield is forecasted. Such information, to provide adequate detail for effective predictions, is typically feasible only on small research sites due to expensive and time-consuming collections. In order for crop assessment systems to be economical, more efficient methods for data collection and analysis are necessary. The purpose of this research is to use satellite data which provides 50 times more spatial information about the environment than the weather station network in a short amount of time at a relatively low cost. Specifically, we are going to use Advanced Very High Resolution Radiometer (AVHRR) based vegetation health (VH) indices as proxies for characterization of weather conditions.
International Nuclear Information System (INIS)
Kaneko, D.; Moriwaki, Y.
2008-01-01
This study presents a crop production model improvement: the previously adopted Michaelis-Menten (MM) type photosynthesis response function (fsub(rad-MM)) was replaced with a Prioul-Chartier (PC) type function (fsub(rad-PC)). The authors' analysis reflects concerns regarding the background effect of global warming, under simultaneous conditions of high air temperature and strong solar radiation. The MM type function fsub(rad-MM) can give excessive values leading to an overestimate of photosynthesis rate (PSN) and grain yield for paddy-rice. The MM model is applicable to many plants whose (PSN) increases concomitant with increased insolation: wheat, maize, soybean, etc. For paddy rice, the PSN apparently shows a maximum PSN. This paper proves that the MM model overestimated the PSN for paddy rice for sufficient solar radiation: the PSN using the PC model yields 10% lower values. However, the unit crop production index (CPIsub(U)) is almost independent of the MM and PC models because of respective standardization of both PSN and crop production index using average PSNsub(0) and CPIsub(0). The authors improved the estimation method using a photosynthesis-and-sterility based crop situation index (CSIsub(E)) to produce a crop yield index (CYIsub(E)), which is used to estimate rice yields in place of the crop situation index (CSI); the CSI gives a percentage of rice yields compared to normal annual production. The model calculates PSN including biomass effects, low-temperature sterility, and high-temperature injury by incorporating insolation, effective air temperature, the normalized difference vegetation index (NDVI), and effects of temperature on photosynthesis. Based on routine observation data, the method enables automated crop-production monitoring in remote regions without special observations. This method can quantify grain production early to raise an alarm in Southeast Asian countries, which must confront climate fluctuation through this era of global
Spatial Rice Yield Estimation Based on MODIS and Sentinel-1 SAR Data and ORYZA Crop Growth Model
Directory of Open Access Journals (Sweden)
Tri D. Setiyono
2018-02-01
Full Text Available Crop insurance is a viable solution to reduce the vulnerability of smallholder farmers to risks from pest and disease outbreaks, extreme weather events, and market shocks that threaten their household food and income security. In developing and emerging countries, the implementation of area yield-based insurance, the form of crop insurance preferred by clients and industry, is constrained by the limited availability of detailed historical yield records. Remote-sensing technology can help to fill this gap by providing an unbiased and replicable source of the needed data. This study is dedicated to demonstrating and validating the methodology of remote sensing and crop growth model-based rice yield estimation with the intention of historical yield data generation for application in crop insurance. The developed system combines MODIS and SAR-based remote-sensing data to generate spatially explicit inputs for rice using a crop growth model. MODIS reflectance data were used to generate multitemporal LAI maps using the inverted Radiative Transfer Model (RTM. SAR data were used to generate rice area maps using MAPScape-RICE to mask LAI map products for further processing, including smoothing with logistic function and running yield simulation using the ORYZA crop growth model facilitated by the Rice Yield Estimation System (Rice-YES. Results from this study indicate that the approach of assimilating MODIS and SAR data into a crop growth model can generate well-adjusted yield estimates that adequately describe spatial yield distribution in the study area while reliably replicating official yield data with root mean square error, RMSE, of 0.30 and 0.46 t ha−1 (normalized root mean square error, NRMSE of 5% and 8% for the 2016 spring and summer seasons, respectively, in the Red River Delta of Vietnam, as evaluated at district level aggregation. The information from remote-sensing technology was also useful for identifying geographic locations with
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
Directory of Open Access Journals (Sweden)
Rebecca Boehm
2016-04-01
Full Text Available Farmers in China’s tea-growing regions report that monsoon dynamics and other weather factors are changing and that this is affecting tea harvest decisions. To assess the effect of climate change on tea production in China, this study uses historical weather and production data from 1980 to 2011 to construct a yield response model that estimates the partial effect of weather factors on tea yields in China, with a specific focus on East Asian Monsoon dynamics. Tea (Camellia sinensis (L. Kunze has not been studied using these methods even though it is an important crop for human nutrition and the economic well-being of rural communities in many countries. Previous studies have approximated the monsoon period using historical average onset and retreat dates, which we believe limits our understanding of how changing monsoon patterns affect crop productivity. In our analysis, we instead estimate the monsoon season across China’s tea growing regions empirically by identifying the unknown breakpoints in the year-by-province cumulative precipitation. We find that a 1% increase in the monsoon retreat date is associated with 0.481%–0.535% reduction in tea yield. In the previous year, we also find that a 1% increase in the date of the monsoon retreat is associated with a 0.604% decrease in tea yields. For precipitation, we find that a 1% increase in average daily precipitation occurring during the monsoon period is associated with a 0.184%–0.262% reduction in tea yields. In addition, our models show that 1% increase in the average daily monsoon precipitation from the previous growing season is associated with 0.258%–0.327% decline in yields. We also find that a 1% decrease in solar radiation in the previous growing season is associated with 0.554%-0.864% decrease in tea yields. These findings suggest the need for adaptive management and harvesting strategies given climate change projections and the known negative association between excess
Directory of Open Access Journals (Sweden)
Andrew E. Suyker
2013-11-01
Full Text Available Remote sensing techniques that provide synoptic and repetitive observations over large geographic areas have become increasingly important in studying the role of agriculture in global carbon cycles. However, it is still challenging to model crop yields based on remotely sensed data due to the variation in radiation use efficiency (RUE across crop types and the effects of spatial heterogeneity. In this paper, we propose a production efficiency model-based method to estimate corn and soybean yields with MODerate Resolution Imaging Spectroradiometer (MODIS data by explicitly handling the following two issues: (1 field-measured RUE values for corn and soybean are applied to relatively pure pixels instead of the biome-wide RUE value prescribed in the MODIS vegetation productivity product (MOD17; and (2 contributions to productivity from vegetation other than crops in mixed pixels are deducted at the level of MODIS resolution. Our estimated yields statistically correlate with the national survey data for rainfed counties in the Midwestern US with low errors for both corn (R2 = 0.77; RMSE = 0.89 MT/ha and soybeans (R2 = 0.66; RMSE = 0.38 MT/ha. Because the proposed algorithm does not require any retrospective analysis that constructs empirical relationships between the reported yields and remotely sensed data, it could monitor crop yields over large areas.
Principal component regression for crop yield estimation
Suryanarayana, T M V
2016-01-01
This book highlights the estimation of crop yield in Central Gujarat, especially with regard to the development of Multiple Regression Models and Principal Component Regression (PCR) models using climatological parameters as independent variables and crop yield as a dependent variable. It subsequently compares the multiple linear regression (MLR) and PCR results, and discusses the significance of PCR for crop yield estimation. In this context, the book also covers Principal Component Analysis (PCA), a statistical procedure used to reduce a number of correlated variables into a smaller number of uncorrelated variables called principal components (PC). This book will be helpful to the students and researchers, starting their works on climate and agriculture, mainly focussing on estimation models. The flow of chapters takes the readers in a smooth path, in understanding climate and weather and impact of climate change, and gradually proceeds towards downscaling techniques and then finally towards development of ...
Yield loss prediction models based on early estimation of weed pressure
DEFF Research Database (Denmark)
Asif, Ali; Streibig, Jens Carl; Andreasen, Christian
2013-01-01
thresholds are more relevant for site-specific weed management, because weeds are unevenly distributed in fields. Precision of prediction of yield loss is influenced by various factors such as locations, yield potential at the site, variation in competitive ability of mix stands of weed species and emergence...
Directory of Open Access Journals (Sweden)
Rafael de Ávila Rodrigues
2012-01-01
Full Text Available In recent years, crop models have increasingly been used to simulate agricultural features. The DSSAT (Decision Support System for Agrotechnology Transfer is an important tool in modeling growth; however, one of its limitations is related to the unaccounted-for effect of diseases. Therefore, the goals of this study were to calibrate and validate the CSM CROPGRO-Soybean for the soybean cultivars M-SOY 6101 and MG/BR 46 (Conquista, analyze the performance and the effect of Asian soybean rust on these cultivars under the environmental conditions of Viçosa, Minas Gerais, Brazil. The experimental data for the evaluation, testing, and adjustment of the genetic coefficients for the cultivars, M-SOY 6101 and MG/BR 46 (Conquista, were obtained during the 2006/2007, 2007/2008 and 2009/2010 growing seasons. GLUE (Generalized Likelihood Uncertainty Estimation was used for the estimation of the genetic coefficients, and pedotransfer functions have been utilized to estimate the physical characteristics of the soil. For all of the sowing dates, the early season cultivar, M-SOY 6101, exhibited a lower variance in yield, which represents more stability with regard to the interannual climate variability, i.e., the farmers who use this cultivar will have in 50% of the crop years analyzed, a higher yield than a late-season cultivar. The MG/BR 46 (Conquista cultivar demonstrated a greater probability of obtaining higher yield in years with favorable weather conditions. However, in the presence of the Asian soybean rust, yield is heavily affected. The early cultivar, M-SOY 6101, showed a lower risk of being affected by the rust and consequently exhibited less yield loss considering the scenario D90 (condensation on the leaf surface occurs when the relative humidity is greater than or equal to 90%, for a sowing date of November 14.
Berry, D P; Buckley, F; Dillon, P; Evans, R D; Rath, M; Veerkamp, R F
2003-11-01
Genetic (co)variances between body condition score (BCS), body weight (BW), milk yield, and fertility were estimated using a random regression animal model extended to multivariate analysis. The data analyzed included 81,313 BCS observations, 91,937 BW observations, and 100,458 milk test-day yields from 8725 multiparous Holstein-Friesian cows. A cubic random regression was sufficient to model the changing genetic variances for BCS, BW, and milk across different days in milk. The genetic correlations between BCS and fertility changed little over the lactation; genetic correlations between BCS and interval to first service and between BCS and pregnancy rate to first service varied from -0.47 to -0.31, and from 0.15 to 0.38, respectively. This suggests that maximum genetic gain in fertility from indirect selection on BCS should be based on measurements taken in midlactation when the genetic variance for BCS is largest. Selection for increased BW resulted in shorter intervals to first service, but more services and poorer pregnancy rates; genetic correlations between BW and pregnancy rate to first service varied from -0.52 to -0.45. Genetic selection for higher lactation milk yield alone through selection on increased milk yield in early lactation is likely to have a more deleterious effect on genetic merit for fertility than selection on higher milk yield in late lactation.
Chen, Yanling; Gong, Adu; Li, Jing; Wang, Jingmei
2017-04-01
Accurate crop growth monitoring and yield predictive information are significant to improve the sustainable development of agriculture and ensure the security of national food. Remote sensing observation and crop growth simulation models are two new technologies, which have highly potential applications in crop growth monitoring and yield forecasting in recent years. However, both of them have limitations in mechanism or regional application respectively. Remote sensing information can not reveal crop growth and development, inner mechanism of yield formation and the affection of environmental meteorological conditions. Crop growth simulation models have difficulties in obtaining data and parameterization from single-point to regional application. In order to make good use of the advantages of these two technologies, the coupling technique of remote sensing information and crop growth simulation models has been studied. Filtering and optimizing model parameters are key to yield estimation by remote sensing and crop model based on regional crop assimilation. Winter wheat of GaoCheng was selected as the experiment object in this paper. And then the essential data was collected, such as biochemical data and farmland environmental data and meteorological data about several critical growing periods. Meanwhile, the image of environmental mitigation small satellite HJ-CCD was obtained. In this paper, research work and major conclusions are as follows. (1) Seven vegetation indexes were selected to retrieve LAI, and then linear regression model was built up between each of these indexes and the measured LAI. The result shows that the accuracy of EVI model was the highest (R2=0.964 at anthesis stage and R2=0.920 at filling stage). Thus, EVI as the most optimal vegetation index to predict LAI in this paper. (2) EFAST method was adopted in this paper to conduct the sensitive analysis to the 26 initial parameters of the WOFOST model and then a sensitivity index was constructed
Marshall, M.; Tu, K. P.
2015-12-01
Large-area crop yield models (LACMs) are commonly employed to address climate-driven changes in crop yield and inform policy makers concerned with climate change adaptation. Production efficiency models (PEMs), a class of LACMs that rely on the conservative response of carbon assimilation to incoming solar radiation absorbed by a crop contingent on environmental conditions, have increasingly been used over large areas with remote sensing spectral information to improve the spatial resolution of crop yield estimates and address important data gaps. Here, we present a new PEM that combines model principles from the remote sensing-based crop yield and evapotranspiration (ET) model literature. One of the major limitations of PEMs is that they are evaluated using data restricted in both space and time. To overcome this obstacle, we first validated the model using 2009-2014 eddy covariance flux tower Gross Primary Production data in a rice field in the Central Valley of California- a critical agro-ecosystem of the United States. This evaluation yielded a Willmot's D and mean absolute error of 0.81 and 5.24 g CO2/d, respectively, using CO2, leaf area, temperature, and moisture constraints from the MOD16 ET model, Priestley-Taylor ET model, and the Global Production Efficiency Model (GLOPEM). A Monte Carlo simulation revealed that the model was most sensitive to the Enhanced Vegetation Index (EVI) input, followed by Photosynthetically Active Radiation, vapor pressure deficit, and air temperature. The model will now be evaluated using 30 x 30m (Landsat resolution) biomass transects developed in 2011 and 2012 from spectroradiometric and other non-destructive in situ metrics for several cotton, maize, and rice fields across the Central Valley. Finally, the model will be driven by Daymet and MODIS data over the entire State of California and compared with county-level crop yield statistics. It is anticipated that the new model will facilitate agro-climatic decision-making in
Veerkamp, R F; Koenen, E P; De Jong, G
2001-10-01
Twenty type classifiers scored body condition (BCS) of 91,738 first-parity cows from 601 sires and 5518 maternal grandsires. Fertility data during first lactation were extracted for 177,220 cows, of which 67,278 also had a BCS observation, and first-lactation 305-d milk, fat, and protein yields were added for 180,631 cows. Heritabilities and genetic correlations were estimated using a sire-maternal grandsire model. Heritability of BCS was 0.38. Heritabilities for fertility traits were low (0.01 to 0.07), but genetic standard deviations were substantial, 9 d for days to first service and calving interval, 0.25 for number of services, and 5% for first-service conception. Phenotypic correlations between fertility and yield or BCS were small (-0.15 to 0.20). Genetic correlations between yield and all fertility traits were unfavorable (0.37 to 0.74). Genetic correlations with BCS were between -0.4 and -0.6 for calving interval and days to first service. Random regression analysis (RR) showed that correlations changed with days in milk for BCS. Little agreement was found between variances and correlations from RR, and analysis including a single month (mo 1 to 10) of data for BCS, especially during early and late lactation. However, this was due to excluding data from the conventional analysis, rather than due to the polynomials used. RR and a conventional five-traits model where BCS in mo 1, 4, 7, and 10 was treated as a separate traits (plus yield or fertility) gave similar results. Thus a parsimonious random regression model gave more realistic estimates for the (co)variances than a series of bivariate analysis on subsets of the data for BCS. A higher genetic merit for yield has unfavorable effects on fertility, but the genetic correlation suggests that BCS (at some stages of lactation) might help to alleviate the unfavorable effect of selection for higher yield on fertility.
Reducing a cortical network to a Potts model yields storage capacity estimates
Naim, Michelangelo; Boboeva, Vezha; Kang, Chol Jun; Treves, Alessandro
2018-04-01
An autoassociative network of Potts units, coupled via tensor connections, has been proposed and analysed as an effective model of an extensive cortical network with distinct short- and long-range synaptic connections, but it has not been clarified in what sense it can be regarded as an effective model. We draw here the correspondence between the two, which indicates the need to introduce a local feedback term in the reduced model, i.e. in the Potts network. An effective model allows the study of phase transitions. As an example, we study the storage capacity of the Potts network with this additional term, the local feedback w, which contributes to drive the activity of the network towards one of the stored patterns. The storage capacity calculation, performed using replica tools, is limited to fully connected networks, for which a Hamiltonian can be defined. To extend the results to the case of intermediate partial connectivity, we also derive the self-consistent signal-to-noise analysis for the Potts network; and finally we discuss the implications for semantic memory in humans.
Modeling of Yield Estimation for The Main Crops in Iran Based on Mechanization Index (hp ha-1
Directory of Open Access Journals (Sweden)
K Abbasi
2014-09-01
Full Text Available Agricultural mechanization is a method for transiting from traditional agriculture towards industrial and sustainable one. Due to the limitation of natural resources and increasing population we need to have economical production of agricultural crops. For reaching this destination; agricultural mechanization has a remarkable role. So it is necessary to have an extensive view for mechanization, because with the help of mechanization the agricultural inputs such as seeds, fertilizer and even water and soil can effectively be managed for an economical and sustainable production. This study has been carried out in many provinces of Iran. The data of agricultural tractors and cereal combine harvesters were firstly gathered by means of questionnaire. The tractors were categorized in four power levels of less than 45, 45 to 80, 80 to 110, and more than 110 hp. In addition, it was also carried out for cereal combine harvesters; it was in three power levels, i.e. between 100 to 110, 110 to 155 and 155 to 210 horse-power in 3 ages, i.e. less than 13, between 13 to 20, and more than 20 years. Information regarding to cultivation areas, production volume, and yield of main crops gathered from statistics of Ministry of Jihad-e-Agriculture. Then agriculture mechanization level index (hp ha-1 in each province was calculated. Four main crops including irrigated and rain-fed wheat and irrigated and rain-fed barley, which met the required criteria to be used in the model, were statistically analyzed. Correlation analysis was carried out in order to get an effective model between yield of the four main crops in Iran and agriculture mechanization level index. Pearson correlation index showed that there is a direct and significant correlation between these variables. Subsequently, outliers were identified in order to get a model with necessary efficiency to predict the yield through mechanization level index, by scatter diagram and estimating regression lines in 1
Berry, D.P.; Buckley, F.; Dillon, P.; Evans, R.D.; Rath, M.; Veerkamp, R.F.
2003-01-01
(Co)variance components for milk yield, body condition score (BCS), body weight (BW), BCS change and BW change over different herd-year mean milk yields (HMY) and nutritional environments (concentrate feeding level, grazing severity and silage quality) were estimated using a random regression model.
Directory of Open Access Journals (Sweden)
Ajay Singh
2016-06-01
Full Text Available A single trait linear mixed random regression test-day model was applied for the first time for analyzing the first lactation monthly test-day milk yield records in Karan Fries cattle. The test-day milk yield data was modeled using a random regression model (RRM considering different order of Legendre polynomial for the additive genetic effect (4th order and the permanent environmental effect (5th order. Data pertaining to 1,583 lactation records spread over a period of 30 years were recorded and analyzed in the study. The variance component, heritability and genetic correlations among test-day milk yields were estimated using RRM. RRM heritability estimates of test-day milk yield varied from 0.11 to 0.22 in different test-day records. The estimates of genetic correlations between different test-day milk yields ranged 0.01 (test-day 1 [TD-1] and TD-11 to 0.99 (TD-4 and TD-5. The magnitudes of genetic correlations between test-day milk yields decreased as the interval between test-days increased and adjacent test-day had higher correlations. Additive genetic and permanent environment variances were higher for test-day milk yields at both ends of lactation. The residual variance was observed to be lower than the permanent environment variance for all the test-day milk yields.
Directory of Open Access Journals (Sweden)
Kaare Græsbøll
2016-12-01
Full Text Available Typically, central milk recording data from dairy herds are recorded less than monthly. Over-fitting early in lactation periods is a challenge, which we explored in different ways by reducing the number of parameters needed to describe the milk yield and somatic cell count of individual cows. Furthermore, we investigated how the parameters of lactation models correlate between parities and from dam to offspring. The aim of the study was to provide simple and robust models for cow level milk yield and somatic cell count (SCC for fitting to sparse data to parameterise herd- and cow-specific simulation of dairy herds.Data from 610 Danish Holstein herds were used to determine parity traits in milk production regarding milk yield and SCC of individual cows. Parity was stratified in first, second and third and higher for milk, and first to sixth and higher for SCC. Fitting of herd level parameters allowed for cow level lactation curves with three, two or one-parameters per lactation. Correlations of milk yield and SCC were estimated between lactations and between dam and offspring.The shape of the lactation curves varied markedly between farms. The correlation between lactations for milk yield and SCC were 0.2-0.6 and significant on more than 95% of farms. The variation in the daily milk yield was observed to be a source of variation to the SCC, and the total SCC was less correlated with the milk production than somatic cells per ml. A positive correlation was found between relative levels of the total SCC and the milk yield.The variation of lactation and SCC curves between farms highlights the importance of a herd level approach. The one-parameter per cow model using a herd level curve allows for estimating the cow production level from first the recording in the parity, while a two-parameter model requires more recordings for a credible estimate, but may more precisely predict persistence, and given the independence of parameters, these can be
Lee, J.; Kang, S.; Jang, K.; Ko, J.; Hong, S.
2012-12-01
Crop productivity is associated with the food security and hence, several models have been developed to estimate crop yield by combining remote sensing data with carbon cycle processes. In present study, we attempted to estimate crop GPP and NPP using algorithm based on the LUE model and a simplified respiration model. The state of Iowa and Illinois was chosen as the study site for estimating the crop yield for a period covering the 5 years (2006-2010), as it is the main Corn-Belt area in US. Present study focuses on developing crop-specific parameters for corn and soybean to estimate crop productivity and yield mapping using satellite remote sensing data. We utilized a 10 km spatial resolution daily meteorological data from WRF to provide cloudy-day meteorological variables but in clear-say days, MODIS-based meteorological data were utilized to estimate daily GPP, NPP, and biomass. County-level statistics on yield, area harvested, and productions were used to test model predicted crop yield. The estimated input meteorological variables from MODIS and WRF showed with good agreements with the ground observations from 6 Ameriflux tower sites in 2006. For examples, correlation coefficients ranged from 0.93 to 0.98 for Tmin and Tavg ; from 0.68 to 0.85 for daytime mean VPD; from 0.85 to 0.96 for daily shortwave radiation, respectively. We developed county-specific crop conversion coefficient, i.e. ratio of yield to biomass on 260 DOY and then, validated the estimated county-level crop yield with the statistical yield data. The estimated corn and soybean yields at the county level ranged from 671 gm-2 y-1 to 1393 gm-2 y-1 and from 213 gm-2 y-1 to 421 gm-2 y-1, respectively. The county-specific yield estimation mostly showed errors less than 10%. Furthermore, we estimated crop yields at the state level which were validated against the statistics data and showed errors less than 1%. Further analysis for crop conversion coefficient was conducted for 200 DOY and 280 DOY
Science yield estimation for AFTA coronagraphs
Traub, Wesley A.; Belikov, Ruslan; Guyon, Olivier; Kasdin, N. Jeremy; Krist, John; Macintosh, Bruce; Mennesson, Bertrand; Savransky, Dmitry; Shao, Michael; Serabyn, Eugene; Trauger, John
2014-08-01
We describe the algorithms and results of an estimation of the science yield for five candidate coronagraph designs for the WFIRST-AFTA space mission. The targets considered are of three types, known radial-velocity planets, expected but as yet undiscovered exoplanets, and debris disks, all around nearby stars. The results of the original estimation are given, as well as those from subsequently updated designs that take advantage of experience from the initial estimates.
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.
Zhang, Yuan; Yang, Bin; Liu, Xiaohui; Wang, Cuizhen
2017-05-01
Fast and accurate estimation of rice yield plays a role in forecasting rice productivity for ensuring regional or national food security. Microwave synthetic aperture radar (SAR) data has been proved to have a great potential for rice monitoring and parameters retrieval. In this study, a rice canopy scattering model (RCSM) was revised and then was applied to simulate the backscatter of rice canopy. The combination of RCSM and genetic algorithm (GA) was proposed for retrieving two important rice parameters relating to grain yield, ear length and ear number density, from a C-band, dual-polarization (HH and HV) Radarsat-2 SAR data. The stability of retrieved results of GA inversion was also evaluated by changing various parameter configurations. Results show that RCSM can effectively simulate backscattering coefficients of rice canopy at HH and HV mode with an error of <1 dB. Reasonable selection of GA's parameters is essential for stability and efficiency of rice parameter retrieval. Two rice parameters are retrieved by the proposed RCSM-GA technology with better accuracy. The rice ear length are estimated with error of <1.5 cm, and ear number density with error of <23 #/m2. Rice grain yields are effectively estimated and mapped by the retrieved ear length and number density via a simple yield regression equation. This study further illustrates the capability of C-band Radarsat-2 SAR data on retrieval of rice ear parameters and the practicability of radar remote sensing technology for operational yield estimation.
Rice yield estimation with multi-temporal Radarsat-2 data
Chen, Chi-Farn; Son, Nguyen-Thanh; Chen, Cheng-Ru
2015-04-01
Rice is the most important food crop in Taiwan. Monitoring rice crop yield is thus crucial for agronomic planners to formulate successful strategies to address national food security and rice grain export issues. However, there is a real challenge for this monitoring purpose because the size of rice fields in Taiwan was generally small and fragmented, and the cropping calendar was also different from region to region. Thus, satellite-based estimation of rice crop yield requires the data that have sufficient spatial and temporal resolutions. This study aimed to develop models to estimate rice crop yield from multi-temporal Radarsat-2 data (5 m resolution). Data processing were carried out for the first rice cropping season from February to July in 2014 in the western part of Taiwan, consisting of four main steps: (1) constructing time-series backscattering coefficient data, (2) spatiotemporal noise filtering of the time-series data, (3) establishment of crop yield models using the time-series backscattering coefficients and in-situ measured yield data, and (4) model validation using field data and government's yield statistics. The results indicated that backscattering behavior varied from region to region due to changes in cultural practices and cropping calendars. The highest correlation coefficient (R2 > 0.8) was obtained at the ripening period. The robustness of the established models was evaluated by comparisons between the estimated yields and in-situ measured yield data showed satisfactory results, with the root mean squared error (RMSE) smaller than 10%. Such results were reaffirmed by the correlation analysis between the estimated yields and government's rice yield statistics (R2 > 0.8). This study demonstrates advantages of using multi-temporal Radarsat-2 backscattering data for estimating rice crop yields in Taiwan prior to the harvesting period, and thus the methods were proposed for rice yield monitoring in other regions.
Directory of Open Access Journals (Sweden)
Javier Hernandez
2015-02-01
Full Text Available Plant breeding based on grain yield (GY is an expensive and time-consuming method, so new indirect estimation techniques to evaluate the performance of crops represent an alternative method to improve grain yield. The present study evaluated the ability of canopy reflectance spectroscopy at the range from 350 to 2500 nm to predict GY in a large panel (368 genotypes of wheat (Triticum aestivum L. through multivariate ridge regression models. Plants were treated under three water regimes in the Mediterranean conditions of central Chile: severe water stress (SWS, rain fed, mild water stress (MWS; one irrigation event around booting and full irrigation (FI with mean GYs of 1655, 4739, and 7967 kg∙ha−1, respectively. Models developed from reflectance data during anthesis and grain filling under all water regimes explained between 77% and 91% of the GY variability, with the highest values in SWS condition. When individual models were used to predict yield in the rest of the trials assessed, models fitted during anthesis under MWS performed best. Combined models using data from different water regimes and each phenological stage were used to predict grain yield, and the coefficients of determination (R2 increased to 89.9% and 92.0% for anthesis and grain filling, respectively. The model generated during anthesis in MWS was the best at predicting yields when it was applied to other conditions. Comparisons against conventional reflectance indices were made, showing lower predictive abilities. It was concluded that a Ridge Regression Model using a data set based on spectral reflectance at anthesis or grain filling represents an effective method to predict grain yield in genotypes under different water regimes.
Directory of Open Access Journals (Sweden)
Luciana Miura Sugawara Berka
2003-01-01
Full Text Available Agrometeorological models interfaced with the Geographic Information System - GIS are an alternative to simulate and quantify the effect of weather spatial and temporal variability on crop yield. The objective of this work was to adapt and interface an agrometeorological model with a GIS to estimate soybean [Glycine max (L. Merr.] yield. Yield estimates were generated for 144 municipalities in the State of Paraná, Brazil, responsible for 90% of the soybean production in the State, from 1996/1997 to 2000/2001. The model uses agronomical parameters and meteorological data to calculate maximum yield which will be penalized under drought stress. Comparative analyses between the yield estimated by the model and that reported by the Paraná State Department of Agriculture (SEAB were performed using the "t" test for paired observations. For the 1996/1997 year the model overestimated yield by 10.8%, which may be attributed to the occurrence of fungal diseases not considered by the model. For 1997/1998, 1998/1999 and 1999/2000 no differences (P > 0.05 were found between the yield estimated by the model and SEAB's data. For 2000/2001 the model underestimated yield by 10.5% and the cause for this difference needs further investigation. The model interfaced with a GIS is an useful tool to monitor soybean crop during growing season to estimate crop yield.Os modelos agrometeorológicos integrados em Sistemas de Informação Geográfica - SIG são uma alternativa para simular e quantificar o efeito da variabilidade espacial e temporal do clima sobre a produtividade agrícola. O objetivo deste trabalho foi adaptar e integrar um modelo agrometeorológico num SIG para estimar a produtividade da soja [Glycine max (L. Merr.]. Foram geradas estimativas de produtividade para 144 municípios do Estado do Paraná, responsáveis por 90% da produção de soja no Estado, em cinco anos-safra no período de 1996/1997 a 2000/2001. O modelo utiliza parâmetros agronômicos e
Veerkamp, R.F.; Koenen, E.P.C.; Jong, de G.
2001-01-01
Twenty type classifiers scored body condition (BCS) of 91,738 first-parity cows from 601 sires and 5518 maternal grandsires. Fertility data during first lactation were extracted for 177,220 cows, of which 67,278 also had a BCS observation, and first-lactation 305-d milk, fat, and protein yields were
CSIR Research Space (South Africa)
Malherbe, J
2014-07-01
Full Text Available -1 Meteorological Applications Vol. 21(3) Seasonal forecasts of the SINTEX-F coupled model applied to maize yield and streamflow estimates over north-eastern South Africa J. Malherbe,a* W. A. Landman,b,c C. Olivier,d H. Sakumae and J- J. Luof a Institute... for Soil, Climate and Water, Agricultural Research Council, Pretoria, South Africa b Council for Scientific and Industrial Research, Natural Resources and the Environment, Pretoria, South Africa c Department of Geography, Geoinformatics and Meteorology...
Katiyar, M.; van Balkom, M.W.; Rindt, C.C.M.; de Keizer, C.; Zondag, H.A.
2017-01-01
It is a common practice to test solar thermal and photovoltaic-thermal (PVT) collectors outdoors. This requires testing over several weeks to account for different weather conditions encountered throughout the year, which is costly and time consuming. The outcome of these tests is an estimation of
Braga, Ana Cláudia F. Medeiros; Silva, Richarde Marques da; Santos, Celso Augusto Guimarães; Galvão, Carlos de Oliveira; Nobre, Paulo
2013-08-01
The coastal zone of northeastern Brazil is characterized by intense human activities and by large settlements and also experiences high soil losses that can contribute to environmental damage. Therefore, it is necessary to build an integrated modeling-forecasting system for rainfall-runoff erosion that assesses plans for water availability and sediment yield that can be conceived and implemented. In this work, we present an evaluation of an integrated modeling system for a basin located in this region with a relatively low predictability of seasonal rainfall and a small area (600 km2). The National Center for Environmental Predictions - NCEP’s Regional Spectral Model (RSM) nested within the Center for Weather Forecasting and Climate Studies - CPTEC’s Atmospheric General Circulation Model (AGCM) were investigated in this study, and both are addressed in the simulation work. The rainfall analysis shows that: (1) the dynamic downscaling carried out by the regional RSM model approximates the frequency distribution of the daily observed data set although errors were detected in the magnitude and timing (anticipation of peaks, for example) at the daily scale, (2) an unbiased precipitation forecast seemed to be essential for use of the results in hydrological models, and (3) the information directly extracted from the global model may also be useful. The simulated runoff and reservoir-stored volumes are strongly linked to rainfall, and their estimation accuracy was significantly improved at the monthly scale, thus rendering the results useful for management purposes. The runoff-erosion forecasting displayed a large sediment yield that was consistent with the predicted rainfall.
Estimation of Rice Crop Yields Using Random Forests in Taiwan
Chen, C. F.; Lin, H. S.; Nguyen, S. T.; Chen, C. R.
2017-12-01
Rice is globally one of the most important food crops, directly feeding more people than any other crops. Rice is not only the most important commodity, but also plays a critical role in the economy of Taiwan because it provides employment and income for large rural populations. The rice harvested area and production are thus monitored yearly due to the government's initiatives. Agronomic planners need such information for more precise assessment of food production to tackle issues of national food security and policymaking. This study aimed to develop a machine-learning approach using physical parameters to estimate rice crop yields in Taiwan. We processed the data for 2014 cropping seasons, following three main steps: (1) data pre-processing to construct input layers, including soil types and weather parameters (e.g., maxima and minima air temperature, precipitation, and solar radiation) obtained from meteorological stations across the country; (2) crop yield estimation using the random forests owing to its merits as it can process thousands of variables, estimate missing data, maintain the accuracy level when a large proportion of the data is missing, overcome most of over-fitting problems, and run fast and efficiently when handling large datasets; and (3) error verification. To execute the model, we separated the datasets into two groups of pixels: group-1 (70% of pixels) for training the model and group-2 (30% of pixels) for testing the model. Once the model is trained to produce small and stable out-of-bag error (i.e., the mean squared error between predicted and actual values), it can be used for estimating rice yields of cropping seasons. The results obtained from the random forests-based regression were compared with the actual yield statistics indicated the values of root mean square error (RMSE) and mean absolute error (MAE) achieved for the first rice crop were respectively 6.2% and 2.7%, while those for the second rice crop were 5.3% and 2
Directory of Open Access Journals (Sweden)
Paulo Augusto Manfron
2012-10-01
Full Text Available Despite the great importance of soybeans in Brazil, there have been few applications of soybean crop modeling on Brazilian conditions. Thus, the objective of this study was to use modified crop models to estimate the depleted and potential soybean crop yield in Brazil. The climatic variable data used in the modified simulation of the soybean crop models were temperature, insolation and rainfall. The data set was taken from 33 counties (28 Sao Paulo state counties, and 5 counties from other states that neighbor São Paulo. Among the models, modifications in the estimation of the leaf area of the soybean crop, which includes corrections for the temperature, shading, senescence, CO2, and biomass partition were proposed; also, the methods of input for the model’s simulation of the climatic variables were reconsidered. The depleted yields were estimated through a water balance, from which the depletion coefficient was estimated. It can be concluded that the adaptation soybean growth crop model might be used to predict the results of the depleted and potential yield of soybeans, and it can also be used to indicate better locations and periods of tillage.Aplicações de modelos de previsão de produtividade na cultura da soja são muito raros. Assim, o objetivo desta pesquisa foi realizar a estimação da produtividade deplecionada e potencial da cultura de soja, usando modelos de previsão modificados. Os dados climáticos utilizados nos modelos de simulação foram a temperatura, precipitação e insolação. Os dados foram proveniente de 33 municípios (28 do estado de São Paulo, e cinco municípios de estados vizinhos. Dentre os modelos propostos modificados está a estimação da área foliar da soja, com correções para temperatura, sombreamento, senescência, CO2, partição de biomassa, bem como os métodos de simulação das variávies climáticas do “input” para o modelo. As produções deplecionadas foram estimadas através do balan
Performance of a procedure for yield estimation in fruit orchards
DEFF Research Database (Denmark)
Aravena Zamora, Felipe; Potin, Camila; Wulfsohn, Dvora-Laio
Early estimation of expected fruit tree yield is important for the market planning and for growers and exporters to plan for labour and boxes. Large variations in tree yield may be found, posing a challenge for accurate yield estimation. We evaluated a multilevel systematic sampling procedure...
ESTIMATION OF PEA GRAIN YIELD STABILITY (Pisum sativum L.
Directory of Open Access Journals (Sweden)
Tihomir Čupić
2003-06-01
Full Text Available The paper aimed to determine yield and estimate pea grain yield stability of newly-created lines JSG-1 (cultivar in recognition process as well as compare with foreign origin cultivars in agroecological area of east Slavonia. The trial was set up by a randomized block design on the experimental field of Agricultural Institute Osijek in four replicates in the five-year period (1998 – 2002. Six (five foreign and one inland cultivars were included by the trial: Eiffil, Erbi, JP-5, JSG-1 (in a recognition process, Torsz and Baccara. Stability parameters were calculated by the grouping method after Francis and Kannenberg (1978 and by the model of individual stability estimation after Eberhart and Russel method (1966. According to Francis and Kannenberg, cultivars Eiffil, Erbi, JSG-1 and Baccara belonged to group I known for high yield and low trait varying coefficient, thus, represent stabile yield cultivars. According to regression coefficient and regression deviation variance the most stabile cultivar appeared to be cultivar JSG-1 (bi =1.06 and S2 di=0.010 and the lowest one was Torsz (bi =0.67 and S2 di =0.160. Cultivar Baccara (bi = 1.22 and S2 di =0.034 was comprised by the group of unstabile and adaptible for high-yielding environments.
Rasmussen, Teresa J.; Lee, Casey J.; Ziegler, Andrew C.
2008-01-01
Johnson County is one of the most rapidly developing counties in Kansas. Population growth and expanding urban land use affect the quality of county streams, which are important for human and environmental health, water supply, recreation, and aesthetic value. This report describes estimates of streamflow and constituent concentrations, loads, and yields in relation to watershed characteristics in five Johnson County streams using continuous in-stream sensor measurements. Specific conductance, pH, water temperature, turbidity, and dissolved oxygen were monitored in five watersheds from October 2002 through December 2006. These continuous data were used in conjunction with discrete water samples to develop regression models for continuously estimating concentrations of other constituents. Continuous regression-based concentrations were estimated for suspended sediment, total suspended solids, dissolved solids and selected major ions, nutrients (nitrogen and phosphorus species), and fecal-indicator bacteria. Continuous daily, monthly, seasonal, and annual loads were calculated from concentration estimates and streamflow. The data are used to describe differences in concentrations, loads, and yields and to explain these differences relative to watershed characteristics. Water quality at the five monitoring sites varied according to hydrologic conditions; contributing drainage area; land use (including degree of urbanization); relative contributions from point and nonpoint constituent sources; and human activity within each watershed. Dissolved oxygen (DO) concentrations were less than the Kansas aquatic-life-support criterion of 5.0 mg/L less than 10 percent of the time at all sites except Indian Creek, which had DO concentrations less than the criterion about 15 percent of the time. Concentrations of suspended sediment, chloride (winter only), indicator bacteria, and pesticides were substantially larger during periods of increased streamflow. Suspended
Exoplanet Yield Estimation for Decadal Study Concepts using EXOSIMS
Morgan, Rhonda; Lowrance, Patrick; Savransky, Dmitry; Garrett, Daniel
2016-01-01
The anticipated upcoming large mission study concepts for the direct imaging of exo-earths present an exciting opportunity for exoplanet discovery and characterization. While these telescope concepts would also be capable of conducting a broad range of astrophysical investigations, the most difficult technology challenges are driven by the requirements for imaging exo-earths. The exoplanet science yield for these mission concepts will drive design trades and mission concept comparisons.To assist in these trade studies, the Exoplanet Exploration Program Office (ExEP) is developing a yield estimation tool that emphasizes transparency and consistent comparison of various design concepts. The tool will provide a parametric estimate of science yield of various mission concepts using contrast curves from physics-based model codes and Monte Carlo simulations of design reference missions using realistic constraints, such as solar avoidance angles, the observatory orbit, propulsion limitations of star shades, the accessibility of candidate targets, local and background zodiacal light levels, and background confusion by stars and galaxies. The python tool utilizes Dmitry Savransky's EXOSIMS (Exoplanet Open-Source Imaging Mission Simulator) design reference mission simulator that is being developed for the WFIRST Preliminary Science program. ExEP is extending and validating the tool for future mission concepts under consideration for the upcoming 2020 decadal review. We present a validation plan and preliminary yield results for a point design.
Directory of Open Access Journals (Sweden)
José Eduardo Boffino de Almeida Monteiro
2013-02-01
Full Text Available The objective of this work was to evaluate an estimation system for rice yield in Brazil, based on simple agrometeorological models and on the technological level of production systems. This estimation system incorporates the conceptual basis proposed by Doorenbos & Kassam for potential and attainable yields with empirical adjusts for maximum yield and crop sensitivity to water deficit, considering five categories of rice yield. Rice yield was estimated from 2000/2001 to 2007/2008, and compared to IBGE yield data. Regression analyses between model estimates and data from IBGE surveys resulted in significant coefficients of determination, with less dispersion in the South than in the North and Northeast regions of the country. Index of model efficiency (E1' ranged from 0.01 in the lower yield classes to 0.45 in higher ones, and mean absolute error ranged from 58 to 250 kg ha‑1, respectively.
Estimation of Maize grain yield using multispectral satellite data sets ...
African Journals Online (AJOL)
Dr-Adeline
Crop yield prediction is production estimates that are made a couple of months or ... involving the effect of biotic and abiotic factors cumulatively which could however ...... Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence.
A Comparison of Machine Learning Approaches for Corn Yield Estimation
Kim, N.; Lee, Y. W.
2017-12-01
Machine learning is an efficient empirical method for classification and prediction, and it is another approach to crop yield estimation. The objective of this study is to estimate corn yield in the Midwestern United States by employing the machine learning approaches such as the support vector machine (SVM), random forest (RF), and deep neural networks (DNN), and to perform the comprehensive comparison for their results. We constructed the database using satellite images from MODIS, the climate data of PRISM climate group, and GLDAS soil moisture data. In addition, to examine the seasonal sensitivities of corn yields, two period groups were set up: May to September (MJJAS) and July and August (JA). In overall, the DNN showed the highest accuracies in term of the correlation coefficient for the two period groups. The differences between our predictions and USDA yield statistics were about 10-11 %.
Seismic Methods of Identifying Explosions and Estimating Their Yield
Walter, W. R.; Ford, S. R.; Pasyanos, M.; Pyle, M. L.; Myers, S. C.; Mellors, R. J.; Pitarka, A.; Rodgers, A. J.; Hauk, T. F.
2014-12-01
Seismology plays a key national security role in detecting, locating, identifying and determining the yield of explosions from a variety of causes, including accidents, terrorist attacks and nuclear testing treaty violations (e.g. Koper et al., 2003, 1999; Walter et al. 1995). A collection of mainly empirical forensic techniques has been successfully developed over many years to obtain source information on explosions from their seismic signatures (e.g. Bowers and Selby, 2009). However a lesson from the three DPRK declared nuclear explosions since 2006, is that our historic collection of data may not be representative of future nuclear test signatures (e.g. Selby et al., 2012). To have confidence in identifying future explosions amongst the background of other seismic signals, and accurately estimate their yield, we need to put our empirical methods on a firmer physical footing. Goals of current research are to improve our physical understanding of the mechanisms of explosion generation of S- and surface-waves, and to advance our ability to numerically model and predict them. As part of that process we are re-examining regional seismic data from a variety of nuclear test sites including the DPRK and the former Nevada Test Site (now the Nevada National Security Site (NNSS)). Newer relative location and amplitude techniques can be employed to better quantify differences between explosions and used to understand those differences in term of depth, media and other properties. We are also making use of the Source Physics Experiments (SPE) at NNSS. The SPE chemical explosions are explicitly designed to improve our understanding of emplacement and source material effects on the generation of shear and surface waves (e.g. Snelson et al., 2013). Finally we are also exploring the value of combining seismic information with other technologies including acoustic and InSAR techniques to better understand the source characteristics. Our goal is to improve our explosion models
PREDICTION MODELS OF GRAIN YIELD AND CHARACTERIZATION
Directory of Open Access Journals (Sweden)
Narciso Ysac Avila Serrano
2009-06-01
Full Text Available With the objective to characterize the grain yield of five cowpea cultivars and to find linear regression models to predict it, a study was developed in La Paz, Baja California Sur, Mexico. A complete randomized blocks design was used. Simple and multivariate analyses of variance were carried out using the canonical variables to characterize the cultivars. The variables cluster per plant, pods per plant, pods per cluster, seeds weight per plant, seeds hectoliter weight, 100-seed weight, seeds length, seeds wide, seeds thickness, pods length, pods wide, pods weight, seeds per pods, and seeds weight per pods, showed significant differences (Pâ‰¤ 0.05 among cultivars. PaceÃ±o and IT90K-277-2 cultivars showed the higher seeds weight per plant. The linear regression models showed correlation coefficients â‰¥0.92. In these models, the seeds weight per plant, pods per cluster, pods per plant, cluster per plant and pods length showed significant correlations (Pâ‰¤ 0.05. In conclusion, the results showed that grain yield differ among cultivars and for its estimation, the prediction models showed determination coefficients highly dependable.
modelling relationship between rainfall variability and yields
African Journals Online (AJOL)
, S. and ... factors to rice yield. Adebayo and Adebayo (1997) developed double log multiple regression model to predict rice yield in Adamawa State, Nigeria. The general form of .... the second are the crop yield/values for millet and sorghum ...
minimum variance estimation of yield parameters of rubber tree
African Journals Online (AJOL)
2013-03-01
Mar 1, 2013 ... It is our opinion that Kalman filter is a robust estimator of the ... Kalman filter, parameter estimation, rubber clones, Chow failure test, autocorrelation, STAMP, data ...... Mills, T.C. Modelling Current Temperature Trends.
Soniat, Thomas M.; Klinck, John M.; Powell, Eric N.; Cooper, Nathan; Abdelguerfi, Mahdi; Hofmann, Eileen E.; Dahal, Janak; Tu, Shengru; Finigan, John; Eberline, Benjamin S.; La Peyre, Jerome F.; LaPeyre, Megan K.; Qaddoura, Fareed
2012-01-01
A numerical model is presented that defines a sustainability criterion as no net loss of shell, and calculates a sustainable harvest of seed (<75 mm) and sack or market oysters (≥75 mm). Stock assessments of the Primary State Seed Grounds conducted east of the Mississippi from 2009 to 2011 show a general trend toward decreasing abundance of sack and seed oysters. Retrospective simulations provide estimates of annual sustainable harvests. Comparisons of simulated sustainable harvests with actual harvests show a trend toward unsustainable harvests toward the end of the time series. Stock assessments combined with shell-neutral models can be used to estimate sustainable harvest and manage cultch through shell planting when actual harvest exceeds sustainable harvest. For exclusive restoration efforts (no fishing allowed), the model provides a metric for restoration success-namely, shell accretion. Oyster fisheries that remove shell versus reef restorations that promote shell accretion, although divergent in their goals, are convergent in their management; both require vigilant attention to shell budgets.
Large-area dry bean yield prediction modeling in Mexico
Given the importance of dry bean in Mexico, crop yield predictions before harvest are valuable for authorities of the agricultural sector, in order to define support for producers. The aim of this study was to develop an empirical model to estimate the yield of dry bean at the regional level prior t...
Estimating yield gaps at the cropping system level.
Guilpart, Nicolas; Grassini, Patricio; Sadras, Victor O; Timsina, Jagadish; Cassman, Kenneth G
2017-05-01
Yield gap analyses of individual crops have been used to estimate opportunities for increasing crop production at local to global scales, thus providing information crucial to food security. However, increases in crop production can also be achieved by improving cropping system yield through modification of spatial and temporal arrangement of individual crops. In this paper we define the cropping system yield potential as the output from the combination of crops that gives the highest energy yield per unit of land and time, and the cropping system yield gap as the difference between actual energy yield of an existing cropping system and the cropping system yield potential. Then, we provide a framework to identify alternative cropping systems which can be evaluated against the current ones. A proof-of-concept is provided with irrigated rice-maize systems at four locations in Bangladesh that represent a range of climatic conditions in that country. The proposed framework identified (i) realistic alternative cropping systems at each location, and (ii) two locations where expected improvements in crop production from changes in cropping intensity (number of crops per year) were 43% to 64% higher than from improving the management of individual crops within the current cropping systems. The proposed framework provides a tool to help assess food production capacity of new systems ( e.g. with increased cropping intensity) arising from climate change, and assess resource requirements (water and N) and associated environmental footprint per unit of land and production of these new systems. By expanding yield gap analysis from individual crops to the cropping system level and applying it to new systems, this framework could also be helpful to bridge the gap between yield gap analysis and cropping/farming system design.
Real-time yield estimation based on deep learning
Rahnemoonfar, Maryam; Sheppard, Clay
2017-05-01
Crop yield estimation is an important task in product management and marketing. Accurate yield prediction helps farmers to make better decision on cultivation practices, plant disease prevention, and the size of harvest labor force. The current practice of yield estimation based on the manual counting of fruits is very time consuming and expensive process and it is not practical for big fields. Robotic systems including Unmanned Aerial Vehicles (UAV) and Unmanned Ground Vehicles (UGV), provide an efficient, cost-effective, flexible, and scalable solution for product management and yield prediction. Recently huge data has been gathered from agricultural field, however efficient analysis of those data is still a challenging task. Computer vision approaches currently face diffident challenges in automatic counting of fruits or flowers including occlusion caused by leaves, branches or other fruits, variance in natural illumination, and scale. In this paper a novel deep convolutional network algorithm was developed to facilitate the accurate yield prediction and automatic counting of fruits and vegetables on the images. Our method is robust to occlusion, shadow, uneven illumination and scale. Experimental results in comparison to the state-of-the art show the effectiveness of our algorithm.
Estimation of rice yield affected by drought and relation between rice yield and TVDI
Hongo, C.; Tamura, E.; Sigit, G.
2016-12-01
Impact of climate change is not only seen on food production but also on food security and sustainable development of society. Adaptation to climate change is a pressing issue throughout the world to reduce the risks along with the plans and strategies for food security and sustainable development. As a key adaptation to the climate change, agricultural insurance is expected to play an important role in stabilizing agricultural production through compensating the losses caused by the climate change. As the adaptation, the Government of Indonesia has launched agricultural insurance program for damage of rice by drought, flood and pest and disease. The Government started a pilot project in 2013 and this year the pilot project has been extended to 22 provinces. Having the above as background, we conducted research on development of new damage assessment method for rice using remote sensing data which could be used for evaluation of damage ratio caused by drought in West Java, Indonesia. For assessment of the damage ratio, estimation of rice yield is a key. As the result of our study, rice yield affected by drought in dry season could be estimated at level of 1 % significance using SPOT 7 data taken in 2015, and the validation result was 0.8t/ha. Then, the decrease ratio in rice yield about each individual paddy field was calculated using data of the estimated result and the average yield of the past 10 years. In addition, TVDI (Temperature Vegetation Dryness Index) which was calculated from Landsat8 data in heading season indicated the dryness in low yield area. The result suggests that rice yield was affected by irrigation water shortage around heading season as a result of the decreased precipitation by El Nino. Through our study, it becomes clear that the utilization of remote sensing data can be promising for assessment of the damage ratio of rice production precisely, quickly and quantitatively, and also it can be incorporated into the insurance procedures.
A new system for seismic yield estimation of underground explosions
International Nuclear Information System (INIS)
Murphy, J.R.
1991-01-01
Research conducted over the past decade has led to the development of a number of innovative procedures for estimating the yields of underground nuclear explosions based on systematic analyses of digital seismic data recorded from these tests. In addition, a wide variety of new data regarding the geophysical environments at Soviet test locations have now become available as a result of the Joint Verification Experiment (JVE) and associated data exchanges. The system described in this paper represents an attempt to integrate all these new capabilities and data into a comprehensive operational prototype which can be used to obtain optimum seismic estimates of explosion yield together with quantitative measures of the uncertainty in those estimates. The implementation of this system has involved a wide variety of technical tasks, including the development of a comprehensive seismic database and related database access software, formulation of a graphical test site information interface for accessing available information on explosion source conditions, design of an interactive seismic analyst station for use in processing the observed data to extract the required magnitude measures and the incorporation of formal statistical analysis modules for use in yield estimation and assessment
Sugarcane yield estimation for climatic conditions in the state of Goiás
Directory of Open Access Journals (Sweden)
Jordana Moura Caetano
Full Text Available ABSTRACT Models that estimate potential and depleted crop yield according to climatic variable enable the crop planning and production quantification for a specific region. Therefore, the objective of this study was to compare methods to sugarcane yield estimates grown in the climatic condition in the central part of Goiás, Brazil. So, Agroecological Zone Method (ZAE and the model proposed by Scarpari (S were correlated with real data of sugarcane yield from an experimental area, located in Santo Antônio de Goiás, state of Goiás, Brazil. Data yield refer to the crops of 2008/2009 (sugarcane plant, 2009/2010, 2010/2011 and 2011/2012 (ratoon sugarcane. Yield rates were calculated as a function of atmospheric water demand and water deficit in the area under study. Real and estimated yields were adjusted in function of productivity loss due to cutting stage of sugarcane, using an average reduction in productivity observed in the experimental area and the average reduction in the state of Goiás. The results indicated that the ZAE method, considering the water deficit, displayed good yield estimates for cane-plant (d > 0.90. Water deficit decreased the yield rates (r = -0.8636; α = 0.05 while the thermal sum increased that rate for all evaluated harvests (r > 0.68; α = 0.05.
Ethiopian Wheat Yield and Yield Gap Estimation: A Spatial Small Area Integrated Data Approach
Mann, M.; Warner, J.
2015-12-01
Despite the collection of routine annual agricultural surveys and significant advances in GIS and remote sensing products, little econometric research has been undertaken in predicting developing nation's agricultural yields. In this paper, we explore the determinants of wheat output per hectare in Ethiopia during the 2011-2013 Meher crop seasons aggregated to the woreda administrative area. Using a panel data approach, combining national agricultural field surveys with relevant GIS and remote sensing products, the model explains nearly 40% of the total variation in wheat output per hectare across the country. The model also identifies specific contributors to wheat yields that include farm management techniques (eg. area planted, improved seed, fertilizer, irrigation), weather (eg. rainfall), water availability (vegetation and moisture deficit indexes) and policy intervention. Our findings suggest that woredas produce between 9.8 and 86.5% of their potential wheat output per hectare given their altitude, weather conditions, terrain, and plant health. At the median, Amhara, Oromiya, SNNP, and Tigray produce 48.6, 51.5, 49.7, and 61.3% of their local attainable yields, respectively. This research has a broad range of applications, especially from a public policy perspective: identifying causes of yield fluctuations, remotely evaluating larger agricultural intervention packages, and analyzing relative yield potential. Overall, the combination of field surveys with spatial data can be used to identify management priorities for improving production at a variety of administrative levels.
Estimation of heterosis in yield and yield attributing traits in single cross hybrids of maize
Directory of Open Access Journals (Sweden)
Hari Prasad Sharma
2016-12-01
Full Text Available A field experiment was conducted at National Maize Research Program, Rampur, Chitwan, Nepal during winter season from 6th October, 2015 to 5th March 2016 to estimate different heterosis on single cross maize hybrids . Thirteen maize hybrids were tested randomized complete block design with three replications. Hybrid namely RML-98/RL-105 gave the highest standard heterosis (57.5% for grain yield over CP-666 followed by RML-4/NML-2 (32.6%, RML-95/RL-105 (29% and RML-5/RL-105 (20.6%. The hybrid RML-98/RL-105 produced the highest standard heterosis (75.1% for grain yield over Rajkumar followed by RML-4/NML-2(50.2%, RML-95/RL-105(46.6%, RML-5/RL-105 and (35.7%. Mid and better parent heterosis were significantly higher for yield and yield attributes viz. ear length, ear diameter, no of kernel row per ear, no of kernel per row and test weight. The highest positive mid-parent heterosis for grain yield was found in RML-98/RL-105 followed by RML-5/RL-105, RML-95/RL-105, and RML-4/NML-2. For the grain yield the better parent heterosis was the highest in RML-98/RL-105, followed by RML-5/RL-105, RML-95/RL-105, and RML-4/NML-2. These results suggested that maize production can be maximized by cultivating hybrids namely RML-98/RL-105, RML-5/RL-105, RML-95/RL-105, and RML-4/NML-2 .
26Al yields from rotating Wolf--Rayet star models
Vuissoz, C.; Meynet, G.; Knoedlseder, J.; Cervino, M.; Schaerer, D.; Palacios, A.; Mowlavi, N.
2003-01-01
We present new $^{26}$Al stellar yields from rotating Wolf--Rayet stellar models which, at solar metallicity, well reproduce the observed properties of the Wolf-Rayet populations. These new yields are enhanced with respect to non--rotating models, even with respect to non--rotating models computed with enhanced mass loss rates. We briefly discuss some implications of the use of these new yields for estimating the global contribution of Wolf-Rayet stars to the quantity of $^{26}$Al now present...
Verification of “Channel-Probability Model” of Grain Yield Estimation
Directory of Open Access Journals (Sweden)
ZHENG Hong-yan
2016-07-01
Full Text Available The "channel-probability model" of grain yield estimation was verified and discussed systematically by using the grain production data from 1949 to 2014 in 16 typical counties, and 6 typical districts, and 31 provinces of China. The results showed as follows:(1Due to the geographical spatial scale was large enough, different climate zones and different meteorological conditions could compensated, and grain yield estimation error was small in the scale of nation. Therefore, it was not necessary to modify the grain yield estimation error by mirco-trend and the climate year types in the scale of nation. However, the grain yield estimation in the scale of province was located at the same of a climate zone,the scale was small, so the impact of the meteorological conditions on grain yield was less complementary than the scale of nation. While the spatial scale of districts and counties was smaller, accordingly the compensation of the impact of the meteorological conditions on grain yield was least. Therefore, it was necessary to use mrico-trend amendment and the climate year types amendment to modify the grain yield estimation in districts and counties.(2Mirco-trend modification had two formulas, generally, when the error of grain yield estimation was less than 10%, it could be modified by Y×(1-K; while the error of grain yield estimation was more than 10%, it could be modified by Y/(1+K.(3Generally, the grain estimation had 5 grades, and some had 7 grades because of large error fluctuation. The parameters modified of super-high yield year and super-low yield year must be depended on the real-time crop growth and the meteorological condition. (4By plenty of demonstration analysis, it was proved that the theory and method of "channel-probability model" was scientific and practical. In order to improve the accuracy of grain yield estimation, the parameters could be modified with micro-trend amendment and the climate year types amendment. If the
A Remote-Sensing Driven Tool for Estimating Crop Stress and Yields
Directory of Open Access Journals (Sweden)
Martha C. Anderson
2013-07-01
Full Text Available Biophysical crop simulation models are normally forced with precipitation data recorded with either gauges or ground-based radar. However, ground-based recording networks are not available at spatial and temporal scales needed to drive the models at many critical places on earth. An alternative would be to employ satellite-based observations of either precipitation or soil moisture. Satellite observations of precipitation are currently not considered capable of forcing the models with sufficient accuracy for crop yield predictions. However, deduction of soil moisture from space-based platforms is in a more advanced state than are precipitation estimates so that these data may be capable of forcing the models with better accuracy. In this study, a mature two-source energy balance model, the Atmosphere Land Exchange Inverse (ALEXI model, was used to deduce root zone soil moisture for an area of North Alabama, USA. The soil moisture estimates were used in turn to force the state-of-the-art Decision Support System for Agrotechnology Transfer (DSSAT crop simulation model. The study area consisted of a mixture of rainfed and irrigated cornfields. The results indicate that the model forced with the ALEXI moisture estimates produced yield simulations that compared favorably with observed yields and with the rainfed model. The data appear to indicate that the ALEXI model did detect the soil moisture signal from the mixed rainfed/irrigation corn fields and this signal was of sufficient strength to produce adequate simulations of recorded yields over a 10 year period.
Lowland rice yield estimates based on air temperature and solar radiation
International Nuclear Information System (INIS)
Pedro Júnior, M.J.; Sentelhas, P.C.; Moraes, A.V.C.; Villela, O.V.
1995-01-01
Two regression equations were developed to estimate lowland rice yield as a function of air temperature and incoming solar radiation, during the crop yield production period in Pindamonhangaba, SP, Brazil. The following rice cultivars were used: IAC-242, IAC-100, IAC-101 and IAC-102. The value of optimum air temperature obtained was 25.0°C and of optimum global solar radiation was 475 cal.cm -2 , day -1 . The best agrometeorological model was the one that related least deviation of air temperature and solar radiation in relation to the optimum value obtained through a multiple linear regression. The yield values estimated by the model showed good fit to actual yields of lowland rice (less than 10%). (author) [pt
Schauberger, Bernhard; Gornott, Christoph; Wechsung, Frank
2017-11-01
Quantifying the influence of weather on yield variability is decisive for agricultural management under current and future climate anomalies. We extended an existing semiempirical modeling scheme that allows for such quantification. Yield anomalies, measured as interannual differences, were modeled for maize, soybeans, and wheat in the United States and 32 other main producer countries. We used two yield data sets, one derived from reported yields and the other from a global yield data set deduced from remote sensing. We assessed the capacity of the model to forecast yields within the growing season. In the United States, our model can explain at least two-thirds (63%-81%) of observed yield anomalies. Its out-of-sample performance (34%-55%) suggests a robust yield projection capacity when applied to unknown weather. Out-of-sample performance is lower when using remote sensing-derived yield data. The share of weather-driven yield fluctuation varies spatially, and estimated coefficients agree with expectations. Globally, the explained variance in yield anomalies based on the remote sensing data set is similar to the United States (71%-84%). But the out-of-sample performance is lower (15%-42%). The performance discrepancy is likely due to shortcomings of the remote sensing yield data as it diminishes when using reported yield anomalies instead. Our model allows for robust forecasting of yields up to 2 months before harvest for several main producer countries. An additional experiment suggests moderate yield losses under mean warming, assuming no major changes in temperature extremes. We conclude that our model can detect weather influences on yield anomalies and project yields with unknown weather. It requires only monthly input data and has a low computational demand. Its within-season yield forecasting capacity provides a basis for practical applications like local adaptation planning. Our study underlines high-quality yield monitoring and statistics as critical
Chatterji, Gano
2011-01-01
Conclusions: Validated the fuel estimation procedure using flight test data. A good fuel model can be created if weight and fuel data are available. Error in assumed takeoff weight results in similar amount of error in the fuel estimate. Fuel estimation error bounds can be determined.
Yield Estimation of Sugar Beet Based on Plant Canopy Using Machine Vision Methods
Directory of Open Access Journals (Sweden)
S Latifaltojar
2014-09-01
Full Text Available Crop yield estimation is one of the most important parameters for information and resources management in precision agriculture. This information is employed for optimizing the field inputs for successive cultivations. In the present study, the feasibility of sugar beet yield estimation by means of machine vision was studied. For the field experiments stripped images were taken during the growth season with one month intervals. The image of horizontal view of plants canopy was prepared at the end of each month. At the end of growth season, beet roots were harvested and the correlation between the sugar beet canopy in each month of growth period and corresponding weight of the roots were investigated. Results showed that there was a strong correlation between the beet yield and green surface area of autumn cultivated sugar beets. The highest coefficient of determination was 0.85 at three months before harvest. In order to assess the accuracy of the final model, the second year of study was performed with the same methodology. The results depicted a strong relationship between the actual and estimated beet weights with R2=0.94. The model estimated beet yield with about 9 percent relative error. It is concluded that this method has appropriate potential for estimation of sugar beet yield based on band imaging prior to harvest
Added-values of high spatiotemporal remote sensing data in crop yield estimation
Gao, F.; Anderson, M. C.
2017-12-01
Timely and accurate estimation of crop yield before harvest is critical for food market and administrative planning. Remote sensing derived parameters have been used for estimating crop yield by using either empirical or crop growth models. The uses of remote sensing vegetation index (VI) in crop yield modeling have been typically evaluated at regional and country scales using coarse spatial resolution (a few hundred to kilo-meters) data or assessed over a small region at field level using moderate resolution spatial resolution data (10-100m). Both data sources have shown great potential in capturing spatial and temporal variability in crop yield. However, the added value of data with both high spatial and temporal resolution data has not been evaluated due to the lack of such data source with routine, global coverage. In recent years, more moderate resolution data have become freely available and data fusion approaches that combine data acquired from different spatial and temporal resolutions have been developed. These make the monitoring crop condition and estimating crop yield at field scale become possible. Here we investigate the added value of the high spatial and temporal VI for describing variability of crop yield. The explanatory ability of crop yield based on high spatial and temporal resolution remote sensing data was evaluated in a rain-fed agricultural area in the U.S. Corn Belt. Results show that the fused Landsat-MODIS (high spatial and temporal) VI explains yield variability better than single data source (Landsat or MODIS alone), with EVI2 performing slightly better than NDVI. The maximum VI describes yield variability better than cumulative VI. Even though VI is effective in explaining yield variability within season, the inter-annual variability is more complex and need additional information (e.g. weather, water use and management). Our findings augment the importance of high spatiotemporal remote sensing data and supports new moderate
Functional dynamic factor models with application to yield curve forecasting
Hays, Spencer
2012-09-01
Accurate forecasting of zero coupon bond yields for a continuum of maturities is paramount to bond portfolio management and derivative security pricing. Yet a universal model for yield curve forecasting has been elusive, and prior attempts often resulted in a trade-off between goodness of fit and consistency with economic theory. To address this, herein we propose a novel formulation which connects the dynamic factor model (DFM) framework with concepts from functional data analysis: a DFM with functional factor loading curves. This results in a model capable of forecasting functional time series. Further, in the yield curve context we show that the model retains economic interpretation. Model estimation is achieved through an expectation- maximization algorithm, where the time series parameters and factor loading curves are simultaneously estimated in a single step. Efficient computing is implemented and a data-driven smoothing parameter is nicely incorporated. We show that our model performs very well on forecasting actual yield data compared with existing approaches, especially in regard to profit-based assessment for an innovative trading exercise. We further illustrate the viability of our model to applications outside of yield forecasting.
Exoplanet Classification and Yield Estimates for Direct Imaging Missions
Kopparapu, Ravi Kumar; Hébrard, Eric; Belikov, Rus; Batalha, Natalie M.; Mulders, Gijs D.; Stark, Chris; Teal, Dillon; Domagal-Goldman, Shawn; Mandell, Avi
2018-04-01
Future NASA concept missions that are currently under study, like the Habitable Exoplanet Imaging Mission (HabEx) and the Large Ultra-violet Optical Infra Red Surveyor, could discover a large diversity of exoplanets. We propose here a classification scheme that distinguishes exoplanets into different categories based on their size and incident stellar flux, for the purpose of providing the expected number of exoplanets observed (yield) with direct imaging missions. The boundaries of this classification can be computed using the known chemical behavior of gases and condensates at different pressures and temperatures in a planetary atmosphere. In this study, we initially focus on condensation curves for sphalerite ZnS, {{{H}}}2{{O}}, {CO}}2, and {CH}}4. The order in which these species condense in a planetary atmosphere define the boundaries between different classes of planets. Broadly, the planets are divided into rocky planets (0.5–1.0 R ⊕), super-Earths (1.0–1.75 R ⊕), sub-Neptunes (1.75–3.5 R ⊕), sub-Jovians (3.5–6.0 R ⊕), and Jovians (6–14.3 R ⊕) based on their planet sizes, and “hot,” “warm,” and “cold” based on the incident stellar flux. We then calculate planet occurrence rates within these boundaries for different kinds of exoplanets, η planet, using the community coordinated results of NASA’s Exoplanet Program Analysis Group’s Science Analysis Group-13 (SAG-13). These occurrence rate estimates are in turn used to estimate the expected exoplanet yields for direct imaging missions of different telescope diameters.
Heritability estimates for yield and related traits in bread wheat
International Nuclear Information System (INIS)
Din, R.; Jehan, S.; Ibraullah, A.
2009-01-01
A set of 22 experimental wheat lines along with four check cultivars were evaluated in in-irrigated and unirrgated environments with objectives to determine genetic and phenotypic variation and heritability estimates for yield and its traits- The two environments were statistically at par for physiological maturity, plant height, spikes m/sub -2/. spike lets spike/sup -1/ and 1000-grain weight. Highly significant genetic variability existed among wheat lines (P < 0.0 I) in the combined analysis across two test environments for traits except 1000- grain weight. Genotypes x environment interactions were non-significant for traits indicating consistent performance of lines in two test environments. However lines and check cultivars were two to five days early in maturity under unirrigated environment. Plant height, spikes m/sup -2/ and 1000-grain weight also reduced under unirrigated environments. Genetic variances were greater than Environmental variances for most of traits- Heritability estimates were of higher magnitude (0.74 to 0.96) for plant height, medium (0.31 to 0.56) for physiological maturity. spikelets spike/sup -1/ (unirrigated) and 1000-grain weight, and low for spikes m/sup -2/. (author)
A numerical integration-based yield estimation method for integrated circuits
International Nuclear Information System (INIS)
Liang Tao; Jia Xinzhang
2011-01-01
A novel integration-based yield estimation method is developed for yield optimization of integrated circuits. This method tries to integrate the joint probability density function on the acceptability region directly. To achieve this goal, the simulated performance data of unknown distribution should be converted to follow a multivariate normal distribution by using Box-Cox transformation (BCT). In order to reduce the estimation variances of the model parameters of the density function, orthogonal array-based modified Latin hypercube sampling (OA-MLHS) is presented to generate samples in the disturbance space during simulations. The principle of variance reduction of model parameters estimation through OA-MLHS together with BCT is also discussed. Two yield estimation examples, a fourth-order OTA-C filter and a three-dimensional (3D) quadratic function are used for comparison of our method with Monte Carlo based methods including Latin hypercube sampling and importance sampling under several combinations of sample sizes and yield values. Extensive simulations show that our method is superior to other methods with respect to accuracy and efficiency under all of the given cases. Therefore, our method is more suitable for parametric yield optimization. (semiconductor integrated circuits)
A numerical integration-based yield estimation method for integrated circuits
Energy Technology Data Exchange (ETDEWEB)
Liang Tao; Jia Xinzhang, E-mail: tliang@yahoo.cn [Key Laboratory of Ministry of Education for Wide Bandgap Semiconductor Materials and Devices, School of Microelectronics, Xidian University, Xi' an 710071 (China)
2011-04-15
A novel integration-based yield estimation method is developed for yield optimization of integrated circuits. This method tries to integrate the joint probability density function on the acceptability region directly. To achieve this goal, the simulated performance data of unknown distribution should be converted to follow a multivariate normal distribution by using Box-Cox transformation (BCT). In order to reduce the estimation variances of the model parameters of the density function, orthogonal array-based modified Latin hypercube sampling (OA-MLHS) is presented to generate samples in the disturbance space during simulations. The principle of variance reduction of model parameters estimation through OA-MLHS together with BCT is also discussed. Two yield estimation examples, a fourth-order OTA-C filter and a three-dimensional (3D) quadratic function are used for comparison of our method with Monte Carlo based methods including Latin hypercube sampling and importance sampling under several combinations of sample sizes and yield values. Extensive simulations show that our method is superior to other methods with respect to accuracy and efficiency under all of the given cases. Therefore, our method is more suitable for parametric yield optimization. (semiconductor integrated circuits)
A Remote Sensing-Derived Corn Yield Assessment Model
Shrestha, Ranjay Man
be further associated with the actual yield. Utilizing satellite remote sensing products, such as daily NDVI derived from Moderate Resolution Imaging Spectroradiometer (MODIS) at 250 m pixel size, the crop yield estimation can be performed at a very fine spatial resolution. Therefore, this study examined the potential of these daily NDVI products within agricultural studies and crop yield assessments. In this study, a regression-based approach was proposed to estimate the annual corn yield through changes in MODIS daily NDVI time series. The relationship between daily NDVI and corn yield was well defined and established, and as changes in corn phenology and yield were directly reflected by the changes in NDVI within the growing season, these two entities were combined to develop a relational model. The model was trained using 15 years (2000-2014) of historical NDVI and county-level corn yield data for four major corn producing states: Kansas, Nebraska, Iowa, and Indiana, representing four climatic regions as South, West North Central, East North Central, and Central, respectively, within the U.S. Corn Belt area. The model's goodness of fit was well defined with a high coefficient of determination (R2>0.81). Similarly, using 2015 yield data for validation, 92% of average accuracy signified the performance of the model in estimating corn yield at county level. Besides providing the county-level corn yield estimations, the derived model was also accurate enough to estimate the yield at finer spatial resolution (field level). The model's assessment accuracy was evaluated using the randomly selected field level corn yield within the study area for 2014, 2015, and 2016. A total of over 120 plot level corn yield were used for validation, and the overall average accuracy was 87%, which statistically justified the model's capability to estimate plot-level corn yield. Additionally, the proposed model was applied to the impact estimation by examining the changes in corn yield
Directory of Open Access Journals (Sweden)
Daniel S. Grohs
2009-03-01
Full Text Available Áreas com diferentes potenciais de rendimento dentro de uma lavoura necessitam ser manejadas separadamente, para fins de aplicação da adubação nitrogenada em cobertura. O equipamento baseado em sensoriamento remoto terrestre (GreenSeeker é um dos instrumentos utilizados para separar diferentes zonas de manejo. Para fazer isso, o sensor permite a definição de classes para estimar o potencial produtivo de forma ágil, precisa e em tempo real. Com o instrumento, foi desenvolvido um modelo para estimativa do potencial produtivo em trigo e cevada, correlacionando o Índice de Vegetação por Diferença Normalizada (NDVI com a biomassa seca acumulada na parte aérea, por ocasião da emissão da sexta folha do colmo principal. A base do modelo foi a formação de classes de potencial produtivo correspondentes a zonas específicas de manejo da lavoura. Essas classes não necessitam ser específicas para diferentes cultivares e/ou espécies, visto que não se detectaram diferenças que justificassem a formação de grupos para elas. As superfícies de fundo (resíduos de restevas de soja e milho tiveram efeitos significativos nas leituras do sensor. O modelo continua válido mesmo se as leituras de NDVI forem feitas antes ou após o período recomendado para tal, podendo ser ajustado com sub ou superestimação. As análises de variabilidade espacial, futuramente, podem avaliar se, as zonas de potencial produtivo estimadas pelas classes de NDVI propostas pelo modelo, correspondem à flutuação espacial da biomassa, doses de N aplicadas e rendimento de grãos.Areas with different yield potential within a field need to be managed separately as for nitrogen application in small grain cereals. Terrestrial remote sensing-based equipment such as the GreenSeeker sensor is one of the tools available to handle different management zones. To do this, the sensor allows the definition of classes to estimate yield potential. A model which correlated the
Estimation of the yield of poplars in plantations of fast-growing species within current results
Directory of Open Access Journals (Sweden)
Martin Fajman
2009-01-01
Full Text Available Current results are presented of allometric yield estimates of the poplar short rotation coppice. According to a literature review it is obvious that yield estimates, based on measurable quantities of a growing stand, depend not only on the selected tree specie or its clone, but also on the site location. The Jap-105 poplar clone (P. nigra x P. maximowiczii allometric relations were analyzed by regression methods aimed at the creation of the yield estimation methodology at a testing site in Domanínek. Altogether, the twelve polynomial dependences of particular measured quantities approved the high empirical data conformity with the tested regression model (correlation index from 0.9033 to 0.9967. Within the forward stepwise regression, factors were selected, which explain best examined estimates of the total biomass DM; i.e. d.b.h. and stem height. Furthermore, the KESTEMONT’s (1971 model was verified with a satisfying conformity as well. Approving presented yield estimation methods, the presented models will be checked in a large-scale field trial.
Explosive Yield Estimation using Fourier Amplitude Spectra of Velocity Histories
Steedman, D. W.; Bradley, C. R.
2016-12-01
The Source Physics Experiment (SPE) is a series of explosive shots of various size detonated at varying depths in a borehole in jointed granite. The testbed includes an extensive array of accelerometers for measuring the shock environment close-in to the explosive source. One goal of SPE is to develop greater understanding of the explosion phenomenology in all regimes: from near-source, non-linear response to the far-field linear elastic region, and connecting the analyses from the respective regimes. For example, near-field analysis typically involves review of kinematic response (i.e., acceleration, velocity and displacement) in the time domain and looks at various indicators (e.g., peaks, pulse duration) to facilitate comparison among events. Review of far-field data more often is based on study of response in the frequency domain to facilitate comparison of event magnitudes. To try to "bridge the gap" between approaches, we have developed a scaling law for Fourier amplitude spectra of near-field velocity histories that successfully collapses data from a wide range of yields (100 kg to 5000 kg) and range to sensors in jointed granite. Moreover, we show that we can apply this scaling law to data from a new event to accurately estimate the explosive yield of that event. This approach presents a new way of working with near-field data that will be more compatible with traditional methods of analysis of seismic data and should serve to facilitate end-to-end event analysis. The goal is that this new approach to data analysis will eventually result in improved methods for discrimination of event type (i.e., nuclear or chemical explosion, or earthquake) and magnitude.
A global water supply reservoir yield model with uncertainty analysis
International Nuclear Information System (INIS)
Kuria, Faith W; Vogel, Richard M
2014-01-01
Understanding the reliability and uncertainty associated with water supply yields derived from surface water reservoirs is central for planning purposes. Using a global dataset of monthly river discharge, we introduce a generalized model for estimating the mean and variance of water supply yield, Y, expected from a reservoir for a prespecified reliability, R, and storage capacity, S assuming a flow record of length n. The generalized storage–reliability–yield (SRY) relationships reported here have numerous water resource applications ranging from preliminary water supply investigations, to economic and climate change impact assessments. An example indicates how our generalized SRY relationship can be combined with a hydroclimatic model to determine the impact of climate change on surface reservoir water supply yields. We also document that the variability of estimates of water supply yield are invariant to characteristics of the reservoir system, including its storage capacity and reliability. Standardized metrics of the variability of water supply yields are shown to depend only on the sample size of the inflows and the statistical characteristics of the inflow series. (paper)
Battude, Marjorie; Bitar, Ahmad Al; Brut, Aurore; Cros, Jérôme; Dejoux, Jean-François; Huc, Mireille; Marais Sicre, Claire; Tallec, Tiphaine; Demarez, Valérie
2016-04-01
Water resources are under increasing pressure as a result of global change and of a raising competition among the different users (agriculture, industry, urban). It is therefore important to develop tools able to estimate accurately crop water requirements in order to optimize irrigation while maintaining acceptable production. In this context, remote sensing is a valuable tool to monitor vegetation development and water demand. This work aims at developing a robust and generic methodology mainly based on high resolution remote sensing data to provide accurate estimates of maize yield and water needs at the watershed scale. Evapotranspiration (ETR) and dry aboveground biomass (DAM) of maize crops were modeled using time series of GAI images used to drive a simple agro-meteorological crop model (SAFYE, Duchemin et al., 2005). This model is based on a leaf partitioning function (Maas, 1993) for the simulation of crop biomass and on the FAO-56 methodology for the ETR simulation. The model also contains a module to simulate irrigation. This study takes advantage of the SPOT4 and SPOT5 Take5 experiments initiated by CNES (http://www.cesbio.ups-tlse.fr/multitemp/). They provide optical images over the watershed from February to May 2013 and from April to August 2015 respectively, with a temporal and spatial resolution similar to future images from the Sentinel-2 and VENμS missions. This dataset was completed with LandSat8 and Deimos1 images in order to cover the whole growing season while reducing the gaps in remote sensing time series. Radiometric, geometric and atmospheric corrections were achieved by the THEIA land data center, and the KALIDEOS processing chain. The temporal dynamics of the green area index (GAI) plays a key role in soil-plant-atmosphere interactions and in biomass accumulation process. Consistent seasonal dynamics of the remotely sensed GAI was estimated by applying a radiative transfer model based on artificial neural networks (BVNET, Baret
Using normalized difference vegetation index (NDVI) to estimate sugarcane yield and yield components
Sugarcane (Saccharum spp.) yield and yield components are important traits for growers and scientists to evaluate and select cultivars. Collection of these yield data would be labor intensive and time consuming in the early selection stages of sugarcane breeding cultivar development programs with a ...
Remote Estimation of Vegetation Fraction and Yield in Oilseed Rape with Unmanned Aerial Vehicle Data
Peng, Y.; Fang, S.; Liu, K.; Gong, Y.
2017-12-01
This study developed an approach for remote estimation of Vegetation Fraction (VF) and yield in oilseed rape, which is a crop species with conspicuous flowers during reproduction. Canopy reflectance in green, red, red edge and NIR bands was obtained by a camera system mounted on an unmanned aerial vehicle (UAV) when oilseed rape was in the vegetative growth and flowering stage. The relationship of several widely-used Vegetation Indices (VI) vs. VF was tested and found to be different in different phenology stages. At the same VF when oilseed rape was flowering, canopy reflectance increased in all bands, and the tested VI decreased. Therefore, two algorithms to estimate VF were calibrated respectively, one for samples during vegetative growth and the other for samples during flowering stage. During the flowering season, we also explored the potential of using canopy reflectance or VIs to estimate Flower Fraction (FF) in oilseed rape. Based on FF estimates, rape yield can be estimated using canopy reflectance data. Our model was validated in oilseed rape planted under different nitrogen fertilization applications and in different phenology stages. The results showed that it was able to predict VF and FF accurately in oilseed rape with estimation error below 6% and predict yield with estimation error below 20%.
Use of vegetation health data for estimation of aus rice yield in bangladesh.
Rahman, Atiqur; Roytman, Leonid; Krakauer, Nir Y; Nizamuddin, Mohammad; Goldberg, Mitch
2009-01-01
Rice is a vital staple crop for Bangladesh and surrounding countries, with interannual variation in yields depending on climatic conditions. We compared Bangladesh yield of aus rice, one of the main varieties grown, from official agricultural statistics with Vegetation Health (VH) Indices [Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI)] computed from Advanced Very High Resolution Radiometer (AVHRR) data covering a period of 15 years (1991-2005). A strong correlation was found between aus rice yield and VCI and VHI during the critical period of aus rice development that occurs during March-April (weeks 8-13 of the year), several months in advance of the rice harvest. Stepwise principal component regression (PCR) was used to construct a model to predict yield as a function of critical-period VHI. The model reduced the yield prediction error variance by 62% compared with a prediction of average yield for each year. Remote sensing is a valuable tool for estimating rice yields well in advance of harvest and at a low cost.
Use of Vegetation Health Data for Estimation of Aus Rice Yield in Bangladesh
Directory of Open Access Journals (Sweden)
Mohammad Nizamuddin
2009-04-01
Full Text Available Rice is a vital staple crop for Bangladesh and surrounding countries, with interannual variation in yields depending on climatic conditions. We compared Bangladesh yield of aus rice, one of the main varieties grown, from official agricultural statistics with Vegetation Health (VH Indices [Vegetation Condition Index (VCI, Temperature Condition Index (TCI and Vegetation Health Index (VHI] computed from Advanced Very High Resolution Radiometer (AVHRR data covering a period of 15 years (1991–2005. A strong correlation was found between aus rice yield and VCI and VHI during the critical period of aus rice development that occurs during March-April (weeks 8–13 of the year, several months in advance of the rice harvest. Stepwise principal component regression (PCR was used to construct a model to predict yield as a function of critical-period VHI. The model reduced the yield prediction error variance by 62% compared with a prediction of average yield for each year. Remote sensing is a valuable tool for estimating rice yields well in advance of harvest and at a low cost.
Track models and radiation chemical yields
International Nuclear Information System (INIS)
Chatterjee, A.; Magee, J.L.
1987-01-01
The authors are concerned only with systems in which single track effects dominate and radiation chemical yields are sums of yields for individual tracks. The authors know that the energy deposits of heavy particle tracks are composed of spurs along the particle trajectory (about one-half of the energy) and a more diffuse pattern composed of the tracks of knock-on electrons, called the penumbra (about one-half of the energy). The simplest way to introduce the concept of a unified track model for heavy particles is to consider the special case of the track of a heavy particle with an LET below 0.2-0.3eV/A, which in practice limits us to protons, deuterons, or particles with energy above 100 MeV per nucleon. At these LET values, to a good approximation, spurs formed by the main particle track can be considered to remain isolated throughout the radiation chemical reactions
Directory of Open Access Journals (Sweden)
Kehinde Anthony Mogaji
2016-07-01
Full Text Available This study developed a GIS-based multivariate regression (MVR yield rate prediction model of groundwater resource sustainability in the hard-rock geology terrain of southwestern Nigeria. This model can economically manage the aquifer yield rate potential predictions that are often overlooked in groundwater resources development. The proposed model relates the borehole yield rate inventory of the area to geoelectrically derived parameters. Three sets of borehole yield rate conditioning geoelectrically derived parameters—aquifer unit resistivity (ρ, aquifer unit thickness (D and coefficient of anisotropy (λ—were determined from the acquired and interpreted geophysical data. The extracted borehole yield rate values and the geoelectrically derived parameter values were regressed to develop the MVR relationship model by applying linear regression and GIS techniques. The sensitivity analysis results of the MVR model evaluated at P ⩽ 0.05 for the predictors ρ, D and λ provided values of 2.68 × 10−05, 2 × 10−02 and 2.09 × 10−06, respectively. The accuracy and predictive power tests conducted on the MVR model using the Theil inequality coefficient measurement approach, coupled with the sensitivity analysis results, confirmed the model yield rate estimation and prediction capability. The MVR borehole yield prediction model estimates were processed in a GIS environment to model an aquifer yield potential prediction map of the area. The information on the prediction map can serve as a scientific basis for predicting aquifer yield potential rates relevant in groundwater resources sustainability management. The developed MVR borehole yield rate prediction mode provides a good alternative to other methods used for this purpose.
Modelling crop yield in Iberia under drought conditions
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
Directory of Open Access Journals (Sweden)
Patrick D. SHAW
2010-08-01
Full Text Available Runoff or water yield is an important input to the Steady-State Water Chemistry (SSWC model for estimating critical loads of acidity. Herein, we present site-specific water yield estimates for a large number of lakes (779 across three provinces of western Canada (Manitoba, Saskatchewan, and British Columbia using an isotope mass balance (IMB approach. We explore the impact of applying site-specific hydrology as compared to use of regional runoff estimates derived from gridded datasets in assessing critical loads of acidity to these lakes. In general, the average water yield derived from IMB is similar to the long-term average runoff; however, IMB results suggest a much larger range in hydrological settings of the lakes, attributed to spatial heterogeneity in watershed characteristics and landcover. The comparison of critical loads estimates from the two methods suggests that use of average regional runoff data in the SSWC model may overestimate critical loads for the majority of lakes due to systematic skewness in the actual runoff distributions. Implications for use of site-specific hydrology in regional critical loads assessments across western Canada are discussed.
Couralt, D.; Hadria, R.; Ruget, F.; Duchemin, B.; Hagolle, O.
2009-09-01
This study focused on the feasibility of using remote sensing data acquired at high spatial and temporal resolution (FORMOSAT-2 images(http://www.spotimage.fr/web/en/977--formosat-2-images.php) for crop monitoring at regional scale. The monitoring of agricultural practices such as grassland mowing and irrigation is essential to simulate accurately all processes related to crop system. This information is needed for example in crop simulation models to estimate production, water and fertilizer consumption and can thus serve to better understand the interactions between agriculture and climate. The analysis of these interactions is especially important in Mediterranean region where the effects of climate changes and crop management modifications are increasingly marked. In this context, an experiment was conducted in 2006 in Crau region in the South-Eastern France. In this area, permanent grassland represents 67 % of the usable agricultural area, and it is often used with irrigation (47 % of the permanent grassland). A time series of 36 FORMOSAT-2 images was acquired with a three days frequency from March to October 2006. Information concerning grassland mowing and irrigation was collected through a survey over 120 fields. The high FORMOSAT-2 revisit frequency allowed replicating the dynamics of Leaf Area index (LAI), and detecting to some extents cultural practices like vegetation cut. Simple automatic algorithms were developed to obtain daily values of LAI for each grasslands field linked with the main agricultural practices performed (cut and irrigation dates). This information was then used in a crop model called STICS (http://147.100.66.194/stics/) to estimate the spatial variability of evapotranspiration and drainage associated with the aerial biomass productions. Comparisons between simulated and observed yields gave satisfactory results. The great spatial variations of evapotranspiration were strongly related to the crop and water management. Such
Assefa S. Desta
2006-01-01
A stochastic precipitation-runoff modeling is used to estimate a cold and warm-seasons water yield from a ponderosa pine forested watershed in the north-central Arizona. The model consists of two parts namely, simulation of the temporal and spatial distribution of precipitation using a stochastic, event-based approach and estimation of water yield from the watershed...
DEFF Research Database (Denmark)
Diouf, Abdoul Aziz; Hiernaux, Pierre; Brandt, Martin Stefan
2016-01-01
evapotranspiration satellite gridded data to estimate the annual herbaceous yield in the semi-arid areas of Senegal. It showed that a machine-learning model combining FAPAR seasonal metrics with various agrometeorological data provided better estimations of the in situ annual herbaceous yield (R2 = 0.69; RMSE = 483...... kg·DM/ha) than models based exclusively on FAPAR metrics (R2 = 0.63; RMSE = 550 kg·DM/ha) or agrometeorological variables (R2 = 0.55; RMSE = 585 kg·DM/ha). All the models provided reasonable outputs and showed a decrease in the mean annual yield with increasing latitude, together with an increase...
Modeling Growth and Yield of Schizolobium amazonicum under Different Spacings
Directory of Open Access Journals (Sweden)
Gilson Fernandes da Silva
2013-01-01
Full Text Available This study aimed to present an approach to model the growth and yield of the species Schizolobium amazonicum (Paricá based on a study of different spacings located in Pará, Brazil. Whole-stand models were employed, and two modeling strategies (Strategies A and B were tested. Moreover, the following three scenarios were evaluated to assess the accuracy of the model in estimating total and commercial volumes at five years of age: complete absence of data (S1; available information about the variables basal area, site index, dominant height, and number of trees at two years of age (S2; and this information available at five years of age (S3. The results indicated that the 3 × 2 spacing has a higher mortality rate than normal, and, in general, greater spacing corresponds to larger diameter and average height and smaller basal area and volume per hectare. In estimating the total and commercial volumes for the three scenarios tested, Strategy B seems to be the most appropriate method to estimate the growth and yield of Paricá plantations in the study region, particularly because Strategy A showed a significant bias in its estimates.
Integrated model for predicting rice yield with climate change
Park, Jin-Ki; Das, Amrita; Park, Jong-Hwa
2018-04-01
Rice is the chief agricultural product and one of the primary food source. For this reason, it is of pivotal importance for worldwide economy and development. Therefore, in a decision-support-system both for the farmers and in the planning and management of the country's economy, forecasting yield is vital. However, crop yield, which is a dependent of the soil-bio-atmospheric system, is difficult to represent in statistical language. This paper describes a novel approach for predicting rice yield using artificial neural network, spatial interpolation, remote sensing and GIS methods. Herein, the variation in the yield is attributed to climatic parameters and crop health, and the normalized difference vegetation index from MODIS is used as an indicator of plant health and growth. Due importance was given to scaling up the input parameters using spatial interpolation and GIS and minimising the sources of error in every step of the modelling. The low percentage error (2.91) and high correlation (0.76) signifies the robust performance of the proposed model. This simple but effective approach is then used to estimate the influence of climate change on South Korean rice production. As proposed in the RCP8.5 scenario, an upswing in temperature may increase the rice yield throughout South Korea.
Calculational estimations of neutron yield from ADS target
International Nuclear Information System (INIS)
Degtyarev, I.I.; Liashenko, O.A.; Yazynin, I.A.; Belyakov-Bodin, V.I.; Blokhin, A.I.
2002-01-01
Results of computational studies of high power spallation thick ADS (Accelerator-Driven System) targets with 0.8-1.2 GeV proton beams are given. Comparisons of experiments and calculations of double differential and integral n/p yield are also described. (author)
Smith, T.; McLaughlin, D.
2017-12-01
Growing more crops to provide a secure food supply to an increasing global population will further stress land and water resources that have already been significantly altered by agriculture. The connection between production and resource use depends on crop yields and unit evapotranspiration (UET) rates that vary greatly, over both time and space. For regional and global analyses of food security it is appropriate to treat yield and UET as uncertain variables conditioned on climatic and soil properties. This study describes how probability distributions of these variables can be estimated by combining remotely sensed land use and evapotranspiration data with in situ agronomic and soils data, all available at different resolutions and coverages. The results reveal the influence of water and temperature stress on crop yield at large spatial scales. They also provide a basis for stochastic modeling and optimization procedures that explicitly account for uncertainty in the environmental factors that affect food production.
Yield estimation based on calculated comparisons to particle velocity data recorded at low stress
International Nuclear Information System (INIS)
Rambo, J.
1993-01-01
This paper deals with the problem of optimizing the yield estimation process if some of the material properties are known from geophysical measurements and others are inferred from in-situ dynamic measurements. The material models and 2-D simulations of the event are combined to determine the yield. Other methods of yield determination from peak particle velocity data have mostly been based on comparisons of nearby events in similar media at NTS. These methods are largely empirical and are subject to additional error when a new event has different properties than the population being used for a basis of comparison. The effect of material variations can be examined using LLNL's KDYNA computer code. The data from an NTS event provide the instructive example for simulation
Estimating agricultural yield gap in Africa using MODIS NDVI dataset
Luan, Y.; Zhu, W.; Luo, X.; Liu, J.; Cui, X.
2013-12-01
Global agriculture has undergone a period of rapid intensification characterized as 'Green Revolution', except for Africa, which is the region most affected by unreliable food access and undernourishment. Increasing crop production will be one of the most challenges and most effectual way to mitigate food insecurity there, as Africa's agricultural yield is on a much lower level comparing to global average. In this study we characterize cropland vegetation phenology in Africa based on MODIS NDVI time series between 2000 and 2012. Cumulated NDVI is a proxy for net primary productivity and used as an indicator for evaluating the potential yield gap in Africa. It is achieved via translating the gap between optimum attainable productivity level in each classification of cropping systems and actual productivity level by the relationship of cumulated NDVI and cereal-equivalent production. The results show most of cropland area in Africa have decreasing trend in cumulated NDVI, distributing in the Nile Delta, Eastern Africa and central of semi-arid to arid savanna area, except significant positive cumulated NDVI trends are mainly found between Senegal and Benin. Using cumulated NDVI and statistics of cereal equivalent production, we find remarkable potential yield gap at the Horn of East Africa (especially in Somalia), Northern Africa (Morocco, Algeria and Tunisia). Meanwhile, countries locating at the savanna area near Sahel desert and South Africa also show significant potential, though they already have a relatively high level of productivity. Our results can help provide policy recommendation for local government or NGO to tackle food security problems by identifying zones with high potential of yield improvement.
Quantum molecular dynamics approach to estimate spallation yield ...
Indian Academy of Sciences (India)
Consequently, the need for reliable data to design and construct spallation neutron sources has prompted ... A major disadvantage of the QMD code .... have estimated the average neutron multiplicities per primary reaction and kinetic energy.
Growth models for ponderosa pine: I. Yield of unthinned plantations in northern California.
William W. Oliver; Robert F. Powers
1978-01-01
Yields for high-survival, unthinned ponderosa pine (Pinus ponderosa Laws.) plantations in northern California are estimated. Stems of 367 trees in 12 plantations were analyzed to produce a growth model simulating stand yields. Diameter, basal area, and net cubic volume yields by Site Indices50 40 through 120 are tabulated for...
Similar estimates of temperature impacts on global wheat yield by three independent methods
DEFF Research Database (Denmark)
Liu, Bing; Asseng, Senthold; Müller, Christoph
2016-01-01
The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produ......-method ensemble, it was possible to quantify ‘method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.......The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce...... similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries...
Similar Estimates of Temperature Impacts on Global Wheat Yield by Three Independent Methods
Liu, Bing; Asseng, Senthold; Muller, Christoph; Ewart, Frank; Elliott, Joshua; Lobell, David B.; Martre, Pierre; Ruane, Alex C.; Wallach, Daniel; Jones, James W.;
2016-01-01
The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify 'method uncertainty' in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.
Similar estimates of temperature impacts on global wheat yield by three independent methods
Liu, Bing; Asseng, Senthold; Müller, Christoph; Ewert, Frank; Elliott, Joshua; Lobell, David B.; Martre, Pierre; Ruane, Alex C.; Wallach, Daniel; Jones, James W.; Rosenzweig, Cynthia; Aggarwal, Pramod K.; Alderman, Phillip D.; Anothai, Jakarat; Basso, Bruno; Biernath, Christian; Cammarano, Davide; Challinor, Andy; Deryng, Delphine; Sanctis, Giacomo De; Doltra, Jordi; Fereres, Elias; Folberth, Christian; Garcia-Vila, Margarita; Gayler, Sebastian; Hoogenboom, Gerrit; Hunt, Leslie A.; Izaurralde, Roberto C.; Jabloun, Mohamed; Jones, Curtis D.; Kersebaum, Kurt C.; Kimball, Bruce A.; Koehler, Ann-Kristin; Kumar, Soora Naresh; Nendel, Claas; O'Leary, Garry J.; Olesen, Jørgen E.; Ottman, Michael J.; Palosuo, Taru; Prasad, P. V. Vara; Priesack, Eckart; Pugh, Thomas A. M.; Reynolds, Matthew; Rezaei, Ehsan E.; Rötter, Reimund P.; Schmid, Erwin; Semenov, Mikhail A.; Shcherbak, Iurii; Stehfest, Elke; Stöckle, Claudio O.; Stratonovitch, Pierre; Streck, Thilo; Supit, Iwan; Tao, Fulu; Thorburn, Peter; Waha, Katharina; Wall, Gerard W.; Wang, Enli; White, Jeffrey W.; Wolf, Joost; Zhao, Zhigan; Zhu, Yan
2016-12-01
The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO2 fertilization effects, produce similar estimates of temperature impact on wheat yields at global and national scales. With a 1 °C global temperature increase, global wheat yield is projected to decline between 4.1% and 6.4%. Projected relative temperature impacts from different methods were similar for major wheat-producing countries China, India, USA and France, but less so for Russia. Point-based and grid-based simulations, and to some extent the statistical regressions, were consistent in projecting that warmer regions are likely to suffer more yield loss with increasing temperature than cooler regions. By forming a multi-method ensemble, it was possible to quantify `method uncertainty’ in addition to model uncertainty. This significantly improves confidence in estimates of climate impacts on global food security.
Soybean yield modeling using bootstrap methods for small samples
Energy Technology Data Exchange (ETDEWEB)
Dalposso, G.A.; Uribe-Opazo, M.A.; Johann, J.A.
2016-11-01
One of the problems that occur when working with regression models is regarding the sample size; once the statistical methods used in inferential analyzes are asymptotic if the sample is small the analysis may be compromised because the estimates will be biased. An alternative is to use the bootstrap methodology, which in its non-parametric version does not need to guess or know the probability distribution that generated the original sample. In this work we used a set of soybean yield data and physical and chemical soil properties formed with fewer samples to determine a multiple linear regression model. Bootstrap methods were used for variable selection, identification of influential points and for determination of confidence intervals of the model parameters. The results showed that the bootstrap methods enabled us to select the physical and chemical soil properties, which were significant in the construction of the soybean yield regression model, construct the confidence intervals of the parameters and identify the points that had great influence on the estimated parameters. (Author)
Primary and Secondary Yield Losses Caused by Pests and Diseases: Assessment and Modeling in Coffee.
Cerda, Rolando; Avelino, Jacques; Gary, Christian; Tixier, Philippe; Lechevallier, Esther; Allinne, Clémentine
2017-01-01
The assessment of crop yield losses is needed for the improvement of production systems that contribute to the incomes of rural families and food security worldwide. However, efforts to quantify yield losses and identify their causes are still limited, especially for perennial crops. Our objectives were to quantify primary yield losses (incurred in the current year of production) and secondary yield losses (resulting from negative impacts of the previous year) of coffee due to pests and diseases, and to identify the most important predictors of coffee yields and yield losses. We established an experimental coffee parcel with full-sun exposure that consisted of six treatments, which were defined as different sequences of pesticide applications. The trial lasted three years (2013-2015) and yield components, dead productive branches, and foliar pests and diseases were assessed as predictors of yield. First, we calculated yield losses by comparing actual yields of specific treatments with the estimated attainable yield obtained in plots which always had chemical protection. Second, we used structural equation modeling to identify the most important predictors. Results showed that pests and diseases led to high primary yield losses (26%) and even higher secondary yield losses (38%). We identified the fruiting nodes and the dead productive branches as the most important and useful predictors of yields and yield losses. These predictors could be added in existing mechanistic models of coffee, or can be used to develop new linear mixed models to estimate yield losses. Estimated yield losses can then be related to production factors to identify corrective actions that farmers can implement to reduce losses. The experimental and modeling approaches of this study could also be applied in other perennial crops to assess yield losses.
SCS-CN based time-distributed sediment yield model
Tyagi, J. V.; Mishra, S. K.; Singh, Ranvir; Singh, V. P.
2008-05-01
SummaryA sediment yield model is developed to estimate the temporal rates of sediment yield from rainfall events on natural watersheds. The model utilizes the SCS-CN based infiltration model for computation of rainfall-excess rate, and the SCS-CN-inspired proportionality concept for computation of sediment-excess. For computation of sedimentographs, the sediment-excess is routed to the watershed outlet using a single linear reservoir technique. Analytical development of the model shows the ratio of the potential maximum erosion (A) to the potential maximum retention (S) of the SCS-CN method is constant for a watershed. The model is calibrated and validated on a number of events using the data of seven watersheds from India and the USA. Representative values of the A/S ratio computed for the watersheds from calibration are used for the validation of the model. The encouraging results of the proposed simple four parameter model exhibit its potential in field application.
Evaluation of weather-based rice yield models in India
Sudharsan, D.; Adinarayana, J.; Reddy, D. Raji; Sreenivas, G.; Ninomiya, S.; Hirafuji, M.; Kiura, T.; Tanaka, K.; Desai, U. B.; Merchant, S. N.
2013-01-01
The objective of this study was to compare two different rice simulation models—standalone (Decision Support System for Agrotechnology Transfer [DSSAT]) and web based (SImulation Model for RIce-Weather relations [SIMRIW])—with agrometeorological data and agronomic parameters for estimation of rice crop production in southern semi-arid tropics of India. Studies were carried out on the BPT5204 rice variety to evaluate two crop simulation models. Long-term experiments were conducted in a research farm of Acharya N G Ranga Agricultural University (ANGRAU), Hyderabad, India. Initially, the results were obtained using 4 years (1994-1997) of data with weather parameters from a local weather station to evaluate DSSAT simulated results with observed values. Linear regression models used for the purpose showed a close relationship between DSSAT and observed yield. Subsequently, yield comparisons were also carried out with SIMRIW and DSSAT, and validated with actual observed values. Realizing the correlation coefficient values of SIMRIW simulation values in acceptable limits, further rice experiments in monsoon (Kharif) and post-monsoon (Rabi) agricultural seasons (2009, 2010 and 2011) were carried out with a location-specific distributed sensor network system. These proximal systems help to simulate dry weight, leaf area index and potential yield by the Java based SIMRIW on a daily/weekly/monthly/seasonal basis. These dynamic parameters are useful to the farming community for necessary decision making in a ubiquitous manner. However, SIMRIW requires fine tuning for better results/decision making.
Estimates of fission yields in nuclear criticality excursions
International Nuclear Information System (INIS)
Choi, J.S.; Thompson, J.W.; Reed, R.
1995-06-01
There is a need for computer simulation of hypothetical criticality excursions involving significant quantities of fissionable materials, especially in fissile aqueous system. The need arises due to the requirements for the emergency planning of facilities where the fissionable materials are handled, processed, or stored; and the regulatory requirements associated with facility operation or conversion. It is proposed here that a data base of fission yeilds for critical experiments and known accidents (both aqueous and solid) should be generated by using existing or new computer codes. The success in compiling this data base would provide useful source-terms for criticality excursions, realistic estimates of emergency-response boundary, as well as a replacement for the ''rule-of-thumb'' or ''bounding'' method. 10 refs
Rahayu, A. P.; Hartatik, T.; Purnomoadi, A.; Kurnianto, E.
2018-02-01
The aims of this study were to estimate 305 day first lactation milk yield of Indonesian Holstein cattle from cumulative monthly and bimonthly test day records and to analyze its accuracy.The first lactation records of 258 dairy cows from 2006 to 2014 consisted of 2571 monthly (MTDY) and 1281 bimonthly test day yield (BTDY) records were used. Milk yields were estimated by regression method. Correlation coefficients between actual and estimated milk yield by cumulative MTDY were 0.70, 0.78, 0.83, 0.86, 0.89, 0.92, 0.94 and 0.96 for 2-9 months, respectively, meanwhile by cumulative BTDY were 0.69, 0.81, 0.87 and 0.92 for 2, 4, 6 and 8 months, respectively. The accuracy of fitting regression models (R2) increased with the increasing in the number of cumulative test day used. The used of 5 cumulative MTDY was considered sufficient for estimating 305 day first lactation milk yield with 80.6% accuracy and 7% error percentage of estimation. The estimated milk yield from MTDY was more accurate than BTDY by 1.1 to 2% less error percentage in the same time.
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.
Comparative Evaluation of Some Crop Yield Prediction Models ...
African Journals Online (AJOL)
A computer program was adopted from the work of Hill et al. (1982) to calibrate and test three of the existing yield prediction models using tropical cowpea yieldÐweather data. The models tested were Hanks Model (first and second versions). Stewart Model (first and second versions) and HallÐButcher Model. Three sets of ...
Directory of Open Access Journals (Sweden)
Makram J. Geha
2011-01-01
Full Text Available Milk yield records (305d, 2X, actual milk yield of 123,639 registered first lactation Holstein cows were used to compare linear regression (y = β0 + β1X + e ,quadratic regression, (y = β0 + β1X + β2X2 + e cubic regression (y = β0 + β1X + β2X2 + β3X3 + e and fixed factor models, with cubic-spline interpolation models, for estimating the effects of inbreeding on milk yield. Ten animal models, all with herd-year-season of calving as fixed effect, were compared using the Akaike corrected-Information Criterion (AICc. The cubic-spline interpolation model with seven knots had the lowest AICc, whereas for all those labeled as "traditional", AICc was higher than the best model. Results from fitting inbreeding using a cubic-spline with seven knots were compared to results from fitting inbreeding as a linear covariate or as a fixed factor with seven levels. Estimates of inbreeding effects were not significantly different between the cubic-spline model and the fixed factor model, but were significantly different from the linear regression model. Milk yield decreased significantly at inbreeding levels greater than 9%. Variance component estimates were similar for the three models. Ranking of the top 100 sires with daughter records remained unaffected by the model used.
Self-consistent technique for estimating the dynamic yield strength of a shock-loaded material
International Nuclear Information System (INIS)
Asay, J.R.; Lipkin, J.
1978-01-01
A technique is described for estimating the dynamic yield stress in a shocked material. This method employs reloading and unloading data from a shocked state along with a general assumption of yield and hardening behavior to estimate the yield stress in the precompressed state. No other data are necessary for this evaluation, and, therefore, the method has general applicability at high shock pressures and in materials undergoing phase transitions. In some special cases, it is also possible to estimate the complete state of stress in a shocked state. Using this method, the dynamic yield strength of aluminum at 2.06 GPa has been estimated to be 0.26 GPa. This value agrees reasonably well with previous estimates
Directory of Open Access Journals (Sweden)
Davinia Font
2015-04-01
Full Text Available This paper presents a method for vineyard yield estimation based on the analysis of high-resolution images obtained with artificial illumination at night. First, this paper assesses different pixel-based segmentation methods in order to detect reddish grapes: threshold based, Mahalanobis distance, Bayesian classifier, linear color model segmentation and histogram segmentation, in order to obtain the best estimation of the area of the clusters of grapes in this illumination conditions. The color spaces tested were the original RGB and the Hue-Saturation-Value (HSV. The best segmentation method in the case of a non-occluded reddish table-grape variety was the threshold segmentation applied to the H layer, with an estimation error in the area of 13.55%, improved up to 10.01% by morphological filtering. Secondly, after segmentation, two procedures for yield estimation based on a previous calibration procedure have been proposed: (1 the number of pixels corresponding to a cluster of grapes is computed and converted directly into a yield estimate; and (2 the area of a cluster of grapes is converted into a volume by means of a solid of revolution, and this volume is converted into a yield estimate; the yield errors obtained were 16% and −17%, respectively.
Statistical modelling and deconvolution of yield meter data
DEFF Research Database (Denmark)
Tøgersen, Frede Aakmann; Waagepetersen, Rasmus Plenge
2004-01-01
and an impulse response function. This results in an unusual spatial covariance structure (depending on the driving pattern of the combine harverster) for the yield monitoring system data. Parameters of the impulse response function and the spatial covariance function of the yield are estimated using maximum...
Directory of Open Access Journals (Sweden)
S. M. Hsu
2012-06-01
Full Text Available A comprehensive approach estimating sediment yield from a watershed is needed to develop better measures for mitigating sediment disasters and assessing downstream impacts. In the present study, an attempt has been made to develop an integrated method, considering sediment supplies associated with soil erosion, shallow landslide and debris flow to estimate sediment yield from a debris-flow-prone watershed on a storm event basis. The integrated method is based on the HSPF and TRIGRS models for predicting soil erosion and shallow landslide sediment yield, and the FLO-2D model for calculating debris flow sediment yield. The proposed method was applied to potential debris-flow watersheds located in the Sioulin Township of Hualien County. The available data such as hourly rainfall data, historical streamflow and sediment records as well as event-based landslide inventory maps have been used for model calibration and validation. Results for simulating sediment yield have been confirmed by comparisons of observed data from several typhoon events. The verified method employed a 24-h design hyetograph with the 100-yr return period to simulate sediment yield within the study area. The results revealed that the influence of shallow landslides on sediment supply as compared with soil erosion was significant. The estimate of landslide transport capacity into a main channel indicated the sediment delivery ratio on a typhoon event basis was approximately 38.4%. In addition, a comparison of sediment yields computed from occurrence and non-occurrence of debris flow scenarios showed that the sediment yield from an occurrence condition was found to be increasing at about 14.2 times more than estimated under a non-occurrence condition. This implied watershed sediment hazard induced by debris flow may cause severe consequences.
Forest Growth and Yield Models Viewed From a Different Perspective
Jeffery C. Goelz
2002-01-01
Typically, when different forms of growth and yield models are considered, they are grouped into convenient discrete classes. As a heuristic device, I chose to use a contrasting perspective, that all growth and yield models are diameter distribution models that merely differ in regard to which diameter distribution is employed and how the distribution is projected to...
Validation of crop weather models for crop assessment arid yield ...
African Journals Online (AJOL)
IRSIS and CRPSM models were used in this study to see how closely they could predict grain yields for selected stations in Tanzania. Input for the models comprised of weather, crop and soil data collected from five selected stations. Simulation results show that IRSIS model tends to over predict grain yields of maize, ...
Experimental formulas and curves for estimating reactivity loss and radio-isotope yields on HWRR
International Nuclear Information System (INIS)
Liu Xi Zhi; Zhu HuanNan
1999-01-01
Based on the elemental conception of reactor physics and experiments on HWRR for years. A set of experimental formulas and curves has been got, which can be used to estimate reactivity loss and radio isotopes yield. (author)
A Model for Quantifying Sources of Variation in Test-day Milk Yield ...
African Journals Online (AJOL)
A cow's test-day milk yield is influenced by several systematic environmental effects, which have to be removed when estimating the genetic potential of an animal. The present study quantified the variation due to test date and month of test in test-day lactation yield records using full and reduced models. The data consisted ...
Regional crop gross primary production and yield estimation using fused Landsat-MODIS data
He, M.; Kimball, J. S.; Maneta, M. P.; Maxwell, B. D.; Moreno, A.
2017-12-01
Accurate crop yield assessments using satellite-based remote sensing are of interest for the design of regional policies that promote agricultural resiliency and food security. However, the application of current vegetation productivity algorithms derived from global satellite observations are generally too coarse to capture cropland heterogeneity. Merging information from sensors with reciprocal spatial and temporal resolution can improve the accuracy of these retrievals. In this study, we estimate annual crop yields for seven important crop types -alfalfa, barley, corn, durum wheat, peas, spring wheat and winter wheat over Montana, United States (U.S.) from 2008 to 2015. Yields are estimated as the product of gross primary production (GPP) and a crop-specific harvest index (HI) at 30 m spatial resolution. To calculate GPP we used a modified form of the MOD17 LUE algorithm driven by a 30 m 8-day fused NDVI dataset constructed by blending Landsat (5 or 7) and MODIS Terra reflectance data. The fused 30-m NDVI record shows good consistency with the original Landsat and MODIS data, but provides better spatiotemporal information on cropland vegetation growth. The resulting GPP estimates capture characteristic cropland patterns and seasonal variations, while the estimated annual 30 m crop yield results correspond favorably with county-level crop yield data (r=0.96, pcrop yield performance was generally lower, but still favorable in relation to field-scale crop yield surveys (r=0.42, p<0.01). Our methods and results are suitable for operational applications at regional scales.
International Nuclear Information System (INIS)
Naghizadeh, M.; Javaherian, A.; Sadidkhooy, A.
2005-01-01
Surface wave amplitudes from explosion sources show less variation for a given event than body wave amplitudes, so it is natural to expect that yield estimation derived from surface waves will be more accurate than yield estimation derived from body waves. However yield estimation from surface waves is complicated by the presence of tectonic strain release, which acts like one or more earthquake sources superimposed on the explosion. Explosions on an island or near a mountain slope can exhibit anomalous surface waves similar to those caused by tectonic strain release. One of the methods in estimating the yield of nuclear explosions is to determine a relationship between the magnitude and the yield of an explosion. The kind of magnitude employed has an important role in this regard. In this paper, vertical component of long period seismograms at SRO, Mashhad from explosions occurred in semipalatinsk test site, semipalatinsk test site east of Kazakhstan) are considered. First, by using the relationships of IASPEI and Rezapour and Pearce (1998), we determined surface wave magnitude (MS) which is defined as the logarithm of the amplitude plus a distance correction. Then we derived a relation for M S versus yield for a data set which includes a 15 long period seismograms recorded at SRO Mashhad station from semipalatinsk test site nuclear explosions. Furthermore, by digitizing the vertical component of seismograms and transforming them to the frequency domain, the mean amplitude of records at frequency ranges of 0.04-0.06 Hz were calculated. Then, surface wave magnitudes in the frequency domain (M Sf ) and their corresponding yield-magnitude relationship were obtained. By comparing correlation coefficients of these two yield-magnitude relationships, following relationship M S = 1.079 log(Y) + 1.714, was chosen for estimating the yield of semipalatinsk test site nuclear explosion from seismograms of SRO
Multilevel systematic sampling to estimate total fruit number for yield forecasts
DEFF Research Database (Denmark)
Wulfsohn, Dvora-Laio; Zamora, Felipe Aravena; Tellez, Camilla Potin
2012-01-01
procedure for unbiased estimation of fruit number for yield forecasts. In the Spring of 2009 we estimated the total number of fruit in several rows of each of 14 commercial fruit orchards growing apple (11 groves), kiwifruit (two groves), and table grapes (one grove) in central Chile. Survey times were 10...
Estimates of the relative specific yield of aquifers from geo-electrical ...
African Journals Online (AJOL)
This paper discusses a method of estimating aquifer specific yield based on surface resistivity sounding measurements supplemented with data on water conductivity. The practical aim of the method is to suggest a parallel low cost method of estimating aquifer properties. The starting point is the Archie's law, which relates ...
Johnson, D. M.; Dorn, M. F.; Crawford, C.
2015-12-01
Since the dawn of earth observation imagery, particularly from systems like Landsat and the Advanced Very High Resolution Radiometer, there has been an overarching desire to regionally estimate crop production remotely. Research efforts integrating space-based imagery into yield models to achieve this need have indeed paralleled these systems through the years, yet development of a truly useful crop production monitoring system has been arguably mediocre in coming. As a result, relatively few organizations have yet to operationalize the concept, and this is most acute in regions of the globe where there are not even alternative sources of crop production data being collected. However, the National Agricultural Statistics Service (NASS) has continued to push for this type of data source as a means to complement its long-standing, traditional crop production survey efforts which are financially costly to the government and create undue respondent burden on farmers. Corn and soybeans, the two largest field crops in the United States, have been the focus of satellite-based production monitoring by NASS for the past decade. Data from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been seen as the most pragmatic input source for modeling yields primarily based on its daily revisit capabilities and reasonable ground sample resolution. The research methods presented here will be broad but provides a summary of what is useful and adoptable with satellite imagery in terms of crop yield estimation. Corn and soybeans will be of particular focus but other major staple crops like wheat and rice will also be presented. NASS will demonstrate that while MODIS provides a slew of vegetation related products, the traditional normalized difference vegetation index (NDVI) is still ideal. Results using land surface temperature products, also generated from MODIS, will also be shown. Beyond the MODIS data itself, NASS research has also focused efforts on understanding a
ZNJPrice/Earnings Ratio Model through Dividend Yield and Required Yield Above Expected Inflation
Directory of Open Access Journals (Sweden)
Emil Mihalina
2010-07-01
Full Text Available Price/earnings ratio is the most popular and most widespread evaluation model used to assess relative capital asset value on financial markets. In functional terms, company earnings in the very long term can be described with high significance. Empirically, it is visible from long-term statistics that the demanded (required yield on capital markets has certain regularity. Thus, investors first require a yield above the stable inflation rate and then a dividend yield and a capital increase caused by the growth of earnings that influence the price, with the assumption that the P/E ratio is stable. By combining the Gordon model for current dividend value, the model of market capitalization of earnings (price/earnings ratio and bearing in mind the influence of the general price levels on company earnings, it is possible to adjust the price/earnings ratio by deriving a function of the required yield on capital markets measured by a market index through dividend yield and inflation rate above the stable inflation rate increased by profit growth. The S&P 500 index for example, has in the last 100 years grown by exactly the inflation rate above the stable inflation rate increased by profit growth. The comparison of two series of price/earnings ratios, a modelled one and an average 7-year ratio, shows a notable correlation in the movement of two series of variables, with a three year deviation. Therefore, it could be hypothesized that three years of the expected inflation level, dividend yield and profit growth rate of the market index are discounted in the current market prices. The conclusion is that, at the present time, the relationship between the adjusted average price/earnings ratio and its effect on the market index on one hand and the modelled price/earnings ratio on the other can clearly show the expected dynamics and course in the following period.
Sheehan, J. J.
2016-12-01
We report here a first-of-its-kind analysis of the potential for intensification of global grazing systems. Intensification is calculated using the statistical yield gap methodology developed previously by others (Mueller et al 2012 and Licker et al 2010) for global crop systems. Yield gaps are estimated by binning global pasture land area into 100 equal area sized bins of similar climate (defined by ranges of rainfall and growing degree days). Within each bin, grid cells of pastureland are ranked from lowest to highest productivity. The global intensification potential is defined as the sum of global production across all bins at a given percentile ranking (e.g. performance at the 90th percentile) divided by the total current global production. The previous yield gap studies focused on crop systems because productivity data on these systems is readily available. Nevertheless, global crop land represents only one-third of total global agricultural land, while pasture systems account for the remaining two-thirds. Thus, it is critical to conduct the same kind of analysis on what is the largest human use of land on the planet—pasture systems. In 2013, Herrero et al announced the completion of a geospatial data set that augmented the animal census data with data and modeling about production systems and overall food productivity (Herrero et al, PNAS 2013). With this data set, it is now possible to apply yield gap analysis to global pasture systems. We used the Herrero et al data set to evaluate yield gaps for meat and milk production from pasture based systems for cattle, sheep and goats. The figure included with this abstract shows the intensification potential for kcal per hectare per year of meat and milk from global cattle, sheep and goats as a function of increasing levels of performance. Performance is measured as the productivity achieved at a given ranked percentile within each bin.We find that if all pasture land were raised to their 90th percentile of
Carlos A. Gonzalez-Benecke; Salvador A. Gezan; Daniel J. Leduc; Timothy A. Martin; Wendell P. Cropper Jr; Lisa J Samuelson
2012-01-01
Longleaf pine (Pinus palustris Mill.) is an important tree species of the southeast U.S. Currently there is no comprehensive stand-level growth and yield model for the species. The model system described here estimates site index (SI) if dominant height (Hdom) and stand age are known (inversely, the model can project H
PROMAB-GIS: A GIS based Tool for Estimating Runoff and Sediment Yield in running Waters
Jenewein, S.; Rinderer, M.; Ploner, A.; Sönser, T.
2003-04-01
In recent times settlements have expanded, traffic and tourist activities have increased in most alpine regions. As a consequence, on the one hand humans and goods are affected by natural hazard processes more often, while on the other hand the demand for protection by both technical constructions and planning measures carried out by public authorities is growing. This situation results in an ever stronger need of reproducibility, comparability, transparency of all methods applied in modern natural hazard management. As a contribution to a new way of coping this situation Promab-GIS Version 1.0 has been developed. Promab-Gis has been designed as a model for time- and space-dependent determination of both runoff and bedload transport in rivers of small alpine catchment areas. The estimation of the unit hydrograph relies upon the "rational formula" and the time-area curves of the watershed. The time area diagram is a graph of cumulative drainage area contributing to discharge at the watershed outlet within a specified time of travel. The sediment yield is estimated for each cell of the channel network by determining the actual process type (erosion, transport or accumulation). Two types of transport processes are considered, sediment transport and debris flows. All functions of Promab-GIS are integrated in the graphical user interface of ArcView as pull-up menus and tool buttons. Hence the application of Promab-GIS does not rely on a sophisticated knowledge of GIS in general, respectively the ArcView software. However, despite the use of computer assistance, Promab-GIS still is an expert support system. In order to obtain plausible results, the users must be familiar with all the relevant processes controlling runoff and sediment yield in torrent catchments.
Graphical user interface for yield and dose estimations for cyclotron-produced technetium.
Hou, X; Vuckovic, M; Buckley, K; Bénard, F; Schaffer, P; Ruth, T; Celler, A
2014-07-07
The cyclotron-based (100)Mo(p,2n)(99m)Tc reaction has been proposed as an alternative method for solving the shortage of (99m)Tc. With this production method, however, even if highly enriched molybdenum is used, various radioactive and stable isotopes will be produced simultaneously with (99m)Tc. In order to optimize reaction parameters and estimate potential patient doses from radiotracers labeled with cyclotron produced (99m)Tc, the yields for all reaction products must be estimated. Such calculations, however, are extremely complex and time consuming. Therefore, the objective of this study was to design a graphical user interface (GUI) that would automate these calculations, facilitate analysis of the experimental data, and predict dosimetry. The resulting GUI, named Cyclotron production Yields and Dosimetry (CYD), is based on Matlab®. It has three parts providing (a) reaction yield calculations, (b) predictions of gamma emissions and (c) dosimetry estimations. The paper presents the outline of the GUI, lists the parameters that must be provided by the user, discusses the details of calculations and provides examples of the results. Our initial experience shows that the proposed GUI allows the user to very efficiently calculate the yields of reaction products and analyze gamma spectroscopy data. However, it is expected that the main advantage of this GUI will be at the later clinical stage when entering reaction parameters will allow the user to predict production yields and estimate radiation doses to patients for each particular cyclotron run.
Top ten models constrained by b {yields} s{gamma}
Energy Technology Data Exchange (ETDEWEB)
Hewett, J.L. [Stanford Univ., CA (United States)
1994-12-01
The radiative decay b {yields} s{gamma} is examined in the Standard Model and in nine classes of models which contain physics beyond the Standard Model. The constraints which may be placed on these models from the recent results of the CLEO Collaboration on both inclusive and exclusive radiative B decays is summarized. Reasonable bounds are found for the parameters in some cases.
NASA Software Cost Estimation Model: An Analogy Based Estimation Model
Hihn, Jairus; Juster, Leora; Menzies, Tim; Mathew, George; Johnson, James
2015-01-01
The cost estimation of software development activities is increasingly critical for large scale integrated projects such as those at DOD and NASA especially as the software systems become larger and more complex. As an example MSL (Mars Scientific Laboratory) developed at the Jet Propulsion Laboratory launched with over 2 million lines of code making it the largest robotic spacecraft ever flown (Based on the size of the software). Software development activities are also notorious for their cost growth, with NASA flight software averaging over 50% cost growth. All across the agency, estimators and analysts are increasingly being tasked to develop reliable cost estimates in support of program planning and execution. While there has been extensive work on improving parametric methods there is very little focus on the use of models based on analogy and clustering algorithms. In this paper we summarize our findings on effort/cost model estimation and model development based on ten years of software effort estimation research using data mining and machine learning methods to develop estimation models based on analogy and clustering. The NASA Software Cost Model performance is evaluated by comparing it to COCOMO II, linear regression, and K- nearest neighbor prediction model performance on the same data set.
Directory of Open Access Journals (Sweden)
K. Ono
2011-01-01
Full Text Available The objective of this study was to estimate the potential sediment yield distribution in Japan attributed to extreme-rainfall-induced slope failures in the future. For this purpose, a regression relationship between the slope failure probability and the subsequent sediment yield was developed by using sediment yield observations from 59 dams throughout Japan. The slope failure probability accounts for the effects of topography (as relief energy, geology and hydro-climate variations (hydraulic gradient changes due to extreme rainfall variations and determines the potential slope failure occurrence with a 1-km resolution. The applicability of the developed relationship was then validated by comparing the simulated and observed sediment yields in another 43 dams. To incorporate the effects of a changing climate, extreme rainfall variations were estimated by using two climate change scenarios (the MRI-RCM20 Ver.2 model A2 scenario and the MIROC A1B scenario for the future and by accounting for the slope failure probability through the effect of extreme rainfall on the hydraulic gradient. Finally, the developed slope failure hazard-sediment yield relationship was employed to estimate the potential sediment yield distribution under a changing climate in Japan.
Time series analyses of annual sediment yields covering 15–20 years in 59 dams reveal that extreme sedimentation events have a high probability of occurring on average every 5–7 years. Therefore, the extreme-rainfall-induced slope failure probability with a five-year return period has a statistically robust relationship with specific sediment yield observations (with r^{2} = 0.65. The verification demonstrated that the model is effective for use in simulating specific sediment yields with r^{2} = 0.74. The results of the GCM scenarios suggest that the sediment yield issue will be critical in Japan in the future. When the spatially averaged sediment
Directory of Open Access Journals (Sweden)
Jianping Qian
Full Text Available ABSTRACT: Apple yield estimation using a smartphone with image processing technology offers advantages such as low cost, quick access and simple operation. This article proposes distribution framework consisting of the acquisition of fruit tree images, yield prediction in smarphone client, data processing and model calculation in server client for estimating the potential fruit yield. An image processing method was designed including the core steps of image segmentation with R/B value combined with V value and circle-fitting using curvature analysis. This method enabled four parameters to be obtained, namely, total identified pixel area (TP, fitting circle amount (FC, average radius of the fitting circle (RC and small polygon pixel area (SP. A individual tree yield estimation model on an ANN (Artificial Neural Network was developed with three layers, four input parameters, 14 hidden neurons, and one output parameter. The system was used on an experimental Fuji apple (Malus domestica Borkh. cv. Red Fuji orchard. Twenty-six tree samples were selected from a total of 80 trees according to the multiples of the number three for the establishment model, whereby 21 groups of data were trained and 5 groups o data were validated. The R2 value for the training datasets was 0.996 and the relative root mean squared error (RRMSE value 0.063. The RRMSE value for the validation dataset was 0.284 Furthermore, a yield map with 80 apple trees was generated, and the space distribution o the yield was identified. It provided appreciable decision support for site-specific management.
Estimating national crop yield potential and the relevance of weather data sources
Van Wart, Justin
2011-12-01
To determine where, when, and how to increase yields, researchers often analyze the yield gap (Yg), the difference between actual current farm yields and crop yield potential. Crop yield potential (Yp) is the yield of a crop cultivar grown under specific management limited only by temperature and solar radiation and also by precipitation for water limited yield potential (Yw). Yp and Yw are critical components of Yg estimations, but are very difficult to quantify, especially at larger scales because management data and especially daily weather data are scarce. A protocol was developed to estimate Yp and Yw at national scales using site-specific weather, soils and management data. Protocol procedures and inputs were evaluated to determine how to improve accuracy of Yp, Yw and Yg estimates. The protocol was also used to evaluate raw, site-specific and gridded weather database sources for use in simulations of Yp or Yw. The protocol was applied to estimate crop Yp in US irrigated maize and Chinese irrigated rice and Yw in US rainfed maize and German rainfed wheat. These crops and countries account for >20% of global cereal production. The results have significant implications for past and future studies of Yp, Yw and Yg. Accuracy of national long-term average Yp and Yw estimates was significantly improved if (i) > 7 years of simulations were performed for irrigated and > 15 years for rainfed sites, (ii) > 40% of nationally harvested area was within 100 km of all simulation sites, (iii) observed weather data coupled with satellite derived solar radiation data were used in simulations, and (iv) planting and harvesting dates were specified within +/- 7 days of farmers actual practices. These are much higher standards than have been applied in national estimates of Yp and Yw and this protocol is a substantial step in making such estimates more transparent, robust, and straightforward. Finally, this protocol may be a useful tool for understanding yield trends and directing
Software Cost-Estimation Model
Tausworthe, R. C.
1985-01-01
Software Cost Estimation Model SOFTCOST provides automated resource and schedule model for software development. Combines several cost models found in open literature into one comprehensive set of algorithms. Compensates for nearly fifty implementation factors relative to size of task, inherited baseline, organizational and system environment and difficulty of task.
Directory of Open Access Journals (Sweden)
Roderick HAZEWINKEL
2010-08-01
Full Text Available Stable isotopes of water were applied to estimate water yield to fifty lakes in northeastern Alberta as part of an acid sensitivity study underway since 2002 in the Athabasca Oil Sands Region (AOSR. Herein, we apply site-specific water yields for each lake to calculate critical loads of acidity using water chemistry data and a steady-state water chemistry model. The main goal of this research was to improve site-specific critical load estimates and to understand the sensitivity to hydrologic variability across a Boreal Plains region under significant oil sands development pressure. Overall, catchment water yields were found to vary significantly over the seven year monitoring period, with distinct variations among lakes and between different regions, overprinted by inter-annual climate-driven shifts. Analysis of critical load estimates based on site-specific water yields suggests that caution must be applied to establish hydrologic conditions and define extremes at specific sites in order to protect more sensitive ecosystems. In general, lakes with low (high water yield tended to be more (less acid sensitive but were typically less (more affected by interannual hydrological variations. While it has been customary to use long-term water yields to define a static critical load for lakes, we find that spatial and temporal variability in water yield may limit effectiveness of this type of assessment in areas of the Boreal Plain characterized by heterogeneous runoff and without a long-term lake-gauging network. Implications for predicting acidification risk are discussed for the AOSR.
Estimates of Sputter Yields of Solar-Wind Heavy Ions of Lunar Regolith Materials
Barghouty, Abdulmasser F.; Adams, James H., Jr.
2008-01-01
At energies of approximately 1 keV/amu, solar-wind protons and heavy ions interact with the lunar surface materials via a number of microscopic interactions that include sputtering. Solar-wind induced sputtering is a main mechanism by which the composition of the topmost layers of the lunar surface can change, dynamically and preferentially. This work concentrates on sputtering induced by solar-wind heavy ions. Sputtering associated with slow (speeds the electrons speed in its first Bohr orbit) and highly charged ions are known to include both kinetic and potential sputtering. Potential sputtering enjoys some unique characteristics that makes it of special interest to lunar science and exploration. Unlike the yield from kinetic sputtering where simulation and approximation schemes exist, the yield from potential sputtering is not as easy to estimate. This work will present a preliminary numerical scheme designed to estimate potential sputtering yields from reactions relevant to this aspect of solar-wind lunar-surface coupling.
Yield estimation using SPOT-VEGETATION products: A case study of wheat in European countries
Kowalik, W.; Dabrowska-Zielinska, K.; Meroni, M.; Raczka, T.U.; Wit, de A.J.W.
2014-01-01
In the period 1999-2009 ten-day SPOT-VEGETATION products of the Normalized Difference Vegetation Index (NDVI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) at 1 km spatial resolution were used in order to estimate and forecast the wheat yield over Europe. The products were
Modeling the effects of ozone on soybean growth and yield.
Kobayashi, K; Miller, J E; Flagler, R B; Heck, W W
1990-01-01
A simple mechanistic model was developed based on an existing growth model in order to address the mechanisms of the effects of ozone on growth and yield of soybean [Glycine max. (L.) Merr. 'Davis'] and interacting effects of other environmental stresses. The model simulates daily growth of soybean plants using environmental data including shortwave radiation, temperature, precipitation, irrigation and ozone concentration. Leaf growth, dry matter accumulation, water budget, nitrogen input and seed growth linked to senescence and abscission of leaves are described in the model. The effects of ozone are modeled as reduced photosynthate production and accelerated senescence. The model was applied to the open-top chamber experiments in which soybean plants were exposed to ozone under two levels of soil moisture regimes. After calibrating the model to the growth data and seed yield, goodness-of-fit of the model was tested. The model fitted well for top dry weight in the vegetative growth phase and also at maturity. The effect of ozone on seen yield was also described satisfactorily by the model. The simulation showed apparent interaction between the effect of ozone and soil moisture stress on the seed yield. The model revealed that further work is needed concerning the effect of ozone on the senescence process and the consequences of alteration of canopy microclimate by the open-top chambers.
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.
An analytical model of nonproportional scintillator light yield in terms of recombination rates
International Nuclear Information System (INIS)
Bizarri, G.; Moses, W. W.; Singh, J.; Vasil'ev, A. N.; Williams, R. T.
2009-01-01
Analytical expressions for the local light yield as a function of the local deposited energy (-dE/dx) and total scintillation yield integrated over the track of an electron of initial energy E are derived from radiative and/or nonradiative rates of first through third order in density of electronic excitations. The model is formulated in terms of rate constants, some of which can be determined independently from time-resolved spectroscopy and others estimated from measured light yield efficiency as a constraint assumed to apply in each kinetic order. The rates and parameters are used in the theory to calculate scintillation yield versus primary electron energy for comparison to published experimental results on four scintillators. Influence of the track radius on the yield is also discussed. Results are found to be qualitatively consistent with the observed scintillation light yield. The theory can be applied to any scintillator if the rates of the radiative and nonradiative processes are known
Nearing, Grey S.; Crow, Wade T.; Thorp, Kelly R.; Moran, Mary S.; Reichle, Rolf H.; Gupta, Hoshin V.
2012-01-01
Observing system simulation experiments were used to investigate ensemble Bayesian state updating data assimilation of observations of leaf area index (LAI) and soil moisture (theta) for the purpose of improving single-season wheat yield estimates with the Decision Support System for Agrotechnology Transfer (DSSAT) CropSim-Ceres model. Assimilation was conducted in an energy-limited environment and a water-limited environment. Modeling uncertainty was prescribed to weather inputs, soil parameters and initial conditions, and cultivar parameters and through perturbations to model state transition equations. The ensemble Kalman filter and the sequential importance resampling filter were tested for the ability to attenuate effects of these types of uncertainty on yield estimates. LAI and theta observations were synthesized according to characteristics of existing remote sensing data, and effects of observation error were tested. Results indicate that the potential for assimilation to improve end-of-season yield estimates is low. Limitations are due to a lack of root zone soil moisture information, error in LAI observations, and a lack of correlation between leaf and grain growth.
Estimating grain yield losses caused by septoria leaf blotch on durum wheat in Tunisia
Directory of Open Access Journals (Sweden)
Samia Berraies
2014-12-01
Full Text Available Septoria leaf blotch (SLB, caused by Zymoseptoria tritici (Desm. Quaedvlieg & Crous, 2011 (teleomorph: Mycosphaerella graminicola (Fuckel J. Schrot., is an important wheat disease in the Mediterranean region. In Tunisia, SLB has become a major disease of durum wheat (Triticum turgidum L. subsp. durum [Desf.] Husn. particularly during favorable growing seasons where significant yield losses and increase of fungicides use were recorded over the last three decades. The objectives of this study were to evaluate the effect of SLB severity on grain yield of new elite durum wheat breeding lines and to measure the relative effect of fungicide control on grain yield. Experiments were conducted during 2007-2008 and 2008-2009 cropping seasons. A set of 800 breeding lines were screened for reaction to SLB under natural infection at Beja research station. To estimate the disease effect, correlation between disease severity at early grain filling stage and grain yield was performed. Results showed that susceptible varieties yield was significantly reduced by SLB. Average yield reduction was as high as 384 and 325 kg ha-1 for every increment in disease severity on a 0-9 scale in both seasons, respectively. A negative correlation coefficient varied between -0.61 and -0.66 in both seasons. Treated and untreated trials conducted during 2008-2009 and 2009-2010 showed that yield of treated plots increased by 50% on the commonly cultivated susceptible varieties. The results of this investigation suggested that septoria incidence is related to large grain yield losses particularly on susceptible high yielding cultivars. However, appropriate fungicide application at booting growth stage could be beneficial for farmers. The development and use of more effective fungicide could be sought to alleviate the disease effects and therefore could be considered as a part of the integrated pest management and responsible use strategy on septoria leaf blotch in Tunisia.
Nizamuddin, Mohammad; Akhand, Kawsar; Roytman, Leonid; Kogan, Felix; Goldberg, Mitch
2015-06-01
Rice is a dominant food crop of Bangladesh accounting about 75 percent of agricultural land use for rice cultivation and currently Bangladesh is the world's fourth largest rice producing country. Rice provides about two-third of total calorie supply and about one-half of the agricultural GDP and one-sixth of the national income in Bangladesh. Aus is one of the main rice varieties in Bangladesh. Crop production, especially rice, the main food staple, is the most susceptible to climate change and variability. Any change in climate will, thus, increase uncertainty regarding rice production as climate is major cause year-to-year variability in rice productivity. This paper shows the application of remote sensing data for estimating Aus rice yield in Bangladesh using official statistics of rice yield with real time acquired satellite data from Advanced Very High Resolution Radiometer (AVHRR) sensor and Principal Component Regression (PCR) method was used to construct a model. The simulated result was compared with official agricultural statistics showing that the error of estimation of Aus rice yield was less than 10%. Remote sensing, therefore, is a valuable tool for estimating crop yields well in advance of harvest, and at a low cost.
Estimation of the Hiroshima bomb yield and weather conditions at the time of the bomb
International Nuclear Information System (INIS)
Tajima, Eizo
1984-01-01
The results of the survey made immediately after the bombings in Hiroshima and Nagasaki were compiled in Collection of Reports on the Investigation of the Atomic Bomb Casualties published in 1953. Much valuable information for the reassessment of dose are included in this document. One of the major problems to be solved for the dose reassessment is the yield of the Hiroshima bomb. Two articles with relatively detailed description were selected, and the estimation of the yield was attempted, based on them. The data on roof tile melting were used for the purpose. Assuming the yield of the Nagasaki bomb as 22 kt, the yield of the Hiroshima bomb was given as 12.4 kt. By the experiment using the charred state of cypress boards, the total radiant energy from the bomb was calculated as 4.6 x 10 12 cal, and the yield of the Hiroshima bomb was estimated as 14.2 kt and 13.2 kt. The true value is likely between 12 and 13 kt. The vapor pressure at the time of bombing significantly affected the neutron spectrum. On the day of bombing, Japan was covered by hot, humid maritime air mass, namely summer monsoon pattern. The air density and water vapor content in the atmosphere were determined by the Japan Weather Association, and compared with the data of Dr. Kerr et al. (Kako, I.)
Crop yield estimation in 2014 for Vojvodina using methods of remote sensing
Directory of Open Access Journals (Sweden)
Jovanović Dušan
2014-01-01
Full Text Available Monitoring phenology of crops and yield estimate based on vegetation indices as well as other parameters such as temperature or amount of rainfall were largely reported in literature. In this research, MODIS Normalized Difference Vegetation Index (NDVI was used as an indicator of specific crop condition; the other parameter was Land Surface Temperature (LST which can indicate the amount of crop moisture. Trial years were 2011, 2012, and 2013. For those years sowing structure was acquired from agricultural organizations Nova Budućnost from Žarkovac and Sava Kovačević from Vrbas, both in Serbia. Also, satellite images with high and medium resolution for these areas and years were available. Multiple linear regression was used for crop yield estimate for Vojvodina Province, Serbia where the NDVI and LST were independent variables and the average yield for specific crop was the dependent variable. The results of crop yield estimate two months before harvest are presented (excluding wheat.
Estimated yield of double-strand breaks from internal exposure to tritium
Energy Technology Data Exchange (ETDEWEB)
Chen, Jing [Health Canada, Radiation Protection Bureau, Ottawa, ON (Canada)
2012-08-15
Internal exposure to tritium may result in DNA lesions. Of those, DNA double-strand breaks (DSBs) are believed to be important. However, experimental and computational data of DSBs induction by tritium are very limited. In this study, microdosimetric characteristics of uniformly distributed tritium were determined in dimensions of critical significance in DNA DSBs. Those characteristics were used to identify other particles comparable to tritium in terms of microscopic energy deposition. The yield of DSBs could be strongly dependent on biological systems and cellular environments. After reviewing theoretically predicted and experimentally determined DSB yields available in the literature for low-energy electrons and high-energy protons of comparable microdosimetric characteristics to tritium in the dimensions relevant to DSBs, it is estimated that the average DSB yields of 2.7 x 10{sup -11}, 0.93 x 10{sup -11}, 2.4 x 10{sup -11} and 1.6 x 10{sup -11} DSBs Gy{sup -1} Da{sup -1} could be reasonable estimates for tritium in plasmid DNAs, yeast cells, Chinese hamster V79 cells and human fibroblasts, respectively. If a biological system is not specified, the DSB yield from tritium exposure can be estimated as (2.3 ± 0.7) x 10{sup -11} DSBs Gy{sup -1} Da{sup -1}, which is a simple average over experimentally determined yields of DSBs for low-energy electrons in various biological systems without considerations of variations caused by different techniques used and obvious differences among different biological systems where the DSB yield was measured. (orig.)
Estimated yield of double-strand breaks from internal exposure to tritium.
Chen, Jing
2012-08-01
Internal exposure to tritium may result in DNA lesions. Of those, DNA double-strand breaks (DSBs) are believed to be important. However, experimental and computational data of DSBs induction by tritium are very limited. In this study, microdosimetric characteristics of uniformly distributed tritium were determined in dimensions of critical significance in DNA DSBs. Those characteristics were used to identify other particles comparable to tritium in terms of microscopic energy deposition. The yield of DSBs could be strongly dependent on biological systems and cellular environments. After reviewing theoretically predicted and experimentally determined DSB yields available in the literature for low-energy electrons and high-energy protons of comparable microdosimetric characteristics to tritium in the dimensions relevant to DSBs, it is estimated that the average DSB yields of 2.7 × 10(-11), 0.93 × 10(-11), 2.4 × 10(-11) and 1.6 × 10(-11) DSBs Gy(-1) Da(-1) could be reasonable estimates for tritium in plasmid DNAs, yeast cells, Chinese hamster V79 cells and human fibroblasts, respectively. If a biological system is not specified, the DSB yield from tritium exposure can be estimated as (2.3 ± 0.7) × 10(-11) DSBs Gy(-1) Da(-1), which is a simple average over experimentally determined yields of DSBs for low-energy electrons in various biological systems without considerations of variations caused by different techniques used and obvious differences among different biological systems where the DSB yield was measured.
Evaluation of Rgb-Based Vegetation Indices from Uav Imagery to Estimate Forage Yield in Grassland
Lussem, U.; Bolten, A.; Gnyp, M. L.; Jasper, J.; Bareth, G.
2018-04-01
Monitoring forage yield throughout the growing season is of key importance to support management decisions on grasslands/pastures. Especially on intensely managed grasslands, where nitrogen fertilizer and/or manure are applied regularly, precision agriculture applications are beneficial to support sustainable, site-specific management decisions on fertilizer treatment, grazing management and yield forecasting to mitigate potential negative impacts. To support these management decisions, timely and accurate information is needed on plant parameters (e.g. forage yield) with a high spatial and temporal resolution. However, in highly heterogeneous plant communities such as grasslands, assessing their in-field variability non-destructively to determine e.g. adequate fertilizer application still remains challenging. Especially biomass/yield estimation, as an important parameter in assessing grassland quality and quantity, is rather laborious. Forage yield (dry or fresh matter) is mostly measured manually with rising plate meters (RPM) or ultrasonic sensors (handheld or mounted on vehicles). Thus the in-field variability cannot be assessed for the entire field or only with potential disturbances. Using unmanned aerial vehicles (UAV) equipped with consumer grade RGB cameras in-field variability can be assessed by computing RGB-based vegetation indices. In this contribution we want to test and evaluate the robustness of RGB-based vegetation indices to estimate dry matter forage yield on a recently established experimental grassland site in Germany. Furthermore, the RGB-based VIs are compared to indices computed from the Yara N-Sensor. The results show a good correlation of forage yield with RGB-based VIs such as the NGRDI with R2 values of 0.62.
Lazzari, Maurizio; Danese, Maria; Gioia, Dario; Piccarreta, Marco
2013-04-01
Sedimentary budget estimation is an important topic for both scientific and social community, because it is crucial to understand both dynamics of orogenic belts and many practical problems, such as soil conservation and sediment accumulation in reservoir. Estimations of sediment yield or denudation rates in southern-central Italy are generally obtained by simple empirical relationships based on statistical regression between geomorphic parameters of the drainage network and the measured suspended sediment yield at the outlet of several drainage basins or through the use of models based on sediment delivery ratio or on soil loss equations. In this work, we perform a study of catchment dynamics and an estimation of sedimentary yield for several mountain catchments of the central-western sector of the Basilicata region, southern Italy. Sediment yield estimation has been obtained through both an indirect estimation of suspended sediment yield based on the Tu index (mean annual suspension sediment yield, Ciccacci et al., 1980) and the application of the Rusle (Renard et al., 1997) and the USPED (Mitasova et al., 1996) empirical methods. The preliminary results indicate a reliable difference between the RUSLE and USPED methods and the estimation based on the Tu index; a critical data analysis of results has been carried out considering also the present-day spatial distribution of erosion, transport and depositional processes in relation to the maps obtained from the application of those different empirical methods. The studied catchments drain an artificial reservoir (i.e. the Camastra dam), where a detailed evaluation of the amount of historical sediment storage has been collected. Sediment yield estimation obtained by means of the empirical methods have been compared and checked with historical data of sediment accumulation measured in the artificial reservoir of the Camastra dam. The validation of such estimations of sediment yield at the scale of large catchments
Yield models for Eucalyptus globulus fuelwood plantations in Ethiopia
Energy Technology Data Exchange (ETDEWEB)
Pukkala, T.; Pohjonen, V. (Joensuu Univ. (FI). Faculty of Forestry)
1990-01-01
Based on 53 tree analyses and 105 sample plots of Eucalyptus globulus, models for volume and biomass at single tree and stand levels were developed. The possible growing sites were divided into four site classes. In seedling stands, the site class I corresponds to yield class 44 m{sup 3} ha{sup -1} year{sup -1}, in coppice stands to yield class 46 m{sup 3} ha{sup -1} year{sup -1}. The site class IV corresponds in seedling stand to yield class 9 m{sup 3} ha{sup -1} year{sup -1}, in coppice stands to yield class 13 m{sup 3} ha{sup -1} year{sup -1}. The maximum mean annual increment was reached in seedling stands at the age of 18-19 years, in coppice stands at the age of 14 years. (author).
Spatial Distribution of Hydrologic Ecosystem Service Estimates: Comparing Two Models
Dennedy-Frank, P. J.; Ghile, Y.; Gorelick, S.; Logsdon, R. A.; Chaubey, I.; Ziv, G.
2014-12-01
We compare estimates of the spatial distribution of water quantity provided (annual water yield) from two ecohydrologic models: the widely-used Soil and Water Assessment Tool (SWAT) and the much simpler water models from the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) toolbox. These two models differ significantly in terms of complexity, timescale of operation, effort, and data required for calibration, and so are often used in different management contexts. We compare two study sites in the US: the Wildcat Creek Watershed (2083 km2) in Indiana, a largely agricultural watershed in a cold aseasonal climate, and the Upper Upatoi Creek Watershed (876 km2) in Georgia, a mostly forested watershed in a temperate aseasonal climate. We evaluate (1) quantitative estimates of water yield to explore how well each model represents this process, and (2) ranked estimates of water yield to indicate how useful the models are for management purposes where other social and financial factors may play significant roles. The SWAT and InVEST models provide very similar estimates of the water yield of individual subbasins in the Wildcat Creek Watershed (Pearson r = 0.92, slope = 0.89), and a similar ranking of the relative water yield of those subbasins (Spearman r = 0.86). However, the two models provide relatively different estimates of the water yield of individual subbasins in the Upper Upatoi Watershed (Pearson r = 0.25, slope = 0.14), and very different ranking of the relative water yield of those subbasins (Spearman r = -0.10). The Upper Upatoi watershed has a significant baseflow contribution due to its sandy, well-drained soils. InVEST's simple seasonality terms, which assume no change in storage over the time of the model run, may not accurately estimate water yield processes when baseflow provides such a strong contribution. Our results suggest that InVEST users take care in situations where storage changes are significant.
Santos, João A.; Malheiro, Aureliano C.; Karremann, Melanie K.; Pinto, Joaquim G.
2011-03-01
The impact of projected climate change on wine production was analysed for the Demarcated Region of Douro, Portugal. A statistical grapevine yield model (GYM) was developed using climate parameters as predictors. Statistically significant correlations were identified between annual yield and monthly mean temperatures and monthly precipitation totals during the growing cycle. These atmospheric factors control grapevine yield in the region, with the GYM explaining 50.4% of the total variance in the yield time series in recent decades. Anomalously high March rainfall (during budburst, shoot and inflorescence development) favours yield, as well as anomalously high temperatures and low precipitation amounts in May and June (May: flowering and June: berry development). The GYM was applied to a regional climate model output, which was shown to realistically reproduce the GYM predictors. Finally, using ensemble simulations under the A1B emission scenario, projections for GYM-derived yield in the Douro Region, and for the whole of the twenty-first century, were analysed. A slight upward trend in yield is projected to occur until about 2050, followed by a steep and continuous increase until the end of the twenty-first century, when yield is projected to be about 800 kg/ha above current values. While this estimate is based on meteorological parameters alone, changes due to elevated CO2 may further enhance this effect. In spite of the associated uncertainties, it can be stated that projected climate change may significantly benefit wine yield in the Douro Valley.
A photometric method for the estimation of the oil yield of oil shale
Cuttitta, Frank
1951-01-01
A method is presented for the distillation and photometric estimation of the oil yield of oil-bearing shales. The oil shale is distilled in a closed test tube and the oil extracted with toluene. The optical density of the toluene extract is used in the estimation of oil content and is converted to percentage of oil by reference to a standard curve. This curve is obtained by relating the oil yields determined by the Fischer assay method to the optical density of the toluene extract of the oil evolved by the new procedure. The new method gives results similar to those obtained by the Fischer assay method in a much shorter time. The applicability of the new method to oil-bearing shale and phosphatic shale has been tested.
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.
Estimates of genetics and phenotypics parameters for the yield and quality of soybean seeds.
Zambiazzi, E V; Bruzi, A T; Guilherme, S R; Pereira, D R; Lima, J G; Zuffo, A M; Ribeiro, F O; Mendes, A E S; Godinho, S H M; Carvalho, M L M
2017-09-27
Estimating genotype x environment (GxE) parameters for quality and yield in soybean seed grown in different environments in Minas Gerais State was the goal of this study, as well as to evaluate interaction effects of GxE for soybean seeds yield and quality. Seeds were produced in three locations in Minas Gerais State (Lavras, Inconfidentes, and Patos de Minas) in 2013/14 and 2014/15 seasons. Field experiments were conducted in randomized blocks in a factorial 17 x 6 (GxE), and three replications. Seed yield and quality were evaluated for germination in substrates paper and sand, seedling emergence, speed emergency index, mechanical damage by sodium hypochlorite, electrical conductivity, speed aging, vigor and viability of seeds by tetrazolium test in laboratory using completely randomized design. Quadratic component genotypic, GXE variance component, genotype determination coefficient, genetic variation coefficient and environmental variation coefficient were estimated using the Genes software. Percentage analysis of genotypes contribution, environments and genotype x environment interaction were conducted by sites combination two by two and three sites combination, using the R software. Considering genotypes selection of broad adaptation, TMG 1179 RR, CD 2737 RR, and CD 237 RR associated better yield performance at high physical and physiological potential of seed. Environmental effect was more expressive for most of the characters related to soybean seed quality. GxE interaction effects were expressive though genotypes did not present coincidental behavior in different environments.
Remote sensing and gis based wheat crop acreage and yield estimation of district hyderabad, pakistan
International Nuclear Information System (INIS)
Siyal, A.
2015-01-01
Pre-harvest reliable and timely yield forecast and area estimates of cropped area is vital to planners and policy makers for making important and timely decisions with respect to food security in a country. The present study was conducted to estimate the wheat cropped area and crop yield in Hyderabad District, Pakistan from the Landsat 8 satellite imagery for Rabi 2013-14 and ground trothing. The required imagery of district Hyderabad was acquired from GLOVIS and was classified with maximum likelihood algorithm using ArcGIS 10.1. The classified image revealed that in district Hyderabad wheat covered 10,210 hectares (9.74% of total area) during Rabi season 2013-14 against 15,000 hectares (14.3% of total area) reported by Crop reporting Services (CRS), Sindh which is 30% less than that of reported by CRS. A positive linear relation between the wheat crop yield and the peak NDVI with coefficient of determination R2 = 0.91 was observed. Crop area and yield forecast through remote sensing is easy, cost effective, quick and reliable hence this technology needs to be introduced and propagated in the concerned government departments of Pakistan. (author)
Simulation Models of Leaf Area Index and Yield for Cotton Grown with Different Soil Conditioners.
Directory of Open Access Journals (Sweden)
Lijun Su
Full Text Available Simulation models of leaf area index (LAI and yield for cotton can provide a theoretical foundation for predicting future variations in yield. This paper analyses the increase in LAI and the relationships between LAI, dry matter, and yield for cotton under three soil conditioners near Korla, Xinjiang, China. Dynamic changes in cotton LAI were evaluated using modified logistic, Gaussian, modified Gaussian, log normal, and cubic polynomial models. Universal models for simulating the relative leaf area index (RLAI were established in which the application rate of soil conditioner was used to estimate the maximum LAI (LAIm. In addition, the relationships between LAIm and dry matter mass, yield, and the harvest index were investigated, and a simulation model for yield is proposed. A feasibility analysis of the models indicated that the cubic polynomial and Gaussian models were less accurate than the other three models for simulating increases in RLAI. Despite significant differences in LAIs under the type and amount of soil conditioner applied, LAIm could be described by aboveground dry matter using Michaelis-Menten kinetics. Moreover, the simulation model for cotton yield based on LAIm and the harvest index presented in this work provided important theoretical insights for improving water use efficiency in cotton cultivation and for identifying optimal application rates of soil conditioners.
Gribovszki, Zoltán
2018-05-01
Methods that use diurnal groundwater-level fluctuations are commonly used for shallow water-table environments to estimate evapotranspiration (ET) and recharge. The key element needed to obtain reliable estimates is the specific yield (Sy), a soil-water storage parameter that depends on unsaturated soil-moisture and water-table fluxes, among others. Soil-moisture profile measurement down to the water table, along with water-table-depth measurements, can provide a good opportunity to calculate Sy values even on a sub-daily scale. These values were compared with Sy estimates derived by traditional techniques, and it was found that slug-test-based Sy values gave the most similar results in a sandy soil environment. Therefore, slug-test methods, which are relatively cheap and require little time, were most suited to estimate Sy using diurnal fluctuations. The reason for this is that the timeframe of the slug-test measurement is very similar to the dynamic of the diurnal signal. The dynamic characteristic of Sy was also analyzed on a sub-daily scale (depending mostly on the speed of drainage from the soil profile) and a remarkable difference was found in Sy with respect to the rate of change of the water table. When comparing constant and sub-daily (dynamic) Sy values for ET estimation, the sub-daily Sy application yielded higher correlation, but only a slightly smaller deviation from the control ET method, compared with the usage of constant Sy.
Estimating the Impact and Spillover Effect of Climate Change on Crop Yield in Northern Ghana.
Botchway, E.
2016-12-01
In tropical regions of the world human-induced climate change is likely to impact negatively on crop yields. To investigate the impact of climate change and its spillover effect on mean and variance of crop yields in northern Ghana, the Just and Pope stochastic production function and the Spatial Durbin model were adopted. Surprisingly, the results suggest that both precipitation and average temperature have positive effects on mean crop yield during the wet season. Wet season average temperature has a significant spillover effect in the region, whereas precipitation during the wet season has only one significant spillover effect on maize yield. Wet season precipitation does not have a strong significant effect on crop yield despite the rainfed nature of agriculture in the region. Thus, even if there are losers and winners as a result of future climate change at the regional level, future crop yield would largely depend on future technological development in agriculture, which may improve yields over time despite the changing climate. We argue, therefore, that technical improvement in farm management such as improved seeds and fertilizers, conservation tillage and better pest control, may have a more significant role in increasing observed crop productivity levels over time. So investigating the relative importance of non-climatic factors on crop yield may shed more light on where appropriate interventions can help in improving crop yields. Climate change, also, needs to be urgently assessed at the level of the household, so that poor and vulnerable people dependent on agriculture can be appropriately targeted in research and development activities whose object is poverty alleviation.
Estimation of variation and correlation analysis for yield components in black currant cultivars
Directory of Open Access Journals (Sweden)
Rakonjac Vera
2015-01-01
Full Text Available Creating genotypes that will be characterized by high yields, good quality and other favorable agronomic characters is a major objective of most currant breeding programs worldwide. For easier and faster achievement of these goals and identification of superior genotypes suitable for use as parents in future hybridization programs, study of genetic parameters seems to be obligatory. In this regard, the aims of our study were to estimate components of variability and heritability, and do correlation analysis for yield components in order to determine efficient strategies for improving yield in black currant breeding programs. Significant differences between cultivars were established for all studied traits. A high proportion of genotypic variance was found with bush width, no. of shoots per bush, bunch weight and berry weight indicating that genetic improvement for these traits through breeding was achievable. Opposite, seasonal variance was high for bush height, no. of bunch per bush and yield. The high heritability coefficients (0.80-0.94 detected for all traits studied reflect the close agreement between their phenotypic and genotypic values. Also, most pairs of traits were similarly correlated at both phenotypic and genotypic levels. So, yield was significantly and positively correlated with bush height, no of bunch per bush and bunch weight. These results imply a rapid response of black currants to selection. [Projekat Ministarstva nauke Republike Srbije, br. 46013 i FP7 Project AREA 316004
Sulistyo, A.; Purwantoro; Sari, K. P.
2018-01-01
Selection is a routine activity in plant breeding programs that must be done by plant breeders in obtaining superior plant genotypes. The use of appropriate selection criteria will determine the effectiveness of selection activities. The purpose of this study was to analysis the inheritable agronomic traits that contribute to soybean yield. A total of 91 soybean lines were planted in Muneng Experimental Station, Probolinggo District, East Java Province, Indonesia in 2016. All soybean lines were arranged in randomized complete block design with two replicates. Correlation analysis, path analysis and heritability estimation were performed on days to flowering, days to maturing, plant height, number of branches, number of fertile nodes, number of filled pods, weight of 100 seeds, and yield to determine selection criteria on soybean breeding program. The results showed that the heritability value of almost all agronomic traits observed is high except for the number of fertile nodes with low heritability. The result of correlation analysis shows that days to flowering, plant height and number of fertile nodes have positive correlation with seed yield per plot (0.056, 0.444, and 0.100, respectively). In addition, path analysis showed that plant height and number of fertile nodes have highest positive direct effect on soybean yield. Based on this result, plant height can be selected as one of selection criteria in soybean breeding program to obtain high yielding soybean variety.
Terziotti, Silvia; Capel, Paul D.; Tesoriero, Anthony J.; Hopple, Jessica A.; Kronholm, Scott C.
2018-03-07
The water quality of the Chesapeake Bay may be adversely affected by dissolved nitrate carried in groundwater discharge to streams. To estimate the concentrations, loads, and yields of nitrate from groundwater to streams for the Chesapeake Bay watershed, a regression model was developed based on measured nitrate concentrations from 156 small streams with watersheds less than 500 square miles (mi2 ) at baseflow. The regression model has three predictive variables: geologic unit, percent developed land, and percent agricultural land. Comparisons of estimated and actual values within geologic units were closely matched. The coefficient of determination (R2 ) for the model was 0.6906. The model was used to calculate baseflow nitrate concentrations at over 83,000 National Hydrography Dataset Plus Version 2 catchments and aggregated to 1,966 total 12-digit hydrologic units in the Chesapeake Bay watershed. The modeled output geospatial data layers provided estimated annual loads and yields of nitrate from groundwater into streams. The spatial distribution of annual nitrate yields from groundwater estimated by this method was compared to the total watershed yields of all sources estimated from a Chesapeake Bay SPAtially Referenced Regressions On Watershed attributes (SPARROW) water-quality model. The comparison showed similar spatial patterns. The regression model for groundwater contribution had similar but lower yields, suggesting that groundwater is an important source of nitrogen for streams in the Chesapeake Bay watershed.
Growth and yield models for Eucalyptus grandis grown in Swaziland ...
African Journals Online (AJOL)
The aim of this study was to develop a stand-level growth and yield model for short-rotationEucalyptus grandis grown for pulp wood production at Piggs Peak in Swaziland. The data were derived from a Nelder 1a spacing trial established with E. grandis clonal cuttings in 1998 and terminated in 2005. Planting density ...
Modelling daily sediment yield from a meso-scale catchment, a case study in SW Poland
International Nuclear Information System (INIS)
Keesstra, S. D.; Schoorl, J.; Temme, A. J. A. M.
2009-01-01
For management purposes it is important to be able to assess the sediment yield of a catchment. however, at this moment models designed for estimating sediment yield are only capable to give either very detailed storm-based information or year averages. The storm-based models require input data that are not available for most catchment. However, models that estimate yearly averages, ignore a lot of other detailed information, like daily discharge and precipitation data. There are currently no models available that model sediment yield on the temporal scale of one day and the spatial scale of a meso-scale catchment, without making use of very detailed input data. To fill this scientific and management gap, landscape evolution model LAPSUS has been adapted to model sediment yield on a daily basis. This model has the water balance as a base. To allow calibration with the discharge at the outlet, a subsurface flow module has been added to the model. (Author) 12 refs.
Modelling daily sediment yield from a meso-scale catchment, a case study in SW Poland
Energy Technology Data Exchange (ETDEWEB)
Keesstra, S. D.; Schoorl, J.; Temme, A. J. A. M.
2009-07-01
For management purposes it is important to be able to assess the sediment yield of a catchment. however, at this moment models designed for estimating sediment yield are only capable to give either very detailed storm-based information or year averages. The storm-based models require input data that are not available for most catchment. However, models that estimate yearly averages, ignore a lot of other detailed information, like daily discharge and precipitation data. There are currently no models available that model sediment yield on the temporal scale of one day and the spatial scale of a meso-scale catchment, without making use of very detailed input data. To fill this scientific and management gap, landscape evolution model LAPSUS has been adapted to model sediment yield on a daily basis. This model has the water balance as a base. To allow calibration with the discharge at the outlet, a subsurface flow module has been added to the model. (Author) 12 refs.
Local yield stress statistics in model amorphous solids
Barbot, Armand; Lerbinger, Matthias; Hernandez-Garcia, Anier; García-García, Reinaldo; Falk, Michael L.; Vandembroucq, Damien; Patinet, Sylvain
2018-03-01
We develop and extend a method presented by Patinet, Vandembroucq, and Falk [Phys. Rev. Lett. 117, 045501 (2016), 10.1103/PhysRevLett.117.045501] to compute the local yield stresses at the atomic scale in model two-dimensional Lennard-Jones glasses produced via differing quench protocols. This technique allows us to sample the plastic rearrangements in a nonperturbative manner for different loading directions on a well-controlled length scale. Plastic activity upon shearing correlates strongly with the locations of low yield stresses in the quenched states. This correlation is higher in more structurally relaxed systems. The distribution of local yield stresses is also shown to strongly depend on the quench protocol: the more relaxed the glass, the higher the local plastic thresholds. Analysis of the magnitude of local plastic relaxations reveals that stress drops follow exponential distributions, justifying the hypothesis of an average characteristic amplitude often conjectured in mesoscopic or continuum models. The amplitude of the local plastic rearrangements increases on average with the yield stress, regardless of the system preparation. The local yield stress varies with the shear orientation tested and strongly correlates with the plastic rearrangement locations when the system is sheared correspondingly. It is thus argued that plastic rearrangements are the consequence of shear transformation zones encoded in the glass structure that possess weak slip planes along different orientations. Finally, we justify the length scale employed in this work and extract the yield threshold statistics as a function of the size of the probing zones. This method makes it possible to derive physically grounded models of plasticity for amorphous materials by directly revealing the relevant details of the shear transformation zones that mediate this process.
Estimating milk yield and value losses from increased somatic cell count on US dairy farms.
Hadrich, J C; Wolf, C A; Lombard, J; Dolak, T M
2018-04-01
Milk loss due to increased somatic cell counts (SCC) results in economic losses for dairy producers. This research uses 10 mo of consecutive dairy herd improvement data from 2013 and 2014 to estimate milk yield loss using SCC as a proxy for clinical and subclinical mastitis. A fixed effects regression was used to examine factors that affected milk yield while controlling for herd-level management. Breed, milking frequency, days in milk, seasonality, SCC, cumulative months with SCC greater than 100,000 cells/mL, lactation, and herd size were variables included in the regression analysis. The cumulative months with SCC above a threshold was included as a proxy for chronic mastitis. Milk yield loss increased as the number of test days with SCC ≥100,000 cells/mL increased. Results from the regression were used to estimate a monetary value of milk loss related to SCC as a function of cow and operation related explanatory variables for a representative dairy cow. The largest losses occurred from increased cumulative test days with a SCC ≥100,000 cells/mL, with daily losses of $1.20/cow per day in the first month to $2.06/cow per day in mo 10. Results demonstrate the importance of including the duration of months above a threshold SCC when estimating milk yield losses. Cows with chronic mastitis, measured by increased consecutive test days with SCC ≥100,000 cells/mL, resulted in higher milk losses than cows with a new infection. This provides farm managers with a method to evaluate the trade-off between treatment and culling decisions as it relates to mastitis control and early detection. Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.
Biomass yield and modeling of logging residues of Terminalia ...
African Journals Online (AJOL)
The use of Dbh as an independent variable in the prediction of models for estimating the biomass residues of the tree species was adjudged best because it performed well. The validation results showed that the selected models satisfied the assumptions of regression analysis. The practical implication of the models is that ...
Antwi, Philip; Li, Jianzheng; Boadi, Portia Opoku; Meng, Jia; Shi, En; Deng, Kaiwen; Bondinuba, Francis Kwesi
2017-03-01
Three-layered feedforward backpropagation (BP) artificial neural networks (ANN) and multiple nonlinear regression (MnLR) models were developed to estimate biogas and methane yield in an upflow anaerobic sludge blanket (UASB) reactor treating potato starch processing wastewater (PSPW). Anaerobic process parameters were optimized to identify their importance on methanation. pH, total chemical oxygen demand, ammonium, alkalinity, total Kjeldahl nitrogen, total phosphorus, volatile fatty acids and hydraulic retention time selected based on principal component analysis were used as input variables, whiles biogas and methane yield were employed as target variables. Quasi-Newton method and conjugate gradient backpropagation algorithms were best among eleven training algorithms. Coefficient of determination (R 2 ) of the BP-ANN reached 98.72% and 97.93% whiles MnLR model attained 93.9% and 91.08% for biogas and methane yield, respectively. Compared with the MnLR model, BP-ANN model demonstrated significant performance, suggesting possible control of the anaerobic digestion process with the BP-ANN model. Copyright © 2016 Elsevier Ltd. All rights reserved.
Estimating the effect of urease inhibitor on rice yield based on NDVI at key growth stages
Directory of Open Access Journals (Sweden)
Kailou LIU,Yazhen LI,Huiwen HU
2014-06-01
Full Text Available The effect of the urease inhibitor, N-(n-butyl thiophosphoric triamide (NBPT at a range of application rates on rice production was examined in a field experiment at Jinxian County, Jiangxi Province, China. The normalized difference vegetation index (NDVI was measured at key growth stages in both early and late rice. The results showed that the grain yield increased significantly when urea was applied with NBPT, with the highest yield observed at 1.00% NBPT (wt/wt. NDVI differed with the growth stage of rice; it remained steady from the heading to the filling stage. Rice yield could be predicted from the NDVI taken at key rice growing stages, with R2 ranging from 0.34 to 0.69 in early rice and 0.49 to 0.70 in late rice. The validation test showed that RMSE (t·hm-2 values were 0.77 and 0.87 in early and late rice, respectively. Therefore, it was feasible to estimate rice yield for different amounts of urease inhibitor using NDVI.
Yin, Xinyou; Belay, Daniel W; van der Putten, Peter E L; Struik, Paul C
2014-12-01
Maximum quantum yield for leaf CO2 assimilation under limiting light conditions (Φ CO2LL) is commonly estimated as the slope of the linear regression of net photosynthetic rate against absorbed irradiance over a range of low-irradiance conditions. Methodological errors associated with this estimation have often been attributed either to light absorptance by non-photosynthetic pigments or to some data points being beyond the linear range of the irradiance response, both causing an underestimation of Φ CO2LL. We demonstrate here that a decrease in photosystem (PS) photochemical efficiency with increasing irradiance, even at very low levels, is another source of error that causes a systematic underestimation of Φ CO2LL. A model method accounting for this error was developed, and was used to estimate Φ CO2LL from simultaneous measurements of gas exchange and chlorophyll fluorescence on leaves using various combinations of species, CO2, O2, or leaf temperature levels. The conventional linear regression method under-estimated Φ CO2LL by ca. 10-15%. Differences in the estimated Φ CO2LL among measurement conditions were generally accounted for by different levels of photorespiration as described by the Farquhar-von Caemmerer-Berry model. However, our data revealed that the temperature dependence of PSII photochemical efficiency under low light was an additional factor that should be accounted for in the model.
Tan, Z.; Leung, L. R.; Li, H. Y.; Tesfa, T. K.
2017-12-01
Sediment yield (SY) has significant impacts on river biogeochemistry and aquatic ecosystems but it is rarely represented in Earth System Models (ESMs). Existing SY models focus on estimating SY from large river basins or individual catchments so it is not clear how well they simulate SY in ESMs at larger spatial scales and globally. In this study, we compare the strengths and weaknesses of eight well-known SY models in simulating annual mean SY at about 400 small catchments ranging in size from 0.22 to 200 km2 in the US, Canada and Puerto Rico. In addition, we also investigate the performance of these models in simulating event-scale SY at six catchments in the US using high-quality hydrological inputs. The model comparison shows that none of the models can reproduce the SY at large spatial scales but the Morgan model performs the better than others despite its simplicity. In all model simulations, large underestimates occur in catchments with very high SY. A possible pathway to reduce the discrepancies is to incorporate sediment detachment by landsliding, which is currently not included in the models being evaluated. We propose a new SY model that is based on the Morgan model but including a landsliding soil detachment scheme that is being developed. Along with the results of the model comparison and evaluation, preliminary findings from the revised Morgan model will be presented.
Yield estimation is a critical task in crop management. A number of traditional methods are available for crop yield estimation but they are costly, time-consuming and difficult to expand to a relatively large field. Remote sensing provides techniques to develop quick coverage over a field at any sc...
Statistical rice yield modeling using blended MODIS-Landsat based crop phenology metrics in Taiwan
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.
Yield stress of duplex stainless steel specimens estimated using a compound Hall–Petch equation
Directory of Open Access Journals (Sweden)
Noriaki Hirota, Fuxing Yin, Tsukasa Azuma and Tadanobu Inoue
2010-01-01
Full Text Available In this study, the 0.2% yield stress of duplex stainless steel was evaluated using a compound Hall–Petch equation. The compound Hall–Petch equation was derived from four types of duplex stainless steel, which contained 0.2–64.4 wt% δ-ferrite phase, had different chemical compositions and were annealed at different temperatures. Intragranular yield stress was measured with an ultra-microhardness tester and evaluated with the yield stress model proposed by Dao et al. Grain size, volume fraction and texture were monitored by electron backscattering diffraction measurement. The kγ constant in the compound equation for duplex stainless steel agrees well with that for γ-phase SUS316L steel in the temperature range of 1323–1473 K. The derived compound Hall–Petch equation predicts that the yield stress will be in good agreement with the experimental results for the Cr, Mn, Si, Ni and N solid-solution states. We find that the intragranular yield stress of the δ-phase of duplex stainless steel is rather sensitive to the chemical composition and annealing conditions, which is attributed to the size misfit parameter.
Modelling of the process yields of a whey fermentation
Energy Technology Data Exchange (ETDEWEB)
Blakebrough, N; Moresi, M
1981-01-01
The biomass yields (y) and COD reduction efficiencies (eta) of a whey fermentation by Kluyveromyces fragilis were studied in a 100-l fermenter at various stirrer speeds and lactose concentrations, and compared to those obtained in 10-l and 15-l fermenters at constant values of the oxygen transfer coefficient (kla) and air velocity. The empirical models previously constructed by using the 15-l fermenter data could be used to predict the yields on the other scales by calculating for each run the 15-l fermenter which would provide the same oxygen transfer coefficient measured by the sulfite method on each fermenter under study. To make this model independent of stirrer speeds used in each generic fermenter, the effect of aeration and mixing was incorporated into an overall parameter (kla) and the values of y and eta were correlated only with temperature, lactose level and kla, since these variables were approximately orthogonal. The validity of this model was finally checked against the yields reported by Wasserman et al. (1961) in a 6-cubic metre fermenter, thus confirming the capability of the model to provide a reliable basis for further scale-up on the production scale. (Refs. 17).
Frost trends and their estimated impact on yield in the Australian wheatbelt.
Zheng, Bangyou; Chapman, Scott C; Christopher, Jack T; Frederiks, Troy M; Chenu, Karine
2015-06-01
Radiant spring frosts occurring during reproductive developmental stages can result in catastrophic yield loss for wheat producers. To better understand the spatial and temporal variability of frost, the occurrence and impact of frost events on rain-fed wheat production was estimated across the Australian wheatbelt for 1957-2013 using a 0.05 ° gridded weather data set. Simulated yield outcomes at 60 key locations were compared with those for virtual genotypes with different levels of frost tolerance. Over the last six decades, more frost events, later last frost day, and a significant increase in frost impact on yield were found in certain regions of the Australian wheatbelt, in particular in the South-East and West. Increasing trends in frost-related yield losses were simulated in regions where no significant trend of frost occurrence was observed, due to higher mean temperatures accelerating crop development and causing sensitive post-heading stages to occur earlier, during the frost risk period. Simulations indicated that with frost-tolerant lines the mean national yield could be improved by up to 20% through (i) reduced frost damage (~10% improvement) and (ii) the ability to use earlier sowing dates (adding a further 10% improvement). In the simulations, genotypes with an improved frost tolerance to temperatures 1 °C lower than the current 0 °C reference provided substantial benefit in most cropping regions, while greater tolerance (to 3 °C lower temperatures) brought further benefits in the East. The results indicate that breeding for improved reproductive frost tolerance should remain a priority for the Australian wheat industry, despite warming climates. © The Author 2015. Published by Oxford University Press on behalf of the Society for Experimental Biology.
Modelling of the process yields of a whey fermentation
Energy Technology Data Exchange (ETDEWEB)
Blakebrough, N; Moresi, M
1981-09-01
The biomass yields (y) and COD reduction efficiencies (eta) of a whey fermentation by Kluyveromyces fragilis were studied in a 100-l fermenter at various stirrer speeds and lactose concentrations, and compared to those obtained in 10-l and 15-l fermenters at constant values of the oxygen transfer coefficient (ksub(L)a) and air velocity. The empirical models previously constructed by using the 15-l fermenter data could be used to predict the yields on the other scales by calculating for each run the 15-l fermenter which would provide the same oxygen transfer coefficient measured by the sulphite method on each fermenter under study. To make this model independent of stirrer speeds used in each generic fermenter, the effect of aeration and mixing was incorporated into an overall parameter (ksub(L)a) and the values of y and eta were correlated only with temperature, lactose level and ksub(L)a since these variables were approximately orthogonal.
Directory of Open Access Journals (Sweden)
H. Tonhati
2010-02-01
Full Text Available The aim of this study was analyze the (covariance components and genetic and phenotypic relationships in the following traits: accumulated milk yield at 270 days (MY270, observed until 305 days of lactation; accumulated milk yield at 270 days (MY270/ A and at 305 days (MY305, observed until 335 days of lactation; mozzarella cheese yield (MCY and fat (FP and protein (PP percentage, observed until 335 days of lactation. The (covariance components were estimated by Restricted Maximum Likelihood methodology in analyses single, two and three-traits using animal models. Heritability estimated for MY270, MY270/A, MY305, MCY, FP and PP were 0.22; 0.24, 0.25, 0.14, 0.29 and 0.40 respectively. The genetic correlations between MCY and the variables MY270, MY270/A, MY305, PP and FP was: 0.85; 1.00; 0.89; 0.14 and 0.06, respectively. This way, the selection for the production of milk in long period should increase MCY. However, in the search of animals that produce milk with quality, the genetic parameters suggest that another index should be composed allying these studied traits.
A GUIDED SWAT MODEL APPLICATION ON SEDIMENT YIELD MODELING IN PANGANI RIVER BASIN: LESSONS LEARNT
Directory of Open Access Journals (Sweden)
Preksedis Marco Ndomba
2008-12-01
Full Text Available The overall objective of this paper is to report on the lessons learnt from applying Soil and Water Assessment Tool (SWAT in a well guided sediment yield modelling study. The study area is the upstream of Pangani River Basin (PRB, the Nyumba Ya Mungu (NYM reservoir catchment, located in the North Eastern part of Tanzania. It should be noted that, previous modeling exercises in the region applied SWAT with preassumption that inter-rill or sheet erosion was the dominant erosion type. In contrast, in this study SWAT model application was guided by results of analysis of high temporal resolution of sediment flow data and hydro-meteorological data. The runoff component of the SWAT model was calibrated from six-years (i.e. 1977–1982 of historical daily streamflow data. The sediment component of the model was calibrated using one-year (1977–1988 daily sediment loads estimated from one hydrological year sampling programme (between March and November, 2005 rating curve. A long-term period over 37 years (i.e. 1969–2005 simulation results of the SWAT model was validated to downstream NYM reservoir sediment accumulation information. The SWAT model captured 56 percent of the variance (CE and underestimated the observed daily sediment loads by 0.9 percent according to Total Mass Control (TMC performance indices during a normal wet hydrological year, i.e., between November 1, 1977 and October 31, 1978, as the calibration period. SWAT model predicted satisfactorily the long-term sediment catchment yield with a relative error of 2.6 percent. Also, the model has identified erosion sources spatially and has replicated some erosion processes as determined in other studies and field observations in the PRB. This result suggests that for catchments where sheet erosion is dominant SWAT model may substitute the sediment-rating curve. However, the SWAT model could not capture the dynamics of sediment load delivery in some seasons to the catchment outlet.
A GUIDED SWAT MODEL APPLICATION ON SEDIMENT YIELD MODELING IN PANGANI RIVER BASIN: LESSONS LEARNT
Directory of Open Access Journals (Sweden)
Preksedis M. Ndomba
2008-01-01
Full Text Available The overall objective of this paper is to report on the lessons learnt from applying Soil and Water Assessment Tool (SWAT in a well guided sediment yield modelling study. The study area is the upstream of Pangani River Basin (PRB, the Nyumba Ya Mungu (NYM reservoir catchment, located in the North Eastern part of Tanzania. It should be noted that, previous modeling exercises in the region applied SWAT with preassumption that inter-rill or sheet erosion was the dominant erosion type. In contrast, in this study SWAT model application was guided by results of analysis of high temporal resolution of sediment flow data and hydro-meteorological data. The runoff component of the SWAT model was calibrated from six-years (i.e. 1977¿1982 of historical daily streamflow data. The sediment component of the model was calibrated using one-year (1977-1988 daily sediment loads estimated from one hydrological year sampling programme (between March and November, 2005 rating curve. A long-term period over 37 years (i.e. 1969-2005 simulation results of the SWAT model was validated to downstream NYM reservoir sediment accumulation information. The SWAT model captured 56 percent of the variance (CE and underestimated the observed daily sediment loads by 0.9 percent according to Total Mass Control (TMC performance indices during a normal wet hydrological year, i.e., between November 1, 1977 and October 31, 1978, as the calibration period. SWAT model predicted satisfactorily the long-term sediment catchment yield with a relative error of 2.6 percent. Also, the model has identified erosion sources spatially and has replicated some erosion processes as determined in other studies and field observations in the PRB. This result suggests that for catchments where sheet erosion is dominant SWAT model may substitute the sediment-rating curve. However, the SWAT model could not capture the dynamics of sediment load delivery in some seasons to the catchment outlet.
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.
Development of a Coupled Hydrological/Sediment Yield Model for a Watershed at Regional Level
Rajbhandaril, Narayan; Crosson, William; Tsegaye, Teferi; Coleman, Tommy; Liu, Yaping; Soman, Vishwas
1998-01-01
Development of a hydrologic model for the study of environmental conservation requires a comprehensive understanding of individual-storm affecting hydrologic and sedimentologic processes. The hydrologic models that we are currently coupling are the Simulator for Hydrology and Energy Exchange at the Land Surface (SHEELS) and the Distributed Runoff Model (DRUM). SHEELS runs continuously to estimate surface energy fluxes and sub-surface soil water fluxes, while DRUM operates during and following precipitation events to predict surface runoff and peak flow through channel routing. The lateral re-distribution of surface water determined by DRUM is passed to SHEELS, which then adjusts soil water contents throughout the profile. The model SHEELS is well documented in Smith et al. (1993) and Laymen and Crosson (1995). The model DRUM is well documented in Vieux et al. (1990) and Vieux and Gauer (1994). The coupled hydrologic model, SHEELS/DRUM, does not simulate sedimentologic processes. The simulation of the sedimentologic process is important for environmental conservation planning and management. Therefore, we attempted to develop a conceptual frame work for coupling a sediment yield model with SHEELS/DRUM to estimate individual-storm sediment yield from a watershed at a regional level. The sediment yield model that will be used for this study is the Universal Soil Loss Equation (USLE) with some modifications to enable the model to predict individual-storm sediment yield. The predicted sediment yield does not include wind erosion and erosion caused by irrigation and snow melt. Units used for this study are those given by Foster et al. (1981) for SI units.
NEST: a comprehensive model for scintillation yield in liquid xenon
Energy Technology Data Exchange (ETDEWEB)
Szydagis, M; Barry, N; Mock, J; Stolp, D; Sweany, M; Tripathi, M; Uvarov, S; Walsh, N; Woods, M [University of California, Davis, One Shields Ave., Davis, CA 95616 (United States); Kazkaz, K, E-mail: mmszydagis@ucdavis.edu [Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA 94550 (United States)
2011-10-15
A comprehensive model for explaining scintillation yield in liquid xenon is introduced. We unify various definitions of work function which abound in the literature and incorporate all available data on electron recoil scintillation yield. This results in a better understanding of electron recoil, and facilitates an improved description of nuclear recoil. An incident gamma energy range of O(1 keV) to O(1 MeV) and electric fields between 0 and O(10 kV/cm) are incorporated into this heuristic model. We show results from a Geant4 implementation, but because the model has a few free parameters, implementation in any simulation package should be simple. We use a quasi-empirical approach with an objective of improving detector calibrations and performance verification. The model will aid in the design and optimization of future detectors. This model is also easy to extend to other noble elements. In this paper we lay the foundation for an exhaustive simulation code which we call NEST (Noble Element Simulation Technique).
NEST: a comprehensive model for scintillation yield in liquid xenon
International Nuclear Information System (INIS)
Szydagis, M; Barry, N; Mock, J; Stolp, D; Sweany, M; Tripathi, M; Uvarov, S; Walsh, N; Woods, M; Kazkaz, K
2011-01-01
A comprehensive model for explaining scintillation yield in liquid xenon is introduced. We unify various definitions of work function which abound in the literature and incorporate all available data on electron recoil scintillation yield. This results in a better understanding of electron recoil, and facilitates an improved description of nuclear recoil. An incident gamma energy range of O(1 keV) to O(1 MeV) and electric fields between 0 and O(10 kV/cm) are incorporated into this heuristic model. We show results from a Geant4 implementation, but because the model has a few free parameters, implementation in any simulation package should be simple. We use a quasi-empirical approach with an objective of improving detector calibrations and performance verification. The model will aid in the design and optimization of future detectors. This model is also easy to extend to other noble elements. In this paper we lay the foundation for an exhaustive simulation code which we call NEST (Noble Element Simulation Technique).
Directory of Open Access Journals (Sweden)
Soshma Jan
2015-06-01
Full Text Available An experiment comprising of 24 wheat genotypes was undertaken during 2011-12, at New Developmental Research Farm, The University of Agriculture Peshawar, to elucidate information on the nature and magnitude of genetic variability, index of transmissibility and assessing the level of genetic improvement of the quantitative characters. The experimental material comprising 19 F3 populations along with their 5 parents of bread wheat were evaluated in randomized complete block design (RCBD with three replications. Analysis of variance exhibited highly significant (P ≤ 0.01 differences among genotypes for all the traits studied. F3 population Ghaznavi-98 x Pirsabak-05 showed maximum mean value for 1000-grain weight (47.3 g and biological yield (11474.9 kg ha-1, whereas, maximum values for grain yield (4027.3 kg ha-1, and harvest index (48.1% were observed for Pirsabak-05 x AUP-4006. Moreover, maximum spike length (11 cm was recorded for cross combination Pirsabak-05 x Pirsabak-04 and Janbaz x Pirsabak-05, respectively. In addition, Pirsabak-04 showed maximum value for number of grains spike-1 (55.0. Genetic variances were of greater magnitude than environmental variances for all the traits except for spike length and 1000-grain weight. Heritability estimates were of higher magnitude ranged from 0.64 to 0.92 for harvest index, biological yield, grain yield, and grains spike-1. Moderate to low heritability (0.40-0.46 was observed for 1000-grain weight, and spike length, respectively. Genetic gain was for spike length (0.48 cm, grains spike-1 (8.57, 1000-grain weight (2.93 g, grain yield (639.87 kg ha-1, biological yield (1790.03 kg ha-1, and harvest index (5.32 %. From high values of heritability and genetic advance, it could be concluded that selection for traits like grains spike-1 suggested good selection criteria and could be effective for future breeding programs. DOI: http://dx.doi.org/10.3126/ije.v4i2.12630 International Journal of Environment
DEFF Research Database (Denmark)
Palosuo, Taru; Kersebaum, Kurt Christian; Angulo, Carlos
2011-01-01
observations at all sites and in all years, and none could unequivocally be labelled robust and accurate in terms of yield prediction across different environments and crop cultivars with only minimum calibration. The best performance regarding yield estimation was for DAISY and DSSAT, for which the RMSE...... and WOFOST furnished high total above-ground biomass estimates, whereas CROPSYST, DSSAT and FASSET provided low total above-ground estimates. Consequently, DSSAT and FASSET produced very high harvest index values, followed by HERMES and WOFOST. APES and DAISY, on the other hand, returned low harvest index...... of grain yield estimates provided by the models for all sites and years reflects substantial uncertainties in model estimates achieved with only minimum calibration. Mean predictions from the eight models, on the other hand, were in good agreement with measured data. This applies to both results across all...
Model for traffic emissions estimation
Alexopoulos, A.; Assimacopoulos, D.; Mitsoulis, E.
A model is developed for the spatial and temporal evaluation of traffic emissions in metropolitan areas based on sparse measurements. All traffic data available are fully employed and the pollutant emissions are determined with the highest precision possible. The main roads are regarded as line sources of constant traffic parameters in the time interval considered. The method is flexible and allows for the estimation of distributed small traffic sources (non-line/area sources). The emissions from the latter are assumed to be proportional to the local population density as well as to the traffic density leading to local main arteries. The contribution of moving vehicles to air pollution in the Greater Athens Area for the period 1986-1988 is analyzed using the proposed model. Emissions and other related parameters are evaluated. Emissions from area sources were found to have a noticeable share of the overall air pollution.
Study on the Method of Grass Yield Model in the Source Region of Three Rivers with Multivariate Data
International Nuclear Information System (INIS)
You, Haoyan; Luo, Chengfeng; Liu, Zhengjun; Wang, Jiao
2014-01-01
This paper uses remote sensing and GIS technology to analyse the Source Region of Three Rivers (SRTR) to establish a grass yield estimation model during 2010 with remote sensing data, meteorological data, grassland type data and ground measured data. Analysis of the correlation between ground measured data, vegetation index based HJ-1A/B satellite data, meteorological data and grassland type data were used to establish the grass yield model. The grass yield model was studied by several statistical methods, such as multiple linear regression and Geographically Weighted Regression (GWR). The model's precision was validated. Finally, the best model to estimate the grass yield of Maduo County in SRTR was contrasted with the TM degraded grassland interpretation image of Maduo County from 2009. The result shows that: (1) Comparing with the multiple linear regression model, the GWR model gave a much better fitting result with the quality of fit increasing significantly from less than 0.3 to more than 0.8; (2) The most sensitive factors affecting the grass yield in SRTR were precipitation from May to August and drought index from May to August. From calculation of the five vegetation indices, MSAVI fitted the best; (3) The Maduo County grass yield estimated by the optimal model was consistent with the TM degraded grassland interpretation image, the spatial distribution of grass yield in Maduo County for 2010 showed a ''high south and low north'' pattern
Yield surface investigation of alloys during model disk spin tests
Directory of Open Access Journals (Sweden)
E. P. Kuzmin
2014-01-01
Full Text Available Gas-turbine engines operate under heavy subsequently static loading conditions. Disks of gas-turbine engine are high loaded parts of irregular shape having intensive stress concentrators wherein a 3D stress strain state occurs. The loss of load-carrying capability or burst of disk can lead to severe accident or disaster. Therefore, development of methods to assess deformations and to predict burst is one of the most important problems.Strength assessment approaches are used at all levels of engine creation. In recent years due to actively developing numerical method, particularly FEA, it became possible to investigate load-carrying capability of irregular shape disks, to use 3D computing schemes including flow theory and different options of force and deformation failure criteria. In spite of a wide progress and practical use of strength assessment approaches, there is a lack of detailed research data on yield surface of disk alloys. The main purpose of this work is to validate the use of basis hypothesis of flow theory and investigate the yield surface of disk alloys during the disks spin test.The results of quasi-static numerical simulation of spin tests of model disk made from high-temperature forged alloy are presented. To determine stress-strain state of disk during loading finite element analysis is used. Simulation of elastic-plastic strain fields was carried out using incremental theory of plasticity with isotropic hardening. Hardening function was taken from the results of specimens tensile test. Specimens were cut from a sinkhead of model disk. The paper investigates the model sensitivity affected by V.Mises and Tresca yield criteria as well as the Hosford model. To identify the material model parameters the eddy current sensors were used in the experimental approach to measure rim radial displacements during the load-unload of spin test. The results of calculation made using different material models were compared with the
Directory of Open Access Journals (Sweden)
Khanchoul Kamel
2014-01-01
Full Text Available Knowledge of sediment yield and the factors controlling it provides useful information for estimating erosion intensities within river basins. The objective of this study was to build a model from which suspended sediment yield could be estimated from ungauged rivers using computed sediment yield and physical factors. Researchers working on suspended sediment transported by wadis in the Maghreb are usually facing the lack of available data for such river types. Further study of the prediction of sediment transport in these regions and its variability is clearly required. In this work, ANNs were built between sediment yield established from longterm measurement series at gauging stations in Algerian catchments and corresponding basic physiographic parameters such as rainfall, runoff, lithology index, coefficient of torrentiality, and basin area. The proposed Levenberg-Marquardt and Multilayer Perceptron algorithms to train the neural networks of the current research study was based on the feed-forward backpropagation method with combinations of number of neurons in each hidden layer, transfer function, error goal. Additionally, three statistical measurements, namely the root mean square error (RMSE, the coefficient of determination (R², and the efficiency factor (EF have been reported for examining the forecasting accuracy of the developed model. Single plot displays of network outputs with respect to targets for training have provided good performance results and good fitting . Thus, ANNs were a promising method for predicting suspended sediment yield in ungauged Algerian catchments.
Colvin, M.E.; Bettoli, Phillip William; Scholten, G.D.
2013-01-01
Equilibrium yield models predict the total biomass removed from an exploited stock; however, traditional yield models must be modified to simulate roe yields because a linear relationship between age (or length) and mature ovary weight does not typically exist. We extended the traditional Beverton-Holt equilibrium yield model to predict roe yields of Paddlefish Polyodon spathula in Kentucky Lake, Tennessee-Kentucky, as a function of varying conditional fishing mortality rates (10-70%), conditional natural mortality rates (cm; 9% and 18%), and four minimum size limits ranging from 864 to 1,016mm eye-to-fork length. These results were then compared to a biomass-based yield assessment. Analysis of roe yields indicated the potential for growth overfishing at lower exploitation rates and smaller minimum length limits than were suggested by the biomass-based assessment. Patterns of biomass and roe yields in relation to exploitation rates were similar regardless of the simulated value of cm, thus indicating that the results were insensitive to changes in cm. Our results also suggested that higher minimum length limits would increase roe yield and reduce the potential for growth overfishing and recruitment overfishing at the simulated cm values. Biomass-based equilibrium yield assessments are commonly used to assess the effects of harvest on other caviar-based fisheries; however, our analysis demonstrates that such assessments likely underestimate the probability and severity of growth overfishing when roe is targeted. Therefore, equilibrium roe yield-per-recruit models should also be considered to guide the management process for caviar-producing fish species.
International codes and model intercomparison for intermediate energy activation yields
International Nuclear Information System (INIS)
Rolf, M.; Nagel, P.
1997-01-01
The motivation for this intercomparison came from data needs of accelerator-based waste transmutation, energy amplification and medical therapy. The aim of this exercise is to determine the degree of reliability of current nuclear reaction models and codes when calculating activation yields in the intermediate energy range up to 5000 MeV. Emphasis has been placed for a wide range of target elements ( O, Al, Fe, Co, Zr and Au). This work is mainly based on calculation of (P,xPyN) integral cross section for incident proton. A qualitative description of some of the nuclear models and code options employed is made. The systematics of graphical presentation of the results allows a quick quantitative measure of agreement or deviation. This code intercomparison highlights the fact that modeling calculations of energy activation yields may at best have uncertainties of a factor of two. The causes of such discrepancies are multi-factorial. Problems are encountered which are connected with the calculation of nuclear masses, binding energies, Q-values, shell effects, medium energy fission and Fermi break-up. (A.C.)
Yield Stress Model for Molten Composition B-3
Davis, Stephen; Zerkle, David
2017-06-01
Composition B-3 (Comp B-3) is a melt-castable explosive composed of 60/40 wt% RDX/TNT (hexahydro-1,3,5-trinitro-1,3,5-triazine/2,4,6-trinitrotoluene). During casting operations thermal conditions are controlled which along with the low melting point of TNT and the insensitivity of the mixture to external stimuli leading to safe use. Outside these standard operating conditions a more rigorous model of Comp B-3 rheological properties is necessary to model thermal transport as Comp B-3 evolves from quiescent solid through vaporization/decomposition upon heating. One particular rheological phenomena of interest is Bingham plasticity, where a material behaves as a quiescent solid unless a sufficient load is applied, resulting in fluid flow. In this study falling ball viscometer data is used to model the change in Bingham plastic yield stresses as a function of RDX particle volume fraction; a function of temperature. Results show the yield stress of Comp B-3 (τy) follows the expression τy = B ϕ -ϕc N , where Φ and Φc are the volume fraction of RDX and a critical volume fraction, respectively and B and N are experimentally evaluated constants.
Directory of Open Access Journals (Sweden)
Nathaniel K. Newlands
2014-06-01
Full Text Available We present a novel forecasting method for generating agricultural crop yield forecasts at the seasonal and regional-scale, integrating agroclimate variables and remotely-sensed indices. The method devises a multivariate statistical model to compute bias and uncertainty in forecasted yield at the Census of Agricultural Region (CAR scale across the Canadian Prairies. The method uses robust variable-selection to select the best predictors within spatial subregions. Markov-Chain Monte Carlo (MCMC simulation and random forest-tree machine learning techniques are then integrated to generate sequential forecasts through the growing season. Cross-validation of the model was performed by hindcasting/backcasting it and comparing its forecasts against available historical data (1987-2011 for spring wheat (Triticum aestivum L.. The model was also validated for the 2012 growing season by comparing its forecast skill at the CAR, provincial and Canadian Prairie region scales against available statistical survey data. Mean percent departures between wheat yield forecasted were under-estimated by 1-4 % in mid-season and over-estimated by 1 % at the end of the growing season. This integrated methodology offers a consistent, generalizable approach for sequentially forecasting crop yield at the regional-scale. It provides a statistically robust, yet flexible way to concurrently adjust to data-rich and data-sparse situations, adaptively select different predictors of yield to changing levels of environmental uncertainty, and to update forecasts sequentially so as to incorporate new data as it becomes available. This integrated method also provides additional statistical support for assessing the accuracy and reliability of model-based crop yield forecasts in time and space.
Directory of Open Access Journals (Sweden)
R Deihimfard
2015-12-01
Full Text Available Introduction Crop productivity is highly constrained by water and nitrogen limitations in many areas of the world (Kalra et al., 2007. Therefore, there is a need to investigate more on nitrogen and water management to achieve higher production as well as quality. Irrigated sugar beet in the cropping systems of Khorasan province in northeastern of Iran accounts for about 34% of the land area under sugar beet production (~115,000 ha with an average yield of around 36 t.ha-1 (Anonymous, 2009. However, there is a huge yield gap (the difference between potential and water and nitrogen-limited yield mainly due to biotic and abiotic factors causing major reduction in farmers’ yield. Accordingly, yield gap analysis should be carried out to reduce the yield reduction and reach the farmer’s yield to the potential yield. The current study aimed to simulate potential yield as well as yield gap related to water and nitrogen shortage in the major sugar beet-growing areas of Khorasan province of Iran. Materials and methods This study was carried out in 6 locations across Khorasan province, which is located in the northeast of Iran. Long term weather data for 1986 to 2009 were obtained from Iran Meteorological Organization for 6 selected locations. The weather data included daily sunshine hours (h, daily maximum and minimum temperatures (◦C, and daily rainfall (mm. Daily solar radiation was estimated using the Goudriaan (1993 method. The validated SUCROSBEET model (Deihimfard, 2011; Deihimfard et al., 2011 was then used to estimate potential, water and nitrogen-limited yield and yield gap of sugar beet for 6 selected locations across the Khorasan province in the northeast of Iran. This model simulates the impacts of weather, genotype and management factors on crop growth and development, soil water and nitrogen balance on a daily basis and finally it predicts crop yield. The model requires input data, including local weather and soil conditions, cultivar
Bastin, Catherine; Gillon, Alain; Massart, Xavier; Bertozzi, Carlo; Vanderick, Sylvie; Gengler, Nicolas
2010-01-01
Genetic correlations between body condition score (BCS) in lactation 1 to 3 and four economically important traits (days open, 305-days milk, fat, and protein yields recorded in the first 3 lactations) were estimated on about 12,500 Walloon Holstein cows using 4-trait random regression models. Results indicated moderate favorable genetic correlations between BCS and days open (from -0.46 to -0.62) and suggested the use of BCS for indirect selection on fertility. However, unfavorable genetic c...
Ozaki, Vitor A.; Ghosh, Sujit K.; Goodwin, Barry K.; Shirota, Ricardo
2009-01-01
This article presents a statistical model of agricultural yield data based on a set of hierarchical Bayesian models that allows joint modeling of temporal and spatial autocorrelation. This method captures a comprehensive range of the various uncertainties involved in predicting crop insurance premium rates as opposed to the more traditional ad hoc, two-stage methods that are typically based on independent estimation and prediction. A panel data set of county-average yield data was analyzed for 290 counties in the State of Paraná (Brazil) for the period of 1990 through 2002. Posterior predictive criteria are used to evaluate different model specifications. This article provides substantial improvements in the statistical and actuarial methods often applied to the calculation of insurance premium rates. These improvements are especially relevant to situations where data are limited. PMID:19890450
Directory of Open Access Journals (Sweden)
Rajeev Kumar
Full Text Available We present a comprehensive analysis of estimation of fisheries Maximum Sustainable Yield (MSY reference points using an ecosystem model built for Mille Lacs Lake, the second largest lake within Minnesota, USA. Data from single-species modelling output, extensive annual sampling for species abundances, annual catch-survey, stomach-content analysis for predatory-prey interactions, and expert opinions were brought together within the framework of an Ecopath with Ecosim (EwE ecosystem model. An increase in the lake water temperature was observed in the last few decades; therefore, we also incorporated a temperature forcing function in the EwE model to capture the influences of changing temperature on the species composition and food web. The EwE model was fitted to abundance and catch time-series for the period 1985 to 2006. Using the ecosystem model, we estimated reference points for most of the fished species in the lake at single-species as well as ecosystem levels with and without considering the influence of temperature change; therefore, our analysis investigated the trophic and temperature effects on the reference points. The paper concludes that reference points such as MSY are not stationary, but change when (1 environmental conditions alter species productivity and (2 fishing on predators alters the compensatory response of their prey. Thus, it is necessary for the management to re-estimate or re-evaluate the reference points when changes in environmental conditions and/or major shifts in species abundance or community structure are observed.
Energy Technology Data Exchange (ETDEWEB)
Leng, Guoyong
2017-12-01
The United States is responsible for 35% and 60% of global corn supply and exports. Enhanced supply stability through a reduction in the year-to-year variability of US corn yield would greatly benefit global food security. Important in this regard is to understand how corn yield variability has evolved geographically in the history and how it relates to climatic and non-climatic factors. Results showed that year-to-year variation of US corn yield has decreased significantly during 1980-2010, mainly in Midwest Corn Belt, Nebraska and western arid regions. Despite the country-scale decreasing variability, corn yield variability exhibited an increasing trend in South Dakota, Texas and Southeast growing regions, indicating the importance of considering spatial scales in estimating yield variability. The observed pattern is partly reproduced by process-based crop models, simulating larger areas experiencing increasing variability and underestimating the magnitude of decreasing variability. And 3 out of 11 models even produced a differing sign of change from observations. Hence, statistical model which produces closer agreement with observations is used to explore the contribution of climatic and non-climatic factors to the changes in yield variability. It is found that climate variability dominate the change trends of corn yield variability in the Midwest Corn Belt, while the ability of climate variability in controlling yield variability is low in southeastern and western arid regions. Irrigation has largely reduced the corn yield variability in regions (e.g. Nebraska) where separate estimates of irrigated and rain-fed corn yield exist, demonstrating the importance of non-climatic factors in governing the changes in corn yield variability. The results highlight the distinct spatial patterns of corn yield variability change as well as its influencing factors at the county scale. I also caution the use of process-based crop models, which have substantially underestimated
Adushkin, V. V.
- A statistical procedure is described for estimating the yields of underground nuclear tests at the former Soviet Semipalatinsk test site using the peak amplitudes of short-period surface waves observed at near-regional distances (Δ Semipalatinsk explosions, including the Soviet JVE explosion of September 14, 1988, and it is demonstrated that it provides seismic estimates of explosion yield which are typically within 20% of the yields determined for these same explosions using more accurate, non-seismic techniques based on near-source observations.
A toy model for the yield of a tamped fission bomb
Reed, B. Cameron
2018-02-01
A simple expression is developed for estimating the yield of a tamped fission bomb, that is, a basic nuclear weapon comprising a fissile core jacketed by a surrounding neutron-reflecting tamper. This expression is based on modeling the nuclear chain reaction as a geometric progression in combination with a previously published expression for the threshold-criticality condition for such a core. The derivation is especially straightforward, as it requires no knowledge of diffusion theory and should be accessible to students of both physics and policy. The calculation can be set up as a single page spreadsheet. Application to the Little Boy and Fat Man bombs of World War II gives results in reasonable accord with published yield estimates for these weapons.
COMPARISON OF THREE MODELS TO PREDICT ANNUAL SEDIMENT YIELD IN CARONI RIVER BASIN, VENEZUELA
Directory of Open Access Journals (Sweden)
Edilberto Guevara-Pérez
2007-01-01
Full Text Available Caroní River Basin is located in the south-eastern part of Venezuela; with an area of 92.000 km2, 40% of which belongs to the main affluent, the Paragua River. Caroní basin is the source of 66% of energy of the country. About 85% of the hydro electrical energy is generated in Guri reservoir located in the lower part of the watershed. To take provisions to avoid the reservoir silting it is very important the study of sediment yield of the basin. In this paper result of three empirical sediment yield models: Langbein- Schumm, Universal Soil Loss Equation-USLE and Poesen, are compared with observed data from five sub basins with records of twenty to thirty years. Men values of sediment yield for low, middle and upper Caroní are of 27, 76, 17 t/km2-year, respectively; and 46 and 78 t/km2-year for low and upper Paragua sub basins are. Standard errors of estimates vary between 13 and 29 for Langbein-Schumm model; between 8 and 32 for USLE procedure; and between 9 and 79, for Poesen model. Sediment yield predictions by Langbein-Schumm model seem to the best in Caroní basin.
Statistical modelling and deconvolution of yield meter data
DEFF Research Database (Denmark)
Tøgersen, Frede Aakmann; Waagepetersen, Rasmus Plenge
Data for yield maps can be obtained from modern combine harvesters equipped with a differential global positioning system and a yield monitoring system. Due to delay and smoothing effects in the combine harvester the recorded yield data for a location represents a shifted weighted average of yiel...
Directory of Open Access Journals (Sweden)
D. K. Henze
2008-05-01
Full Text Available Formation of SOA from the aromatic species toluene, xylene, and, for the first time, benzene, is added to a global chemical transport model. A simple mechanism is presented that accounts for competition between low and high-yield pathways of SOA formation, wherein secondary gas-phase products react further with either nitric oxide (NO or hydroperoxy radical (HO_{2} to yield semi- or non-volatile products, respectively. Aromatic species yield more SOA when they react with OH in regions where the [NO]/[HO_{2}] ratios are lower. The SOA yield thus depends upon the distribution of aromatic emissions, with biomass burning emissions being in areas with lower [NO]/[HO_{2}] ratios, and the reactivity of the aromatic with respect to OH, as a lower initial reactivity allows transport away from industrial source regions, where [NO]/[HO_{2}] ratios are higher, to more remote regions, where this ratio is lower and, hence, the ultimate yield of SOA is higher. As a result, benzene is estimated to be the most important aromatic species with regards to global formation of SOA, with a total production nearly equal that of toluene and xylene combined. Global production of SOA from aromatic sources via the mechanisms identified here is estimated at 3.5 Tg/yr, resulting in a global burden of 0.08 Tg, twice as large as previous estimates. The contribution of these largely anthropogenic sources to global SOA is still small relative to biogenic sources, which are estimated to comprise 90% of the global SOA burden, about half of which comes from isoprene. Uncertainty in these estimates owing to factors ranging from the atmospheric relevance of chamber conditions to model deficiencies result in an estimated range of SOA production from aromatics of 2–12 Tg/yr. Though this uncertainty range affords a significant anthropogenic contribution to global SOA, it is evident from comparisons to recent observations that additional pathways for
Rymszewicz, A; Bruen, M; O'Sullivan, J J; Turner, J N; Lawler, D M; Harrington, J R; Conroy, E; Kelly-Quinn, M
2018-04-01
Estimates of sediment yield are important for ecological and geomorphological assessment of fluvial systems and for assessment of soil erosion within a catchment. Many regulatory frameworks, such as the Convention for the Protection of the Marine Environment of the North-East Atlantic, derived from the Oslo and Paris Commissions (OSPAR) require reporting of annual sediment fluxes. While they may be measured in large rivers, sediment flux is rarely measured in smaller rivers. Measurements of sediment transport at a national scale can be also challenging and therefore, sediment yield models are often utilised by water resource managers for the predictions of sediment yields in the ungauged catchments. Regression based models, calibrated to field measurements, can offer an advantage over complex and computational models due to their simplicity, easy access to input data and due to the additional insights into factors controlling sediment export in the study sites. While traditionally calibrated to long-term average values of sediment yields such predictions cannot represent temporal variations. This study addresses this issue in a novel way by taking account of the variation from year to year in hydrological variables in the developed models (using annual mean runoff, annual mean flow, flows exceeded in five percentage of the time (Q5) and seasonal rainfall estimated separately for each year of observations). Other parameters included in the models represent spatial differences influenced by factors such as soil properties (% poorly drained soils and % peaty soils), land-use (% pasture or % arable lands), channel slope (S1085) and drainage network properties (drainage density). Catchment descriptors together with year-specific hydrological variables can explain both spatial differences and inter-annual variability of suspended sediment yields. The methodology is demonstrated by deriving equations from Irish data-sets (compiled in this study) with the best model
Using operational data to estimate the reliable yields of water-supply wells
Misstear, Bruce D. R.; Beeson, Sarah
The reliable yield of a water-supply well depends on many different factors, including the properties of the well and the aquifer; the capacities of the pumps, raw-water mains, and treatment works; the interference effects from other wells; and the constraints imposed by ion licences, water quality, and environmental issues. A relatively simple methodology for estimating reliable yields has been developed that takes into account all of these factors. The methodology is based mainly on an analysis of water-level and source-output data, where such data are available. Good operational data are especially important when dealing with wells in shallow, unconfined, fissure-flow aquifers, where actual well performance may vary considerably from that predicted using a more analytical approach. Key issues in the yield-assessment process are the identification of a deepest advisable pumping water level, and the collection of the appropriate well, aquifer, and operational data. Although developed for water-supply operators in the United Kingdom, this approach to estimating the reliable yields of water-supply wells using operational data should be applicable to a wide range of hydrogeological conditions elsewhere. Résumé La productivité d'un puits capté pour l'adduction d'eau potable dépend de différents facteurs, parmi lesquels les propriétés du puits et de l'aquifère, la puissance des pompes, le traitement des eaux brutes, les effets d'interférences avec d'autres puits et les contraintes imposées par les autorisations d'exploitation, par la qualité des eaux et par les conditions environnementales. Une méthodologie relativement simple d'estimation de la productivité qui prenne en compte tous ces facteurs a été mise au point. Cette méthodologie est basée surtout sur une analyse des données concernant le niveau piézométrique et le débit de prélèvement, quand ces données sont disponibles. De bonnes données opérationnelles sont particuli
Application of Bayesian Model Selection for Metal Yield Models using ALEGRA and Dakota.
Energy Technology Data Exchange (ETDEWEB)
Portone, Teresa; Niederhaus, John Henry; Sanchez, Jason James; Swiler, Laura Painton
2018-02-01
This report introduces the concepts of Bayesian model selection, which provides a systematic means of calibrating and selecting an optimal model to represent a phenomenon. This has many potential applications, including for comparing constitutive models. The ideas described herein are applied to a model selection problem between different yield models for hardened steel under extreme loading conditions.
Estimating Stochastic Volatility Models using Prediction-based Estimating Functions
DEFF Research Database (Denmark)
Lunde, Asger; Brix, Anne Floor
to the performance of the GMM estimator based on conditional moments of integrated volatility from Bollerslev and Zhou (2002). The case where the observed log-price process is contaminated by i.i.d. market microstructure (MMS) noise is also investigated. First, the impact of MMS noise on the parameter estimates from......In this paper prediction-based estimating functions (PBEFs), introduced in Sørensen (2000), are reviewed and PBEFs for the Heston (1993) stochastic volatility model are derived. The finite sample performance of the PBEF based estimator is investigated in a Monte Carlo study, and compared...... to correctly account for the noise are investigated. Our Monte Carlo study shows that the estimator based on PBEFs outperforms the GMM estimator, both in the setting with and without MMS noise. Finally, an empirical application investigates the possible challenges and general performance of applying the PBEF...
A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield
Syazwan Wahab, Nur; Saifullah Rusiman, Mohd; Mohamad, Mahathir; Amira Azmi, Nur; Che Him, Norziha; Ghazali Kamardan, M.; Ali, Maselan
2018-04-01
In this paper, we propose a hybrid model which is a combination of multiple linear regression model and fuzzy c-means method. This research involved a relationship between 20 variates of the top soil that are analyzed prior to planting of paddy yields at standard fertilizer rates. Data used were from the multi-location trials for rice carried out by MARDI at major paddy granary in Peninsular Malaysia during the period from 2009 to 2012. Missing observations were estimated using mean estimation techniques. The data were analyzed using multiple linear regression model and a combination of multiple linear regression model and fuzzy c-means method. Analysis of normality and multicollinearity indicate that the data is normally scattered without multicollinearity among independent variables. Analysis of fuzzy c-means cluster the yield of paddy into two clusters before the multiple linear regression model can be used. The comparison between two method indicate that the hybrid of multiple linear regression model and fuzzy c-means method outperform the multiple linear regression model with lower value of mean square error.
Possible effects of climatic change on estimated crop yields in Canada: A review
International Nuclear Information System (INIS)
Stewart, R.
1990-01-01
An overview is presented of research relating to the possible effects of climatic change on crop yields in Canada. Possible changes in the long-term climate resulting from a doubling in atmospheric carbon dioxide would have a major impact on agriculture in Canada. The Goddard Institute for Space Studies' general circulation model suggests that Canada will be significantly warmer and somewhat drier, with an annual temperature increase of more than 3 degree C and an annual precipitation increase of 11-13%. The increased annual precipitation will not compensate for the increased temperatures, and most agricultural regions will be somewhat drier. Current crop varieties and cropping systems will be unsuitable. Existing varieties will be eliminated or moved north into more suitable agricultural areas. Longer growing season varieties or alternative crops will be required for existing agricultural areas. Production opportunities for hard and soft winter wheat, corn, soybeans, horticulture and tender fruits could be enhanced. 21 refs
Directory of Open Access Journals (Sweden)
Julien Morel
2014-07-01
Full Text Available Estimating sugarcane biomass is difficult to achieve when working with highly variable spatial distributions of growing conditions, like on Reunion Island. We used a dataset of in-farm fields with contrasted climatic conditions and farming practices to compare three methods of yield estimation based on remote sensing: (1 an empirical relationship method with a growing season-integrated Normalized Difference Vegetation Index NDVI, (2 the Kumar-Monteith efficiency model, and (3 a forced-coupling method with a sugarcane crop model (MOSICAS and satellite-derived fraction of absorbed photosynthetically active radiation. These models were compared with the crop model alone and discussed to provide recommendations for a satellite-based system for the estimation of yield at the field scale. Results showed that the linear empirical model produced the best results (RMSE = 10.4 t∙ha−1. Because this method is also the simplest to set up and requires less input data, it appears that it is the most suitable for performing operational estimations and forecasts of sugarcane yield at the field scale. The main limitation is the acquisition of a minimum of five satellite images. The upcoming open-access Sentinel-2 Earth observation system should overcome this limitation because it will provide 10-m resolution satellite images with a 5-day frequency.
ExEP yield modeling tool and validation test results
Morgan, Rhonda; Turmon, Michael; Delacroix, Christian; Savransky, Dmitry; Garrett, Daniel; Lowrance, Patrick; Liu, Xiang Cate; Nunez, Paul
2017-09-01
EXOSIMS is an open-source simulation tool for parametric modeling of the detection yield and characterization of exoplanets. EXOSIMS has been adopted by the Exoplanet Exploration Programs Standards Definition and Evaluation Team (ExSDET) as a common mechanism for comparison of exoplanet mission concept studies. To ensure trustworthiness of the tool, we developed a validation test plan that leverages the Python-language unit-test framework, utilizes integration tests for selected module interactions, and performs end-to-end crossvalidation with other yield tools. This paper presents the test methods and results, with the physics-based tests such as photometry and integration time calculation treated in detail and the functional tests treated summarily. The test case utilized a 4m unobscured telescope with an idealized coronagraph and an exoplanet population from the IPAC radial velocity (RV) exoplanet catalog. The known RV planets were set at quadrature to allow deterministic validation of the calculation of physical parameters, such as working angle, photon counts and integration time. The observing keepout region was tested by generating plots and movies of the targets and the keepout zone over a year. Although the keepout integration test required the interpretation of a user, the test revealed problems in the L2 halo orbit and the parameterization of keepout applied to some solar system bodies, which the development team was able to address. The validation testing of EXOSIMS was performed iteratively with the developers of EXOSIMS and resulted in a more robust, stable, and trustworthy tool that the exoplanet community can use to simulate exoplanet direct-detection missions from probe class, to WFIRST, up to large mission concepts such as HabEx and LUVOIR.
Sequential Path Model for Grain Yield in Soybean
Directory of Open Access Journals (Sweden)
Mohammad SEDGHI
2010-09-01
Full Text Available This study was performed to determine some physiological traits that affect soybean,s grain yield via sequential path analysis. In a factorial experiment, two cultivars (Harcor and Williams were sown under four levels of nitrogen and two levels of weed management at the research station of Tabriz University, Iran, during 2004 and 2005. Grain yield, some yield components and physiological traits were measured. Correlation coefficient analysis showed that grain yield had significant positive and negative association with measured traits. A sequential path analysis was done in order to evaluate associations among grain yield and related traits by ordering the various variables in first, second and third order paths on the basis of their maximum direct effects and minimal collinearity. Two first-order variables, namely number of pods per plant and pre-flowering net photosynthesis revealed highest direct effect on total grain yield and explained 49, 44 and 47 % of the variation in grain yield based on 2004, 2005, and combined datasets, respectively. Four traits i.e. post-flowering net photosynthesis, plant height, leaf area index and intercepted radiation at the bottom layer of canopy were found to fit as second-order variables. Pre- and post-flowering chlorophyll content, main root length and intercepted radiation at the middle layer of canopy were placed at the third-order path. From the results concluded that, number of pods per plant and pre-flowering net photosynthesis are the best selection criteria in soybean for grain yield.
Dynamic growth and yield model for Black pine stands in Spain
Energy Technology Data Exchange (ETDEWEB)
Mora, J. V.; Rio, M. del; Bravo-Oviedo, A.
2012-07-01
In a forestry context, modelling stand development over time relies on estimates of different stand characteristics obtained from equations which usually constitute a multivariate system. In this study we have developed a stand growth model for even-aged stands of Black pine (Pinus nigra Arn.) in Spain. The 53 plots used to fit the equations came from the permanent sample plot network established by the Forest Research Centre (INIA) in 1963 and 1964 in the main distribution regions of Black pine. The model is made up of a system of equations to predict growth and yield in volume and basal area. In the fitting phase we took into account the correlation between the measurements within the same plot and the cross-equation residual correlations. The model incorporates a control function to estimate the thinning effect and a function for predicting the reduction in tree number due to regular mortality. In addition, we use the three parameter Weibull distribution function to estimate the number of trees in each diameter class by recovering the parameters using the moment method. The developed model is useful for simulating the evolution of even-aged stands with and without thinnings and allows the estimation of number of trees by diameter classes. (Author) 44 refs.
Nonparametric estimation in models for unobservable heterogeneity
Hohmann, Daniel
2014-01-01
Nonparametric models which allow for data with unobservable heterogeneity are studied. The first publication introduces new estimators and their asymptotic properties for conditional mixture models. The second publication considers estimation of a function from noisy observations of its Radon transform in a Gaussian white noise model.
MCMC estimation of multidimensional IRT models
Beguin, Anton; Glas, Cornelis A.W.
1998-01-01
A Bayesian procedure to estimate the three-parameter normal ogive model and a generalization to a model with multidimensional ability parameters are discussed. The procedure is a generalization of a procedure by J. Albert (1992) for estimating the two-parameter normal ogive model. The procedure will
Application of a rising plate meter to estimate forage yield on dairy farms in Pennsylvania
Accurately assessing pasture forage yield is necessary for producers who want to budget feed expenses and make informed pasture management decisions. Clipping and weighing forage from a known area is a direct method to measure pasture forage yield, however it is time consuming. The rising plate mete...
Estimating Canopy Dark Respiration for Crop Models
Monje Mejia, Oscar Alberto
2014-01-01
Crop production is obtained from accurate estimates of daily carbon gain.Canopy gross photosynthesis (Pgross) can be estimated from biochemical models of photosynthesis using sun and shaded leaf portions and the amount of intercepted photosyntheticallyactive radiation (PAR).In turn, canopy daily net carbon gain can be estimated from canopy daily gross photosynthesis when canopy dark respiration (Rd) is known.
Estimating Model Probabilities using Thermodynamic Markov Chain Monte Carlo Methods
Ye, M.; Liu, P.; Beerli, P.; Lu, D.; Hill, M. C.
2014-12-01
Markov chain Monte Carlo (MCMC) methods are widely used to evaluate model probability for quantifying model uncertainty. In a general procedure, MCMC simulations are first conducted for each individual model, and MCMC parameter samples are then used to approximate marginal likelihood of the model by calculating the geometric mean of the joint likelihood of the model and its parameters. It has been found the method of evaluating geometric mean suffers from the numerical problem of low convergence rate. A simple test case shows that even millions of MCMC samples are insufficient to yield accurate estimation of the marginal likelihood. To resolve this problem, a thermodynamic method is used to have multiple MCMC runs with different values of a heating coefficient between zero and one. When the heating coefficient is zero, the MCMC run is equivalent to a random walk MC in the prior parameter space; when the heating coefficient is one, the MCMC run is the conventional one. For a simple case with analytical form of the marginal likelihood, the thermodynamic method yields more accurate estimate than the method of using geometric mean. This is also demonstrated for a case of groundwater modeling with consideration of four alternative models postulated based on different conceptualization of a confining layer. This groundwater example shows that model probabilities estimated using the thermodynamic method are more reasonable than those obtained using the geometric method. The thermodynamic method is general, and can be used for a wide range of environmental problem for model uncertainty quantification.
Improved diagnostic model for estimating wind energy
Energy Technology Data Exchange (ETDEWEB)
Endlich, R.M.; Lee, J.D.
1983-03-01
Because wind data are available only at scattered locations, a quantitative method is needed to estimate the wind resource at specific sites where wind energy generation may be economically feasible. This report describes a computer model that makes such estimates. The model uses standard weather reports and terrain heights in deriving wind estimates; the method of computation has been changed from what has been used previously. The performance of the current model is compared with that of the earlier version at three sites; estimates of wind energy at four new sites are also presented.
Fission yields data generation and benchmarks of decay heat estimation of a nuclear fuel
Gil, Choong-Sup; Kim, Do Heon; Yoo, Jae Kwon; Lee, Jounghwa
2017-09-01
Fission yields data with the ENDF-6 format of 235U, 239Pu, and several actinides dependent on incident neutron energies have been generated using the GEF code. In addition, fission yields data libraries of ORIGEN-S, -ARP modules in the SCALE code, have been generated with the new data. The decay heats by ORIGEN-S using the new fission yields data have been calculated and compared with the measured data for validation in this study. The fission yields data ORIGEN-S libraries based on ENDF/B-VII.1, JEFF-3.1.1, and JENDL/FPY-2011 have also been generated, and decay heats were calculated using the ORIGEN-S libraries for analyses and comparisons.
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 form...
Large-scale crop monitoring and yield estimation are important for both scientific research and practical applications. Satellite remote sensing provides an effective means for regional and global cropland monitoring, particularly in data-sparse regions that lack reliable ground observations and rep...
Modeling and estimating system availability
International Nuclear Information System (INIS)
Gaver, D.P.; Chu, B.B.
1976-11-01
Mathematical models to infer the availability of various types of more or less complicated systems are described. The analyses presented are probabilistic in nature and consist of three parts: a presentation of various analytic models for availability; a means of deriving approximate probability limits on system availability; and a means of statistical inference of system availability from sparse data, using a jackknife procedure. Various low-order redundant systems are used as examples, but extension to more complex systems is not difficult
Use of Vegetation Health Data for Estimation of Aus Rice Yield in Bangladesh
Rahman, Atiqur; Roytman, Leonid; Krakauer, Nir Y.; Nizamuddin, Mohammad; Goldberg, Mitch
2009-01-01
Rice is a vital staple crop for Bangladesh and surrounding countries, with interannual variation in yields depending on climatic conditions. We compared Bangladesh yield of aus rice, one of the main varieties grown, from official agricultural statistics with Vegetation Health (VH) Indices [Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Vegetation Health Index (VHI)] computed from Advanced Very High Resolution Radiometer (AVHRR) data covering a period of 15 years (1991...
Fieuzal, R.; Marais Sicre, C.; Baup, F.
2017-05-01
The yield forecasting of corn constitutes a key issue in agricultural management, particularly in the context of demographic pressure and climate change. This study presents two methods to estimate yields using artificial neural networks: a diagnostic approach based on all the satellite data acquired throughout the agricultural season, and a real-time approach, where estimates are updated after each image was acquired in the microwave and optical domains (Formosat-2, Spot-4/5, TerraSAR-X, and Radarsat-2) throughout the crop cycle. The results are based on the Multispectral Crop Monitoring experimental campaign conducted by the CESBIO (Centre d'Études de la BIOsphère) laboratory in 2010 over an agricultural region in southwestern France. Among the tested sensor configurations (multi-frequency, multi-polarization or multi-source data), the best yield estimation performance (using the diagnostic approach) is obtained with reflectance acquired in the red wavelength region, with a coefficient of determination of 0.77 and an RMSE of 6.6 q ha-1. In the real-time approach the combination of red reflectance and CHH backscattering coefficients provides the best compromise between the accuracy and earliness of the yield estimate (more than 3 months before the harvest), with an R2 of 0.69 and an RMSE of 7.0 q ha-1 during the development of the central stem. The two best yield estimates are similar in most cases (for more than 80% of the monitored fields), and the differences are related to discrepancies in the crop growth cycle and/or the consequences of pests.
Adapting SWAT hillslope erosion model to predict sediment concentrations and yields in large Basins.
Vigiak, Olga; Malagó, Anna; Bouraoui, Fayçal; Vanmaercke, Matthias; Poesen, Jean
2015-12-15
The Soil and Water Assessment Tool (SWAT) is used worldwide for water quality assessment and planning. This paper aimed to assess and adapt SWAT hillslope sediment yield model (Modified Universal Soil Loss Equation, MUSLE) for applications in large basins, i.e. when spatial data is coarse and model units are large; and to develop a robust sediment calibration method for large regions. The Upper Danube Basin (132,000km(2)) was used as case study representative of large European Basins. The MUSLE was modified to reduce sensitivity of sediment yields to the Hydrologic Response Unit (HRU) size, and to identify appropriate algorithms for estimating hillslope length (L) and slope-length factor (LS). HRUs gross erosion was broadly calibrated against plot data and soil erosion map estimates. Next, mean annual SWAT suspended sediment concentrations (SSC, mg/L) were calibrated and validated against SSC data at 55 gauging stations (622 station-years). SWAT annual specific sediment yields in subbasin reaches (RSSY, t/km(2)/year) were compared to yields measured at 33 gauging stations (87station-years). The best SWAT configuration combined a MUSLE equation modified by the introduction of a threshold area of 0.01km(2) where L and LS were estimated with flow accumulation algorithms. For this configuration, the SSC residual interquartile was less than +/-15mg/L both for the calibration (1995-2004) and the validation (2005-2009) periods. The mean SSC percent bias for 1995-2009 was 24%. RSSY residual interquartile was within +/-10t/km(2)/year, with a mean RSSY percent bias of 12%. Residuals showed no bias with respect to drainage area, slope, or spatial distribution. The use of multiple data types at multiple sites enabled robust simulation of sediment concentrations and yields of the region. The MUSLE modifications are recommended for use in large basins. Based on SWAT simulations, we present a sediment budget for the Upper Danube Basin. Copyright © 2015. Published by Elsevier B.V.
Parameter Estimation of Partial Differential Equation Models.
Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Carroll, Raymond J; Maity, Arnab
2013-01-01
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 present 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 LIDAR data.
Modelling and water yield assessment of Lake Sibhayi | Smithers ...
African Journals Online (AJOL)
A yield analysis of simulated results with historical developments in the catchment for the 65-year period of observed climate record was undertaken using both a fixed minimum allowable lake level or a maximum drop from a reference lake level as criteria for system failure. Results from simulating lake levels using the ...
Similar estimates of temperature impacts on global wheat yield by three independent methods
Liu, Bing; Asseng, Senthold; Müller, Christoph; Ewert, Frank; Elliott, Joshua; Lobell, David B.; Martre, Pierre; Ruane, Alex C.; Wallach, Daniel; Jones, James W.; Supit, Iwan; Wolf, Joost
2016-01-01
The potential impact of global temperature change on global crop yield has recently been assessed with different methods. Here we show that grid-based and point-based simulations and statistical regressions (from historic records), without deliberate adaptation or CO 2 fertilization effects,
Comparison of estimated and measured sediment yield in the Gualala River
Matthew O’Connor; Jack Lewis; Robert Pennington
2012-01-01
This study compares quantitative erosion rate estimates developed at different spatial and temporal scales. It is motivated by the need to assess potential water quality impacts of a proposed vineyard development project in the Gualala River watershed. Previous erosion rate estimates were developed using sediment source assessment techniques by the North Coast Regional...
Directory of Open Access Journals (Sweden)
Luis Gabriel González Herrera
2008-09-01
of Gyr cows calving between 1990 and 2005 were used to estimate genetic parameters of monthly test-day milk yield (TDMY. Records were analyzed by random regression models (MRA that included the additive genetic and permanent environmental random effects and the contemporary group, age of cow at calving (linear and quadratic components and the average trend of the population as fixed effects. Random trajectories were fitted by Wilmink's (WIL and Ali & Schaeffer's (AS parametric functions. Residual variances were fitted by step functions with 1, 4, 6 or 10 classes. The contemporary group was defined by herd-year-season of test-day and included at least three animals. Models were compared by Akaike's and Schwarz's Bayesian (BIC information criterion. The AS function used for modeling the additive genetic and permanent environmental effects with heterogeneous residual variances adjusted with a step function with four classes was the best fitted model. Heritability estimates ranged from 0.21 to 0.33 for the AS function and from 0.17 to 0.30 for WIL function and were larger in the first half of the lactation period. Genetic correlations between TDMY were high and positive for adjacent test-days and decreased as days between records increased. Predicted breeding values for total 305-day milk yield (MRA305 and specific periods of lactation (obtained by the mean of all breeding values in the periods using the AS function were compared with that predicted by a standard model using accumulated 305-day milk yield (PTA305 by rank correlation. The magnitude of correlations suggested differences may be observed in ranking animals by using the different criteria which were compared in this study.
Sloto, Ronald A.; Olson, Leif E.
2011-01-01
Turbidity and suspended-sediment concentration data were collected by the U.S. Geological Survey (USGS) at four stream stations--French Creek near Phoenixville, West Branch Brandywine Creek near Honey Brook, West Branch Brandywine Creek at Modena, and East Branch Brandywine Creek below Downingtown--in Chester County, Pa. Sedimentation and siltation is the leading cause of stream impairment in Chester County, and these data are critical for quantifying sediment transport. This study was conducted by the USGS in cooperation with the Chester County Water Resources Authority and the Chester County Health Department. Data from optical turbidity sensors deployed at the four stations were recorded at 15- or 30-minute intervals by a data logger and uploaded every 1 to 4 hours to the USGS database. Most of the suspended-sediment samples were collected using automated samplers. The use of optical sensors to continuously monitor turbidity provided an accurate estimate of sediment fluctuations without the collection and analysis costs associated with intensive sampling during storms. Turbidity was used as a surrogate for suspended-sediment concentration (SSC), which is a measure of sedimentation and siltation. Regression models were developed between SSC and turbidity for each of the monitoring stations using SSC data collected from the automated samplers and turbidity data collected at each station. Instantaneous suspended-sediment loads (SSL) were computed from time-series turbidity and discharge data for the 2008 and 2009 water years using the regression equations. The instantaneous computations of SSL were summed to provide daily, storm, and water year annual loads. The annual SSL contributed from each basin was divided by the upstream drainage area to estimate the annual sediment yield. For all four basins, storms provided more than 96 percent of the annual SSL. In each basin, four storms generally provided over half the annual SSL each water year. Stormflows with the
Directory of Open Access Journals (Sweden)
Fernando Pérez-Rodríguez
2016-07-01
Full Text Available Aim of the study: To introduce and describe FlorNExT®, a free cloud computing application to estimate growth and yield of maritime pine (Pinus pinaster Ait. even-aged stands in the Northeast of Portugal (NE Portugal. Area of study: NE Portugal. Material and methods: FlorNExT® implements a dynamic growth and yield modelling framework which integrates transition functions for dominant height (site index curves and basal area, as well as output functions for tree and stand volume, biomass, and carbon content. Main results: FlorNExT® is freely available from any device with an Internet connection at: http://flornext.esa.ipb.pt/. Research highlights: This application has been designed to make it possible for any stakeholder to easily estimate standing volume, biomass, and carbon content in maritime pine stands from stand data, as well as to estimate growth and yield based on four stand variables: age, density, dominant height, and basal area. FlorNExT® allows planning thinning treatments. FlorNExT® is a fundamental tool to support forest mobilization at local and regional scales in NE Portugal. Keywords: forest management; maritime pine; forest modelling; knowledge transfer tool.
NEW MODEL FOR SOLAR RADIATION ESTIMATION FROM ...
African Journals Online (AJOL)
NEW MODEL FOR SOLAR RADIATION ESTIMATION FROM MEASURED AIR TEMPERATURE AND ... Nigerian Journal of Technology ... Solar radiation measurement is not sufficient in Nigeria for various reasons such as maintenance and ...
Development of a remote sensing-based rice yield forecasting model
Energy Technology Data Exchange (ETDEWEB)
Mosleh, M.K.; Hassan, Q.K.; Chowdhury, E.H.
2016-11-01
This study aimed to develop a remote sensing-based method for forecasting rice yield by considering vegetation greenness conditions during initial and peak greenness stages of the crop; and implemented for “boro” rice in Bangladeshi context. In this research, we used Moderate Resolution Imaging Spectroradiometer (MODIS)-derived two 16-day composite of normalized difference vegetation index (NDVI) images at 250 m spatial resolution acquired during the initial (January 1 to January 16) and peak greenness (March 23/24 to April 6/7 depending on leap year) stages in conjunction with secondary datasets (i.e., boro suitability map, and ground-based information) during 2007-2012 period. The method consisted of two components: (i) developing a model for delineating area under rice cultivation before harvesting; and (ii) forecasting rice yield as a function of NDVI. Our results demonstrated strong agreements between the model (i.e., MODIS-based) and ground-based area estimates during 2010-2012 period, i.e., coefficient of determination (R2); root mean square error (RMSE); and relative error (RE) in between 0.93 to 0.95; 30,519 to 37,451 ha; and ±10% respectively at the 23 district-levels. We also found good agreements between forecasted (i.e., MODIS-based) and ground-based yields during 2010-2012 period (R2 between 0.76 and 0.86; RMSE between 0.21 and 0.29 Mton/ha, and RE between -5.45% and 6.65%) at the 23 district-levels. We believe that our developments of forecasting the boro rice yield would be useful for the decision makers in addressing food security in Bangladesh. (Author)
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
Towards Better Simulation of US Maize Yield Responses to Climate in the Community Earth System Model
Peng, B.; Guan, K.; Chen, M.; Lawrence, D. M.; Jin, Z.; Bernacchi, C.; Ainsworth, E. A.; DeLucia, E. H.; Lombardozzi, D. L.; Lu, Y.
2017-12-01
Global food security is undergoing continuing pressure from increased population and climate change despites the potential advancement in breeding and management technologies. Earth system models (ESMs) are essential tools to study the impacts of historical and future climate on regional and global food production, as well as to assess the effectiveness of possible adaptations and their potential feedback to climate. Here we developed an improved maize representation within the Community Earth System Model (CESM) by combining the strengths of both the Community Land Model version 4.5 (CLM4.5) and the Agricultural Production Systems sIMulator (APSIM) models. Specifically, we modified the maize planting scheme, incorporated the phenology scheme adopted from the APSIM model, added a new carbon allocation scheme into CLM4.5, and improved the estimation of canopy structure parameters including leaf area index (LAI) and canopy height. Unique features of the new model (CLM-APSIM) include more detailed phenology stages, an explicit implementation of the impacts of various abiotic environmental stresses (including nitrogen, water, temperature and heat stresses) on maize phenology and carbon allocation, as well as an explicit simulation of grain number and grain size. We conducted a regional simulation of this new model over the US Corn Belt during 1990 to 2010. The simulated maize yield as well as its responses to climate (growing season mean temperature and precipitation) are benchmarked with data from UADA NASS statistics. Our results show that the CLM-APSIM model outperforms the CLM4.5 in simulating county-level maize yield production and reproduces more realistic yield responses to climate variations than CLM4.5. However, some critical processes (such as crop failure due to frost and inundation and suboptimal growth condition due to biotic stresses) are still missing in both CLM-APSIM and CLM4.5, making the simulated yield responses to climate slightly deviate from the
Modeling Long-Term Corn Yield Response to Nitrogen Rate and Crop Rotation.
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
Modelling of seed yield and its components in tall fescue (Festuca ...
African Journals Online (AJOL)
AJL
2011-10-03
Oct 3, 2011 ... Ridge regression analysis was used to derive a steady algorithmic .... included three replicates [3 × 6 = 18 plots (treatments), stochastic ..... The parameter estimates of the five seed yield components of a total of 327 samples.
Parameter Estimation of Nonlinear Models in Forestry.
Fekedulegn, Desta; Mac Siúrtáin, Máirtín Pádraig; Colbert, Jim J.
1999-01-01
Partial derivatives of the negative exponential, monomolecular, Mitcherlich, Gompertz, logistic, Chapman-Richards, von Bertalanffy, Weibull and the Richard’s nonlinear growth models are presented. The application of these partial derivatives in estimating the model parameters is illustrated. The parameters are estimated using the Marquardt iterative method of nonlinear regression relating top height to age of Norway spruce (Picea abies L.) from the Bowmont Norway Spruce Thinnin...
Torres, F E; Teodoro, P E; Rodrigues, E V; Santos, A; Corrêa, A M; Ceccon, G
2016-04-29
The aim of this study was to select erect cowpea (Vigna unguiculata L.) genotypes simultaneously for high adaptability, stability, and yield grain in Mato Grosso do Sul, Brazil using mixed models. We conducted six trials of different cowpea genotypes in 2005 and 2006 in Aquidauana, Chapadão do Sul, Dourados, and Primavera do Leste. The experimental design was randomized complete blocks with four replications and 20 genotypes. Genetic parameters were estimated by restricted maximum likelihood/best linear unbiased prediction, and selection was based on the harmonic mean of the relative performance of genetic values method using three strategies: selection based on the predicted breeding value, having considered the performance mean of the genotypes in all environments (no interaction effect); the performance in each environment (with an interaction effect); and the simultaneous selection for grain yield, stability, and adaptability. The MNC99542F-5 and MNC99-537F-4 genotypes could be grown in various environments, as they exhibited high grain yield, adaptability, and stability. The average heritability of the genotypes was moderate to high and the selective accuracy was 82%, indicating an excellent potential for selection.
Mean size estimation yields left-side bias: Role of attention on perceptual averaging.
Li, Kuei-An; Yeh, Su-Ling
2017-11-01
The human visual system can estimate mean size of a set of items effectively; however, little is known about whether information on each visual field contributes equally to the mean size estimation. In this study, we examined whether a left-side bias (LSB)-perceptual judgment tends to depend more heavily on left visual field's inputs-affects mean size estimation. Participants were instructed to estimate the mean size of 16 spots. In half of the trials, the mean size of the spots on the left side was larger than that on the right side (the left-larger condition) and vice versa (the right-larger condition). Our results illustrated an LSB: A larger estimated mean size was found in the left-larger condition than in the right-larger condition (Experiment 1), and the LSB vanished when participants' attention was effectively cued to the right side (Experiment 2b). Furthermore, the magnitude of LSB increased with stimulus-onset asynchrony (SOA), when spots on the left side were presented earlier than the right side. In contrast, the LSB vanished and then induced a reversed effect with SOA when spots on the right side were presented earlier (Experiment 3). This study offers the first piece of evidence suggesting that LSB does have a significant influence on mean size estimation of a group of items, which is induced by a leftward attentional bias that enhances the prior entry effect on the left side.
INTEGRATED SPEED ESTIMATION MODEL FOR MULTILANE EXPREESSWAYS
Hong, Sungjoon; Oguchi, Takashi
In this paper, an integrated speed-estimation model is developed based on empirical analyses for the basic sections of intercity multilane expressway un der the uncongested condition. This model enables a speed estimation for each lane at any site under arb itrary highway-alignment, traffic (traffic flow and truck percentage), and rainfall conditions. By combin ing this model and a lane-use model which estimates traffic distribution on the lanes by each vehicle type, it is also possible to es timate an average speed across all the lanes of one direction from a traffic demand by vehicle type under specific highway-alignment and rainfall conditions. This model is exp ected to be a tool for the evaluation of traffic performance for expressways when the performance me asure is travel speed, which is necessary for Performance-Oriented Highway Planning and Design. Regarding the highway-alignment condition, two new estimators, called effective horizo ntal curvature and effective vertical grade, are proposed in this paper which take into account the influence of upstream and downstream alignment conditions. They are applied to the speed-estimation model, and it shows increased accuracy of the estimation.
Efficiently adapting graphical models for selectivity estimation
DEFF Research Database (Denmark)
Tzoumas, Kostas; Deshpande, Amol; Jensen, Christian S.
2013-01-01
cardinality estimation without making the independence assumption. By carefully using concepts from the field of graphical models, we are able to factor the joint probability distribution over all the attributes in the database into small, usually two-dimensional distributions, without a significant loss...... in estimation accuracy. We show how to efficiently construct such a graphical model from the database using only two-way join queries, and we show how to perform selectivity estimation in a highly efficient manner. We integrate our algorithms into the PostgreSQL DBMS. Experimental results indicate...
Semi-parametric estimation for ARCH models
Directory of Open Access Journals (Sweden)
Raed Alzghool
2018-03-01
Full Text Available In this paper, we conduct semi-parametric estimation for autoregressive conditional heteroscedasticity (ARCH model with Quasi likelihood (QL and Asymptotic Quasi-likelihood (AQL estimation methods. The QL approach relaxes the distributional assumptions of ARCH processes. The AQL technique is obtained from the QL method when the process conditional variance is unknown. We present an application of the methods to a daily exchange rate series. Keywords: ARCH model, Quasi likelihood (QL, Asymptotic Quasi-likelihood (AQL, Martingale difference, Kernel estimator
Xu, Xiangying; Gao, Ping; Zhu, Xinkai; Guo, Wenshan; Ding, Jinfeng; Li, Chunyan
2018-01-01
Jiangsu is an important agricultural province in China. Winter wheat, as the second major grain crop in the province, is greatly affected by moisture variations. The objective of this study was to investigate whether there were significant trends in changes in the moisture conditions during wheat growing seasons over the past decades and how the wheat yields responded to different moisture levels by means of a popular drought index, the Standardized Precipitation Evapotranspiration Index (SPEI). The study started with a trend analysis and quantification of the moisture conditions with the Mann-Kendall test and Sen's Slope method, respectively. Then, correlation analysis was carried out to determine the relationship between de-trended wheat yields and multi-scalar SPEI. Finally, a multivariate panel regression model was established to reveal the quantitative yield responses to moisture variations. The results showed that the moisture conditions in Jiangsu were generally at a normal level, but this century appeared slightly drier in because of the relatively high temperatures. There was a significant correlation between short time scale SPEI values and wheat yields. Among the three critical stages of wheat development, the SPEI values in the late growth stage (April-June) had a closer linkage to the yields than in the seedling stage (October-November) and the over-wintering stage (December-February). Moreover, the yield responses displayed an asymmetric characteristic, namely, moisture excess led to higher yield losses compared to moisture deficit in this region. The maximum yield increment could be obtained under the moisture level of slight drought according to the 3-month SPEI at the late growth stage, while extreme wetting resulted in the most severe yield losses. The moisture conditions in the first 15 years of the 21st century were more favorable than in the last 20 years of the 20th century for wheat production in Jiangsu.
Directory of Open Access Journals (Sweden)
Xiangying Xu
Full Text Available Jiangsu is an important agricultural province in China. Winter wheat, as the second major grain crop in the province, is greatly affected by moisture variations. The objective of this study was to investigate whether there were significant trends in changes in the moisture conditions during wheat growing seasons over the past decades and how the wheat yields responded to different moisture levels by means of a popular drought index, the Standardized Precipitation Evapotranspiration Index (SPEI. The study started with a trend analysis and quantification of the moisture conditions with the Mann-Kendall test and Sen's Slope method, respectively. Then, correlation analysis was carried out to determine the relationship between de-trended wheat yields and multi-scalar SPEI. Finally, a multivariate panel regression model was established to reveal the quantitative yield responses to moisture variations. The results showed that the moisture conditions in Jiangsu were generally at a normal level, but this century appeared slightly drier in because of the relatively high temperatures. There was a significant correlation between short time scale SPEI values and wheat yields. Among the three critical stages of wheat development, the SPEI values in the late growth stage (April-June had a closer linkage to the yields than in the seedling stage (October-November and the over-wintering stage (December-February. Moreover, the yield responses displayed an asymmetric characteristic, namely, moisture excess led to higher yield losses compared to moisture deficit in this region. The maximum yield increment could be obtained under the moisture level of slight drought according to the 3-month SPEI at the late growth stage, while extreme wetting resulted in the most severe yield losses. The moisture conditions in the first 15 years of the 21st century were more favorable than in the last 20 years of the 20th century for wheat production in Jiangsu.
Modelling of the parametric yield in decananometer SRAM-Arrays
Directory of Open Access Journals (Sweden)
Th. Fischer
2006-01-01
Full Text Available In today's decananometer (90 nm, 65 nm, ..., CMOS technologies variations of device parameters play an ever more important role. Due to the demand for low leakage systems, supply voltage is decreased on one hand and the transistor threshold voltage is increased on the other hand. This reduces the overdrive voltage of the transistors and leads to decreasing read and write security margins in static memories (SRAM. In addition, smaller dimensions of the devices lead to increasing variations of the device parameters, thus mismatch effects increase. It can be shown that local variations of the transistor parameters limit the functionality of circuits stronger than variations on a global scale or hard defects. We show a method to predict the yield for a large number of SRAM devices without time consuming Monte Carlo simulations in dependence of various parameters (Vdd, temperature, technology options, transistor dimensions, .... This helps the designer to predict the yield for various system options and transistor dimensions, to choose the optimal solution for a specific product.
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
International Nuclear Information System (INIS)
Khanchoul, Kamel; Assassi Fella; Altschul, Robert
2009-01-01
This paper is an assessment of the suspended sediment yield in the Mellah Catchment of northern Algeria. We use discharge-sediment load relationships to explore the variability of water discharge and sediment load, and to investigate the impact of geomorphic factors disturbance on erosion and sedimentation. Suspended sediment load was analyzed in the Mellah Catchment (550 squre kms ) which was controlled by a gauging station to measure discharge and sediment transport. The relations between daily mean sediment concentration and daily mean water discharge were analyzed to develop sediment rating curves. For storms with no water samples, a sediment rating curve was developed. The technique involves stratification of data into discharge-based classes, the mean of which are used to fit a rating curve according to single flow data and season to provide various rating relationships. The mean annual sediment yield during the 24 years of the study period was 562 T km -2 in the Mellah Catchment. This drainage basin had high rainfall and runoff, the erosion was high. The high sediment yield in the Mellah basin could be explained by a high percentage of sparse grassland and cultivation developed on shallow marly silty-clayey soils with steep slopes often exceeding 12%. Almost all suspended sediment loads are transported during storm events that mainly occur in the winter and spring heavy and medium downpours. The scarceness of these events leads to a very large interseasonal variability of the wadi sediment fluxes. The negative impacts of this enhanced sediment mobility are directly felt in the western part of the basin which shows many mass movements, bank and gully erosion because cultivated areas are often bared during autumnal brief flash floods and furrowed downslope during the winter season. (author)
Parameter Estimation of Partial Differential Equation Models
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.
Directory of Open Access Journals (Sweden)
G. Bussi
2013-08-01
Full Text Available Soil loss and sediment transport in Mediterranean areas are driven by complex non-linear processes which have been only partially understood. Distributed models can be very helpful tools for understanding the catchment-scale phenomena which lead to soil erosion and sediment transport. In this study, a modelling approach is proposed to reproduce and evaluate erosion and sediment yield processes in a Mediterranean catchment (Rambla del Poyo, Valencia, Spain. Due to the lack of sediment transport records for model calibration and validation, a detailed description of the alluvial stratigraphy infilling a check dam that drains a 12.9 km2 sub-catchment was used as indirect information of sediment yield data. These dam infill sediments showed evidences of at least 15 depositional events (floods over the time period 1990–2009. The TETIS model, a distributed conceptual hydrological and sediment model, was coupled to the Sediment Trap Efficiency for Small Ponds (STEP model for reproducing reservoir retention, and it was calibrated and validated using the sedimentation volume estimated for the depositional units associated with discrete runoff events. The results show relatively low net erosion rates compared to other Mediterranean catchments (0.136 Mg ha−1 yr−1, probably due to the extensive outcrops of limestone bedrock, thin soils and rather homogeneous vegetation cover. The simulated sediment production and transport rates offer model satisfactory results, further supported by in-site palaeohydrological evidences and spatial validation using additional check dams, showing the great potential of the presented data assimilation methodology for the quantitative analysis of sediment dynamics in ungauged Mediterranean basins.
Using hardness to model yield and tensile strength
Energy Technology Data Exchange (ETDEWEB)
Hawk, Jeffrey A.; Dogan, Omer N.; Schrems, Karol K.
2005-02-01
The current direction in hardness research is towards smaller and smaller loads as nano-scale materials are developed. There remains, however, a need to investigate the mechanical behavior of complex alloys for severe environment service. In many instances this entails casting large ingots and making numerous tensile samples as the bounds of the operating environment are explored. It is possible to gain an understanding of the tensile strength of these alloys using room and elevated temperature hardness in conjunction with selected tensile tests. The approach outlined here has its roots in the work done by Tabor for metals and low alloy and carbon steels. This research seeks to extend the work to elevated temperatures for multi-phase, complex alloys. A review of the approach will be given after which the experimental data will be examined. In particular, the yield stress and tensile strength will be compared to their corresponding hardness based values.
Estimating impact on clover-grass yield caused by traffic intensities
DEFF Research Database (Denmark)
Jørgensen, R N; Green, Ole; Kristensen, Kristian
2009-01-01
Steer and a 15 m3 Kimadan slurry tanker on two axels, was used to perform the simulated traffic treatment on the parcels. The different traffic intensities are combinations of different tire pressure (1,0 and 2,5 bar), tire load (3000 and 6000 kg), time of year and number of passes (variating from 0 to 8...... components must be determined. The objective of this paper was to measure yield affects on clover-grass as a consequence of different traffic intensities. The experiments were carried out in the context of a full scale field trial. A 14 hectare full scale grass-clover field trial with 24 different traffic......). The harvesting procedure was preformed with a Haldrup plot harvester modified with RTK-GPS. This paper shows the initial results from measuring the yield affects References M.A. Hamza, M.A.; Anderson, W.K 2005. Soil compaction in cropping systems: A review of the nature, causes and possible solutionsRaper , R...
Energy Technology Data Exchange (ETDEWEB)
Barreiro, S.; Rua, J.; Tomé, M.
2016-07-01
Aim of the study. To introduce and describe FlorNExT®, a free cloud computing application to estimate growth and yield of maritime pine (Pinus pinaster Ait.) even-aged stands in the Northeast of Portugal (NE Portugal). Area of study: NE Portugal. Material and methods: FlorNExT® implements a dynamic growth and yield modelling framework which integrates transition functions for dominant height (site index curves) and basal area, as well as output functions for tree and stand volume, biomass, and carbon content. Main results: FlorNExT® is freely available from any device with an Internet connection at: http://flornext.esa.ipb.pt/. Research highlights: This application has been designed to make it possible for any stakeholder to easily estimate standing volume, biomass, and carbon content in maritime pine stands from stand data, as well as to estimate growth and yield based on four stand variables: age, density, dominant height, and basal area. FlorNExT® allows planning thinning treatments. FlorNExT® is a fundamental tool to support forest mobilization at local and regional scales in NE Portugal. (Author)
Conditional shape models for cardiac motion estimation
DEFF Research Database (Denmark)
Metz, Coert; Baka, Nora; Kirisli, Hortense
2010-01-01
We propose a conditional statistical shape model to predict patient specific cardiac motion from the 3D end-diastolic CTA scan. The model is built from 4D CTA sequences by combining atlas based segmentation and 4D registration. Cardiac motion estimation is, for example, relevant in the dynamic...
FUZZY MODELING BY SUCCESSIVE ESTIMATION OF RULES ...
African Journals Online (AJOL)
This paper presents an algorithm for automatically deriving fuzzy rules directly from a set of input-output data of a process for the purpose of modeling. The rules are extracted by a method termed successive estimation. This method is used to generate a model without truncating the number of fired rules, to within user ...
Directory of Open Access Journals (Sweden)
A Koocheki
2017-10-01
intensification. We estimated a stochastic frontier production function to calculate global datasets of maximum attainable grain yields, yield gaps, and efficiencies of grain production at. Applying a stochastic frontier production function facilitates estimating the yield gap based on the actual grain yield data only, instead of using actual and potential grain yield data from different sources. Therefore, the method allows for a robust and consistent analysis of the yield gap. The factors determining the yield gap are quantified at both global and regional scales. For this purpose, climatic information and wheat yield of different provinces were obtained from Iran meteorological organization and Agriculture Jahade organization, respectively. Wheat potential yield in different provinces was simulated by WOFOST model. Wheat gap was gained by difference between actual and potential yield in different provinces. Relative share of climatic variables in potential yield and also relative share of management variables included irrigation, fertilizer application, mechanization, pesticide application and manure in wheat yield gap was calculated by frontier production function. Results and Discussion The results showed that the effect of precipitation and radiation on wheat potential yield was positive and the impact of temperature was negative. Precipitation had the highest impact on wheat potential yield among other climatic variables. The range of wheat yield gap was from 1646 to 4470 kg ha-1 and 29 to 58% in Iran. Generally, the effect of all management variables on wheat yield gap was negative so that wheat yield gap was reduced by improving of these variables. Among studied management variables, irrigation had the highest effect on yield gap reduction, especially in dry-warm climate and fertilizer application was the second factor which had high effect on yield gap reduction. Therefore, to reduce wheat yield gap in Iran, irrigation management and fertilizer application should be
Robust estimation for ordinary differential equation models.
Cao, J; Wang, L; Xu, J
2011-12-01
Applied scientists often like to use ordinary differential equations (ODEs) to model complex dynamic processes that arise in biology, engineering, medicine, and many other areas. It is interesting but challenging to estimate ODE parameters from noisy data, especially when the data have some outliers. We propose a robust method to address this problem. The dynamic process is represented with a nonparametric function, which is a linear combination of basis functions. The nonparametric function is estimated by a robust penalized smoothing method. The penalty term is defined with the parametric ODE model, which controls the roughness of the nonparametric function and maintains the fidelity of the nonparametric function to the ODE model. The basis coefficients and ODE parameters are estimated in two nested levels of optimization. The coefficient estimates are treated as an implicit function of ODE parameters, which enables one to derive the analytic gradients for optimization using the implicit function theorem. Simulation studies show that the robust method gives satisfactory estimates for the ODE parameters from noisy data with outliers. The robust method is demonstrated by estimating a predator-prey ODE model from real ecological data. © 2011, The International Biometric Society.
Statistical Model-Based Face Pose Estimation
Institute of Scientific and Technical Information of China (English)
GE Xinliang; YANG Jie; LI Feng; WANG Huahua
2007-01-01
A robust face pose estimation approach is proposed by using face shape statistical model approach and pose parameters are represented by trigonometric functions. The face shape statistical model is firstly built by analyzing the face shapes from different people under varying poses. The shape alignment is vital in the process of building the statistical model. Then, six trigonometric functions are employed to represent the face pose parameters. Lastly, the mapping function is constructed between face image and face pose by linearly relating different parameters. The proposed approach is able to estimate different face poses using a few face training samples. Experimental results are provided to demonstrate its efficiency and accuracy.
Modeling of secondary organic aerosol yields from laboratory chamber data
Directory of Open Access Journals (Sweden)
M. N. Chan
2009-08-01
Full Text Available Laboratory chamber data serve as the basis for constraining models of secondary organic aerosol (SOA formation. Current models fall into three categories: empirical two-product (Odum, product-specific, and volatility basis set. The product-specific and volatility basis set models are applied here to represent laboratory data on the ozonolysis of α-pinene under dry, dark, and low-NO_{x} conditions in the presence of ammonium sulfate seed aerosol. Using five major identified products, the model is fit to the chamber data. From the optimal fitting, SOA oxygen-to-carbon (O/C and hydrogen-to-carbon (H/C ratios are modeled. The discrepancy between measured H/C ratios and those based on the oxidation products used in the model fitting suggests the potential importance of particle-phase reactions. Data fitting is also carried out using the volatility basis set, wherein oxidation products are parsed into volatility bins. The product-specific model is most likely hindered by lack of explicit inclusion of particle-phase accretion compounds. While prospects for identification of the majority of SOA products for major volatile organic compounds (VOCs classes remain promising, for the near future empirical product or volatility basis set models remain the approaches of choice.
A General Microscopic Traffic Model Yielding Dissipative Shocks
DEFF Research Database (Denmark)
Gaididei, Yuri Borisovich; Caputo, Jean Guy; Christiansen, Peter Leth
2018-01-01
We consider a general microscopic traffic model with a delay. An algebraic traffic function reduces the equation to the Aw-Rascle microscopic model while a sigmoid function gives the standard “follow the leader”. For zero delay we prove that the homogeneous solution is globally stable...
Energy Yield Potential Estimation Using Marine Current Turbine Simulations for the Bosphorus
DEFF Research Database (Denmark)
Yazicioglu, Hasan; Tunc, K. M. Murat; Ozbek, Muammer
2017-01-01
. The differences in elevation and salinity ratios between these two seas cause strong underwater currents. Depending on the morphology of the canal the speed of the flow varies and at some specific locations the energy intensity reaches to sufficient levels where electricity generation by marine current turbines...... becomes economically feasible. In this study, several simulations are performed for a 10 MW marine turbine farm/ cluster whose location is selected by taking into account several factors such as the canal morphology, current speed and passage of vessels. 360 different simulations are performed for 15...... within the selected region, the analyses are performed for three different flow speeds corresponding to 10 % increase and decrease in the average value. For each simulation the annual energy yield and cluster efficiency are calculated....
Direct Importance Estimation with Gaussian Mixture Models
Yamada, Makoto; Sugiyama, Masashi
The ratio of two probability densities is called the importance and its estimation has gathered a great deal of attention these days since the importance can be used for various data processing purposes. In this paper, we propose a new importance estimation method using Gaussian mixture models (GMMs). Our method is an extention of the Kullback-Leibler importance estimation procedure (KLIEP), an importance estimation method using linear or kernel models. An advantage of GMMs is that covariance matrices can also be learned through an expectation-maximization procedure, so the proposed method — which we call the Gaussian mixture KLIEP (GM-KLIEP) — is expected to work well when the true importance function has high correlation. Through experiments, we show the validity of the proposed approach.
Raymond, G M; Bassingthwaighte, J B
This is a practical example of a powerful research strategy: putting together data from studies covering a diversity of conditions can yield a scientifically sound grasp of the phenomenon when the individual observations failed to provide definitive understanding. The rationale is that defining a realistic, quantitative, explanatory hypothesis for the whole set of studies, brings about a "consilience" of the often competing hypotheses considered for individual data sets. An internally consistent conjecture linking multiple data sets simultaneously provides stronger evidence on the characteristics of a system than does analysis of individual data sets limited to narrow ranges of conditions. Our example examines three very different data sets on the clearance of salicylic acid from humans: a high concentration set from aspirin overdoses; a set with medium concentrations from a research study on the influences of the route of administration and of sex on the clearance kinetics, and a set on low dose aspirin for cardiovascular health. Three models were tested: (1) a first order reaction, (2) a Michaelis-Menten (M-M) approach, and (3) an enzyme kinetic model with forward and backward reactions. The reaction rates found from model 1 were distinctly different for the three data sets, having no commonality. The M-M model 2 fitted each of the three data sets but gave a reliable estimates of the Michaelis constant only for the medium level data (K m = 24±5.4 mg/L); analyzing the three data sets together with model 2 gave K m = 18±2.6 mg/L. (Estimating parameters using larger numbers of data points in an optimization increases the degrees of freedom, constraining the range of the estimates). Using the enzyme kinetic model (3) increased the number of free parameters but nevertheless improved the goodness of fit to the combined data sets, giving tighter constraints, and a lower estimated K m = 14.6±2.9 mg/L, demonstrating that fitting diverse data sets with a single model
Thresholding projection estimators in functional linear models
Cardot, Hervé; Johannes, Jan
2010-01-01
We consider the problem of estimating the regression function in functional linear regression models by proposing a new type of projection estimators which combine dimension reduction and thresholding. The introduction of a threshold rule allows to get consistency under broad assumptions as well as minimax rates of convergence under additional regularity hypotheses. We also consider the particular case of Sobolev spaces generated by the trigonometric basis which permits to get easily mean squ...
Clock error models for simulation and estimation
International Nuclear Information System (INIS)
Meditch, J.S.
1981-10-01
Mathematical models for the simulation and estimation of errors in precision oscillators used as time references in satellite navigation systems are developed. The results, based on all currently known oscillator error sources, are directly implementable on a digital computer. The simulation formulation is sufficiently flexible to allow for the inclusion or exclusion of individual error sources as desired. The estimation algorithms, following from Kalman filter theory, provide directly for the error analysis of clock errors in both filtering and prediction
Functional dynamic factor models with application to yield curve forecasting
Hays, Spencer; Shen, Haipeng; Huang, Jianhua Z.
2012-01-01
resulted in a trade-off between goodness of fit and consistency with economic theory. To address this, herein we propose a novel formulation which connects the dynamic factor model (DFM) framework with concepts from functional data analysis: a DFM
Estimation and uncertainty of reversible Markov models.
Trendelkamp-Schroer, Benjamin; Wu, Hao; Paul, Fabian; Noé, Frank
2015-11-07
Reversibility is a key concept in Markov models and master-equation models of molecular kinetics. The analysis and interpretation of the transition matrix encoding the kinetic properties of the model rely heavily on the reversibility property. The estimation of a reversible transition matrix from simulation data is, therefore, crucial to the successful application of the previously developed theory. In this work, we discuss methods for the maximum likelihood estimation of transition matrices from finite simulation data and present a new algorithm for the estimation if reversibility with respect to a given stationary vector is desired. We also develop new methods for the Bayesian posterior inference of reversible transition matrices with and without given stationary vector taking into account the need for a suitable prior distribution preserving the meta-stable features of the observed process during posterior inference. All algorithms here are implemented in the PyEMMA software--http://pyemma.org--as of version 2.0.
Modeling the yield potential of dryland canola under current and future climates in California
George, N.; Kaffka, S.; Beeck, C.; Bucaram, S.; Zhang, J.
2012-12-01
Models predict that the climate of California will become hotter, drier and more variable under future climate change scenarios. This will lead to both increased irrigation demand and reduced irrigation water availability. In addition, it is predicted that most common Californian crops will suffer a concomitant decline in productivity. To remain productive and economically viable, future agricultural systems will need to have greater water use efficiency, tolerance of high temperatures, and tolerance of more erratic temperature and rainfall patterns. Canola (Brassica napus) is the third most important oilseed globally, supporting large and well-established agricultural industries in Canada, Europe and Australia. It is an agronomically useful and economically valuable crop, with multiple end markets, that can be grown in California as a dryland winter rotation with little to no irrigation demand. This gives canola great potential as a new crop for Californian farmers both now and as the climate changes. Given practical and financial limitations it is not always possible to immediately or widely evaluate a crop in a new region. Crop production models are therefore valuable tools for assessing the potential of new crops, better targeting further field research, and refining research questions. APSIM is a modular modeling framework developed by the Agricultural Production Systems Research Unit in Australia, it combines biophysical and management modules to simulate cropping systems. This study was undertaken to examine the yield potential of Australian canola varieties having different water requirements and maturity classes in California using APSIM. The objective of the work was to identify the agricultural regions of California most ideally suited to the production of Australian cultivars of canola and to simulate the production of canola in these regions to estimate yield-potential. This will establish whether the introduction and in-field evaluation of better
Miller, Cherie V.; Gutierrez-Magness, Angelica L.; Feit Majedi, Brenda L.; Foster, Gregory D.
2007-01-01
concentrations of total phosphorus and total nitrogen had lower values of multiple R2 than suspended sediment, but the estimated bias for all the models was similar. The models for total nitrogen and total phosphorus tended to under-predict high concentrations and to over-predict low concentrations as compared to measured values. Annual yields (loads per square area in kilograms per year per square kilometer) were estimated for suspended sediment, total nitrogen, and total phosphorus using the U.S. Geological Survey models ESTIMATOR and LOADEST. The model LOADEST used hourly time steps and allowed the use of turbidity, which is strongly correlated to concentrations of suspended sediment, as a predictor variable. Annual yields for total nitrogen and total phosphorus were slightly higher but similar to previous estimates for other watersheds of the Chesapeake Bay, but annual yields for suspended sediment were higher by an order of magnitude for the two Anacostia River stations. Annual yields of suspended sediment at the two Anacostia River stations ranged from 131,000 to 248,000 kilograms per year per square kilometer for 2004 and 2005. LOADEST estimates were similar to those determined with ESTIMATOR, but had reduced errors associated with the estimates.
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.
A single model procedure for estimating tank calibration equations
International Nuclear Information System (INIS)
Liebetrau, A.M.
1997-10-01
A fundamental component of any accountability system for nuclear materials is a tank calibration equation that relates the height of liquid in a tank to its volume. Tank volume calibration equations are typically determined from pairs of height and volume measurements taken in a series of calibration runs. After raw calibration data are standardized to a fixed set of reference conditions, the calibration equation is typically fit by dividing the data into several segments--corresponding to regions in the tank--and independently fitting the data for each segment. The estimates obtained for individual segments must then be combined to obtain an estimate of the entire calibration function. This process is tedious and time-consuming. Moreover, uncertainty estimates may be misleading because it is difficult to properly model run-to-run variability and between-segment correlation. In this paper, the authors describe a model whose parameters can be estimated simultaneously for all segments of the calibration data, thereby eliminating the need for segment-by-segment estimation. The essence of the proposed model is to define a suitable polynomial to fit to each segment and then extend its definition to the domain of the entire calibration function, so that it (the entire calibration function) can be expressed as the sum of these extended polynomials. The model provides defensible estimates of between-run variability and yields a proper treatment of between-segment correlations. A portable software package, called TANCS, has been developed to facilitate the acquisition, standardization, and analysis of tank calibration data. The TANCS package was used for the calculations in an example presented to illustrate the unified modeling approach described in this paper. With TANCS, a trial calibration function can be estimated and evaluated in a matter of minutes
Creatinine measurements often yielded false estimates of progression in chronic renal failure
International Nuclear Information System (INIS)
Walser, M.; Drew, H.H.; LaFrance, N.D.
1988-01-01
In 9 of 22 observation periods (lasting an average of 15 months) in 17 patients with moderate to severe chronic renal failure (GFR 4 to 23 ml/min), rates of progression as estimated from the linear regression on time of 24-hour creatinine clearance (b1) differed significantly from rates of progression as estimated from the regression on time of urinary clearance of 99mTc-DTPA (b2), during all or part of the period of observation. b1 exceeded b2 in four cases and was less than b2 in the other five. Thus there were gradual changes in the fractional tubular secretion of creatinine in individual patients, in both directions. Owing to these changes, measurements of creatinine clearance gave erroneous impressions of the rate or existence of progression during all or a portion of the period of observation in nearly half of these patients. In the 22 studies as a group, using the entire periods of observation, b1 indicated significantly more rapid progression (by 0.18 +/- 0.06 ml/min/month, P less than 0.01) than did b2, and had a significantly greater variance. Measurements of progression based on the rate of change of reciprocal plasma creatinine (multiplied by an average rate of urinary creatinine excretion in each study) were equally misleading, even though less variable. We conclude that sequential creatinine measurements are often misleading as measures of progression and should, when feasible, be replaced by urinary clearance of isotopes in following patients with chronic renal failure
Fission yield calculation using toy model based on Monte Carlo simulation
International Nuclear Information System (INIS)
Jubaidah; Kurniadi, Rizal
2015-01-01
Toy model is a new approximation in predicting fission yield distribution. Toy model assumes nucleus as an elastic toy consist of marbles. The number of marbles represents the number of nucleons, A. This toy nucleus is able to imitate the real nucleus properties. In this research, the toy nucleons are only influenced by central force. A heavy toy nucleus induced by a toy nucleon will be split into two fragments. These two fission fragments are called fission yield. In this research, energy entanglement is neglected. Fission process in toy model is illustrated by two Gaussian curves intersecting each other. There are five Gaussian parameters used in this research. They are scission point of the two curves (R c ), mean of left curve (μ L ) and mean of right curve (μ R ), deviation of left curve (σ L ) and deviation of right curve (σ R ). The fission yields distribution is analyses based on Monte Carlo simulation. The result shows that variation in σ or µ can significanly move the average frequency of asymmetry fission yields. This also varies the range of fission yields distribution probability. In addition, variation in iteration coefficient only change the frequency of fission yields. Monte Carlo simulation for fission yield calculation using toy model successfully indicates the same tendency with experiment results, where average of light fission yield is in the range of 90yield is in about 135
Fission yield calculation using toy model based on Monte Carlo simulation
Energy Technology Data Exchange (ETDEWEB)
Jubaidah, E-mail: jubaidah@student.itb.ac.id [Nuclear Physics and Biophysics Division, Department of Physics, Bandung Institute of Technology. Jl. Ganesa No. 10 Bandung – West Java, Indonesia 40132 (Indonesia); Physics Department, Faculty of Mathematics and Natural Science – State University of Medan. Jl. Willem Iskandar Pasar V Medan Estate – North Sumatera, Indonesia 20221 (Indonesia); Kurniadi, Rizal, E-mail: rijalk@fi.itb.ac.id [Nuclear Physics and Biophysics Division, Department of Physics, Bandung Institute of Technology. Jl. Ganesa No. 10 Bandung – West Java, Indonesia 40132 (Indonesia)
2015-09-30
Toy model is a new approximation in predicting fission yield distribution. Toy model assumes nucleus as an elastic toy consist of marbles. The number of marbles represents the number of nucleons, A. This toy nucleus is able to imitate the real nucleus properties. In this research, the toy nucleons are only influenced by central force. A heavy toy nucleus induced by a toy nucleon will be split into two fragments. These two fission fragments are called fission yield. In this research, energy entanglement is neglected. Fission process in toy model is illustrated by two Gaussian curves intersecting each other. There are five Gaussian parameters used in this research. They are scission point of the two curves (R{sub c}), mean of left curve (μ{sub L}) and mean of right curve (μ{sub R}), deviation of left curve (σ{sub L}) and deviation of right curve (σ{sub R}). The fission yields distribution is analyses based on Monte Carlo simulation. The result shows that variation in σ or µ can significanly move the average frequency of asymmetry fission yields. This also varies the range of fission yields distribution probability. In addition, variation in iteration coefficient only change the frequency of fission yields. Monte Carlo simulation for fission yield calculation using toy model successfully indicates the same tendency with experiment results, where average of light fission yield is in the range of 90yield is in about 135
Correlation length estimation in a polycrystalline material model
International Nuclear Information System (INIS)
Simonovski, I.; Cizelj, L.
2005-01-01
This paper deals with the correlation length estimated from a mesoscopic model of a polycrystalline material. The correlation length can be used in some macroscopic material models as a material parameter that describes the internal length. It can be estimated directly from the strain and stress fields calculated from a finite-element model, which explicitly accounts for the selected mesoscopic features such as the random orientation, shape and size of the grains. A crystal plasticity material model was applied in the finite-element analysis. Different correlation lengths were obtained depending on the used set of crystallographic orientations. We determined that the different sets of crystallographic orientations affect the general level of the correlation length, however, as the external load is increased the behaviour of correlation length is similar in all the analyzed cases. The correlation lengths also changed with the macroscopic load. If the load is below the yield strength the correlation lengths are constant, and are slightly higher than the average grain size. The correlation length can therefore be considered as an indicator of first plastic deformations in the material. Increasing the load above the yield strength creates shear bands that temporarily increase the values of the correlation lengths calculated from the strain fields. With a further load increase the correlation lengths decrease slightly but stay above the average grain size. (author)
Directory of Open Access Journals (Sweden)
H. Khalilzade
2016-02-01
Full Text Available Introduction Around the world maize is the second crop with the most cultivated areas and amount of production, so as the most important strategic crop, have a special situation in policies, decision making, resources and inputs allocation. On the other side, negative environmental consequences of intensive consumption of agrochemicals resulted to change view concerning food production. One of the most important visions is sustainable production of enough food plus attention to social, economic and environmental aspects. Many researchers stated that the first step to achieve this goal is optimization and improvement of resources use efficiencies. According to little knowledge on relation between soil electrical conductivity and yield of maize, beside the environmental concerns about nitrogen consumption and need to replace chemical nitrogen by ecological inputs, this study designed and aimed to evaluate agroecological characteristics of corn and some soil characteristics as affected by application of organic and biological fertilizers under field conditions. Materials and Methods In order to probing the possibility of grain yield and soil nitrogen estimation via measurement of soil properties, a field experiment was conducted during growing season 2010 at Research Station, Ferdowsi University of Mashhad, Iran. A randomized complete block design (RCBD with three replications was used. Treatments included: 1- manure (30 ton ha-1, 2-vermicompost (10 ton ha-1, 3- nitroxin (containing Azotobacter sp. and Azospirillum sp., inoculation was done according to Kennedy et al., 4- nitrogen as urea (400 kg ha-1 and 5- control (without fertilizer. Studied traits were soil pH, soil EC, soil respiration rate, N content of soil and maize yield. Soil respiration rate was measured using equation 1: CO2= (V0- V× N×22 Equation 1 In which V0 is the volume of consumed acid for control treatment titration, V is of the volume of consumed acid for sample treatment
Development on electromagnetic impedance function modeling and its estimation
Energy Technology Data Exchange (ETDEWEB)
Sutarno, D., E-mail: Sutarno@fi.itb.ac.id [Earth Physics and Complex System Division Faculty of Mathematics and Natural Sciences Institut Teknologi Bandung (Indonesia)
2015-09-30
Today the Electromagnetic methods such as magnetotellurics (MT) and controlled sources audio MT (CSAMT) is used in a broad variety of applications. Its usefulness in poor seismic areas and its negligible environmental impact are integral parts of effective exploration at minimum cost. As exploration was forced into more difficult areas, the importance of MT and CSAMT, in conjunction with other techniques, has tended to grow continuously. However, there are obviously important and difficult problems remaining to be solved concerning our ability to collect process and interpret MT as well as CSAMT in complex 3D structural environments. This talk aim at reviewing and discussing the recent development on MT as well as CSAMT impedance functions modeling, and also some improvements on estimation procedures for the corresponding impedance functions. In MT impedance modeling, research efforts focus on developing numerical method for computing the impedance functions of three dimensionally (3-D) earth resistivity models. On that reason, 3-D finite elements numerical modeling for the impedances is developed based on edge element method. Whereas, in the CSAMT case, the efforts were focused to accomplish the non-plane wave problem in the corresponding impedance functions. Concerning estimation of MT and CSAMT impedance functions, researches were focused on improving quality of the estimates. On that objective, non-linear regression approach based on the robust M-estimators and the Hilbert transform operating on the causal transfer functions, were used to dealing with outliers (abnormal data) which are frequently superimposed on a normal ambient MT as well as CSAMT noise fields. As validated, the proposed MT impedance modeling method gives acceptable results for standard three dimensional resistivity models. Whilst, the full solution based modeling that accommodate the non-plane wave effect for CSAMT impedances is applied for all measurement zones, including near-, transition
Sparse estimation of polynomial dynamical models
Toth, R.; Hjalmarsson, H.; Rojas, C.R.; Kinnaert, M.
2012-01-01
In many practical situations, it is highly desirable to estimate an accurate mathematical model of a real system using as few parameters as possible. This can be motivated either from appealing to a parsimony principle (Occam's razor) or from the view point of the utilization complexity in terms of
MODELING POLLINATION FACTORS THAT INFLUENCE ALFALFA SEED YIELD IN NORTH-CENTRAL NEVADA
BREAZEALE, Don; FERNANDEZ, George; NARAYANAN, Rangesan
2008-01-01
The relative importance of both environmental and management factors on alfalfa seed yield was investigated on North–Central Nevada farms. Multiple linear regression models using 2002-2003 data revealed that cumulative tripped fl owers increased seed yield in both years. Field location does not appear to make a difference in the observed variation in tripped fl ower production. The results suggest that seed yield can be increased by (a) by placing bee shelters closer and (b) cultural practice...
A General Model for Estimating Macroevolutionary Landscapes.
Boucher, Florian C; Démery, Vincent; Conti, Elena; Harmon, Luke J; Uyeda, Josef
2018-03-01
The evolution of quantitative characters over long timescales is often studied using stochastic diffusion models. The current toolbox available to students of macroevolution is however limited to two main models: Brownian motion and the Ornstein-Uhlenbeck process, plus some of their extensions. Here, we present a very general model for inferring the dynamics of quantitative characters evolving under both random diffusion and deterministic forces of any possible shape and strength, which can accommodate interesting evolutionary scenarios like directional trends, disruptive selection, or macroevolutionary landscapes with multiple peaks. This model is based on a general partial differential equation widely used in statistical mechanics: the Fokker-Planck equation, also known in population genetics as the Kolmogorov forward equation. We thus call the model FPK, for Fokker-Planck-Kolmogorov. We first explain how this model can be used to describe macroevolutionary landscapes over which quantitative traits evolve and, more importantly, we detail how it can be fitted to empirical data. Using simulations, we show that the model has good behavior both in terms of discrimination from alternative models and in terms of parameter inference. We provide R code to fit the model to empirical data using either maximum-likelihood or Bayesian estimation, and illustrate the use of this code with two empirical examples of body mass evolution in mammals. FPK should greatly expand the set of macroevolutionary scenarios that can be studied since it opens the way to estimating macroevolutionary landscapes of any conceivable shape. [Adaptation; bounds; diffusion; FPK model; macroevolution; maximum-likelihood estimation; MCMC methods; phylogenetic comparative data; selection.].
A single model procedure for tank calibration function estimation
International Nuclear Information System (INIS)
York, J.C.; Liebetrau, A.M.
1995-01-01
Reliable tank calibrations are a vital component of any measurement control and accountability program for bulk materials in a nuclear reprocessing facility. Tank volume calibration functions used in nuclear materials safeguards and accountability programs are typically constructed from several segments, each of which is estimated independently. Ideally, the segments correspond to structural features in the tank. In this paper the authors use an extension of the Thomas-Liebetrau model to estimate the entire calibration function in a single step. This procedure automatically takes significant run-to-run differences into account and yields an estimate of the entire calibration function in one operation. As with other procedures, the first step is to define suitable calibration segments. Next, a polynomial of low degree is specified for each segment. In contrast with the conventional practice of constructing a separate model for each segment, this information is used to set up the design matrix for a single model that encompasses all of the calibration data. Estimation of the model parameters is then done using conventional statistical methods. The method described here has several advantages over traditional methods. First, modeled run-to-run differences can be taken into account automatically at the estimation step. Second, no interpolation is required between successive segments. Third, variance estimates are based on all the data, rather than that from a single segment, with the result that discontinuities in confidence intervals at segment boundaries are eliminated. Fourth, the restrictive assumption of the Thomas-Liebetrau method, that the measured volumes be the same for all runs, is not required. Finally, the proposed methods are readily implemented using standard statistical procedures and widely-used software packages
Modeling long-term yield trends of Miscanthusxgiganteus using experimental data from across Europe
DEFF Research Database (Denmark)
Lesur, Claire; Jeuffroy, Marie-Hélène; Makowski, David
2013-01-01
and the ceiling phases and (ii) to determine whether M. giganteus ceiling phase is followed by a decline phase where yields decrease across years. Data were analyzed through comparisons between a set of statistical growth models. The model that best fitted the experimental data included a decline phase....... The decline intensity and the value of several other model parameters, such as the maximum yield reached during the ceiling phase or the duration of the establishment phase, were highly variable. The highest maximum yields were obtained in the experiments located in the southern part of the studied area....... giganteus is known to have an establishment phase during which annual yields increased as a function of crop age, followed by a ceiling phase, the duration of which is unknown. We built a database including 16 European long-term experiments (i) to describe the yield evolution during the establishment...
Kurukuri, Srihari; Worswick, Michael J.
2013-12-01
An alternative approach is proposed to utilize symmetric yield functions for modeling the tension-compression asymmetry commonly observed in hcp materials. In this work, the strength differential (SD) effect is modeled by choosing separate symmetric plane stress yield functions (for example, Barlat Yld 2000-2d) for the tension i.e., in the first quadrant of principal stress space, and compression i.e., third quadrant of principal stress space. In the second and fourth quadrants, the yield locus is constructed by adopting interpolating functions between uniaxial tensile and compressive stress states. In this work, different interpolating functions are chosen and the predictive capability of each approach is discussed. The main advantage of this proposed approach is that the yield locus parameters are deterministic and relatively easy to identify when compared to the Cazacu family of yield functions commonly used for modeling SD effect observed in hcp materials.
Schrön, M.; Fersch, B.; Jagdhuber, T.
2017-12-01
The representative determination of soil moisture across different spatial ranges and scales is still an important challenge in hydrology. While in situ measurements are trusted methods at the profile- or point-scale, cosmic-ray neutron sensors (CRNS) are renowned for providing volume averages for several hectares and tens of decimeters depth. On the other hand, airborne remote-sensing enables the coverage of regional scales, however limited to the top few centimeters of the soil.Common to all of these methods is a challenging data processing part, often requiring calibration with independent data. We investigated the performance and potential of three complementary observational methods for the determination of soil moisture below grassland in an alpine front-range river catchment (Rott, 55 km2) of southern Germany.We employ the TERENO preAlpine soil moisture monitoring network, along with additional soil samples taken throughout the catchment. Spatial soil moisture products have been generated using surveys of a car-mounted mobile CRNS (rover), and an aerial acquisition of the polarimetric synthetic aperture radar (F-SAR) of DLR.The study assesses (1) the viability of the different methods to estimate soil moisture for their respective scales and extents, and (2) how either method could support an improvement of the others. We found that in situ data can provide valuable information to calibrate the CRNS rover and to train the vegetation removal part of the polarimetric SAR (PolSAR) retrieval algorithm. Vegetation correction is mandatory to obtain the sub-canopy soil moisture patterns. While CRNS rover surveys can be used to evaluate the F-SAR product across scales, vegetation-related PolSAR products in turn can support the spatial correction of CRNS products for biomass water. Despite the different physical principles, the synthesis of the methods can provide reasonable soil moisture information by integrating from the plot to the landscape scale. The
Modelling maximum likelihood estimation of availability
International Nuclear Information System (INIS)
Waller, R.A.; Tietjen, G.L.; Rock, G.W.
1975-01-01
Suppose the performance of a nuclear powered electrical generating power plant is continuously monitored to record the sequence of failure and repairs during sustained operation. The purpose of this study is to assess one method of estimating the performance of the power plant when the measure of performance is availability. That is, we determine the probability that the plant is operational at time t. To study the availability of a power plant, we first assume statistical models for the variables, X and Y, which denote the time-to-failure and the time-to-repair variables, respectively. Once those statistical models are specified, the availability, A(t), can be expressed as a function of some or all of their parameters. Usually those parameters are unknown in practice and so A(t) is unknown. This paper discusses the maximum likelihood estimator of A(t) when the time-to-failure model for X is an exponential density with parameter, lambda, and the time-to-repair model for Y is an exponential density with parameter, theta. Under the assumption of exponential models for X and Y, it follows that the instantaneous availability at time t is A(t)=lambda/(lambda+theta)+theta/(lambda+theta)exp[-[(1/lambda)+(1/theta)]t] with t>0. Also, the steady-state availability is A(infinity)=lambda/(lambda+theta). We use the observations from n failure-repair cycles of the power plant, say X 1 , X 2 , ..., Xsub(n), Y 1 , Y 2 , ..., Ysub(n) to present the maximum likelihood estimators of A(t) and A(infinity). The exact sampling distributions for those estimators and some statistical properties are discussed before a simulation model is used to determine 95% simulation intervals for A(t). The methodology is applied to two examples which approximate the operating history of two nuclear power plants. (author)
DEFF Research Database (Denmark)
Pedersen, Marie; Siroux, Valérie; Pin, Isabelle
2013-01-01
BACKGROUND: Spatially-resolved air pollution models can be developed in large areas. The resulting increased exposure contrasts and population size offer opportunities to better characterize the effect of atmospheric pollutants on respiratory health. However the heterogeneity of these areas may......: Simulations indicated that adjustment for area limited the bias due to unmeasured confounders varying with area at the costs of a slight decrease in statistical power. In our cohort, rural and urban areas differed for air pollution levels and for many factors associated with respiratory health and exposure....... Area tended to modify effect measures of air pollution on respiratory health. CONCLUSIONS: Increasing the size of the study area also increases the potential for residual confounding. Our simulations suggest that adjusting for type of area is a good option to limit residual confounding due to area...
a metabolic wastage model for the rate-yield trade off
Indian Academy of Sciences (India)
A METABOLIC WASTAGE MODEL FOR THE RATE-YIELD TRADE OFF. There is a growth limiting step in which an intermediate metabolite (m) has to hit a target molecule (t). ... D= rate of diffusing out. S= the rate of formation of the metabolite. The equilibrium loss decides the yield. The no. of activated targets decide the rate ...
High-dimensional model estimation and model selection
CERN. Geneva
2015-01-01
I will review concepts and algorithms from high-dimensional statistics for linear model estimation and model selection. I will particularly focus on the so-called p>>n setting where the number of variables p is much larger than the number of samples n. I will focus mostly on regularized statistical estimators that produce sparse models. Important examples include the LASSO and its matrix extension, the Graphical LASSO, and more recent non-convex methods such as the TREX. I will show the applicability of these estimators in a diverse range of scientific applications, such as sparse interaction graph recovery and high-dimensional classification and regression problems in genomics.
Pervin, Lia; Islam, Md Saiful
2015-02-01
The aim of this study was to develop a system dynamics model for computation of yields and to investigate the dependency of yields on some major climatic parameters, i.e. temperature and rainfall, for Beta vulgaris subsp. (sugar beet crops) under future climate change scenarios. A system dynamics model was developed which takes account of the effects of rainfall and temperature on sugar beet yields under limited irrigation conditions. A relationship was also developed between the seasonal evapotranspiration and seasonal growing degree days for sugar beet crops. The proposed model was set to run for the present time period of 1993-2012 and for the future period 2013-2040 for Lethbridge region (Alberta, Canada). The model provides sugar beet yields on a yearly basis which are comparable to the present field data. It was found that the future average yield will be increased at about 14% with respect to the present average yield. The proposed model can help to improve the understanding of soil water conditions and irrigation water requirements of an area under certain climatic conditions and can be used for future prediction of yields for any crops in any region (with the required information to be provided). The developed system dynamics model can be used as a supporting tool for decision making, for improvement of agricultural management practice of any region. © 2014 Society of Chemical Industry.
Zhu, Shou-Dong; Huang, Lu-Qi; Guo, Lan-Ping; Ma, Xing-Tian; Hao, Qing-Xiu; Le, Zhi-Yong; Zhang, Xiao-Bo; Yang, Guang; Zhang, Yan; Chen, Mei-Lan
2017-04-01
Cordyceps sinensis is a Chinese unique precious herbal material, its genuine producing areas covering Naqu, Changdu in Qinghai Tibet Plateau, Yushu in Qinghai province and other regions. In recent 10 years, C. sinensis resources is decreasing as a result of the blindly and excessively perennial dug. How to rationally protect, develop and utilize of the valuable resources of C. sinensis has been referred to an important field of research on C. sinensis. The ecological environment and climate change trend of Qinghai Tibet plateau happens prior to other regions, which means that the distribution and evolution of C. sinensis are more obvious and intense than those of the other populations. Based on RS (remote sensing)/GIS(geographic information system) technology, this paper utilized the relationship between the snowline elevation, the average temperature, precipitation and sunshine hours in harvest period (April and may) of C. sinensis and the actual production of C. sinensis to establish a weighted geometric mean model. The model's prediction accuracy can reach 82.16% at least in forecasting C. sinensis year yield in Naqu area in every early June. This study can provide basic datum and information for supporting the C. sinensis industry healthful, sustainable development. Copyright© by the Chinese Pharmaceutical Association.
Assessments of Maize Yield Potential in the Korean Peninsula Using Multiple Crop Models
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.
Estimating Coastal Digital Elevation Model (DEM) Uncertainty
Amante, C.; Mesick, S.
2017-12-01
Integrated bathymetric-topographic digital elevation models (DEMs) are representations of the Earth's solid surface and are fundamental to the modeling of coastal processes, including tsunami, storm surge, and sea-level rise inundation. Deviations in elevation values from the actual seabed or land surface constitute errors in DEMs, which originate from numerous sources, including: (i) the source elevation measurements (e.g., multibeam sonar, lidar), (ii) the interpolative gridding technique (e.g., spline, kriging) used to estimate elevations in areas unconstrained by source measurements, and (iii) the datum transformation used to convert bathymetric and topographic data to common vertical reference systems. The magnitude and spatial distribution of the errors from these sources are typically unknown, and the lack of knowledge regarding these errors represents the vertical uncertainty in the DEM. The National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information (NCEI) has developed DEMs for more than 200 coastal communities. This study presents a methodology developed at NOAA NCEI to derive accompanying uncertainty surfaces that estimate DEM errors at the individual cell-level. The development of high-resolution (1/9th arc-second), integrated bathymetric-topographic DEMs along the southwest coast of Florida serves as the case study for deriving uncertainty surfaces. The estimated uncertainty can then be propagated into the modeling of coastal processes that utilize DEMs. Incorporating the uncertainty produces more reliable modeling results, and in turn, better-informed coastal management decisions.
Hoffmann, H.; Zhao, G.; Bussel, van L.G.J.
2015-01-01
Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield
Consistent Estimation of Partition Markov Models
Directory of Open Access Journals (Sweden)
Jesús E. García
2017-04-01
Full Text Available The Partition Markov Model characterizes the process by a partition L of the state space, where the elements in each part of L share the same transition probability to an arbitrary element in the alphabet. This model aims to answer the following questions: what is the minimal number of parameters needed to specify a Markov chain and how to estimate these parameters. In order to answer these questions, we build a consistent strategy for model selection which consist of: giving a size n realization of the process, finding a model within the Partition Markov class, with a minimal number of parts to represent the process law. From the strategy, we derive a measure that establishes a metric in the state space. In addition, we show that if the law of the process is Markovian, then, eventually, when n goes to infinity, L will be retrieved. We show an application to model internet navigation patterns.
Los Alamos Waste Management Cost Estimation Model
International Nuclear Information System (INIS)
Matysiak, L.M.; Burns, M.L.
1994-03-01
This final report completes the Los Alamos Waste Management Cost Estimation Project, and includes the documentation of the waste management processes at Los Alamos National Laboratory (LANL) for hazardous, mixed, low-level radioactive solid and transuranic waste, development of the cost estimation model and a user reference manual. The ultimate goal of this effort was to develop an estimate of the life cycle costs for the aforementioned waste types. The Cost Estimation Model is a tool that can be used to calculate the costs of waste management at LANL for the aforementioned waste types, under several different scenarios. Each waste category at LANL is managed in a separate fashion, according to Department of Energy requirements and state and federal regulations. The cost of the waste management process for each waste category has not previously been well documented. In particular, the costs associated with the handling, treatment and storage of the waste have not been well understood. It is anticipated that greater knowledge of these costs will encourage waste generators at the Laboratory to apply waste minimization techniques to current operations. Expected benefits of waste minimization are a reduction in waste volume, decrease in liability and lower waste management costs
DEFF Research Database (Denmark)
Hagnestam-Nielsen, Christel; Østergaard, Søren
2009-01-01
The main aim of the present study was to examine the economic consequences of a reduction in the incidence of clinical mastitis (CM) at herd level under current Swedish farming conditions. A second objective was to ask whether the estimated cost of CM alters depending upon whether the model...... with 150 cows (9000 kg of energy-corrected milk per cow-year). Four herd types, defined by production level and reproductive performance, were modelled to investigate possible interactions between herd type and response to a reduction in the risk of CM. Technical and economic results, given the initial...... incidence of CM (25.6 per 100 cow-years), were studied together with the consequences of reducing the initial risk of CM by 50% and 90% throughout lactation and the consequences of reducing the initial risk by 50% and 90% before peak yield. A conventional way of modelling yield losses - i.e. one employing...
Energy Technology Data Exchange (ETDEWEB)
Daijiro Kaneko [Department of Civil and Environmental Engineering, Matsue National College of Technology, Matsue (Japan); Toshiro Kumakura [Department of Civil and Environmental Engineering, Nagaoka University of Technology, Nagaoka (Japan); Peng Yang [Laboratory of Resources Remote Sensing and Digital Agriculture, Ministry of Agriculture, Beijing (China)
2008-09-30
This study is intended to develop a model for estimating carbon dioxide (CO{sub 2}) fixation in the carbon cycle and for monitoring grain yields using a photosynthetic-sterility model, which integrates solar radiation and air temperature effects on photosynthesis, along with grain-filling from heading to ripening. Grain production monitoring would support orderly crisis management to maintain food security in Asia, which is facing climate fluctuation through this century of global warming. The author improved a photosynthesis-and-sterility model to compute both the crop yield and crop situation index CSI, which gives a percentage of rice yields compared to normal annual production. The model calculates photosynthesis rates including biomass effects, lowtemperature sterility, and high-temperature injury by incorporating solar radiation, effective air temperature, the normalized difference vegetation index NDVI, and the effect of temperature on photosynthesis by grain plant leaves. A decision-tree method classifies the distribution of crop fields in Asia using MODIS fundamental landcover and SPOT VEGETATION data, which include the Normalized Vegetation index (NDVI) and Land Surface Water Index (LSWI). This study provides daily distributions of the photosynthesis rate, which is the CO2 fixation in Asian areas combined with the land-cover distribution, the Japanese geostationary meteorological satellite (GMS), and meteorological re-analysis data by National Centers for Environmental Prediction (NCEP). The method is based on routine observation data, enabling automated monitoring of crop yields.
Strip yielding model for calculation of COD in spheres with short cracks
International Nuclear Information System (INIS)
Miller, A.G.
1981-08-01
The crack opening displacement at the centre of a crack in a sphere with internal pressure has been calculated, using a strip yielding model. The results have been displayed for a range of geometrical parameters and loads. (author)
International Nuclear Information System (INIS)
Arain, B.T.; Baloch, M.J.; Bughio, Q.U.A.; Sial, P.; Arain, M.A.; Baloch, A.
2015-01-01
The experimental research was conducted so as to determine the general combining ability (GCA) and specific combining ability (SCA) estimates and heterotic effects for seed cotton yield and fibre traits in 5 x 5 half diallel crosses of upland cotton (Gossypium hirsutum L.). The parental genotypes studied were; CRIS-134, IR-3701, IR-1524, FH-113 and MG-6. The characters such as bolls/plant, sympodial branches/plant, boll weight (g), plant height (cm), fibre length (mm), seed cotton yield/plant (g), seed index (g) and ginning out turn percentage were studied. The experiment was laid-out in a randomized complete block design with four replications at experimental field of the Department of Plant Breeding and Genetics, Sindh Agriculture University Tandojam, Pakistan during 2013. The results revealed that, parents and hybrids differed significantly for their mean performance regarding all the traits studied. The importance of heterotic effects was evident from the significance of parents vs. hybrids performance. The variances due to GCA and SCA were significant for all the traits except that GCA was non-significant for boll weight only whereas, SCA was non-significant for boll weight, seed index and ginning out turn percentage. The significance of GCA indicated the importance of additive genes advocating the traits while, the involvement of non-additive genes was evident from the significance of SCA variances. The GCA variances were greater than SCA for bolls per plant, plant height, seed cotton yield and lint percentage while, SCA variances were higher than GCA for sympodial branches/plant and fibre length. Parents IR-3701, FH-113 and MG-6 displayed higher positive GCA effects for bolls/plant, sympodial branches/plant, fibre length, seed cotton yield, seed index and ginning out turn percentage. The per se performance of these three parents was exactly reflected in their GCA effects and such happenings are exceptional. Such results suggested that, all three parents were
RSMASS: A simple model for estimating reactor and shield masses
International Nuclear Information System (INIS)
Marshall, A.C.; Aragon, J.; Gallup, D.
1987-01-01
A simple mathematical model (RSMASS) has been developed to provide rapid estimates of reactor and shield masses for space-based reactor power systems. Approximations are used rather than correlations or detailed calculations to estimate the reactor fuel mass and the masses of the moderator, structure, reflector, pressure vessel, miscellaneous components, and the reactor shield. The fuel mass is determined either by neutronics limits, thermal/hydraulic limits, or fuel damage limits, whichever yields the largest mass. RSMASS requires the reactor power and energy, 24 reactor parameters, and 20 shield parameters to be specified. This parametric approach should be applicable to a very broad range of reactor types. Reactor and shield masses calculated by RSMASS were found to be in good agreement with the masses obtained from detailed calculations
Sparse estimation of model-based diffuse thermal dust emission
Irfan, Melis O.; Bobin, Jérôme
2018-03-01
Component separation for the Planck High Frequency Instrument (HFI) data is primarily concerned with the estimation of thermal dust emission, which requires the separation of thermal dust from the cosmic infrared background (CIB). For that purpose, current estimation methods rely on filtering techniques to decouple thermal dust emission from CIB anisotropies, which tend to yield a smooth, low-resolution, estimation of the dust emission. In this paper, we present a new parameter estimation method, premise: Parameter Recovery Exploiting Model Informed Sparse Estimates. This method exploits the sparse nature of thermal dust emission to calculate all-sky maps of thermal dust temperature, spectral index, and optical depth at 353 GHz. premise is evaluated and validated on full-sky simulated data. We find the percentage difference between the premise results and the true values to be 2.8, 5.7, and 7.2 per cent at the 1σ level across the full sky for thermal dust temperature, spectral index, and optical depth at 353 GHz, respectively. A comparison between premise and a GNILC-like method over selected regions of our sky simulation reveals that both methods perform comparably within high signal-to-noise regions. However, outside of the Galactic plane, premise is seen to outperform the GNILC-like method with increasing success as the signal-to-noise ratio worsens.
Maro, G.P.; Mrema, J.P.; Msanya, B.M.; Janssen, B.H.; Teri, J.M.
2014-01-01
The aim of this study was to develop a simple and quantitative system for coffee yield estimation and nutrient input advice, so as to address the problem of declining annual coffee production in Tanzania (particularly in its Northern coffee zone), which is related to declining soil fertility. The
Parameter Estimation of Spacecraft Fuel Slosh Model
Gangadharan, Sathya; Sudermann, James; Marlowe, Andrea; Njengam Charles
2004-01-01
Fuel slosh in the upper stages of a spinning spacecraft during launch has been a long standing concern for the success of a space mission. Energy loss through the movement of the liquid fuel in the fuel tank affects the gyroscopic stability of the spacecraft and leads to nutation (wobble) which can cause devastating control issues. The rate at which nutation develops (defined by Nutation Time Constant (NTC can be tedious to calculate and largely inaccurate if done during the early stages of spacecraft design. Pure analytical means of predicting the influence of onboard liquids have generally failed. A strong need exists to identify and model the conditions of resonance between nutation motion and liquid modes and to understand the general characteristics of the liquid motion that causes the problem in spinning spacecraft. A 3-D computerized model of the fuel slosh that accounts for any resonant modes found in the experimental testing will allow for increased accuracy in the overall modeling process. Development of a more accurate model of the fuel slosh currently lies in a more generalized 3-D computerized model incorporating masses, springs and dampers. Parameters describing the model include the inertia tensor of the fuel, spring constants, and damper coefficients. Refinement and understanding the effects of these parameters allow for a more accurate simulation of fuel slosh. The current research will focus on developing models of different complexity and estimating the model parameters that will ultimately provide a more realistic prediction of Nutation Time Constant obtained through simulation.
Measurement and Estimation of the 99Mo Production Yield by 100Mo(n,2n)99Mo
Minato, Futoshi; Tsukada, Kazuaki; Sato, Nozomi; Watanabe, Satoshi; Saeki, Hideya; Kawabata, Masako; Hashimoto, Shintaro; Nagai, Yasuki
2017-11-01
We, for the first time, measured the yield of 99Mo, the mother nuclide of 99mTc used in nuclear medicine diagnostic procedures, produced by the 100Mo(n,2n)99Mo reaction with accelerator neutrons. The neutrons with a continuous energy spectrum from the thermal energy up to about 40 MeV were provided by the C(d,n) reaction with 40 MeV deuteron beams. It was proved that the 99Mo yield agrees with that estimated by using the latest data on neutrons from the C(d,n) reaction and the evaluated cross section of the 100Mo(n,2n)99Mo reaction given in the Japanese Evaluated Nuclear Data Library. On the basis of the agreement, a systematic calculation was carried out to search for an optimum condition that enables us to produce as much 99Mo as possible with a good 99Mo/100Mo value from an economical point of view. The calculated 99Mo yield from a 150 g 100MoO3 sample indicated that about 30% of the demand for 99Mo in Japan can be met with a single accelerator capable of 40 MeV, 2 mA deuteron beams. Here, by referring to an existing 18F-fluorodeoxyglucose (FDG) distribution system we assumed that 99mTc radiopharmaceuticals formed after separating 99mTc from 99Mo can be delivered to hospitals from a radiopharmaceutical company within 6 h. The elution of 99mTc from 99Mo twice a day would meet about 50% of the demand for 99Mo.
Flores, P.; Duchêne, L.; Lelotte, T.; Bouffioux, C.; El Houdaigui, F.; Van Bael, A.; He, S.; Duflou, J.; Habraken, A. M.
2005-08-01
The bi-axial experimental equipment developed by Flores enables to perform Baushinger shear tests and successive or simultaneous simple shear tests and plane-strain tests. Such experiments and classical tensile tests investigate the material behavior in order to identify the yield locus and the hardening models. With tests performed on two steel grades, the methods applied to identify classical yield surfaces such as Hill or Hosford ones as well as isotropic Swift type hardening or kinematic Armstrong-Frederick hardening models are explained. Comparison with the Taylor-Bishop-Hill yield locus is also provided. The effect of both yield locus and hardening model choice will be presented for two applications: Single Point Incremental Forming (SPIF) and a cup deep drawing.
International Nuclear Information System (INIS)
Flores, P.; Lelotte, T.; Bouffioux, C.; El Houdaigui, F.; Habraken, A.M.; Duchene, L.; Bael, A. van; He, S.; Duflou, J.
2005-01-01
The bi-axial experimental equipment developed by Flores enables to perform Baushinger shear tests and successive or simultaneous simple shear tests and plane-strain tests. Such experiments and classical tensile tests investigate the material behavior in order to identify the yield locus and the hardening models. With tests performed on two steel grades, the methods applied to identify classical yield surfaces such as Hill or Hosford ones as well as isotropic Swift type hardening or kinematic Armstrong-Frederick hardening models are explained. Comparison with the Taylor-Bishop-Hill yield locus is also provided. The effect of both yield locus and hardening model choice will be presented for two applications: Single Point Incremental Forming (SPIF) and a cup deep drawing
Fronzek, Stefan; Pirttioja, Nina; Carter, Timothy R.; Bindi, Marco; Hoffmann, Holger; Palosuo, Taru; Ruiz-Ramos, Margarita; Tao, Fulu; Trnka, Miroslav; Acutis, Marco;
2017-01-01
Crop growth simulation models can differ greatly in their treatment of key processes and hence in their response to environmental conditions. Here, we used an ensemble of 26 process-based wheat models applied at sites across a European transect to compare their sensitivity to changes in temperature (minus 2 to plus 9 degrees Centigrade) and precipitation (minus 50 to plus 50 percent). Model results were analysed by plotting them as impact response surfaces (IRSs), classifying the IRS patterns of individual model simulations, describing these classes and analysing factors that may explain the major differences in model responses. The model ensemble was used to simulate yields of winter and spring wheat at four sites in Finland, Germany and Spain. Results were plotted as IRSs that show changes in yields relative to the baseline with respect to temperature and precipitation. IRSs of 30-year means and selected extreme years were classified using two approaches describing their pattern. The expert diagnostic approach (EDA) combines two aspects of IRS patterns: location of the maximum yield (nine classes) and strength of the yield response with respect to climate (four classes), resulting in a total of 36 combined classes defined using criteria pre-specified by experts. The statistical diagnostic approach (SDA) groups IRSs by comparing their pattern and magnitude, without attempting to interpret these features. It applies a hierarchical clustering method, grouping response patterns using a distance metric that combines the spatial correlation and Euclidian distance between IRS pairs. The two approaches were used to investigate whether different patterns of yield response could be related to different properties of the crop models, specifically their genealogy, calibration and process description. Although no single model property across a large model ensemble was found to explain the integrated yield response to temperature and precipitation perturbations, the
Estimating Structural Models of Corporate Bond Prices in Indonesian Corporations
Directory of Open Access Journals (Sweden)
Lenny Suardi
2014-08-01
Full Text Available This paper applies the maximum likelihood (ML approaches to implementing the structural model of corporate bond, as suggested by Li and Wong (2008, in Indonesian corporations. Two structural models, extended Merton and Longstaff & Schwartz (LS models, are used in determining these prices, yields, yield spreads and probabilities of default. ML estimation is used to determine the volatility of irm value. Since irm value is unobserved variable, Duan (1994 suggested that the irst step of ML estimation is to derive the likelihood function for equity as the option on the irm value. The second step is to ind parameters such as the drift and volatility of irm value, that maximizing this function. The irm value itself is extracted by equating the pricing formula to the observed equity prices. Equity, total liabilities, bond prices data and the irm's parameters (irm value, volatility of irm value, and default barrier are substituted to extended Merton and LS bond pricing formula in order to valuate the corporate bond.These models are implemented to a sample of 24 bond prices in Indonesian corporation during period of 2001-2005, based on criteria of Eom, Helwege and Huang (2004. The equity and bond prices data were obtained from Indonesia Stock Exchange for irms that issued equity and provided regular inancial statement within this period. The result shows that both models, in average, underestimate the bond prices and overestimate the yields and yield spread. ";} // -->activate javascript
Comparison of HSPF and SWAT models performance for runoff and sediment yield prediction.
Im, Sangjun; Brannan, Kevin M; Mostaghimi, Saied; Kim, Sang Min
2007-09-01
A watershed model can be used to better understand the relationship between land use activities and hydrologic/water quality processes that occur within a watershed. The physically based, distributed parameter model (SWAT) and a conceptual, lumped parameter model (HSPF), were selected and their performance were compared in simulating runoff and sediment yields from the Polecat Creek watershed in Virginia, which is 12,048 ha in size. A monitoring project was conducted in Polecat Creek watershed during the period of October 1994 to June 2000. The observed data (stream flow and sediment yield) from the monitoring project was used in the calibration/validations of the models. The period of September 1996 to June 2000 was used for the calibration and October 1994 to December 1995 was used for the validation of the models. The outputs from the models were compared to the observed data at several sub-watershed outlets and at the watershed outlet of the Polecat Creek watershed. The results indicated that both models were generally able to simulate stream flow and sediment yields well during both the calibration/validation periods. For annual and monthly loads, HSPF simulated hydrologic and sediment yield more accurately than SWAT at all monitoring sites within the watershed. The results of this study indicate that both the SWAT and HSPF watershed models performed sufficiently well in the simulation of stream flow and sediment yield with HSPF performing moderately better than SWAT for simulation time-steps greater than a month.
Resource-estimation models and predicted discovery
International Nuclear Information System (INIS)
Hill, G.W.
1982-01-01
Resources have been estimated by predictive extrapolation from past discovery experience, by analogy with better explored regions, or by inference from evidence of depletion of targets for exploration. Changes in technology and new insights into geological mechanisms have occurred sufficiently often in the long run to form part of the pattern of mature discovery experience. The criterion, that a meaningful resource estimate needs an objective measure of its precision or degree of uncertainty, excludes 'estimates' based solely on expert opinion. This is illustrated by development of error measures for several persuasive models of discovery and production of oil and gas in USA, both annually and in terms of increasing exploration effort. Appropriate generalizations of the models resolve many points of controversy. This is illustrated using two USA data sets describing discovery of oil and of U 3 O 8 ; the latter set highlights an inadequacy of available official data. Review of the oil-discovery data set provides a warrant for adjusting the time-series prediction to a higher resource figure for USA petroleum. (author)
Morton, Michael J; Williams, David L; Hjorth, Heather B; Smith, Jennifer H
2010-04-01
This paper explores using the intensity of the stain on the end of the filter ("filter color") as a vehicle for estimating cigarette tar yield, both by instrument reading of the filter color and by visual comparison to a template. The correlation of machine-measured tar yield to filter color measured with a colorimeter was reasonably strong and was relatively unaffected by different puff volumes or different tobacco moistures. However, the correlation of filter color to machine-measured nicotine yield was affected by the moisture content of the cigarette. Filter color, as measured by a colorimeter, was generally comparable to filter extraction of either nicotine or solanesol in its correlation to machine-smoked tar yields. It was found that the color of the tar stain changes over time. Panelists could generally correctly order the filters from machine-smoked cigarettes by tar yield using the intensity of the tar stain. However, there was considerable variation in the panelist-to-panelist tar yield estimates. The wide person-to-person variation in tar yield estimates, and other factors discussed in the text could severely limit the usefulness and practicality of this approach for visually estimating the tar yield of machine-smoked cigarettes. Copyright 2009 Elsevier Inc. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Schmitz, Martin
2011-05-17
A study of the expected sensitivity of the ATLAS experiment to discover the Standard Model Higgs boson produced via vector boson fusion (VBF) and its decay to H{yields} {tau}{tau}{yields} ll+4{nu} is presented. The study is based on simulated proton-proton collisions at a centre-of-mass energy of 14 TeV. For the first time the discovery potential is evaluated in the presence of additional proton-proton interactions (pile-up) to the process of interest in a complete and consistent way. Special emphasis is placed on the development of background estimation techniques to extract the main background processes Z{yields}{tau}{tau} and t anti t production using data. The t anti t background is estimated using a control sample selected with the VBF analysis cuts and the inverted b-jet veto. The dominant background process Z{yields}{tau}{tau} is estimated using Z{yields}{mu}{mu} events. Replacing the muons of the Z{yields}{mu}{mu} event with simulated {tau}-leptons, Z{yields}{tau}{tau} events are modelled to high precision. For the replacement of the Z boson decay products a dedicated method based on tracks and calorimeter cells is developed. Without pile-up a discovery potential of 3{sigma} to 3.4{sigma} in the mass range 115 GeV
Estimating true evolutionary distances under the DCJ model.
Lin, Yu; Moret, Bernard M E
2008-07-01
Modern techniques can yield the ordering and strandedness of genes on each chromosome of a genome; such data already exists for hundreds of organisms. The evolutionary mechanisms through which the set of the genes of an organism is altered and reordered are of great interest to systematists, evolutionary biologists, comparative genomicists and biomedical researchers. Perhaps the most basic concept in this area is that of evolutionary distance between two genomes: under a given model of genomic evolution, how many events most likely took place to account for the difference between the two genomes? We present a method to estimate the true evolutionary distance between two genomes under the 'double-cut-and-join' (DCJ) model of genome rearrangement, a model under which a single multichromosomal operation accounts for all genomic rearrangement events: inversion, transposition, translocation, block interchange and chromosomal fusion and fission. Our method relies on a simple structural characterization of a genome pair and is both analytically and computationally tractable. We provide analytical results to describe the asymptotic behavior of genomes under the DCJ model, as well as experimental results on a wide variety of genome structures to exemplify the very high accuracy (and low variance) of our estimator. Our results provide a tool for accurate phylogenetic reconstruction from multichromosomal gene rearrangement data as well as a theoretical basis for refinements of the DCJ model to account for biological constraints. All of our software is available in source form under GPL at http://lcbb.epfl.ch.
Model to estimate fractal dimension for ion-bombarded materials
Energy Technology Data Exchange (ETDEWEB)
Hu, A., E-mail: hu77@purdue.edu; Hassanein, A.
2014-03-15
Comprehensive fractal Monte Carlo model ITMC-F (Hu and Hassanein, 2012 [1]) is developed based on the Monte Carlo ion bombardment simulation code, i.e., Ion Transport in Materials and Compounds (ITMC) code (Hassanein, 1985 [2]). The ITMC-F studies the impact of surface roughness on the angular dependence of sputtering yield. Instead of assuming material surfaces to be flat or composed of exact self-similar fractals in simulation, we developed a new method to describe the surface shapes. Random fractal surfaces which are generated by midpoint displacement algorithm and support vector machine algorithm are combined with ITMC. With this new fractal version of ITMC-F, we successfully simulated the angular dependence of sputtering yield for various ion-target combinations, with the input surface roughness exponent directly depicted from experimental data (Hu and Hassanein, 2012 [1]). The ITMC-F code showed good agreement with the experimental data. In advanced, we compare other experimental sputtering yield with the results from ITMC-F to estimate the surface roughness exponent for ion-bombarded material in this research.
Directory of Open Access Journals (Sweden)
M. Sarai Tabrizi
2016-10-01
Full Text Available Introduction: Several mathematical models are being used for assessing the plant response to the salinity of the root zone. The salinity of the soil and water resources is a major challenge for agricultural sector in Iran. Several mathematical models have been developed for plant responses to the salinity stress. However, these models are often applicable in particular conditions. The objectives of this study were to evaluate the threshold value of Basil yield reduction, modeling Basil response to salinity and to evaluate the effectiveness of available mathematical models for the yield estimation of the Basil . Materials and Methods: The extensive experiments were conducted with 13 natural saline water treatments including 1.2, 1.8, 2, 2.2, 2.5, 2.8, 3, 3.5, 4, 5, 6, 8, and 10 dSm-1. Water salinity treatments were prepared by mixing Shoor River water with fresh water. In order to quantify the salinity effect on Basil yield, seven mathematical models including Maas and Hoffman (1977, van Genuchten and Hoffman (1984, Dirksen and Augustijn (1988, and Homaee et al., (2002 were used. One of the relatively recent methods for soil water content measurements is theta probes instrument. Theta probes instrument consists of four probes with 60 mm long and 3 mm diameter, a water proof container (probe structure, and a cable that links input and output signals to the data logger display. The advantages that have been attributed to this method are high precision and direct and rapid measurements in the field and greenhouse. The range of measurements is not limited like tensiometer and is from saturation to wilting point. In this study, Theta probes instrument was calibrated by weighing method for exact irrigation scheduling. Relative transpiration was calculated using daily soil water content changes. A coarse sand layer with 2 centimeters thick was used to decrease evaporation from the surface soil of the pots. Quantity comparison of the used models was done
Modelling Bambara Groundnut Yield in Southern Africa: Towards a Climate-Resilient Future
Karunaratne, A. S.; Walker, S.; Ruane, A. C.
2015-01-01
Current agriculture depends on a few major species grown as monocultures that are supported by global research underpinning current productivity. However, many hundreds of alternative crops have the potential to meet real world challenges by sustaining humanity, diversifying agricultural systems for food and nutritional security, and especially responding to climate change through their resilience to certain climate conditions. Bambara groundnut (Vigna subterranea (L.) Verdc.), an underutilised African legume, is an exemplar crop for climate resilience. Predicted yield performances of Bambara groundnut by AquaCrop (a crop-water productivity model) were evaluated for baseline (1980-2009) and mid-century climates (2040-2069) under 20 downscaled Global Climate Models (CMIP5-RCP8.5), as well as for climate sensitivities (AgMIPC3MP) across 3 locations in Southern Africa (Botswana, South Africa, Namibia). Different land - races of Bambara groundnut originating from various semi-arid African locations showed diverse yield performances with diverse sensitivities to climate. S19 originating from hot-dry conditions in Namibia has greater future yield potential compared to the Swaziland landrace Uniswa Red-UN across study sites. South Africa has the lowest yield under the current climate, indicating positive future yield trends. Namibia reported the highest baseline yield at optimum current temperatures, indicating less yield potential in future climates. Bambara groundnut shows positive yield potential at temperatures of up to 31degC, with further warming pushing yields down. Thus, many regions in Southern Africa can utilize Bambara groundnut successfully in the coming decades. This modelling exercise supports decisions on genotypic suitability for present and future climates at specific locations.
Parameter estimation in fractional diffusion models
Kubilius, Kęstutis; Ralchenko, Kostiantyn
2017-01-01
This book is devoted to parameter estimation in diffusion models involving fractional Brownian motion and related processes. For many years now, standard Brownian motion has been (and still remains) a popular model of randomness used to investigate processes in the natural sciences, financial markets, and the economy. The substantial limitation in the use of stochastic diffusion models with Brownian motion is due to the fact that the motion has independent increments, and, therefore, the random noise it generates is “white,” i.e., uncorrelated. However, many processes in the natural sciences, computer networks and financial markets have long-term or short-term dependences, i.e., the correlations of random noise in these processes are non-zero, and slowly or rapidly decrease with time. In particular, models of financial markets demonstrate various kinds of memory and usually this memory is modeled by fractional Brownian diffusion. Therefore, the book constructs diffusion models with memory and provides s...
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
Determinants of the Government Bond Yield in Spain: A Loanable Funds Model
Directory of Open Access Journals (Sweden)
Yu Hsing
2015-07-01
Full Text Available This paper applies demand and supply analysis to examine the government bond yield in Spain. The sample ranges from 1999.Q1 to 2014.Q2. The EGARCH model is employed in empirical work. The Spanish government bond yield is positively associated with the government debt/GDP ratio, the short-term Treasury bill rate, the expected inflation rate, the U.S. 10 year government bond yield and a dummy variable representing the debt crisis and negatively affected by the GDP growth rate and the expected nominal effective exchange rate.
Yield shear stress model of magnetorheological fluids based on exponential distribution
International Nuclear Information System (INIS)
Guo, Chu-wen; Chen, Fei; Meng, Qing-rui; Dong, Zi-xin
2014-01-01
The magnetic chain model that considers the interaction between particles and the external magnetic field in a magnetorheological fluid has been widely accepted. Based on the chain model, a yield shear stress model of magnetorheological fluids was proposed by introducing the exponential distribution to describe the distribution of angles between the direction of magnetic field and the chain formed by magnetic particles. The main influencing factors were considered in the model, such as magnetic flux density, intensity of magnetic field, particle size, volume fraction of particles, the angle of magnetic chain, and so on. The effect of magnetic flux density on the yield shear stress was discussed. The yield stress of aqueous Fe 3 O 4 magnetreological fluids with volume fraction of 7.6% and 16.2% were measured by a device designed by ourselves. The results indicate that the proposed model can be used for calculation of yield shear stress with acceptable errors. - Highlights: • A yield shear stress model of magnetorheological fluids was proposed. • Use exponential distribution to describe the distribution of magnetic chain angles. • Experimental and predicted results were in good agreement for 2 types of MR
A Stand-Class Growth and Yield Model for Mexico’s Northern Temperate, Mixed and Multiaged Forests
Directory of Open Access Journals (Sweden)
José Návar
2014-12-01
Full Text Available The aim of this research was to develop a stand-class growth and yield model based on the diameter growth dynamics of Pinus spp. and Quercus spp. of Mexico’s mixed temperate forests. Using a total of 2663 temporary, circular-sampling plots of 1000 m2 each, nine Weibull distribution techniques of parameter estimation were fitted to the diameter structures of pines and oaks. Statistical equations using stand attributes and the first three moments of the diameter distribution predicted and recovered the Weibull parameters. Using nearly 1200 and 100 harvested trees for pines and oaks, respectively, I developed the total height versus diameter at breast height relationship by fitting three non-linear functions. The Newnham model predicted stem taper and numerical integration was done to estimate merchantable timber volume for all trees in the stand for each diameter class. The independence of the diameter structures of pines and oaks was tested by regressing the Weibull parameters and projecting diameter structures. The model predicts diameter distributions transition from exponential (J inverse, logarithmic to well-balanced distributions with increasing mean stand diameter at breast height. Pine diameter distributions transition faster and the model predicts independent growth rates between pines and oaks. The stand-class growth and yield model must be completed with the diameter-age relationship for oaks in order to carry a full optimization procedure to find stand density and genera composition to maximize forest growth.
Adaptive Estimation of Heteroscedastic Money Demand Model of Pakistan
Directory of Open Access Journals (Sweden)
Muhammad Aslam
2007-07-01
Full Text Available For the problem of estimation of Money demand model of Pakistan, money supply (M1 shows heteroscedasticity of the unknown form. For estimation of such model we compare two adaptive estimators with ordinary least squares estimator and show the attractive performance of the adaptive estimators, namely, nonparametric kernel estimator and nearest neighbour regression estimator. These comparisons are made on the basis standard errors of the estimated coefficients, standard error of regression, Akaike Information Criteria (AIC value, and the Durban-Watson statistic for autocorrelation. We further show that nearest neighbour regression estimator performs better when comparing with the other nonparametric kernel estimator.
Sheng, Lili; Wang, Xin; Yang, Xiaoyi
2018-01-01
The model of biocrude yield and the nitrogen heterocyclic compounds in biocrude of microalgae hydrothermal liquefaction are two of the most concerned issues in this field at present. This study explored a hydrothermal liquefaction biocrude yield model involved in the interaction among biochemical compounds in microalgae and analysed nitrogen heterocyclic compounds in biocrude. The model compound (castor oil, soya protein and glucose) and Nanochloropsis were liquefied at 280°C for 1h. The products were analyzed by GC-MS, element analysis and FTIR. The results suggested that interactions among different components in microalgae enhanced biocrude yield. The biocrude yield prediction model involved cross-interactions performed more accurate than previous models.When the ratio of protein and carbohydrate around 3, the cross-interaction and nitrogen heterocyclic compounds in biocrude would both reach the highest extent. Copyright © 2017. Published by Elsevier Ltd.
Soil Erosion and Sediment Yield Modelling in the Pra River Basin of ...
African Journals Online (AJOL)
kusimi
sediment delivery ratio; soil erosion modelling; sediment yield modelling. .... The basin falls within the wet semi-equitorial climatic belt which is ... influence of the moist south-west monsoons during the rainy season, with high .... availability of good satellite images covering the study area; because of thick cloud cover most.
Brazilian Soybean Yields and Yield Gaps Vary with Farm Size
Jeffries, G. R.; Cohn, A.; Griffin, T. S.; Bragança, A.
2017-12-01
Understanding the farm size-specific characteristics of crop yields and yield gaps may help to improve yields by enabling better targeting of technical assistance and agricultural development programs. Linking remote sensing-based yield estimates with property boundaries provides a novel view of the relationship between farm size and yield structure (yield magnitude, gaps, and stability over time). A growing literature documents variations in yield gaps, but largely ignores the role of farm size as a factor shaping yield structure. Research on the inverse farm size-productivity relationship (IR) theory - that small farms are more productive than large ones all else equal - has documented that yield magnitude may vary by farm size, but has not considered other yield structure characteristics. We examined farm size - yield structure relationships for soybeans in Brazil for years 2001-2015. Using out-of-sample soybean yield predictions from a statistical model, we documented 1) gaps between the 95th percentile of attained yields and mean yields within counties and individual fields, and 2) yield stability defined as the standard deviation of time-detrended yields at given locations. We found a direct relationship between soy yields and farm size at the national level, while the strength and the sign of the relationship varied by region. Soybean yield gaps were found to be inversely related to farm size metrics, even when yields were only compared to farms of similar size. The relationship between farm size and yield stability was nonlinear, with mid-sized farms having the most stable yields. The work suggests that farm size is an important factor in understanding yield structure and that opportunities for improving soy yields in Brazil are greatest among smaller farms.
Directory of Open Access Journals (Sweden)
Corrado Dimauro
2010-11-01
Full Text Available Test day records for milk yield of 57,390 first lactation Canadian Holsteins were analyzed with a linear model that included the fixed effects of herd-test date and days in milk (DIM interval nested within age and calving season. Residuals from this model were analyzed as a new variable and fitted with a five parameter model, fourth-order Legendre polynomials, with linear, quadratic and cubic spline models with three knots. The fit of the models was rather poor, with about 30-40% of the curves showing an adjusted R-square lower than 0.20 across all models. Results underline a great difficulty in modelling individual deviations around the mean curve for milk yield. However, the Ali and Schaeffer (5 parameter model and the fourth-order Legendre polynomials were able to detect two basic shapes of individual deviations among the mean curve. Quadratic and, especially, cubic spline functions had better fitting performances but a poor predictive ability due to their great flexibility that results in an abrupt change of the estimated curve when data are missing. Parametric and orthogonal polynomials seem to be robust and affordable under this standpoint.
Olivoto, T; Nardino, M; Carvalho, I R; Follmann, D N; Ferrari, M; Szareski, V J; de Pelegrin, A J; de Souza, V Q
2017-03-22
Methodologies using restricted maximum likelihood/best linear unbiased prediction (REML/BLUP) in combination with sequential path analysis in maize are still limited in the literature. Therefore, the aims of this study were: i) to use REML/BLUP-based procedures in order to estimate variance components, genetic parameters, and genotypic values of simple maize hybrids, and ii) to fit stepwise regressions considering genotypic values to form a path diagram with multi-order predictors and minimum multicollinearity that explains the relationships of cause and effect among grain yield-related traits. Fifteen commercial simple maize hybrids were evaluated in multi-environment trials in a randomized complete block design with four replications. The environmental variance (78.80%) and genotype-vs-environment variance (20.83%) accounted for more than 99% of the phenotypic variance of grain yield, which difficult the direct selection of breeders for this trait. The sequential path analysis model allowed the selection of traits with high explanatory power and minimum multicollinearity, resulting in models with elevated fit (R 2 > 0.9 and ε analysis is effective in the evaluation of maize-breeding trials.
Archfield, Stacey A.; Vogel, Richard M.; Steeves, Peter A.; Brandt, Sara L.; Weiskel, Peter K.; Garabedian, Stephen P.
2010-01-01
Federal, State and local water-resource managers require a variety of data and modeling tools to better understand water resources. The U.S. Geological Survey, in cooperation with the Massachusetts Department of Environmental Protection, has developed a statewide, interactive decision-support tool to meet this need. The decision-support tool, referred to as the Massachusetts Sustainable-Yield Estimator (MA SYE) provides screening-level estimates of the sustainable yield of a basin, defined as the difference between the unregulated streamflow and some user-specified quantity of water that must remain in the stream to support such functions as recreational activities or aquatic habitat. The MA SYE tool was designed, in part, because the quantity of surface water available in a basin is a time-varying quantity subject to competing demands for water. To compute sustainable yield, the MA SYE tool estimates a daily time series of unregulated, daily mean streamflow for a 44-year period of record spanning October 1, 1960, through September 30, 2004. Selected streamflow quantiles from an unregulated, daily flow-duration curve are estimated by solving six regression equations that are a function of physical and climate basin characteristics at an ungaged site on a stream of interest. Streamflow is then interpolated between the estimated quantiles to obtain a continuous daily flow-duration curve. A time series of unregulated daily streamflow subsequently is created by transferring the timing of the daily streamflow at a reference streamgage to the ungaged site by equating exceedence probabilities of contemporaneous flow at the two locations. One of 66 reference streamgages is selected by kriging, a geostatistical method, which is used to map the spatial relation among correlations between the time series of the logarithm of daily streamflows at each reference streamgage and the ungaged site. Estimated unregulated, daily mean streamflows show good agreement with observed
Parameters-related uncertainty in modeling sugar cane yield with an agro-Land Surface Model
Valade, A.; Ciais, P.; Vuichard, N.; Viovy, N.; Ruget, F.; Gabrielle, B.
2012-12-01
uptake of water (root profile), and transpiration and respiration (stomatal conductance, growth and maintenance respiration coefficients). We find that the optimal carboxylation rate and optimal photosynthesis temperature parameters contribute most to the uncertainty in NPP and GPP simulations whereas stomatal conductance is the most sensitive parameter controlling SH, followed by optimal photosynthesis temperature and optimal carboxylation rate. The spatial variation of the ranked correlation between input parameters and output variables is well explained by rain and temperature drivers, suggesting that climate mediated regionally different sensitivities of modeled sugarcane yield to the model parameters, for Australia and Brazil.
MODELING OF YIELD AND QUALITY OF TABLE ROOT CROPS WITH THE USE OF DIFFERENT AGROTECHNICAL METHODS
Directory of Open Access Journals (Sweden)
S. M. Nadezhkin
2017-01-01
Full Text Available The effects of different fertilizer rates, irrigation, sowing rate for carrot and red beet were studied in the field condition in food-hills zone of Chechen Republic. The use of N40-80P40-80K40-80 caused the increase in yield from 22.8 to 30.8-33.2 t/ha or by 35-46%, when cultivating a carrot crop. Under irrigation the yield increases by 30-33%. Application of N40P40K40 and maintenance of soil moisture at 70% of moisture rate provoked the improvement in value, market and biochemical characteristics of roots; where the increased contents of dry matter, total sugar and vitamins were observed. The mathematical modeling for the process of yielding abilities and root quality in carrot and red beet showed that highest productivity can be achieved on chernozem soil at Central Pre-Caucasus zone when the level of mineral plant nutrition was N40-60P40-60K40-60. The further increment in fertilizer doses does not bring an improvement to yields and leads to decrease in quality of yields. The increased level of antecedent soil water moisture 70-75% of moisture rates does not raise the yield, on the contrary decreasing at the same time the root quality. The use of mathematical modeling enables to rationally define the fertilizer rates depending on application of irrigation and sowing rates in cultivation of carrot and red beet.
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.
Directory of Open Access Journals (Sweden)
Maryam Baharizadeh
2012-10-01
Full Text Available Buffalo milk yield records were obtained from monthly records of the Animal Breeding Organization of Iran from 1992 to 2009 in 33 herds raised in the Khuzestan province. Variance components, heritability and repeatability were estimated for milk yield, fat yield, fat percentage, protein yield and protein percentage. These estimates were carried out through single trait animal model using DFREML program. Herd-year-season was considered as fixed effect in the model. For milk production traits, age at calving was fitted as a covariate. The additive genetic and permanent environmental effects were also included in the model. The mean values (±SD for milk yield, fat yield, fat percentage, protein yield and protein percentage were 2285.08±762.47 kg, 144.35±54.86 kg, 6.25±0.90%, 97.30±26.73 kg and 4.19±0.27%, respectively. The heritability (±SE of milk yield, fat yield, fat percentage, protein yield and protein percentage were 0.093±0.08, 0.054±0.06, 0.043±0.05, 0.093±0.16 and zero, respectively. These estimates for repeatability were 0.272, 0.132, 0.043, 0.674 and 0.0002, respectively. Lower values of genetic parameter estimates require more data and reliable pedigree records.
Estimators for longitudinal latent exposure models: examining measurement model assumptions.
Sánchez, Brisa N; Kim, Sehee; Sammel, Mary D
2017-06-15
Latent variable (LV) models are increasingly being used in environmental epidemiology as a way to summarize multiple environmental exposures and thus minimize statistical concerns that arise in multiple regression. LV models may be especially useful when multivariate exposures are collected repeatedly over time. LV models can accommodate a variety of assumptions but, at the same time, present the user with many choices for model specification particularly in the case of exposure data collected repeatedly over time. For instance, the user could assume conditional independence of observed exposure biomarkers given the latent exposure and, in the case of longitudinal latent exposure variables, time invariance of the measurement model. Choosing which assumptions to relax is not always straightforward. We were motivated by a study of prenatal lead exposure and mental development, where assumptions of the measurement model for the time-changing longitudinal exposure have appreciable impact on (maximum-likelihood) inferences about the health effects of lead exposure. Although we were not particularly interested in characterizing the change of the LV itself, imposing a longitudinal LV structure on the repeated multivariate exposure measures could result in high efficiency gains for the exposure-disease association. We examine the biases of maximum likelihood estimators when assumptions about the measurement model for the longitudinal latent exposure variable are violated. We adapt existing instrumental variable estimators to the case of longitudinal exposures and propose them as an alternative to estimate the health effects of a time-changing latent predictor. We show that instrumental variable estimators remain unbiased for a wide range of data generating models and have advantages in terms of mean squared error. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
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.
Lloyd, Simon J; Kovats, R Sari; Chalabi, Zaid
2011-12-01
Global climate change is anticipated to reduce future cereal yields and threaten food security, thus potentially increasing the risk of undernutrition. The causation of undernutrition is complex, and there is a need to develop models that better quantify the potential impacts of climate change on population health. We developed a model for estimating future undernutrition that accounts for food and nonfood (socioeconomic) causes and can be linked to available regional scenario data. We estimated child stunting attributable to climate change in five regions in South Asia and sub-Saharan Africa (SSA) in 2050. We used current national food availability and undernutrition data to parameterize and validate a global model, using a process-driven approach based on estimations of the physiological relationship between a lack of food and stunting. We estimated stunting in 2050 using published modeled national calorie availability under two climate scenarios and a reference scenario (no climate change). We estimated that climate change will lead to a relative increase in moderate stunting of 1-29% in 2050 compared with a future without climate change. Climate change will have a greater impact on rates of severe stunting, which we estimated will increase by 23% (central SSA) to 62% (South Asia). Climate change is likely to impair future efforts to reduce child malnutrition in South Asia and SSA, even when economic growth is taken into account. Our model suggests that to reduce and prevent future undernutrition, it is necessary to both increase food access and improve socioeconomic conditions, as well as reduce greenhouse gas emissions.
DEFF Research Database (Denmark)
Salo, T J; Palosuo, T; Kersebaum, K C
2016-01-01
Eleven widely used crop simulation models (APSIM, CERES, CROPSYST, COUP, DAISY, EPIC, FASSET, HERMES, MONICA, STICS and WOFOST) were tested using spring barley (Hordeum vulgare L.) data set under varying nitrogen (N) fertilizer rates from three experimental years in the boreal climate of Jokioinen......, Finland. This is the largest standardized crop model inter-comparison under different levels of N supply to date. The models were calibrated using data from 2002 and 2008, of which 2008 included six N rates ranging from 0 to 150 kg N/ha. Calibration data consisted of weather, soil, phenology, leaf area...... ranged from 170 to 870 kg/ha. During the test year 2009, most models failed to accurately reproduce the observed low yield without N fertilizer as well as the steep yield response to N applications. The multi-model predictions were closer to observations than most single-model predictions, but multi...
DEFF Research Database (Denmark)
Lühr, Armin; Priegnitz, Marlen; Fiedler, Fine
2014-01-01
In ion beam cancer therapy, range verification in patients using positron emission tomography (PET) requires the comparison of measured with simulated positron emitter yields. We found that (1) changes in modeling nuclear interactions strongly affected the positron emitter yields and that (2) Monte...... Carlo simulations with SHIELD-HIT10A reasonably matched the most abundant PET isotopes 11C and 15O. We observed an ion-energy (i.e., depth) dependence of the agreement between SHIELD-HIT10A and measurement. Improved modeling requires more accurate measurements of cross-section values....
International Nuclear Information System (INIS)
Grytsyuk, N.; Arapis, G.; Perepelyatnikova, L.; Ivanova, T.; Vynograds'ka, V.
2006-01-01
Heavy metals (Cu, Cd, Pb, Zn) effect on the productivity of forage crops (clover and perennial cereal grasses) and their accumulation in plants, depending on the concentration of these elements in a soil, has been studied in micro-field experiments on three types of soil. The principle objective was to determine regularities of heavy metals migration in a soil-plant system aiming the estimation of permissible levels of heavy metals content in soils with the following elaboration of methods, which regulate the toxicants transfer to plants. Methods of field experiments, agrochemical and atomic absorption analysis were used. Results were statistically treated by Statistica 6.0, S-Plus 6. Experimental results have shown that the intensity of heavy metals accumulation in plants depends on the type of the soil, the species of plants, the physicochemical properties of heavy metals and their content in the soil. Logarithmic interdependency of heavy metals concentration in soils and their accumulation in plants is suggested. However, the strong correlation between the different heavy metals concentrations in the various soils and the yield of crops was not observed. Toxicants accumulation in crops decreased in time
Grytsyuk, N; Arapis, G; Perepelyatnikova, L; Ivanova, T; Vynograds'ka, V
2006-02-01
Heavy metals (Cu, Cd, Pb, Zn) effect on the productivity of forage crops (clover and perennial cereal grasses) and their accumulation in plants, depending on the concentration of these elements in a soil, has been studied in micro-field experiments on three types of soil. The principle objective was to determine regularities of heavy metals migration in a soil-plant system aiming the estimation of permissible levels of heavy metals content in soils with the following elaboration of methods, which regulate the toxicants transfer to plants. Methods of field experiments, agrochemical and atomic absorption analysis were used. Results were statistically treated by Statistica 6.0, S-Plus 6. Experimental results have shown that the intensity of heavy metals accumulation in plants depends on the type of the soil, the species of plants, the physicochemical properties of heavy metals and their content in the soil. Logarithmic interdependency of heavy metals concentration in soils and their accumulation in plants is suggested. However, the strong correlation between the different heavy metals concentrations in the various soils and the yield of crops was not observed. Toxicants accumulation in crops decreased in time.
Model of yield response of corn to plant population and absorption of solar energy.
Directory of Open Access Journals (Sweden)
Allen R Overman
Full Text Available Biomass yield of agronomic crops is influenced by a number of factors, including crop species, soil type, applied nutrients, water availability, and plant population. This article is focused on dependence of biomass yield (Mg ha(-1 and g plant(-1 on plant population (plants m(-2. Analysis includes data from the literature for three independent studies with the warm-season annual corn (Zea mays L. grown in the United States. Data are analyzed with a simple exponential mathematical model which contains two parameters, viz. Y(m (Mg ha(-1 for maximum yield at high plant population and c (m(2 plant(-1 for the population response coefficient. This analysis leads to a new parameter called characteristic plant population, x(c = 1/c (plants m(-2. The model is shown to describe the data rather well for the three field studies. In one study measurements were made of solar radiation at different positions in the plant canopy. The coefficient of absorption of solar energy was assumed to be the same as c and provided a physical basis for the exponential model. The three studies showed no definitive peak in yield with plant population, but generally exhibited asymptotic approach to maximum yield with increased plant population. Values of x(c were very similar for the three field studies with the same crop species.
Sensitivity of APSIM/ORYZA model due to estimation errors in solar radiation
Alexandre Bryan Heinemann; Pepijn A.J. van Oort; Diogo Simões Fernandes; Aline de Holanda Nunes Maia
2012-01-01
Crop models are ideally suited to quantify existing climatic risks. However, they require historic climate data as input. While daily temperature and rainfall data are often available, the lack of observed solar radiation (Rs) data severely limits site-specific crop modelling. The objective of this study was to estimate Rs based on air temperature solar radiation models and to quantify the propagation of errors in simulated radiation on several APSIM/ORYZA crop model seasonal outputs, yield, ...
Oracle estimation of parametric models under boundary constraints.
Wong, Kin Yau; Goldberg, Yair; Fine, Jason P
2016-12-01
In many classical estimation problems, the parameter space has a boundary. In most cases, the standard asymptotic properties of the estimator do not hold when some of the underlying true parameters lie on the boundary. However, without knowledge of the true parameter values, confidence intervals constructed assuming that the parameters lie in the interior are generally over-conservative. A penalized estimation method is proposed in this article to address this issue. An adaptive lasso procedure is employed to shrink the parameters to the boundary, yielding oracle inference which adapt to whether or not the true parameters are on the boundary. When the true parameters are on the boundary, the inference is equivalent to that which would be achieved with a priori knowledge of the boundary, while if the converse is true, the inference is equivalent to that which is obtained in the interior of the parameter space. The method is demonstrated under two practical scenarios, namely the frailty survival model and linear regression with order-restricted parameters. Simulation studies and real data analyses show that the method performs well with realistic sample sizes and exhibits certain advantages over standard methods. © 2016, The International Biometric Society.
Energy Technology Data Exchange (ETDEWEB)
Studnicki, M.; Mądry, W.; Noras, K.; Wójcik-Gront, E.; Gacek, E.
2016-11-01
The main objectives of multi-environmental trials (METs) are to assess cultivar adaptation patterns under different environmental conditions and to investigate genotype by environment (G×E) interactions. Linear mixed models (LMMs) with more complex variance-covariance structures have become recognized and widely used for analyzing METs data. Best practice in METs analysis is to carry out a comparison of competing models with different variance-covariance structures. Improperly chosen variance-covariance structures may lead to biased estimation of means resulting in incorrect conclusions. In this work we focused on adaptive response of cultivars on the environments modeled by the LMMs with different variance-covariance structures. We identified possible limitations of inference when using an inadequate variance-covariance structure. In the presented study we used the dataset on grain yield for 63 winter wheat cultivars, evaluated across 18 locations, during three growing seasons (2008/2009-2010/2011) from the Polish Post-registration Variety Testing System. For the evaluation of variance-covariance structures and the description of cultivars adaptation to environments, we calculated adjusted means for the combination of cultivar and location in models with different variance-covariance structures. We concluded that in order to fully describe cultivars adaptive patterns modelers should use the unrestricted variance-covariance structure. The restricted compound symmetry structure may interfere with proper interpretation of cultivars adaptive patterns. We found, that the factor-analytic structure is also a good tool to describe cultivars reaction on environments, and it can be successfully used in METs data after determining the optimal component number for each dataset. (Author)
Integrated traffic conflict model for estimating crash modification factors.
Shahdah, Usama; Saccomanno, Frank; Persaud, Bhagwant
2014-10-01
Crash modification factors (CMFs) for road safety treatments are usually obtained through observational models based on reported crashes. Observational Bayesian before-and-after methods have been applied to obtain more precise estimates of CMFs by accounting for the regression-to-the-mean bias inherent in naive methods. However, sufficient crash data reported over an extended period of time are needed to provide reliable estimates of treatment effects, a requirement that can be a challenge for certain types of treatment. In addition, these studies require that sites analyzed actually receive the treatment to which the CMF pertains. Another key issue with observational approaches is that they are not causal in nature, and as such, cannot provide a sound "behavioral" rationale for the treatment effect. Surrogate safety measures based on high risk vehicle interactions and traffic conflicts have been proposed to address this issue by providing a more "causal perspective" on lack of safety for different road and traffic conditions. The traffic conflict approach has been criticized, however, for lacking a formal link to observed and verified crashes, a difficulty that this paper attempts to resolve by presenting and investigating an alternative approach for estimating CMFs using simulated conflicts that are linked formally to observed crashes. The integrated CMF estimates are compared to estimates from an empirical Bayes (EB) crash-based before-and-after analysis for the same sample of treatment sites. The treatment considered involves changing left turn signal priority at Toronto signalized intersections from permissive to protected-permissive. The results are promising in that the proposed integrated method yields CMFs that closely match those obtained from the crash-based EB before-and-after analysis. Copyright © 2014 Elsevier Ltd. All rights reserved.
Sensitivity of APSIM/ORYZA model due to estimation errors in solar radiation
Directory of Open Access Journals (Sweden)
Alexandre Bryan Heinemann
2012-01-01
Full Text Available Crop models are ideally suited to quantify existing climatic risks. However, they require historic climate data as input. While daily temperature and rainfall data are often available, the lack of observed solar radiation (Rs data severely limits site-specific crop modelling. The objective of this study was to estimate Rs based on air temperature solar radiation models and to quantify the propagation of errors in simulated radiation on several APSIM/ORYZA crop model seasonal outputs, yield, biomass, leaf area (LAI and total accumulated solar radiation (SRA during the crop cycle. The accuracy of the 5 models for estimated daily solar radiation was similar, and it was not substantially different among sites. For water limited environments (no irrigation, crop model outputs yield, biomass and LAI was not sensitive for the uncertainties in radiation models studied here.
Crop Yield Simulations Using Multiple Regional Climate Models in the Southwestern United States
Stack, D.; Kafatos, M.; Kim, S.; Kim, J.; Walko, R. L.
2013-12-01
Agricultural productivity (described by crop yield) is strongly dependent on climate conditions determined by meteorological parameters (e.g., temperature, rainfall, and solar radiation). California is the largest producer of agricultural products in the United States, but crops in associated arid and semi-arid regions live near their physiological limits (e.g., in hot summer conditions with little precipitation). Thus, accurate climate data are essential in assessing the impact of climate variability on agricultural productivity in the Southwestern United States and other arid regions. To address this issue, we produced simulated climate datasets and used them as input for the crop production model. For climate data, we employed two different regional climate models (WRF and OLAM) using a fine-resolution (8km) grid. Performances of the two different models are evaluated in a fine-resolution regional climate hindcast experiment for 10 years from 2001 to 2010 by comparing them to the North American Regional Reanalysis (NARR) dataset. Based on this comparison, multi-model ensembles with variable weighting are used to alleviate model bias and improve the accuracy of crop model productivity over large geographic regions (county and state). Finally, by using a specific crop-yield simulation model (APSIM) in conjunction with meteorological forcings from the multi-regional climate model ensemble, we demonstrate the degree to which maize yields are sensitive to the regional climate in the Southwestern United States.
How model and input uncertainty impact maize yield simulations in West Africa
Waha, Katharina; Huth, Neil; Carberry, Peter; Wang, Enli
2015-02-01
Crop models are common tools for simulating crop yields and crop production in studies on food security and global change. Various uncertainties however exist, not only in the model design and model parameters, but also and maybe even more important in soil, climate and management input data. We analyze the performance of the point-scale crop model APSIM and the global scale crop model LPJmL with different climate and soil conditions under different agricultural management in the low-input maize-growing areas of Burkina Faso, West Africa. We test the models’ response to different levels of input information from little to detailed information on soil, climate (1961-2000) and agricultural management and compare the models’ ability to represent the observed spatial (between locations) and temporal variability (between years) in crop yields. We found that the resolution of different soil, climate and management information influences the simulated crop yields in both models. However, the difference between models is larger than between input data and larger between simulations with different climate and management information than between simulations with different soil information. The observed spatial variability can be represented well from both models even with little information on soils and management but APSIM simulates a higher variation between single locations than LPJmL. The agreement of simulated and observed temporal variability is lower due to non-climatic factors e.g. investment in agricultural research and development between 1987 and 1991 in Burkina Faso which resulted in a doubling of maize yields. The findings of our study highlight the importance of scale and model choice and show that the most detailed input data does not necessarily improve model performance.
AMEM-ADL Polymer Migration Estimation Model User's Guide
The user's guide of the Arthur D. Little Polymer Migration Estimation Model (AMEM) provides the information on how the model estimates the fraction of a chemical additive that diffuses through polymeric matrices.
Benefit Estimation Model for Tourist Spaceflights
Goehlich, Robert A.
2003-01-01
It is believed that the only potential means for significant reduction of the recurrent launch cost, which results in a stimulation of human space colonization, is to make the launcher reusable, to increase its reliability, and to make it suitable for new markets such as mass space tourism. But such space projects, that have long range aspects are very difficult to finance, because even politicians would like to see a reasonable benefit during their term in office, because they want to be able to explain this investment to the taxpayer. This forces planners to use benefit models instead of intuitive judgement to convince sceptical decision-makers to support new investments in space. Benefit models provide insights into complex relationships and force a better definition of goals. A new approach is introduced in the paper that allows to estimate the benefits to be expected from a new space venture. The main objective why humans should explore space is determined in this study to ``improve the quality of life''. This main objective is broken down in sub objectives, which can be analysed with respect to different interest groups. Such interest groups are the operator of a space transportation system, the passenger, and the government. For example, the operator is strongly interested in profit, while the passenger is mainly interested in amusement, while the government is primarily interested in self-esteem and prestige. This leads to different individual satisfactory levels, which are usable for the optimisation process of reusable launch vehicles.
Climate Impacts on Chinese Corn Yields: A Fractional Polynomial Regression Model
Kooten, van G.C.; Sun, Baojing
2012-01-01
In this study, we examine the effect of climate on corn yields in northern China using data from ten districts in Inner Mongolia and two in Shaanxi province. A regression model with a flexible functional form is specified, with explanatory variables that include seasonal growing degree days,
Implications of a visco-elastic model of the lithosphere for calculating yield strength envelopes
Ershov, A.V.; Stephenson, R.A.
2006-01-01
The dominant deformation mechanism in the ductile part of the lithosphere is creep. From a mechanical point of view, creep can be modelled as a viscous phenomenon. On the other hand, yield-strength envelopes (YSEs), commonly used to describe lithosphere rheology, are constructed supposing creep to
The Forecasting Power of the Yield Curve, a Supervised Factor Model Approach
DEFF Research Database (Denmark)
Boldrini, Lorenzo; Hillebrand, Eric Tobias
loadings have the Nelson and Siegel (1987) structure and we consider one forecast target at a time. We compare the forecasting performance of our specification to benchmark models such as principal components regression, partial least squares, and ARMA(p,q) processes. We use the yield curve data from G...
Supporting Crop Loss Insurance Policy of Indonesia through Rice Yield Modelling and Forecasting
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.
Model dependencies of risk aversion and working interest estimates
International Nuclear Information System (INIS)
Lerche, I.
1996-01-01
Working interest, W, and risk adjusted value, RAV, are evaluated using both Cozzolino's formula for exponential dependence of risk aversion and also for a hyperbolic tangent dependence. In addition, the general method is given of constructing an RAV formula for any functional choice of risk aversion dependence. Two examples are given to illustrate how the model dependencies influence choices of working interest and risk adjusted value depending on whether the expected value of the project is positive or negative. In general the Cozzolino formula provides a more conservative position for risk than does the hyperbolic tangent formula, reflecting the difference in corporate attitudes to risk aversion. The commonly used Cozzolino formula is shown to have simple exact arithmetic expressions for maximum working interest and maximum RAV; the hyperbolic tangent formula has approximate analytic expressions. Both formulae also yield approximate analytical expressions for the working interest yielding a risk neutral RAV of zero. These arithmetic results are useful for making quick estimates of working interest ranges and risk adjusted values. (Author)
Getting water right: A case study in water yield modelling based on precipitation data.
Pessacg, Natalia; Flaherty, Silvia; Brandizi, Laura; Solman, Silvina; Pascual, Miguel
2015-12-15
Water yield is a key ecosystem service in river basins and especially in dry regions around the World. In this study we carry out a modelling analysis of water yields in the Chubut River basin, located in one of the driest districts of Patagonia, Argentina. We focus on the uncertainty around precipitation data, a driver of paramount importance for water yield. The objectives of this study are to: i) explore the spatial and numeric differences among six widely used global precipitation datasets for this region, ii) test them against data from independent ground stations, and iii) explore the effects of precipitation data uncertainty on simulations of water yield. The simulations were performed using the ecosystem services model InVEST (Integrated Valuation of Ecosystem Services and Tradeoffs) with each of the six different precipitation datasets as input. Our results show marked differences among datasets for the Chubut watershed region, both in the magnitude of precipitations and their spatial arrangement. Five of the precipitation databases overestimate the precipitation over the basin by 50% or more, particularly over the more humid western range. Meanwhile, the remaining dataset (Tropical Rainfall Measuring Mission - TRMM), based on satellite measurements, adjusts well to the observed rainfall in different stations throughout the watershed and provides a better representation of the precipitation gradient characteristic of the rain shadow of the Andes. The observed differences among datasets in the representation of the rainfall gradient translate into large differences in water yield simulations. Errors in precipitation of +30% (-30%) amplify to water yield errors ranging from 50 to 150% (-45 to -60%) in some sub-basins. These results highlight the importance of assessing uncertainties in main input data when quantifying and mapping ecosystem services with biophysical models and cautions about the undisputed use of global environmental datasets. Copyright
Water deficit effects on maize yields modeled under current and greenhouse climates
International Nuclear Information System (INIS)
Muchow, R.C.; Sinclair, T.R.
1991-01-01
The availability of water imposes one of the major limits on rainfed maize (Zea mays L.) productivity. This analysis was undertaken in an attempt to quantify the effects of limited water on maize growth and yield by extending a simple, mechanistic model in which temperature regulates crop development and intercepted solar radiation is used to calculate crop biomass accumulation. A soil water budget was incorporated into the model by accounting for inputs from rainfall and irrigation, and water use by soil evaporation and crop transpiration. The response functions of leaf area development and crop gas exchange to the soil water budget were developed from experimental studies. The model was used to interpret a range of field experiments using observed daily values of temperature, solar radiation, and rainfall or irrigation, where water deficits of varying durations developed at different stages of growth. The relative simplicity of the model and its robustness in simulating maize yields under a range of water-availability conditions allows the model to be readily used for studies of crop performance under alternate conditions. One such study, presented here, was a yield assessment for rainfed maize under possible greenhouse climates where temperature and atmospheric CO 2 concentration were increased. An increase in temperature combined with decreased rainfall lowered grain yield, although the increase in crop water use efficiency associated with elevated CO 2 concentration ameliorated the response to the greenhouse climate. Grain yields for the greenhouse climates as compared to current conditions increased, or decreased only slightly, except when the greenhouse climate was assumed to result in severly decreased rainfall
Determining Rheological Parameters of Generalized Yield-Power-Law Fluid Model
Directory of Open Access Journals (Sweden)
Stryczek Stanislaw
2004-09-01
Full Text Available The principles of determining rheological parameters of drilling muds described by a generalized yield-power-law are presented in the paper. Functions between tangent stresses and shear rate are given. The conditions of laboratory measurements of rheological parameters of generalized yield-power-law fluids are described and necessary mathematical relations for rheological model parameters given. With the block diagrams, the methodics of numerical solution of these relations has been presented. Rheological parameters of an exemplary drilling mud have been calculated with the use of this numerical program.
International Nuclear Information System (INIS)
Brenner, D.J.; Miller, J.H.; Ritchie, R.H.; Bichsel, H.
1985-01-01
An insulator model with four experimental energy bands was used to fit the optical properties of liquid water and to extend these data to non-zero momentum transfer. Inelastic mean free paths derived from this dielectric response function provided the basic information necessary to degrade high energy electrons to the subexcitation energy domain. Two approaches for the transport of subexcitation electrons were investigated. (i) Gas phase cross sections were used to degrade subexcitation electrons to thermal energy and the thermalization lengths were scaled to unit density. (ii) Thermalization lengths were estimated by age-diffusion theory with a stopping power deduced from the data on liquid water and transport cross sections derived from elastic scattering in water vapor. Theoretical ranges were compared to recent experimental results. A stochastic model was used to calculate the rapid diffusion and reaction of hydrated electrons with other radiolysis products. The sensitivity of the calculated yields to the model assumptions and comparison with experimental data are discussed
Wang, Haijun; Liang, Xiaomin; Wang, Hongzhu
2017-07-01
Next to excessive nutrient loading, intensive aquaculture is one of the major anthropogenic impacts threatening lake ecosystems. In China, particularly in the shallow lakes of mid-lower Changjiang (Yangtze) River, continuous overstocking of the Chinese mitten crab ( Eriocheir sinensis) could deteriorate water quality and exhaust natural resources. A series of crab yield models and a general optimum-stocking rate model have been established, which seek to benefit both crab culture and the environment. In this research, independent investigations were carried out to evaluate the crab yield models and modify the optimum-stocking model. Low percentage errors (average 47%, median 36%) between observed and calculated crab yields were obtained. Specific values were defined for adult crab body mass (135 g/ind.) and recapture rate (18% and 30% in lakes with submerged macrophyte biomass above and below 1 000 g/m2) to modify the optimum-stocking model. Analysis based on the modified optimum-stocking model indicated that the actual stocking rates in most lakes were much higher than the calculated optimum-stocking rates. This implies that, for most lakes, the current stocking rates should be greatly reduced to maintain healthy lake ecosystems.
DEFF Research Database (Denmark)
Brock, Andreas Libonati; Kästner, M.; Trapp, Stefan
2017-01-01
NER. Formation of microbial mass can be estimated from the microbial growth yield, but experimental data is rare. Instead, we suggest using prediction methods for the theoretical yield based on thermodynamics. Recently, we presented the Microbial Turnover to Biomass (MTB) method that needs a minimum...... and using the released CO2 as a measure for microbial activity, we predicted a range for the formation of biogenic NER. For the majority of the pesticides, a considerable fraction of the NER was estimated to be biogenic. This novel approach provides a theoretical foundation applicable to the evaluation...
Valentini, Alessio; Rivero, Daniel; Zapata, Felipe; García-Iriepa, Cristina; Marazzi, Marco; Palmeiro, Raúl; Fdez Galván, Ignacio; Sampedro, Diego; Olivucci, Massimo; Frutos, Luis Manuel
2017-03-27
The quantum yield of a photochemical reaction is one of the most fundamental quantities in photochemistry, as it measures the efficiency of the transduction of light energy into chemical energy. Nature has evolved photoreceptors in which the reactivity of a chromophore is enhanced by its molecular environment to achieve high quantum yields. The retinal chromophore sterically constrained inside rhodopsin proteins represents an outstanding example of such a control. In a more general framework, mechanical forces acting on a molecular system can strongly modify its reactivity. Herein, we show that the exertion of tensile forces on a simplified retinal chromophore model provokes a substantial and regular increase in the trans-to-cis photoisomerization quantum yield in a counterintuitive way, as these extension forces facilitate the formation of the more compressed cis photoisomer. A rationale for the mechanochemical effect on this photoisomerization mechanism is also proposed. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.
A model independent determination of the B{yields}X{sub s}{gamma} decay rate
Energy Technology Data Exchange (ETDEWEB)
Bernlochner, Florian U. [Victoria Univ., BC (Canada); Lacker, Heiko [Humboldt-Universitaet, Berlin (Germany); Ligeti, Zoltan [California Univ., Berkeley, CA (United States). Ernest Orlando Lawrence Berkeley National Laboratory; Stewart, Iain W. [Massachusetts Institute of Technology, Cambridge, MA (United States). Center for Theoretical Physics; Tackmann, Frank J.; Tackmann, Kerstin [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany)
2013-03-15
The goal of the SIMBA collaboration is to provide a global fit to the available measurements of inclusive B{yields}X{sub s}{gamma} and B{yields}X{sub u}l{nu} decays. By performing a global fit one is able to simultaneously determine the relevant normalizations, i.e. the total B{yields}X{sub s}{gamma} rate and the CKM-matrix element vertical stroke Vub vertical stroke, together with the required hadronic parameters, most importantly the b-quark mass and the b-quark distribution function in the B-meson, called the shape function. In this talk, the current status on the model-independent determination of the shape function and vertical stroke C{sub 7}{sup incl}V{sub tb}V{sub ts}{sup *} vertical stroke, which parametrizes the total B{yields}X{sub s}{gamma} rate, from a global fit to the available B{yields}X{sub s}{gamma} measurements from Babar and Belle is presented. In particular, the theoretical uncertainties originating from variations of the different factorization scales are evaluated.
Dynamic Diffusion Estimation in Exponential Family Models
Czech Academy of Sciences Publication Activity Database
Dedecius, Kamil; Sečkárová, Vladimíra
2013-01-01
Roč. 20, č. 11 (2013), s. 1114-1117 ISSN 1070-9908 R&D Projects: GA MŠk 7D12004; GA ČR GA13-13502S Keywords : diffusion estimation * distributed estimation * paremeter estimation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.639, year: 2013 http://library.utia.cas.cz/separaty/2013/AS/dedecius-0396518.pdf
UAV State Estimation Modeling Techniques in AHRS
Razali, Shikin; Zhahir, Amzari
2017-11-01
Autonomous unmanned aerial vehicle (UAV) system is depending on state estimation feedback to control flight operation. Estimation on the correct state improves navigation accuracy and achieves flight mission safely. One of the sensors configuration used in UAV state is Attitude Heading and Reference System (AHRS) with application of Extended Kalman Filter (EKF) or feedback controller. The results of these two different techniques in estimating UAV states in AHRS configuration are displayed through position and attitude graphs.
Inflow, Outflow, Yields, and Stellar Population Mixing in Chemical Evolution Models
International Nuclear Information System (INIS)
Andrews, Brett H.; Weinberg, David H.; Schönrich, Ralph; Johnson, Jennifer A.
2017-01-01
Chemical evolution models are powerful tools for interpreting stellar abundance surveys and understanding galaxy evolution. However, their predictions depend heavily on the treatment of inflow, outflow, star formation efficiency (SFE), the stellar initial mass function, the SN Ia delay time distribution, stellar yields, and stellar population mixing. Using flexCE, a flexible one-zone chemical evolution code, we investigate the effects of and trade-offs between parameters. Two critical parameters are SFE and the outflow mass-loading parameter, which shift the knee in [O/Fe]–[Fe/H] and the equilibrium abundances that the simulations asymptotically approach, respectively. One-zone models with simple star formation histories follow narrow tracks in [O/Fe]–[Fe/H] unlike the observed bimodality (separate high- α and low- α sequences) in this plane. A mix of one-zone models with inflow timescale and outflow mass-loading parameter variations, motivated by the inside-out galaxy formation scenario with radial mixing, reproduces the two sequences better than a one-zone model with two infall epochs. We present [X/Fe]–[Fe/H] tracks for 20 elements assuming three different supernova yield models and find some significant discrepancies with solar neighborhood observations, especially for elements with strongly metallicity-dependent yields. We apply principal component abundance analysis to the simulations and existing data to reveal the main correlations among abundances and quantify their contributions to variation in abundance space. For the stellar population mixing scenario, the abundances of α -elements and elements with metallicity-dependent yields dominate the first and second principal components, respectively, and collectively explain 99% of the variance in the model. flexCE is a python package available at https://github.com/bretthandrews/flexCE.
Inflow, Outflow, Yields, and Stellar Population Mixing in Chemical Evolution Models
Energy Technology Data Exchange (ETDEWEB)
Andrews, Brett H. [PITT PACC, Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, PA 15260 (United States); Weinberg, David H.; Schönrich, Ralph; Johnson, Jennifer A., E-mail: andrewsb@pitt.edu [Department of Astronomy, The Ohio State University, 140 West 18th Avenue, Columbus, OH 43210 (United States)
2017-02-01
Chemical evolution models are powerful tools for interpreting stellar abundance surveys and understanding galaxy evolution. However, their predictions depend heavily on the treatment of inflow, outflow, star formation efficiency (SFE), the stellar initial mass function, the SN Ia delay time distribution, stellar yields, and stellar population mixing. Using flexCE, a flexible one-zone chemical evolution code, we investigate the effects of and trade-offs between parameters. Two critical parameters are SFE and the outflow mass-loading parameter, which shift the knee in [O/Fe]–[Fe/H] and the equilibrium abundances that the simulations asymptotically approach, respectively. One-zone models with simple star formation histories follow narrow tracks in [O/Fe]–[Fe/H] unlike the observed bimodality (separate high- α and low- α sequences) in this plane. A mix of one-zone models with inflow timescale and outflow mass-loading parameter variations, motivated by the inside-out galaxy formation scenario with radial mixing, reproduces the two sequences better than a one-zone model with two infall epochs. We present [X/Fe]–[Fe/H] tracks for 20 elements assuming three different supernova yield models and find some significant discrepancies with solar neighborhood observations, especially for elements with strongly metallicity-dependent yields. We apply principal component abundance analysis to the simulations and existing data to reveal the main correlations among abundances and quantify their contributions to variation in abundance space. For the stellar population mixing scenario, the abundances of α -elements and elements with metallicity-dependent yields dominate the first and second principal components, respectively, and collectively explain 99% of the variance in the model. flexCE is a python package available at https://github.com/bretthandrews/flexCE.
Directory of Open Access Journals (Sweden)
Pierre Coenraets
1997-01-01
Full Text Available Comparison of computation time between single-trait and multiple-trait evaluations showed that with the use of the canonicat transformation associated with multiple diagonalization of (covariance matrices, multiple-trait analysis for milk, fat and protein yields is not more expensive than three single-trait analyzes. Rank correlations between breeding values for 54,820 cows with records (for their 1,406 sires estimated with the single-trait and multiple-trait models were over .98 (.99 in fat yield and over .99 (.99 in milk and protein yields. The relative gain expressed as reduction in mean prediction error variance was 3% (1% in milk yield, 6% (3% in fat yield, and .4% (.2% in protein yield for cows (for sires. Relative genetic gains were 3% (1%, 6% (2% and .5% (.2% respectively in milk, fat and protein yields for cows (for sires. The use of multiple-trait models bas therefore the advantages of improved precision and reduced selection bics. Multiple-trait analysis could be extended for the analyzes of test-day records. Results show that this or similar multiple-trait animal model could be implemented immediately in Belgium at low computing cost, using the proposed algorithme and could be the first step to new, more advanced evaluation methods.
Efficient estimation of an additive quantile regression model
Cheng, Y.; de Gooijer, J.G.; Zerom, D.
2011-01-01
In this paper, two non-parametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a more viable alternative to existing kernel-based approaches. The second estimator
Performances of some estimators of linear model with ...
African Journals Online (AJOL)
The estimators are compared by examing the finite properties of estimators namely; sum of biases, sum of absolute biases, sum of variances and sum of the mean squared error of the estimated parameter of the model. Results show that when the autocorrelation level is small (ρ=0.4), the MLGD estimator is best except when ...
Directory of Open Access Journals (Sweden)
Omar Vergara-Diaz
2015-06-01
Full Text Available The biotrophic fungus Puccinia striiformis f. sp. tritici is the causal agent of the yellow rust in wheat. Between the years 2010–2013 a new strain of this pathogen (Warrior/Ambition, against which the present cultivated wheat varieties have no resistance, appeared and spread rapidly. It threatens cereal production in most of Europe. The search for sources of resistance to this strain is proposed as the most efficient and safe solution to ensure high grain production. This will be helped by the development of high performance and low cost techniques for field phenotyping. In this study we analyzed vegetation indices in the Red, Green, Blue (RGB images of crop canopies under field conditions. We evaluated their accuracy in predicting grain yield and assessing disease severity in comparison to other field measurements including the Normalized Difference Vegetation Index (NDVI, leaf chlorophyll content, stomatal conductance, and canopy temperature. We also discuss yield components and agronomic parameters in relation to grain yield and disease severity. RGB-based indices proved to be accurate predictors of grain yield and grain yield losses associated with yellow rust (R2 = 0.581 and R2 = 0.536, respectively, far surpassing the predictive ability of NDVI (R2 = 0.118 and R2 = 0.128, respectively. In comparison to potential yield, we found the presence of disease to be correlated with reductions in the number of grains per spike, grains per square meter, kernel weight and harvest index. Grain yield losses in the presence of yellow rust were also greater in later heading varieties. The combination of RGB-based indices and days to heading together explained 70.9% of the variability in grain yield and 62.7% of the yield losses.
On population size estimators in the Poisson mixture model.
Mao, Chang Xuan; Yang, Nan; Zhong, Jinhua
2013-09-01
Estimating population sizes via capture-recapture experiments has enormous applications. The Poisson mixture model can be adopted for those applications with a single list in which individuals appear one or more times. We compare several nonparametric estimators, including the Chao estimator, the Zelterman estimator, two jackknife estimators and the bootstrap estimator. The target parameter of the Chao estimator is a lower bound of the population size. Those of the other four estimators are not lower bounds, and they may produce lower confidence limits for the population size with poor coverage probabilities. A simulation study is reported and two examples are investigated. © 2013, The International Biometric Society.
Hoffmann, M.; Castaneda Vera, A.; Wijk, van M.T.; Giller, K.E.; Oberthür, T.; Donough, C.; Whitbread, A.M.
2014-01-01
Reducing the gap between water-limited potential yield and actual yield in oil palm production systems through intensification is seen as an important option for sustainably increasing palm oil production. Simulation models can play an important role in quantifying water-limited potential yield, and
Parameter Estimates in Differential Equation Models for Chemical Kinetics
Winkel, Brian
2011-01-01
We discuss the need for devoting time in differential equations courses to modelling and the completion of the modelling process with efforts to estimate the parameters in the models using data. We estimate the parameters present in several differential equation models of chemical reactions of order n, where n = 0, 1, 2, and apply more general…
Radiation risk estimation based on measurement error models
Masiuk, Sergii; Shklyar, Sergiy; Chepurny, Mykola; Likhtarov, Illya
2017-01-01
This monograph discusses statistics and risk estimates applied to radiation damage under the presence of measurement errors. The first part covers nonlinear measurement error models, with a particular emphasis on efficiency of regression parameter estimators. In the second part, risk estimation in models with measurement errors is considered. Efficiency of the methods presented is verified using data from radio-epidemiological studies.
Directory of Open Access Journals (Sweden)
Rafia Mumtaz
2017-10-01
Full Text Available Land management for crop production is an essential human activity that supports life on Earth. The main challenge to be faced by the agriculture sector in coming years is to feed the rapidly growing population while maintaining the key resources such as soil fertility, efficient land use, and water. Climate change is also a critical factor that impacts agricultural production. Among others, a major effect of climate change is the potential alterations in the growth cycle of crops which would likely lead to a decline in the agricultural output. Due to the increasing demand for proper agricultural management, this study explores the effects of meteorological variation on wheat yield in Chakwal and Faisalabad districts of Punjab, Pakistan and used normalised difference vegetation index (NDVI as a predictor for yield estimates. For NDVI data (2001-14, the NDVI product of Moderate Resolution Imaging spectrometer (MODIS 16-day composites data has been used. The crop area mapping has been realised by classifying the satellite data into different land use/land covers using iterative self-organising (ISO data clustering. The land cover for the wheat crop was mapped using a crop calendar. The relation of crop yield with NDVI and the impact of meteorological parameters on wheat growth and its yield has been analysed at various development stages. A strong correlation of rainfall and temperature was found with NDVI data, which determined NDVI as a strong predictor of yield estimation. The wheat yield estimates were obtained by linearly regressing the reported crop yield against the time series of MODIS NDVI profiles. The wheat NDVI profiles have shown a parabolic pattern across the growing season, therefore parabolic least square fit (LSF has been applied prior to linear regression. The coefficients of determination (R2 between the reported and estimated yield was found to be 0.88 and 0.73, respectively, for Chakwal and Faisalabad. This indicates that the
Efficient estimation of semiparametric copula models for bivariate survival data
Cheng, Guang
2014-01-01
A semiparametric copula model for bivariate survival data is characterized by a parametric copula model of dependence and nonparametric models of two marginal survival functions. Efficient estimation for the semiparametric copula model has been recently studied for the complete data case. When the survival data are censored, semiparametric efficient estimation has only been considered for some specific copula models such as the Gaussian copulas. In this paper, we obtain the semiparametric efficiency bound and efficient estimation for general semiparametric copula models for possibly censored data. We construct an approximate maximum likelihood estimator by approximating the log baseline hazard functions with spline functions. We show that our estimates of the copula dependence parameter and the survival functions are asymptotically normal and efficient. Simple consistent covariance estimators are also provided. Numerical results are used to illustrate the finite sample performance of the proposed estimators. © 2013 Elsevier Inc.
A radio-controlled unmanned helicopter-based LARS (Low-Altitude Remote Sensing) platform was used to acquire quality images of high spatial and temporal resolution, in order to estimate yield and total biomass of a rice crop (Oriza Sativa, L.). Fifteen rice field plots with five N-treatments (0, 33,...
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
Potato production ranks fourth in the world after rice, wheat, and maize and it is highly sensitive to water stress. It is thus very important to implement irrigation management strategies to minimize the effects of water stress under different climate conditions. The use of modelling tools...... 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...... (If), deficit irrigated (Id) and not irrigated (I0) were investigated in three-years potato field experiment (2013–15) with four replicates in randomized complete block design. Tuber and total dry matter yield, canopy cover, dry matter production during the crop growth season, and soil water content...
Mathematical model of transmission network static state estimation
Directory of Open Access Journals (Sweden)
Ivanov Aleksandar
2012-01-01
Full Text Available In this paper the characteristics and capabilities of the power transmission network static state estimator are presented. The solving process of the mathematical model containing the measurement errors and their processing is developed. To evaluate difference between the general model of state estimation and the fast decoupled state estimation model, the both models are applied to an example, and so derived results are compared.
Parameter Estimation of Partial Differential Equation Models
Xun, Xiaolei; Cao, Jiguo; Mallick, Bani; Maity, Arnab; Carroll, Raymond J.
2013-01-01
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
Celler, A; Hou, X; Bénard, F; Ruth, T
2011-09-07
Recent acute shortage of medical radioisotopes prompted investigations into alternative methods of production and the use of a cyclotron and ¹⁰⁰Mo(p,2n)(99m)Tc reaction has been considered. In this context, the production yields of (99m)Tc and various other radioactive and stable isotopes which will be created in the process have to be investigated, as these may affect the diagnostic outcome and radiation dosimetry in human studies. Reaction conditions (beam and target characteristics, and irradiation and cooling times) need to be optimized in order to maximize the amount of (99m)Tc and minimize impurities. Although ultimately careful experimental verification of these conditions must be performed, theoretical calculations can provide the initial guidance allowing for extensive investigations at little cost. We report the results of theoretically determined reaction yields for (99m)Tc and other radioactive isotopes created when natural and enriched molybdenum targets are irradiated by protons. The cross-section calculations were performed using a computer program EMPIRE for the proton energy range 6-30 MeV. A computer graphical user interface for automatic calculation of production yields taking into account various reaction channels leading to the same final product has been created. The proposed approach allows us to theoretically estimate the amount of (99m)Tc and its ratio relative to (99g)Tc and other radioisotopes which must be considered reaction contaminants, potentially contributing to additional patient dose in diagnostic studies.
Estimation of radiation hardening in ferritic steels using the cluster dynamics models
Energy Technology Data Exchange (ETDEWEB)
Kwon, Jun Hyun; Kim, Whung Whoe; Hong, Jun Hwa [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)
2005-07-01
Evolution of microstructure under irradiation brings about the mechanical property changes of materials, of which the major concern is radiation hardening in this work. Radiation hardening is generally expressed in terms of an increase in yield strength as a function of radiation dose and temperature. Cluster dynamics model for radiation hardening has been developed to describe the evolution of point defects clusters (PDCs) and copperrich precipitates (CRPs). While the mathematical models developed by Stoller focus on the evolution of PDCs in ferritic steels under neutron irradiation, we slightly modify the model by including the CRP growth and estimate the magnitude of hardening induced by PDC and CRP. The model is then used to calculate the changes in yield strength of RPV steels. The calculation results are compared to measured yield strength values, obtained from surveillance testing of PWR vessel steels in France.
An individual reproduction model sensitive to milk yield and body condition in Holstein dairy cows
Brun-Lafleur, L.; Cutullic, E.; Faverdin, P.; Delaby, L.; Disenhaus, C.
2017-01-01
To simulate the consequences of management in dairy herds, the use of individual-based herd models is very useful and has become common. Reproduction is a key driver of milk production and herd dynamics, whose influence has been magnified by the decrease in reproductive performance over the last decades. Moreover, feeding management influences milk yield (MY) and body reserves, which in turn influence reproductive performance. Therefore, our objective was to build an up-to-date animal reprodu...
A Probabilistic Cost Estimation Model for Unexploded Ordnance Removal
National Research Council Canada - National Science Library
Poppe, Peter
1999-01-01
...) contaminated sites that the services must decontaminate. Existing models for estimating the cost of UXO removal often require a high level of expertise and provide only a point estimate for the costs...
International Nuclear Information System (INIS)
Sarwar, M.
2013-01-01
Field trials were carried out to estimate resistance along with paddy yield in 55 rice germplasm lines (35 aromatic and 20 non-aromatic genotypes) for rice stem borers (Pyralidae: Lepidoptera) to expose their potential in pest management approach. The results expressed significant differences for pest damage build-up and paddy yield among the rice germplasm lines. The findings clearly portrayed that based upon the percentage of pest invasions (dead hearts and white heads damage), no genotype was exclusively resistant to stem borers damage under field conditions. Two aromatic genotypes, Jajai-15A/97 and Basmati-Cr-34, exhibited least borers prevalence and amplified paddy yield while Sonehri Sugdasi (P) and Sada Gulab (P) pointed out a peak pest invasion and declined paddy yield. The estimation of pest incidence build-up and paddy productivity within non-aromatic genotypes confirmed that IR8 (P), IR6-15-2 and IR6 (P) were mainly proficient for bearing condensed pest invasion and augmented paddy yield. IR8-2.5-4, IR6-15-10 and IR6-20-9 demonstrated elevated pest susceptibility and gave poor yield. Rest of the germplasms appeared to be least tolerant or vulnerable to pest build-up and reduced paddy production. The tolerant and high yielding genotypes should be popularised in rice borers endemic areas and can be used in varietals resistance breeding strategy. The outcome of current studies necessitates the integration of existing host plant tolerance along with other management strategies to accomplish a suitable control of rice stem borers and enhance paddy yield. (author)
Simulating and Predicting Cereal Crop Yields in Ethiopia: Model Calibration and Verification
Yang, M.; Wang, G.; Ahmed, K. F.; Eggen, M.; Adugna, B.; Anagnostou, E. N.
2017-12-01
Agriculture in developing countries are extremely vulnerable to climate variability and changes. In East Africa, most people live in the rural areas with outdated agriculture techniques and infrastructure. Smallholder agriculture continues to play a key role in this area, and the rate of irrigation is among the lowest of the world. As a result, seasonal and inter-annual weather patterns play an important role in the spatiotemporal variability of crop yields. This study investigates how various climate variables (e.g., temperature, precipitation, sunshine) and agricultural practice (e.g., fertilization, irrigation, planting date) influence cereal crop yields using a process-based model (DSSAT) and statistical analysis, and focuses on the Blue Nile Basin of Ethiopia. The DSSAT model is driven with meteorological forcing from the ECMWF's latest reanalysis product that cover the past 35 years; the statistical model will be developed by linking the same meteorological reanalysis data with harvest data at the woreda level from the Ethiopian national dataset. Results from this study will set the stage for the development of a seasonal prediction system for weather and crop yields in Ethiopia, which will serve multiple sectors in coping with the agricultural impact of climate variability.
International Nuclear Information System (INIS)
Barnett, C.S.
1991-01-01
The Double Contingency Principle (DCP) is widely applied to criticality safety practice in the United States. Most practitioners base their application of the principle on qualitative, intuitive assessments. The recent trend toward probabilistic safety assessments provides a motive to search for a quantitative, probabilistic foundation for the DCP. A Markov model is tractable and leads to relatively simple results. The model yields estimates of mean time to simultaneous collapse of two contingencies as a function of estimates of mean failure times and mean recovery times of two independent contingencies. The model is a tool that can be used to supplement the qualitative methods now used to assess effectiveness of the DCP. (Author)
Butler, J. J., Jr.; Whittemore, D. O.; Wilson, B. B.; Bohling, G.
2017-12-01
Many large regional aquifers supporting irrigated agriculture are experiencing high rates of water-level decline. The primary means of moderating these rates is to reduce pumping. The key question is what percent pumping reduction will significantly impact decline rates. We have recently developed a water-balance approach to address this question for subareas (100s to 1000s km2 in size) of seasonally pumped aquifers (Butler et al., GRL, 2016). This approach also provides an estimate of specific yield (Sy), which has been difficult to estimate from field data at the scale of modeling analyses. When applied to subareas of the High Plains aquifer in Kansas, this approach reveals that the Sy estimate is much lower (as much as a factor of five or more) than expected for an unconsolidated aquifer. One explanation is that the aquifer is heterogeneous with considerable amounts of fine material, whereas field data, such as drillers' logs, are often biased towards coarser intervals. An additional explanation, which appears to have received little attention, is the impact of entrapped air. In seasonally pumped systems, water levels pass through the same aquifer intervals multiple times, giving ample opportunity for air to be entrapped. This entrapped air imbues the aquifer with a specific yield that is considerably lower than what would be expected from lithology. If unrecognized, a larger-than-actual Sy value is input into the aquifer model. This can lead to the inadvertent use of the same-year recharge assumption, which may not be appropriate for many conditions (e.g., large depths to water), and can also result in artificially low estimates of net inflow for a depleting aquifer. Moreover, failure to recognize this condition can bedevil efforts to model conservation-based water use reductions. In that case, models will leave the range of conditions for which they have been calibrated and can become more vulnerable to parameter errors. Conservation-based water use reductions
An overview of available crop growth and yield models for studies and assessments in agriculture.
Di Paola, Arianna; Valentini, Riccardo; Santini, Monia
2016-02-01
The scientific community offers numerous crop models with different levels of sophistication. In such a wide range of crop models, users should have the possibility to choose the most suitable, in terms of detail, scale and representativeness, to their objectives. However, even when an appropriate choice is made, model limitations should be clarified such that modelling studies are put in the proper perspective and robust applications are achieved. This work is an overview of available models to simulate crop growth and yield. A summary matrix with more than 70 crop models is provided, storing the main model characteristics that can help users to choose the proper tool according to their purposes. Overall, we found that two main aspects of models, despite their importance, are not always clear from the published references, i.e. the versatility of the models, in terms of reliable transferability to different conditions, and the degree of complexity. Hence, the developers of models should be encouraged to pay more attention to clarifying the model limitations and limits of applicability, and users should make an effort in proper model selection, to save time often devoted to iteration of tuning steps to force an inappropriate model to be adapted to their own purpose. © 2015 Society of Chemical Industry.
A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change.
Directory of Open Access Journals (Sweden)
M Irfan Ashraf
Full Text Available Global climate is changing due to increasing anthropogenic emissions of greenhouse gases. Forest managers need growth and yield models that can be used to predict future forest dynamics during the transition period of present-day forests under a changing climatic regime. In this study, we developed a forest growth and yield model that can be used to predict individual-tree growth under current and projected future climatic conditions. The model was constructed by integrating historical tree growth records with predictions from an ecological process-based model using neural networks. The new model predicts basal area (BA and volume growth for individual trees in pure or mixed species forests. For model development, tree-growth data under current climatic conditions were obtained using over 3000 permanent sample plots from the Province of Nova Scotia, Canada. Data to reflect tree growth under a changing climatic regime were projected with JABOWA-3 (an ecological process-based model. Model validation with designated data produced model efficiencies of 0.82 and 0.89 in predicting individual-tree BA and volume growth. Model efficiency is a relative index of model performance, where 1 indicates an ideal fit, while values lower than zero means the predictions are no better than the average of the observations. Overall mean prediction error (BIAS of basal area and volume growth predictions was nominal (i.e., for BA: -0.0177 cm(2 5-year(-1 and volume: 0.0008 m(3 5-year(-1. Model variability described by root mean squared error (RMSE in basal area prediction was 40.53 cm(2 5-year(-1 and 0.0393 m(3 5-year(-1 in volume prediction. The new modelling approach has potential to reduce uncertainties in growth and yield predictions under different climate change scenarios. This novel approach provides an avenue for forest managers to generate required information for the management of forests in transitional periods of climate change. Artificial intelligence
Estimation of Stochastic Volatility Models by Nonparametric Filtering
DEFF Research Database (Denmark)
Kanaya, Shin; Kristensen, Dennis
2016-01-01
/estimated volatility process replacing the latent process. Our estimation strategy is applicable to both parametric and nonparametric stochastic volatility models, and can handle both jumps and market microstructure noise. The resulting estimators of the stochastic volatility model will carry additional biases...... and variances due to the first-step estimation, but under regularity conditions we show that these vanish asymptotically and our estimators inherit the asymptotic properties of the infeasible estimators based on observations of the volatility process. A simulation study examines the finite-sample properties...
Nevison, C. D.; Andrews, A. E.; Thoning, K. W.; Dlugokencky, E. J.; Sweeney, C.; Saikawa, E.; Miller, S. M.; Benmergui, J. S.; Fischer, M. L.
2017-12-01
North American nitrous oxide (N2O) emissions of 1.5 ± 0.2 Tg N/yr over 2008-2013 are estimated using the Carbon Tracker-Lagrange (CT-L) regional inversion framework. The estimated N2O emissions are largely consistent with the EDGAR global inventory and with the results of global atmospheric inversions, but offer more spatial and temporal detail and improved uncertainty quantification over North America. Emissions are strongest from the Midwestern corn/soybean belt, which accounts for about one fourth of the total North American N2O source. The emissions are maximum in spring/early summer, consistent with a nitrogen fertilizer-driven source, but also show a late winter spike suggestive of freeze-thaw effects. Interannual variability in emissions across the primary months of fertilizer application is positively correlated to mean soil moisture and precipitation. The inversion results, in combination with gridded N fertilizer datasets, are used to estimate the fraction of synthetic N fertilizer that is released as N2O. The estimated N2O flux from the Midwestern corn/soybean belt and the more northerly U.S./Canadian wheat belt corresponds to 3.6-4.5% and 1.4-3.5%, respectively, of total synthetic + organic N fertilizer applied to those regions. Consideration of additional N inputs from soybean N2 fixation reduces the N2O yield from the Midwestern corn/soybean belt to 2-2.6% of total N inputs. Figure 1. Posterior N2O flux integrated over the central Midwestern Corn/Soybean belt (38° to 43°N, 102° to 80°W, in grids where 5% or more of land area was planted in corn and/or soybean). Cases 1 (red) and 2 (blue) are defined based on different covariance matrix parameters, representing alternative scenarios of tighter prior/looser model-data mismatch and looser prior/tighter model-data mismatch. Both cases use a standard prior derived from a coarser resolution global inversion. Triangles show the approximate day when soil temperature climbs above 0°C and drops below 10
Tan, Zeli; Leung, L. Ruby; Li, Hongyi; Tesfa, Teklu; Vanmaercke, Matthias; Poesen, Jean; Zhang, Xuesong; Lu, Hui; Hartmann, Jens
2017-12-01
Although sediment yield (SY) from water erosion is ubiquitous and its environmental consequences are well recognized, its impacts on the global carbon cycle remain largely uncertain. This knowledge gap is partly due to the lack of soil erosion modeling in Earth System Models (ESMs), which are important tools used to understand the global carbon cycle and explore its changes. This study analyzed sediment and particulate organic carbon yield (CY) data from 1,081 and 38 small catchments (0.1-200 km2), respectively, in different environments across the globe. Using multiple statistical analysis techniques, we explored environmental factors and hydrological processes important for SY and CY modeling in ESMs. Our results show clear correlations of high SY with traditional agriculture, seismicity and heavy storms, as well as strong correlations between SY and annual peak runoff. These highlight the potential limitation of SY models that represent only interrill and rill erosion because shallow overland flow and rill flow have limited transport capacity due to their hydraulic geometry to produce high SY. Further, our results suggest that SY modeling in ESMs should be implemented at the event scale to produce the catastrophic mass transport during episodic events. Several environmental factors such as seismicity and land management that are often not considered in current catchment-scale SY models can be important in controlling global SY. Our analyses show that SY is likely the primary control on CY in small catchments and a statistically significant empirical relationship is established to calculate SY and CY jointly in ESMs.
Influence of Different Yield Loci on Failure Prediction with Damage Models
Heibel, S.; Nester, W.; Clausmeyer, T.; Tekkaya, A. E.
2017-09-01
Advanced high strength steels are widely used in the automotive industry to simultaneously improve crash performance and reduce the car body weight. A drawback of these multiphase steels is their sensitivity to damage effects and thus the reduction of ductility. For that reason the Forming Limit Curve is only partially suitable for this class of steels. An improvement in failure prediction can be obtained by using damage mechanics. The objective of this paper is to comparatively review the phenomenological damage model GISSMO and the Enhanced Lemaitre Damage Model. GISSMO is combined with three different yield loci, namely von Mises, Hill48 and Barlat2000 to investigate the influence of the choice of the plasticity description on damage modelling. The Enhanced Lemaitre Model is used with Hill48. An inverse parameter identification strategy for a DP1000 based on stress-strain curves and optical strain measurements of shear, uniaxial, notch and (equi-)biaxial tension tests is applied to calibrate the models. A strong dependency of fracture strains on the choice of yield locus can be observed. The identified models are validated on a cross-die cup showing ductile fracture with slight necking.
Energy Technology Data Exchange (ETDEWEB)
Tan, Zeli [Pacific Northwest National Laboratory, Richland WA USA; Leung, L. Ruby [Pacific Northwest National Laboratory, Richland WA USA; Li, Hongyi [Montana State University, Bozeman MT USA; Tesfa, Teklu [Pacific Northwest National Laboratory, Richland WA USA; Vanmaercke, Matthias [Département de Géographie, Université de Liège, Liege Belgium; Poesen, Jean [Department of Earth and Environmental Sciences, Division of Geography, KU Leuven, Leuven Belgium; Zhang, Xuesong [Pacific Northwest National Laboratory, Richland WA USA; Lu, Hui [Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University, Beijing China; Hartmann, Jens [Institute for Geology, Center for Earth System Research and Sustainability, Universität Hamburg, Hamburg Germany
2017-12-01
Although sediment yield (SY) from water erosion is ubiquitous and its environmental consequences are well recognized, its impacts on the global carbon cycle remain largely uncertain. This knowledge gap is partly due to the lack of soil erosion modeling in Earth System Models (ESMs), which are important tools used to understand the global carbon cycle and explore its changes. This study analyzed sediment and particulate organic carbon yield (CY) data from 1081 and 38 small catchments (0.1-200 km27 ), respectively, in different environments across the globe. Using multiple statistical analysis techniques, we explored environmental factors and hydrological processes important for SY and CY modeling in ESMs. Our results show clear correlations of high SY with traditional agriculture, seismicity and heavy storms, as well as strong correlations between SY and annual peak runoff. These highlight the potential limitation of SY models that represent only interrill and rill erosion because shallow overland flow and rill flow have limited transport capacity due to their hydraulic geometry to produce high SY. Further, our results suggest that SY modeling in ESMs should be implemented at the event scale to produce the catastrophic mass transport during episodic events. Several environmental factors such as seismicity and land management that are often not considered in current catchment-scale SY models can be important in controlling global SY. Our analyses show that SY is likely the primary control on CY in small catchments and a statistically significant empirical relationship is established to calculate SY and CY jointly in ESMs.
International Nuclear Information System (INIS)
Linares-Rodriguez, Alvaro; Ruiz-Arias, José Antonio; Pozo-Vazquez, David; Tovar-Pescador, Joaquin
2013-01-01
An optimized artificial neural network ensemble model is built to estimate daily global solar radiation over large areas. The model uses clear-sky estimates and satellite images as input variables. Unlike most studies using satellite imagery based on visible channels, our model also exploits all information within infrared channels of the Meteosat 9 satellite. A genetic algorithm is used to optimize selection of model inputs, for which twelve are selected – eleven 3-km Meteosat 9 channels and one clear-sky term. The model is validated in Andalusia (Spain) from January 2008 through December 2008. Measured data from 83 stations across the region are used, 65 for training and 18 independent ones for testing the model. At the latter stations, the ensemble model yields an overall root mean square error of 6.74% and correlation coefficient of 99%; the generated estimates are relatively accurate and errors spatially uniform. The model yields reliable results even on cloudy days, improving on current models based on satellite imagery. - Highlights: • Daily solar radiation data are generated using an artificial neural network ensemble. • Eleven Meteosat channels observations and a clear sky term are used as model inputs. • Model exploits all information within infrared Meteosat channels. • Measured data for a year from 83 ground stations are used. • The proposed approach has better performance than existing models on daily basis
International Nuclear Information System (INIS)
Song, Weize; Jia, Haifeng; Liang, Shidong; Wang, Zheng; Liu, Shujie; Hao, Lizhuang; Chai, Shatuo
2014-01-01
Estimating forage biomass yield remotely from space is still challenging nowadays. Field experiments were conducted and ground measurements correlated to remote sensing data to estimate the forage biomass yield of Alpine-cold meadow grassland during the grass and grass-withering period in Sanjiangyuan area in Yushu county. Both Shapiro-Wilk and Kolmogorov-Smirnov two-tailed tests showed that the field training samples are normally distributed, the Spearman coefficient indicated that the parametric correlation analysis had significant differences. The optimal regression models were developed based on the Landsat Thematic Mapper Normalized Difference Vegetation Index (TM-NDVI) and the forage biomass field data during the grass and the grass-withering periods, respectively. Then an integration model was used to predict forage biomass yield of alpine-cold meadow in the grass-withering period. The model showed good prediction accuracy and reliability. It was found that this approach can not only estimate forage yield in large scale efficiently but also overcome the seasonal limitation of remote sensing inversion. This technique can provides valuable guidance to animal husbandry to resource more efficiently in winter
Directory of Open Access Journals (Sweden)
2007-01-01
Full Text Available The SHETRAN model for simulating the sediment yield arising from shallow landslides at the scale of a river catchment was applied to the 45-km2 Ijuez catchment in the central Spanish Pyrenees, to investigate the effect of loss of forest cover on landslide and debris flow incidence and on catchment sediment yield. The application demonstrated how such a model, with a large number of parameters to be evaluated, can be used even when directly measured data are not available: rainfall and discharge time series were generated by reference to other local records and data providing the basis for a soil map were obtained by a short field campaign. Uncertainty bounds for the outputs were determined as a function of the uncertainty in the values of key model parameters. For a four-year period and for the existing forested state of the catchment, a good ability to simulate the observed long term spatial distribution of debris flows (represented by a 45-year inventory and to determine catchment sediment yield within the range of regional observations was demonstrated. The lower uncertainty bound on simulated landslide occurrence approximated the observed annual rate of landsliding and suggests that landslides provide a relatively minor proportion of the total sediment yield, at least in drier years. A scenario simulation in which the forest cover was replaced by grassland indicated an increase in landsliding but a decrease in the number of landslides which evolve into debris flows and, at least for drier years, a reduction in sediment delivery to the channel network.
Volatility estimation using a rational GARCH model
Directory of Open Access Journals (Sweden)
Tetsuya Takaishi
2018-03-01
Full Text Available The rational GARCH (RGARCH model has been proposed as an alternative GARCHmodel that captures the asymmetric property of volatility. In addition to the previously proposedRGARCH model, we propose an alternative RGARCH model called the RGARCH-Exp model thatis more stable when dealing with outliers. We measure the performance of the volatility estimationby a loss function calculated using realized volatility as a proxy for true volatility and compare theRGARCH-type models with other asymmetric type models such as the EGARCH and GJR models.We conduct empirical studies of six stocks on the Tokyo Stock Exchange and find that a volatilityestimation using the RGARCH-type models outperforms the GARCH model and is comparable toother asymmetric GARCH models.
Estimation of curve number by DAWAST model
Energy Technology Data Exchange (ETDEWEB)
Kim, Tai Cheol; Park, Seung Ki; Moon, Jong Pil [Chungnam National University, Taejon (Korea, Republic of)
1997-10-31
It is one of the most important factors to determine the effective rainfall for estimation of flood hydrograph in design schedule. SCS curve number (CN) method has been frequently used to estimate the effective rainfall of synthesized design flood hydrograph for hydraulic structures. But, it should be cautious to apply SCS-CN originally developed in U.S.A to watersheds in Korea, because characteristics of watersheds in Korea and cropping patterns especially like a paddy land cultivation are quite different from those in USA. New CN method has been introduced. Maximum storage capacity which was herein defined as U{sub max} can be calibrated from the stream flow data and converted to new CN-I of driest condition of soil moisture in the given watershed. Effective rainfall for design flood hydrograph can be estimated by the curve number developed in the watersheds in Korea. (author). 14 refs., 5 tabs., 3 figs.
Grünhage, Ludger; Pleijel, Håkan; Mills, Gina; Bender, Jürgen; Danielsson, Helena; Lehmann, Yvonne; Castell, Jean-Francois; Bethenod, Olivier
2012-06-01
Field measurements and open-top chamber experiments using nine current European winter wheat cultivars provided a data set that was used to revise and improve the parameterisation of a stomatal conductance model for wheat, including a revised value for maximum stomatal conductance and new functions for phenology and soil moisture. For the calculation of stomatal conductance for ozone a diffusivity ratio between O(3) and H(2)O in air of 0.663 was applied, based on a critical review of the literature. By applying the improved parameterisation for stomatal conductance, new flux-effect relationships for grain yield, grain mass and protein yield were developed for use in ozone risk assessments including effects on food security. An example of application of the flux model at the local scale in Germany shows that negative effects of ozone on wheat grain yield were likely each year and on protein yield in most years since the mid 1980s. Copyright © 2012 Elsevier Ltd. All rights reserved.
Modelling and Forecasting of Rice Yield in support of Crop Insurance
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 .
Possible ecosystem impacts of applying maximum sustainable yield policy in food chain models.
Ghosh, Bapan; Kar, T K
2013-07-21
This paper describes the possible impacts of maximum sustainable yield (MSY) and maximum sustainable total yield (MSTY) policy in ecosystems. In general it is observed that exploitation at MSY (of single species) or MSTY (of multispecies) level may cause the extinction of several species. In particular, for traditional prey-predator system, fishing under combined harvesting effort at MSTY (if it exists) level may be a sustainable policy, but if MSTY does not exist then it is due to the extinction of the predator species only. In generalist prey-predator system, harvesting of any one of the species at MSY level is always a sustainable policy, but harvesting of both the species at MSTY level may or may not be a sustainable policy. In addition, we have also investigated the MSY and MSTY policy in a traditional tri-trophic and four trophic food chain models. Copyright © 2013 Elsevier Ltd. All rights reserved.
Lag space estimation in time series modelling
DEFF Research Database (Denmark)
Goutte, Cyril
1997-01-01
The purpose of this article is to investigate some techniques for finding the relevant lag-space, i.e. input information, for time series modelling. This is an important aspect of time series modelling, as it conditions the design of the model through the regressor vector a.k.a. the input layer...
Relevance of the Lin's and Host hydropedological models to predict grape yield and wine quality
Directory of Open Access Journals (Sweden)
E. A. C. Costantini
2009-09-01
Full Text Available The adoption of precision agriculture in viticulture could be greatly enhanced by the diffusion of straightforward and easy to be applied hydropedological models, able to predict the spatial variability of available soil water. The Lin's and Host hydropedological models were applied to standard soil series descriptions and hillslope position, to predict the distribution of hydrological functional units in two vineyard and their relevance for grape yield and wine quality. A three-years trial was carried out in Chianti (Central Italy on Sangiovese. The soils of the vineyards differentiated in structure, porosity and related hydropedological characteristics, as well as in salinity. Soil spatial variability was deeply affected by earth movement carried out before vine plantation. Six plots were selected in the different hydrological functional units of the two vineyards, that is, at summit, backslope and footslope morphological positions, to monitor soil hydrology, grape production and wine quality. Plot selection was based upon a cluster analysis of local slope, topographic wetness index (TWI, and cumulative moisture up to the root limiting layer, appreciated by means of a detailed combined geophysical survey. Water content, redox processes and temperature were monitored, as well as yield, phenological phases, and chemical analysis of grapes. The isotopic ratio δ^{13}C was measured in the wine ethanol upon harvesting to evaluate the degree of stress suffered by vines. The grapes in each plot were collected for wine making in small barrels. The wines obtained were analysed and submitted to a blind organoleptic testing.
The results demonstrated that the combined application of the two hydropedological models can be used for the prevision of the moisture status of soils cultivated with grape during summertime in Mediterranean climate. As correctly foreseen by the models, the amount of mean daily transpirable soil water (TSW during
Moise Famien, Adjoua; Defrance, Dimitri; Sultan, Benjamin; Janicot, Serge; Vrac, Mathieu
2017-04-01
Different CMIP exercises show that the simulations of the future/current temperature and precipitation are complex with a high uncertainty degree. For example, the African monsoon system is not correctly simulated and most of the CMIP5 models underestimate the precipitation. Therefore, Global Climate Models (GCMs) show significant systematic biases that require bias correction before it can be used in impacts studies. Several methods of bias corrections have been developed for several years and are increasingly using more complex statistical methods. The aims of this work is to show the interest of the CDFt (Cumulative Distribution Function transfom (Michelangeli et al.,2009)) method to reduce the data bias from 29 CMIP5 GCMs over Africa and to assess the impact of bias corrected data on crop yields prediction by the end of the 21st century. In this work, we apply the CDFt to daily data covering the period from 1950 to 2099 (Historical and RCP8.5) and we correct the climate variables (temperature, precipitation, solar radiation, wind) by the use of the new daily database from the EU project WATer and global CHange (WATCH) available from 1979 to 2013 as reference data. The performance of the method is assessed in several cases. First, data are corrected based on different calibrations periods and are compared, on one hand, with observations to estimate the sensitivity of the method to the calibration period and, on other hand, with another bias-correction method used in the ISIMIP project. We find that, whatever the calibration period used, CDFt corrects well the mean state of variables and preserves their trend, as well as daily rainfall occurrence and intensity distributions. However, some differences appear when compared to the outputs obtained with the method used in ISIMIP and show that the quality of the correction is strongly related to the reference data. Secondly, we validate the bias correction method with the agronomic simulations (SARRA-H model (Kouressy
Quantification of the specific yield in a two-layer hard-rock aquifer model
Durand, Véronique; Léonardi, Véronique; de Marsily, Ghislain; Lachassagne, Patrick
2017-08-01
Hard rock aquifers (HRA) have long been considered to be two-layer systems, with a mostly capacitive layer just below the surface, the saprolite layer, and a mainly transmissive layer underneath, the fractured layer. Although this hydrogeological conceptual model is widely accepted today within the scientific community, it is difficult to quantify the respective storage properties of each layer with an equivalent porous medium model. Based on an HRA field site, this paper attempts to quantify in a distinct manner the respective values of the specific yield (Sy) in the saprolite and the fractured layer, with the help of a deterministic hydrogeological model. The study site is the Plancoët migmatitic aquifer located in north-western Brittany, France, with piezometric data from 36 observation wells surveyed every two weeks for eight years. Whereas most of the piezometers (26) are located where the water table lies within the saprolite, thus representing the specific yield of the unconfined layer (Sy1), 10 of them are representative of the unconfined fractured layer (Sy2), due to their position where the saprolite is eroded or unsaturated. The two-layer model, based on field observations of the layer geometry, runs with the MODFLOW code. 81 values of the Sy1/Sy2 parameter sets were tested manually, as an inverse calibration was not able to calibrate these parameters. In order to calibrate the storage properties, a new quality-of-fit criterion called ;AdVar; was also developed, equal to the mean squared deviation of the seasonal piezometric amplitude variation. Contrary to the variance, AdVar is able to select the best values for the specific yield in each layer. It is demonstrated that the saprolite layer is about 2.5 times more capacitive than the fractured layer, with Sy1 = 10% (7% < Sy1 < 15%) against Sy2 = 2% (1% < Sy2 < 3%), in this particular example.
Mkoga, Z. J.; Tumbo, S. D.; Kihupi, N.; Semoka, J.
There is big effort to disseminate conservation tillage practices in Tanzania. Despite wide spread field demonstrations there has been some field experiments meant to assess and verify suitability of the tillage options in local areas. Much of the experiments are short lived and thus long term effects of the tillage options are unknown. Experiments to study long term effects of the tillage options are lacking because they are expensive and cannot be easily managed. Crop simulation models have the ability to use long term weather data and the local soil parameters to assess long term effects of the tillage practices. The Agricultural Production Systems Simulator (APSIM) crop simulation model; was used to simulate long term production series of soil moisture and grain yield based on the soil and weather conditions in Mkoji sub-catchment of the great Ruaha river basin in Tanzania. A 24 year simulated maize yield series based on conventional tillage with ox-plough, without surface crop residues (CT) treatment was compared with similar yield series based on conservation tillage (ox-ripping, with surface crop residues (RR)). Results showed that predicted yield averages were significantly higher in conservation tillage than in conventional tillage ( P APSIM simulation model, showed that average soil moisture in the conservation tillage was significantly higher ( P < 0.05) (about 0.29 mm/mm) than in conventional tillage (0.22 mm/mm) treatment during the seasons which received rainfall between 468 and 770 mm. Similarly the conservation tillage treatment recorded significantly higher yields (4.4 t/ha) ( P < 0.01) than the conventional tillage (3.6 t/ha) treatment in the same range of seasonal rainfall. On the other hand there was no significant difference in soil moisture for the seasons which received rainfall above 770 mm. In these seasons grain yield in conservation tillage treatment was significantly lower (3.1 kg/ha) than in the conventional tillage treatment (4.8 kg
Verhoef, A.; Punalekar, S.; Quaife, T. L.; Humphries, D.; Reynolds, C.
2017-12-01
Currently, 30% of the world's land area is covered by permanent pasture. Grazing ruminants convert forage materials into milk and meat for human consumption; ruminant production is a key agricultural enterprise. Management of pasture farms (nutrient and herbi-/pesticides application, grazing rotations) is often suboptimal. Furthermore, adverse weather can have negative effects on pasture growth and quality. Near real-time herbage monitoring and prediction could help improve farm profitability. While the use of remote sensing (RS) in the context of arable crop growth prediction is becoming more established, the same is not true for pasture. However, recently launched Sentinel satellites offer real opportunities to exploit high spatio-temporal resolution datasets for effective monitoring of pastures, as well as crops. A perennial grazed ryegrass field in the Southwest of the UK was monitored regularly using field hyperspectral spectro-radiometers. Simultaneously, leaf area index (LAI) was measured using a ceptometer, and yield was measured, indirectly using a `plate meter' and directly by destructive sampling. Two sets of spectral data were used to retrieve LAI with the PROSAIL radiative transfer model: (i) Sentinel-2A bands convolved from field spectral data, (ii) actual Sentinel-2A image pixels for the sampling plots. Retrieved LAI was compared against field observations. LAI estimates were assimilated in a bespoke growth model (including grazing and management), driven by weather data, for calibration of sensitive parameters using a 4D-Var scheme, to obtain pasture biomass. The developed approach was used to study a pasture farm in the South of the UK, for which a large number of Sentinel-2A images were available throughout 2016-17. Retrieved LAI compared well with in-situ LAI, and significantly improved yield estimates. The calibrated model parameters compared well with literature values. The model, guided by satellite data and general information on farm
Gao, Xiaoyu; Huo, Zailin; Qu, Zhongyi; Xu, Xu; Huang, Guanhua; Steenhuis, Tammo S
2017-02-21
Capillary rise from shallow groundwater can decrease the need for irrigation water. However, simple techniques do not exist to quantify the contribution of capillary flux to crop water use. In this study we develop the Agricultural Water Productivity Model for Shallow Groundwater (AWPM-SG) for calculating capillary fluxes from shallow groundwater using readily available data. The model combines an analytical solution of upward flux from groundwater with the EPIC crop growth model. AWPM-SG was calibrated and validated with 2-year lysimetric experiment with maize. Predicted soil moisture, groundwater depth and leaf area index agreed with the observations. To investigate the response of model, various scenarios were run in which the irrigation amount and groundwater depth were varied. Simulations shows that at groundwater depth of 1 m capillary upward supplied 41% of the evapotranspiration. This reduced to 6% at groundwater depth of 2 m. The yield per unit water consumed (water productivity) was nearly constant for 2.3 kg/m 3 . The yield per unit water applied (irrigation water productivity) increased with decreasing irrigation water because capillary rise made up in part for the lack of irrigation water. Consequently, using AWPM-SG in irrigation scheduling will be beneficial to save more water in areas with shallow groundwater.
Extreme Quantile Estimation in Binary Response Models
1990-03-01
in Cancer Research," Biometria , VoL 66, pp. 307-316. Hsi, B.P. [1969], ’The Multiple Sample Up-and-Down Method in Bioassay," Journal of the American...New Method of Estimation," Biometria , VoL 53, pp. 439-454. Wetherill, G.B. [1976], Sequential Methods in Statistics, London: Chapman and Hall. Wu, C.FJ
Robust Estimation and Forecasting of the Capital Asset Pricing Model
G. Bian (Guorui); M.J. McAleer (Michael); W.-K. Wong (Wing-Keung)
2013-01-01
textabstractIn this paper, we develop a modified maximum likelihood (MML) estimator for the multiple linear regression model with underlying student t distribution. We obtain the closed form of the estimators, derive the asymptotic properties, and demonstrate that the MML estimator is more
Robust Estimation and Forecasting of the Capital Asset Pricing Model
G. Bian (Guorui); M.J. McAleer (Michael); W.-K. Wong (Wing-Keung)
2010-01-01
textabstractIn this paper, we develop a modified maximum likelihood (MML) estimator for the multiple linear regression model with underlying student t distribution. We obtain the closed form of the estimators, derive the asymptotic properties, and demonstrate that the MML estimator is more
Efficient estimation of an additive quantile regression model
Cheng, Y.; de Gooijer, J.G.; Zerom, D.
2009-01-01
In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). By
Efficient estimation of an additive quantile regression model
Cheng, Y.; de Gooijer, J.G.; Zerom, D.
2010-01-01
In this paper two kernel-based nonparametric estimators are proposed for estimating the components of an additive quantile regression model. The first estimator is a computationally convenient approach which can be viewed as a viable alternative to the method of De Gooijer and Zerom (2003). By
Performances Of Estimators Of Linear Models With Autocorrelated ...
African Journals Online (AJOL)
The performances of five estimators of linear models with Autocorrelated error terms are compared when the independent variable is autoregressive. The results reveal that the properties of the estimators when the sample size is finite is quite similar to the properties of the estimators when the sample size is infinite although ...
Maximum likelihood estimation of finite mixture model for economic data
Phoong, Seuk-Yen; Ismail, Mohd Tahir
2014-06-01
Finite mixture model is a mixture model with finite-dimension. This models are provides a natural representation of heterogeneity in a finite number of latent classes. In addition, finite mixture models also known as latent class models or unsupervised learning models. Recently, maximum likelihood estimation fitted finite mixture models has greatly drawn statistician's attention. The main reason is because maximum likelihood estimation is a powerful statistical method which provides consistent findings as the sample sizes increases to infinity. Thus, the application of maximum likelihood estimation is used to fit finite mixture model in the present paper in order to explore the relationship between nonlinear economic data. In this paper, a two-component normal mixture model is fitted by maximum likelihood estimation in order to investigate the relationship among stock market price and rubber price for sampled countries. Results described that there is a negative effect among rubber price and stock market price for Malaysia, Thailand, Philippines and Indonesia.
Crash data modeling with a generalized estimator.
Ye, Zhirui; Xu, Yueru; Lord, Dominique
2018-05-11
The investigation of relationships between traffic crashes and relevant factors is important in traffic safety management. Various methods have been developed for modeling crash data. In real world scenarios, crash data often display the characteristics of over-dispersion. However, on occasions, some crash datasets have exhibited under-dispersion, especially in cases where the data are conditioned upon the mean. The commonly used models (such as the Poisson and the NB regression models) have associated limitations to cope with various degrees of dispersion. In light of this, a generalized event count (GEC) model, which can be generally used to handle over-, equi-, and under-dispersed data, is proposed in this study. This model was first applied to case studies using data from Toronto, characterized by over-dispersion, and then to crash data from railway-highway crossings in Korea, characterized with under-dispersion. The results from the GEC model were compared with those from the Negative binomial and the hyper-Poisson models. The cases studies show that the proposed model provides good performance for crash data characterized with over- and under-dispersion. Moreover, the proposed model simplifies the modeling process and the prediction of crash data. Copyright © 2018 Elsevier Ltd. All rights reserved.
Energy Technology Data Exchange (ETDEWEB)
Freund, H.; Walters, C.C.; Kelemen, S.R.; Siskin, M.; Gorbaty, M.L.; Curry, D.J.; Bence, A.E. [ExxonMobil Research & Engineering Co., Annandale, NJ (United States)
2007-07-01
We have developed a method to calculate the amounts and composition of products resulting from the thermal decomposition of a solid complex carbonaceous material. This procedure provides a means of using laboratory measurements of complex carbonaceous solids to construct a representative model of its chemical structure (CS) that is then coupled with elementary reaction pathways to predict the chemical yield (CY) upon thermal decomposition. Data from elemental analysis, H, N, O, S, solid state {sup 13}C NMR, X-ray photoelectron spectroscopy (XPS), sulfur X-ray absorption structure spectroscopy (XANES), and pyrolysis-gas chromatography (GC) are used to constrain the construction of core molecular structures representative of the complex carbonaceous material. These core structures are expanded stochastically to describe large macromolecules ({gt} 10{sup 6} cores with similar to 10{sup 6} atoms) with bulk properties that match the experimental results. Gas, liquid and solid product yields, resulting from thermal decomposition, are calculated by identifying reactive functional groups within the CS stochastic ensemble and imposing a reaction network constrained by fundamental thermodynamics and kinetics. An expulsion model is added to the decomposition model to calculate the chemical products in open and closed systems. Product yields may then be predicted under a wide range of time-temperature conditions used in rapid laboratory pyrolysis experiments, refinery processes, or geologic maturation.
Soms, Juris
2015-04-01
The accelerated soil erosion by water and associated fine sediment transfer in river catchments has various negative environmental as well as economic implications in many EU countries. Hence, the scientific community had recognized and ranked soil erosion among other environmental problems. Moreover, these matters might worsen in the near future in the countries of the Baltic Region, e.g. Latvia considering the predicted climate changes - more precisely, the increase in precipitation and shortening of return periods of extreme rainfall events, which in their turn will enable formation of surface runoff, erosion and increase of sediment delivery to receiving streams. Thereby it is essential to carry out studies focused on these issues in order to obtain reliable data in terms of both scientific and applied aims, e.g. environmental protection and sustainable management of soils as well as water resources. During the past decades, many of such studies of soil erosion had focused on the application of modelling techniques implemented in a GIS environment, allowing indirectly to estimate the potential soil losses and to quantify related sediment yield. According to research results published in the scientific literature, this approach currently is widely used all over the world, and most of these studies are based on the USLE model and its revised and modified versions. Considering that, the aim of this research was to estimate soil erosion rates and sediment transport under different hydro-climatic conditions in south-eastern Latvia by application of GIS-based modelling. For research purposes, empirical RUSLE model and ArcGIS software were applied, and five headwater catchments were chosen as model territories. The selected catchments with different land use are located in the Daugava spillway valley, which belongs to the upper Daugava River drainage basin. Considering lithological diversity of Quaternary deposits, a variety of soils can be identified, i.e., Stagnic
Jaszczuk, Marek; Pawlikowski, Arkadiusz
2017-12-01
units giving consideration to the load of the caving shield, a model of support unit was used that allows for unequivocal determination of the yielding capacity of the support with consideration given to the height of the unit in use and the change in the inclination of the canopy resulting from the displacement of the roof of the longwall. The yielding capacity of the support unit and its point of application on the canopy was determined using the method of units which allows for the internal forces to be manifested. The weight of the rock mass depends on the geological and mining conditions, for which the shape and dimensions of the rock mass affecting the support unit are determined. The resultant force of the pressure of gob on the gob shield was calculated by assuming that the load may be understood as a pressure of ground on a wall. This required the specification of the volume of the fallen rocks that affect the unit of powered roof supports (Fig. 2). To determine the support of the roof rock mass by the coal seam, experience of the Australian mining industry was used. Experiments regarding the strength properties of coal have exhibited that vertical deformation, at which the highest seam reaction occurs while supporting the roof rock mass, amounts to 0.5% of the longwall's height. The measure of the width of the contact area between the rock mass and the seam is the width of the additional uncovering of the face roof due to spalling of seam topcorners da (Fig. 2). With the above parameters and the value of the modulus of elasticity of coal in mind, the value of the seam's reaction may be estimated using the dependence (2). The vertical component of the goafs' reaction may be determined based on the strength characteristics of the fallen roof, the contact area of the rock mass with the fallen roof and the mean strain of the fallen roof at the area of contact. In the work by Pawlikowski (2014), a research procedure was proposed which encompasses model tests and
Klamt, Steffen; Müller, Stefan; Regensburger, Georg; Zanghellini, Jürgen
2018-02-07
The optimization of metabolic rates (as linear objective functions) represents the methodical core of flux-balance analysis techniques which have become a standard tool for the study of genome-scale metabolic models. Besides (growth and synthesis) rates, metabolic yields are key parameters for the characterization of biochemical transformation processes, especially in the context of biotechnological applications. However, yields are ratios of rates, and hence the optimization of yields (as nonlinear objective functions) under arbitrary linear constraints is not possible with current flux-balance analysis techniques. Despite the fundamental importance of yields in constraint-based modeling, a comprehensive mathematical framework for yield optimization is still missing. We present a mathematical theory that allows one to systematically compute and analyze yield-optimal solutions of metabolic models under arbitrary linear constraints. In particular, we formulate yield optimization as a linear-fractional program. For practical computations, we transform the linear-fractional yield optimization problem to a (higher-dimensional) linear problem. Its solutions determine the solutions of the original problem and can be used to predict yield-optimal flux distributions in genome-scale metabolic models. For the theoretical analysis, we consider the linear-fractional problem directly. Most importantly, we show that the yield-optimal solution set (like the rate-optimal solution set) is determined by (yield-optimal) elementary flux vectors of the underlying metabolic model. However, yield- and rate-optimal solutions may differ from each other, and hence optimal (biomass or product) yields are not necessarily obtained at solutions with optimal (growth or synthesis) rates. Moreover, we discuss phase planes/production envelopes and yield spaces, in particular, we prove that yield spaces are convex and provide algorithms for their computation. We illustrate our findings by a small
Energy Technology Data Exchange (ETDEWEB)
Celler, A; Hou, X [University of British Columbia, Vancouver, BC, Canada, (Canada); Benard, F; Ruth, T, E-mail: aceller@physics.ubc.ca, E-mail: xinchi@phas.ubc.ca, E-mail: fbenard@bccrc.ca, E-mail: truth@triumf.ca [BC Cancer Agency, Vancouver, BC (Canada)
2011-09-07
Recent acute shortage of medical radioisotopes prompted investigations into alternative methods of production and the use of a cyclotron and {sup 100}Mo(p,2n){sup 99m}Tc reaction has been considered. In this context, the production yields of {sup 99m}Tc and various other radioactive and stable isotopes which will be created in the process have to be investigated, as these may affect the diagnostic outcome and radiation dosimetry in human studies. Reaction conditions (beam and target characteristics, and irradiation and cooling times) need to be optimized in order to maximize the amount of {sup 99m}Tc and minimize impurities. Although ultimately careful experimental verification of these conditions must be performed, theoretical calculations can provide the initial guidance allowing for extensive investigations at little cost. We report the results of theoretically determined reaction yields for {sup 99m}Tc and other radioactive isotopes created when natural and enriched molybdenum targets are irradiated by protons. The cross-section calculations were performed using a computer program EMPIRE for the proton energy range 6-30 MeV. A computer graphical user interface for automatic calculation of production yields taking into account various reaction channels leading to the same final product has been created. The proposed approach allows us to theoretically estimate the amount of {sup 99m}Tc and its ratio relative to {sup 99g}Tc and other radioisotopes which must be considered reaction contaminants, potentially contributing to additional patient dose in diagnostic studies.
Characterization of yield reduction in Ethiopia using a GIS-based crop water balance model
Senay, G.B.; Verdin, J.
2003-01-01
In many parts of sub-Saharan Africa, subsistence agriculture is characterized by significant fluctuations in yield and production due to variations in moisture availability to staple crops. Widespread drought can lead to crop failures, with associated deterioration in food security. Ground data collection networks are sparse, so methods using geospatial rainfall estimates derived from satellite and gauge observations, where available, have been developed to calculate seasonal crop water balances. Using conventional crop production data for 4 years in Ethiopia (1996-1999), it was found that water-limited and water-unlimited growing regions can be distinguished. Furthermore, maize growing conditions are also indicative of conditions for sorghum. However, another major staple, teff, was found to behave sufficiently differently from maize to warrant studies of its own.
Modeling and Parameter Estimation of a Small Wind Generation System
Directory of Open Access Journals (Sweden)
Carlos A. Ramírez Gómez
2013-11-01
Full Text Available The modeling and parameter estimation of a small wind generation system is presented in this paper. The system consists of a wind turbine, a permanent magnet synchronous generator, a three phase rectifier, and a direct current load. In order to estimate the parameters wind speed data are registered in a weather station located in the Fraternidad Campus at ITM. Wind speed data were applied to a reference model programed with PSIM software. From that simulation, variables were registered to estimate the parameters. The wind generation system model together with the estimated parameters is an excellent representation of the detailed model, but the estimated model offers a higher flexibility than the programed model in PSIM software.
Applicability of models to estimate traffic noise for urban roads.
Melo, Ricardo A; Pimentel, Roberto L; Lacerda, Diego M; Silva, Wekisley M
2015-01-01
Traffic noise is a highly relevant environmental impact in cities. Models to estimate traffic noise, in turn, can be useful tools to guide mitigation measures. In this paper, the applicability of models to estimate noise levels produced by a continuous flow of vehicles on urban roads is investigated. The aim is to identify which models are more appropriate to estimate traffic noise in urban areas since several models available were conceived to estimate noise from highway traffic. First, measurements of traffic noise, vehicle count and speed were carried out in five arterial urban roads of a brazilian city. Together with geometric measurements of width of lanes and distance from noise meter to lanes, these data were input in several models to estimate traffic noise. The predicted noise levels were then compared to the respective measured counterparts for each road investigated. In addition, a chart showing mean differences in noise between estimations and measurements is presented, to evaluate the overall performance of the models. Measured Leq values varied from 69 to 79 dB(A) for traffic flows varying from 1618 to 5220 vehicles/h. Mean noise level differences between estimations and measurements for all urban roads investigated ranged from -3.5 to 5.5 dB(A). According to the results, deficiencies of some models are discussed while other models are identified as applicable to noise estimations on urban roads in a condition of continuous flow. Key issues to apply such models to urban roads are highlighted.
Conditional density estimation using fuzzy GARCH models
Almeida, R.J.; Bastürk, N.; Kaymak, U.; Costa Sousa, da J.M.; Kruse, R.; Berthold, M.R.; Moewes, C.; Gil, M.A.; Grzegorzewski, P.; Hryniewicz, O.
2013-01-01
Abstract. Time series data exhibits complex behavior including non-linearity and path-dependency. This paper proposes a flexible fuzzy GARCH model that can capture different properties of data, such as skewness, fat tails and multimodality in one single model. Furthermore, additional information and
Van't Hoog, Anna H; Onozaki, Ikushi; Lonnroth, Knut
2014-10-19
To inform the choice of an appropriate screening and diagnostic algorithm for tuberculosis (TB) screening initiatives in different epidemiological settings, we compare algorithms composed of currently available methods. Of twelve algorithms composed of screening for symptoms (prolonged cough or any TB symptom) and/or chest radiography abnormalities, and either sputum-smear microscopy (SSM) or Xpert MTB/RIF (XP) as confirmatory test we model algorithm outcomes and summarize the yield, number needed to screen (NNS) and positive predictive value (PPV) for different levels of TB prevalence. Screening for prolonged cough has low yield, 22% if confirmatory testing is by SSM and 32% if XP, and a high NNS, exceeding 1000 if TB prevalence is ≤0.5%. Due to low specificity the PPV of screening for any TB symptom followed by SSM is less than 50%, even if TB prevalence is 2%. CXR screening for TB abnormalities followed by XP has the highest case detection (87%) and lowest NNS, but is resource intensive. CXR as a second screen for symptom screen positives improves efficiency. The ideal algorithm does not exist. The choice will be setting specific, for which this study provides guidance. Generally an algorithm composed of CXR screening followed by confirmatory testing with XP can achieve the lowest NNS and highest PPV, and is the least amenable to setting-specific variation. However resource requirements for tests and equipment may be prohibitive in some settings and a reason to opt for symptom screening and SSM. To better inform disease control programs we need empirical data to confirm the modeled yield, cost-effectiveness studies, transmission models and a better screening test.
International Nuclear Information System (INIS)
Ramarohetra, Johanna; Pohl, Benjamin; Sultan, Benjamin
2015-01-01
The challenge of estimating the potential impacts of climate change has led to an increasing use of dynamical downscaling to produce fine spatial-scale climate projections for impact assessments. In this work, we analyze if and to what extent the bias in the simulated crop yield can be reduced by using the Weather Research and Forecasting (WRF) regional climate model to downscale ERA-Interim (European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis) rainfall and radiation data. Then, we evaluate the uncertainties resulting from both the choice of the physical parameterizations of the WRF model and its internal variability. Impact assessments were performed at two sites in Sub-Saharan Africa and by using two crop models to simulate Niger pearl millet and Benin maize yields. We find that the use of the WRF model to downscale ERA-Interim climate data generally reduces the bias in the simulated crop yield, yet this reduction in bias strongly depends on the choices in the model setup. Among the physical parameterizations considered, we show that the choice of the land surface model (LSM) is of primary importance. When there is no coupling with a LSM, or when the LSM is too simplistic, the simulated precipitation and then the simulated yield are null, or respectively very low; therefore, coupling with a LSM is necessary. The convective scheme is the second most influential scheme for yield simulation, followed by the shortwave radiation scheme. The uncertainties related to the internal variability of the WRF model are also significant and reach up to 30% of the simulated yields. These results suggest that regional models need to be used more carefully in order to improve the reliability of impact assessments. (letter)
Estimation of a multivariate mean under model selection uncertainty
Directory of Open Access Journals (Sweden)
Georges Nguefack-Tsague
2014-05-01
Full Text Available Model selection uncertainty would occur if we selected a model based on one data set and subsequently applied it for statistical inferences, because the "correct" model would not be selected with certainty. When the selection and inference are based on the same dataset, some additional problems arise due to the correlation of the two stages (selection and inference. In this paper model selection uncertainty is considered and model averaging is proposed. The proposal is related to the theory of James and Stein of estimating more than three parameters from independent normal observations. We suggest that a model averaging scheme taking into account the selection procedure could be more appropriate than model selection alone. Some properties of this model averaging estimator are investigated; in particular we show using Stein's results that it is a minimax estimator and can outperform Stein-type estimators.
A nonparametric mixture model for cure rate estimation.
Peng, Y; Dear, K B
2000-03-01
Nonparametric methods have attracted less attention than their parametric counterparts for cure rate analysis. In this paper, we study a general nonparametric mixture model. The proportional hazards assumption is employed in modeling the effect of covariates on the failure time of patients who are not cured. The EM algorithm, the marginal likelihood approach, and multiple imputations are employed to estimate parameters of interest in the model. This model extends models and improves estimation methods proposed by other researchers. It also extends Cox's proportional hazards regression model by allowing a proportion of event-free patients and investigating covariate effects on that proportion. The model and its estimation method are investigated by simulations. An application to breast cancer data, including comparisons with previous analyses using a parametric model and an existing nonparametric model by other researchers, confirms the conclusions from the parametric model but not those from the existing nonparametric model.
A simulation of water pollution model parameter estimation
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.
Modelling climate change impacts on viticultural yield, phenology and stress conditions in Europe.
Fraga, Helder; García de Cortázar Atauri, Iñaki; Malheiro, Aureliano C; Santos, João A
2016-11-01
Viticulture is a key socio-economic sector in Europe. Owing to the strong sensitivity of grapevines to atmospheric factors, climate change may represent an important challenge for this sector. This study analyses viticultural suitability, yield, phenology, and water and nitrogen stress indices in Europe, for present climates (1980-2005) and future (2041-2070) climate change scenarios (RCP4.5 and 8.5). The STICS crop model is coupled with climate, soil and terrain databases, also taking into account CO 2 physiological effects, and simulations are validated against observational data sets. A clear agreement between simulated and observed phenology, leaf area index, yield and water and nitrogen stress indices, including the spatial differences throughout Europe, is shown. The projected changes highlight an extension of the climatic suitability for grapevines up to 55°N, which may represent the emergence of new winemaking regions. Despite strong regional heterogeneity, mean phenological timings (budburst, flowering, veraison and harvest) are projected to undergo significant advancements (e.g. budburst/harvest can be >1 month earlier), with implications also in the corresponding phenophase intervals. Enhanced dryness throughout Europe is also projected, with severe water stress over several regions in southern regions (e.g. southern Iberia and Italy), locally reducing yield and leaf area. Increased atmospheric CO 2 partially offsets dryness effects, promoting yield and leaf area index increases in central/northern Europe. Future biomass changes may lead to modifications in nitrogen demands, with higher stress in northern/central Europe and weaker stress in southern Europe. These findings are critical decision support systems for stakeholders from the European winemaking sector. © 2016 John Wiley & Sons Ltd.
Incorporating Yearly Derived Winter Wheat Maps Into Winter Wheat Yield Forecasting Model
Skakun, S.; Franch, B.; Roger, J.-C.; Vermote, E.; Becker-Reshef, I.; Justice, C.; Santamaría-Artigas, A.
2016-01-01
Wheat is one of the most important cereal crops in the world. Timely and accurate forecast of wheat yield and production at global scale is vital in implementing food security policy. Becker-Reshef et al. (2010) developed a generalized empirical model for forecasting winter wheat production using remote sensing data and official statistics. This model was implemented using static wheat maps. In this paper, we analyze the impact of incorporating yearly wheat masks into the forecasting model. We propose a new approach of producing in season winter wheat maps exploiting satellite data and official statistics on crop area only. Validation on independent data showed that the proposed approach reached 6% to 23% of omission error and 10% to 16% of commission error when mapping winter wheat 2-3 months before harvest. In general, we found a limited impact of using yearly winter wheat masks over a static mask for the study regions.
A distribution-free newsvendor model with balking penalty and random yield
Directory of Open Access Journals (Sweden)
Chongfeng Lan
2015-05-01
Full Text Available Purpose: The purpose of this paper is to extend the analysis of the distribution-free newsvendor problem in an environment of customer balking, which occurs when customers are reluctant to buy a product if its available inventory falls below a threshold level. Design/methodology/approach: We provide a new tradeoff tool as a replacement of the traditional one to weigh the holding cost and the goodwill costs segment: in addition to the shortage penalty, we also introduce the balking penalty. Furthermore, we extend our model to the case of random yield. Findings: A model is presented for determining both an optimal order quantity and a lower bound on the profit under the worst possible distribution of the demand. We also study the effects of shortage penalty and the balking penalty on the optimal order quantity, which have been largely bypassed in the existing distribution free single period models with balking. Numerical examples are presented to illustrate the result. Originality/value: The incorporation of balking penalty and random yield represents an important improvement in inventory policy performance for distribution-free newsvendor problem when customer balking occurs and the distributional form of demand is unknown.
Mrg: A Magnitude Scale for 1 s Rayleigh Waves at Local Distances with Focus on Yield Estimation
2016-08-23
definition for filtering Rg near 1 s period at distances between 2-100 km. We used the new formula to estimate MRg for 39 small (37 ≤ Y ≤ 12,270 kg TNT...the Butterworth filter order (Russell, 2006). Filter Definition . To estimate the time-domain amplitude of Rg, we filter the time series with a 2nd... phenomenology experiments, including the Arizona Source Phenomenology Experiment (AZ-SPE), Bighorn Active Seismic Experiment (BASE), the HUMMING ALBATROS
Estimating High-Dimensional Time Series Models
DEFF Research Database (Denmark)
Medeiros, Marcelo C.; Mendes, Eduardo F.
We study the asymptotic properties of the Adaptive LASSO (adaLASSO) in sparse, high-dimensional, linear time-series models. We assume both the number of covariates in the model and candidate variables can increase with the number of observations and the number of candidate variables is, possibly......, larger than the number of observations. We show the adaLASSO consistently chooses the relevant variables as the number of observations increases (model selection consistency), and has the oracle property, even when the errors are non-Gaussian and conditionally heteroskedastic. A simulation study shows...
Optimal covariance selection for estimation using graphical models
Vichik, Sergey; Oshman, Yaakov
2011-01-01
We consider a problem encountered when trying to estimate a Gaussian random field using a distributed estimation approach based on Gaussian graphical models. Because of constraints imposed by estimation tools used in Gaussian graphical models, the a priori covariance of the random field is constrained to embed conditional independence constraints among a significant number of variables. The problem is, then: given the (unconstrained) a priori covariance of the random field, and the conditiona...
Temporal rainfall estimation using input data reduction and model inversion
Wright, A. J.; Vrugt, J. A.; Walker, J. P.; Pauwels, V. R. N.
2016-12-01
Floods are devastating natural hazards. To provide accurate, precise and timely flood forecasts there is a need to understand the uncertainties associated with temporal rainfall and model parameters. The estimation of temporal rainfall and model parameter distributions from streamflow observations in complex dynamic catchments adds skill to current areal rainfall estimation methods, allows for the uncertainty of rainfall input to be considered when estimating model parameters and provides the ability to estimate rainfall from poorly gauged catchments. Current methods to estimate temporal rainfall distributions from streamflow are unable to adequately explain and invert complex non-linear hydrologic systems. This study uses the Discrete Wavelet Transform (DWT) to reduce rainfall dimensionality for the catchment of Warwick, Queensland, Australia. The reduction of rainfall to DWT coefficients allows the input rainfall time series to be simultaneously estimated along with model parameters. The estimation process is conducted using multi-chain Markov chain Monte Carlo simulation with the DREAMZS algorithm. The use of a likelihood function that considers both rainfall and streamflow error allows for model parameter and temporal rainfall distributions to be estimated. Estimation of the wavelet approximation coefficients of lower order decomposition structures was able to estimate the most realistic temporal rainfall distributions. These rainfall estimates were all able to simulate streamflow that was superior to the results of a traditional calibration approach. It is shown that the choice of wavelet has a considerable impact on the robustness of the inversion. The results demonstrate that streamflow data contains sufficient information to estimate temporal rainfall and model parameter distributions. The extent and variance of rainfall time series that are able to simulate streamflow that is superior to that simulated by a traditional calibration approach is a
Estimating Lead (Pb) Bioavailability In A Mouse Model
Children are exposed to Pb through ingestion of Pb-contaminated soil. Soil Pb bioavailability is estimated using animal models or with chemically defined in vitro assays that measure bioaccessibility. However, bioavailability estimates in a large animal model (e.g., swine) can be...
Estimating Dynamic Equilibrium Models using Macro and Financial Data
DEFF Research Database (Denmark)
Christensen, Bent Jesper; Posch, Olaf; van der Wel, Michel
We show that including financial market data at daily frequency, along with macro series at standard lower frequency, facilitates statistical inference on structural parameters in dynamic equilibrium models. Our continuous-time formulation conveniently accounts for the difference in observation...... of the estimators and estimate the model using 20 years of U.S. macro and financial data....
mathematical models for estimating radio channels utilization
African Journals Online (AJOL)
2017-08-08
Aug 8, 2017 ... Mathematical models for radio channels utilization assessment by real-time flows transfer in ... data transmission networks application having dynamic topology ..... Journal of Applied Mathematics and Statistics, 56(2): 85–90.
Linear Regression Models for Estimating True Subsurface ...
Indian Academy of Sciences (India)
47
The objective is to minimize the processing time and computer memory required. 10 to carry out inversion .... to the mainland by two long bridges. .... term. In this approach, the model converges when the squared sum of the differences. 143.
Seo, Seongwon; Hwang, Yongwoo
1999-08-01
Construction and demolition (C&D) debris is generated at the site of various construction activities. However, the amount of the debris is usually so large that it is necessary to estimate the amount of C&D debris as accurately as possible for effective waste management and control in urban areas. In this paper, an effective estimation method using a statistical model was proposed. The estimation process was composed of five steps: estimation of the life span of buildings; estimation of the floor area of buildings to be constructed and demolished; calculation of individual intensity units of C&D debris; and estimation of the future C&D debris production. This method was also applied in the city of Seoul as an actual case, and the estimated amount of C&D debris in Seoul in 2021 was approximately 24 million tons. Of this total amount, 98% was generated by demolition, and the main components of debris were concrete and brick.
International Nuclear Information System (INIS)
Manjunatha, H.C.; Sowmya, N.
2013-01-01
X-rays may be produced following the excitation of target atoms induced by an energetic incident ion beam of protons. Proton induced X-ray emission (PIXE) analysis has been used for many years for the determination of elemental composition of materials using X-rays. Recent interest in the proton induced X-ray emission cross section has arisen due to their importance in the rapidly expanding field of PIXE analysis. One of the steps in the analysis is to fit the measured X-ray spectrum with theoretical spectrum. The theoretical cross section and yields are essential for the evaluation of spectrum. We have theoretically evaluated the PIXE cross sections for trace elements in the lung and breast cancer tissues such as Cl, K, Ca,Ti, Cr, Mn, Fe, Ni, Cu, Zn, As, Se, Br, Rb, P, S, Sr, Hg and Pb. The estimated cross section is used in the evaluation of Proton induced X-ray emission spectrum for the given trace elements.We have also evaluated the Proton induced X-ray emission yields in the thin and thick target of the given trace elements. The evaluated Proton induced X-ray emission cross-section, spectrum and yields are graphically represented. Some of these values are also tabulated. Proton induced X-ray emission cross sections and a yield for the given trace elements varies with the energy. PIXE yield depends on a real density and does not on thickness of the target. (author)
DEFF Research Database (Denmark)
Nielsen, Jesper Ellerbæk; Thorndahl, Søren Liedtke; Rasmussen, Michael R.
2011-01-01
Distributed weather radar precipitation measurements are used as rainfall input for an urban drainage model, to simulate the runoff from a small catchment of Denmark. It is demonstrated how the Generalized Likelihood Uncertainty Estimation (GLUE) methodology can be implemented and used to estimate...
An individual reproduction model sensitive to milk yield and body condition in Holstein dairy cows.
Brun-Lafleur, L; Cutullic, E; Faverdin, P; Delaby, L; Disenhaus, C
2013-08-01
To simulate the consequences of management in dairy herds, the use of individual-based herd models is very useful and has become common. Reproduction is a key driver of milk production and herd dynamics, whose influence has been magnified by the decrease in reproductive performance over the last decades. Moreover, feeding management influences milk yield (MY) and body reserves, which in turn influence reproductive performance. Therefore, our objective was to build an up-to-date animal reproduction model sensitive to both MY and body condition score (BCS). A dynamic and stochastic individual reproduction model was built mainly from data of a single recent long-term experiment. This model covers the whole reproductive process and is composed of a succession of discrete stochastic events, mainly calving, ovulations, conception and embryonic loss. Each reproductive step is sensitive to MY or BCS levels or changes. The model takes into account recent evolutions of reproductive performance, particularly concerning calving-to-first ovulation interval, cyclicity (normal cycle length, prevalence of prolonged luteal phase), oestrus expression and pregnancy (conception, early and late embryonic loss). A sensitivity analysis of the model to MY and BCS at calving was performed. The simulated performance was compared with observed data from the database used to build the model and from the bibliography to validate the model. Despite comprising a whole series of reproductive steps, the model made it possible to simulate realistic global reproduction outputs. It was able to well simulate the overall reproductive performance observed in farms in terms of both success rate (recalving rate) and reproduction delays (calving interval). This model has the purpose to be integrated in herd simulation models to usefully test the impact of management strategies on herd reproductive performance, and thus on calving patterns and culling rates.
Ballistic model to estimate microsprinkler droplet distribution
Directory of Open Access Journals (Sweden)
Conceição Marco Antônio Fonseca
2003-01-01
Full Text Available Experimental determination of microsprinkler droplets is difficult and time-consuming. This determination, however, could be achieved using ballistic models. The present study aimed to compare simulated and measured values of microsprinkler droplet diameters. Experimental measurements were made using the flour method, and simulations using a ballistic model adopted by the SIRIAS computational software. Drop diameters quantified in the experiment varied between 0.30 mm and 1.30 mm, while the simulated between 0.28 mm and 1.06 mm. The greatest differences between simulated and measured values were registered at the highest radial distance from the emitter. The model presented a performance classified as excellent for simulating microsprinkler drop distribution.
Fu, A.; Xue, Y.
2017-12-01
Corn is one of most important agricultural production in China. Research on the simulation of corn yields and the impacts of climate change and agricultural management practices on corn yields is important in maintaining the stable corn production. After climatic data including daily temperature, precipitation, solar radiation, relative humidity, and wind speed from 1948 to 2010, soil properties, observed corn yields, and farmland management information were collected, corn yields grown in humidity and hot environment (Sichuang province) and cold and dry environment (Hebei province) in China in the past 63 years were simulated by Daycent, and the results was evaluated based on published yield record. The relationship between regional climate change, global warming and corn yield were analyzed, the uncertainties of simulation derived from agricultural management practices by changing fertilization levels, land fertilizer maintenance and tillage methods were reported. The results showed that: (1) Daycent model is capable to simulate corn yields under the different climatic background in China. (2) When studying the relationship between regional climate change and corn yields, it has been found that observed and simulated corn yields increased along with total regional climate change. (3) When studying the relationship between the global warming and corn yields, It was discovered that newly-simulated corn yields after removing the global warming trend of original temperature data were lower than before.
Directory of Open Access Journals (Sweden)
Nader Naifar
2016-09-01
Full Text Available The aim of this paper is to investigate the dependence structure between sukuk (Islamic bonds yields and stock market (returns and volatility in the case of Saudi Arabia. We consider three Archimedean copula models with different tail dependence structures namely Gumbel, Clayton, and Frank. This study shows that the sukuk yields exhibit significant dependence only with stock market volatility. In addition, the dependence structure between sukuk yields and stock market volatility are symmetric and linked with the same intensity.
Estimation of some stochastic models used in reliability engineering
International Nuclear Information System (INIS)
Huovinen, T.
1989-04-01
The work aims to study the estimation of some stochastic models used in reliability engineering. In reliability engineering continuous probability distributions have been used as models for the lifetime of technical components. We consider here the following distributions: exponential, 2-mixture exponential, conditional exponential, Weibull, lognormal and gamma. Maximum likelihood method is used to estimate distributions from observed data which may be either complete or censored. We consider models based on homogeneous Poisson processes such as gamma-poisson and lognormal-poisson models for analysis of failure intensity. We study also a beta-binomial model for analysis of failure probability. The estimators of the parameters for three models are estimated by the matching moments method and in the case of gamma-poisson and beta-binomial models also by maximum likelihood method. A great deal of mathematical or statistical problems that arise in reliability engineering can be solved by utilizing point processes. Here we consider the statistical analysis of non-homogeneous Poisson processes to describe the failing phenomena of a set of components with a Weibull intensity function. We use the method of maximum likelihood to estimate the parameters of the Weibull model. A common cause failure can seriously reduce the reliability of a system. We consider a binomial failure rate (BFR) model as an application of the marked point processes for modelling common cause failure in a system. The parameters of the binomial failure rate model are estimated with the maximum likelihood method
Cokriging model for estimation of water table elevation
International Nuclear Information System (INIS)
Hoeksema, R.J.; Clapp, R.B.; Thomas, A.L.; Hunley, A.E.; Farrow, N.D.; Dearstone, K.C.
1989-01-01
In geological settings where the water table is a subdued replica of the ground surface, cokriging can be used to estimate the water table elevation at unsampled locations on the basis of values of water table elevation and ground surface elevation measured at wells and at points along flowing streams. The ground surface elevation at the estimation point must also be determined. In the proposed method, separate models are generated for the spatial variability of the water table and ground surface elevation and for the dependence between these variables. After the models have been validated, cokriging or minimum variance unbiased estimation is used to obtain the estimated water table elevations and their estimation variances. For the Pits and Trenches area (formerly a liquid radioactive waste disposal facility) near Oak Ridge National Laboratory, water table estimation along a linear section, both with and without the inclusion of ground surface elevation as a statistical predictor, illustrate the advantages of the cokriging model
Weibull Parameters Estimation Based on Physics of Failure Model
DEFF Research Database (Denmark)
Kostandyan, Erik; Sørensen, John Dalsgaard
2012-01-01
Reliability estimation procedures are discussed for the example of fatigue development in solder joints using a physics of failure model. The accumulated damage is estimated based on a physics of failure model, the Rainflow counting algorithm and the Miner’s rule. A threshold model is used...... for degradation modeling and failure criteria determination. The time dependent accumulated damage is assumed linearly proportional to the time dependent degradation level. It is observed that the deterministic accumulated damage at the level of unity closely estimates the characteristic fatigue life of Weibull...
A Dynamic Travel Time Estimation Model Based on Connected Vehicles
Directory of Open Access Journals (Sweden)
Daxin Tian
2015-01-01
Full Text Available With advances in connected vehicle technology, dynamic vehicle route guidance models gradually become indispensable equipment for drivers. Traditional route guidance models are designed to direct a vehicle along the shortest path from the origin to the destination without considering the dynamic traffic information. In this paper a dynamic travel time estimation model is presented which can collect and distribute traffic data based on the connected vehicles. To estimate the real-time travel time more accurately, a road link dynamic dividing algorithm is proposed. The efficiency of the model is confirmed by simulations, and the experiment results prove the effectiveness of the travel time estimation method.
These model-based estimates use two surveys, the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health Interview Survey (NHIS). The two surveys are combined using novel statistical methodology.
International Nuclear Information System (INIS)
Alves, J.L.; Oliveira, M.C.; Menezes, L.F.
2004-01-01
Two constitutive models used to describe the plastic behavior of sheet metals in the numerical simulation of sheet metal forming process are studied: a recently proposed advanced constitutive model based on the Teodosiu microstructural model and the Cazacu Barlat yield criterion is compared with a more classical one, based on the Swift law and the Hill 1948 yield criterion. These constitutive models are implemented into DD3IMP, a finite element home code specifically developed to simulate sheet metal forming processes, which generically is a 3-D elastoplastic finite element code with an updated Lagrangian formulation, following a fully implicit time integration scheme, large elastoplastic strains and rotations. Solid finite elements and parametric surfaces are used to model the blank sheet and tool surfaces, respectively. Some details of the numerical implementation of the constitutive models are given. Finally, the theory is illustrated with the numerical simulation of the deep drawing of a cylindrical cup. The results show that the proposed advanced constitutive model predicts with more exactness the final shape (medium height and ears profile) of the formed part, as one can conclude from the comparison with the experimental results
Parameters Estimation of Geographically Weighted Ordinal Logistic Regression (GWOLR) Model
Zuhdi, Shaifudin; Retno Sari Saputro, Dewi; Widyaningsih, Purnami
2017-06-01
A regression model is the representation of relationship between independent variable and dependent variable. The dependent variable has categories used in the logistic regression model to calculate odds on. The logistic regression model for dependent variable has levels in the logistics regression model is ordinal. GWOLR model is an ordinal logistic regression model influenced the geographical location of the observation site. Parameters estimation in the model needed to determine the value of a population based on sample. The purpose of this research is to parameters estimation of GWOLR model using R software. Parameter estimation uses the data amount of dengue fever patients in Semarang City. Observation units used are 144 villages in Semarang City. The results of research get GWOLR model locally for each village and to know probability of number dengue fever patient categories.
A hydrologic regression sediment-yield model for two ungaged watershed outlet stations in Africa
International Nuclear Information System (INIS)
Moussa, O.M.; Smith, S.E.; Shrestha, R.L.
1991-01-01
A hydrologic regression sediment-yield model was established to determine the relationship between water discharge and suspended sediment discharge at the Blue Nile and the Atbara River outlet stations during the flood season. The model consisted of two main submodels: (1) a suspended sediment discharge model, which was used to determine suspended sediment discharge for each basin outlet; and (2) a sediment rating model, which related water discharge and suspended sediment discharge for each outlet station. Due to the absence of suspended sediment concentration measurements at or near the outlet stations, a minimum norm solution, which is based on the minimization of the unknowns rather than the residuals, was used to determine the suspended sediment discharges at the stations. In addition, the sediment rating submodel was regressed by using an observation equations procedure. Verification analyses on the model were carried out and the mean percentage errors were found to be +12.59 and -12.39, respectively, for the Blue Nile and Atbara. The hydrologic regression model was found to be most sensitive to the relative weight matrix, moderately sensitive to the mean water discharge ratio, and slightly sensitive to the concentration variation along the River Nile's course
Cai, Y.
2017-12-01
Accurately forecasting crop yields has broad implications for economic trading, food production monitoring, and global food security. However, the variation of environmental variables presents challenges to model yields accurately, especially when the lack of highly accurate measurements creates difficulties in creating models that can succeed across space and time. In 2016, we developed a sequence of machine-learning based models forecasting end-of-season corn yields for the US at both the county and national levels. We combined machine learning algorithms in a hierarchical way, and used an understanding of physiological processes in temporal feature selection, to achieve high precision in our intra-season forecasts, including in very anomalous seasons. During the live run, we predicted the national corn yield within 1.40% of the final USDA number as early as August. In the backtesting of the 2000-2015 period, our model predicts national yield within 2.69% of the actual yield on average already by mid-August. At the county level, our model predicts 77% of the variation in final yield using data through the beginning of August and improves to 80% by the beginning of October, with the percentage of counties predicted within 10% of the average yield increasing from 68% to 73%. Further, the lowest errors are in the most significant producing regions, resulting in very high precision national-level forecasts. In addition, we identify the changes of important variables throughout the season, specifically early-season land surface temperature, and mid-season land surface temperature and vegetation index. For the 2017 season, we feed 2016 data to the training set, together with additional geospatial data sources, aiming to make the current model even more precise. We will show how our 2017 US corn yield forecasts converges in time, which factors affect the yield the most, as well as present our plans for 2018 model adjustments.
Investigation and modeling of the anomalous yield point phenomenon in pure tantalum
Energy Technology Data Exchange (ETDEWEB)
Colas, D. [Laboratoire Interdisciplinaire Carnot de Bourgogne, UMR 5209 CNRS, Université de Bourgogne, 9 avenue Alain Savary, BP 17870, 21078 Dijon Cedex (France); CEA Valduc, 21120 Is-sur-Tille (France); Mines ParisTech, Centre des Matériaux, CNRS, UMR 7633, BP 87, 91003 Evry Cedex (France); Finot, E. [Laboratoire Interdisciplinaire Carnot de Bourgogne, UMR 5209 CNRS, Université de Bourgogne, 9 avenue Alain Savary, BP 17870, 21078 Dijon Cedex (France); Flouriot, S. [CEA Valduc, 21120 Is-sur-Tille (France); Forest, S. [Mines ParisTech, Centre des Matériaux, CNRS, UMR 7633, BP 87, 91003 Evry Cedex (France); Mazière, M., E-mail: matthieu.maziere@mines-paristech.fr [Mines ParisTech, Centre des Matériaux, CNRS, UMR 7633, BP 87, 91003 Evry Cedex (France); Paris, T. [CEA Valduc, 21120 Is-sur-Tille (France)
2014-10-06
The monotonic and cyclic behavior of commercially pure tantalum has been investigated at room temperature, in order to capture and understand the occurrence of the anomalous yield point phenomenon. Interrupted tests have been performed, with strain reversals (tensile or compressive loading) after an aging period. The stress drop is attributed to the interactions between dislocations and solute atoms (oxygen) and its macroscopic occurrence is not systematically observed. InfraRed Thermography (IRT) measurements supported by Scanning Electron Microscopy (SEM) pictures of the polished gauge length of a specimen during an interrupted tensile test reveal the nucleation and propagation of a strain localization band. The KEMC (Kubin–Estrin–McCormick) phenomenological model accounting for strain aging has been identified for several loadings and strain rates at room temperature. Simulations on full specimen using the KEMC model do not show strain localization, because of the competition between viscosity and strain localization. However, a slight misalignment of the sample can promote strain localization.
Sun, Ruochen; Yuan, Huiling; Liu, Xiaoli
2017-11-01
The heteroscedasticity treatment in residual error models directly impacts the model calibration and prediction uncertainty estimation. This study compares three methods to deal with the heteroscedasticity, including the explicit linear modeling (LM) method and nonlinear modeling (NL) method using hyperbolic tangent function, as well as the implicit Box-Cox transformation (BC). Then a combined approach (CA) combining the advantages of both LM and BC methods has been proposed. In conjunction with the first order autoregressive model and the skew exponential power (SEP) distribution, four residual error models are generated, namely LM-SEP, NL-SEP, BC-SEP and CA-SEP, and their corresponding likelihood functions are applied to the Variable Infiltration Capacity (VIC) hydrologic model over the Huaihe River basin, China. Results show that the LM-SEP yields the poorest streamflow predictions with the widest uncertainty band and unrealistic negative flows. The NL and BC methods can better deal with the heteroscedasticity and hence their corresponding predictive performances are improved, yet the negative flows cannot be avoided. The CA-SEP produces the most accurate predictions with the highest reliability and effectively avoids the negative flows, because the CA approach is capable of addressing the complicated heteroscedasticity over the study basin.
Teolis, B. D.; Plainaki, C.; Cassidy, T. A.; Raut, U.
2017-10-01
O2, H2, and H2O2 radiolysis from water ice is pervasive on icy astrophysical bodies, but the lack of a self-consistent, quantitative model of the yields of these water products versus irradiation projectile species and energy has been an obstacle to estimating the radiolytic oxidant sources to the surfaces and exospheres of these objects. A major challenge is the wide variation of O2 radiolysis yields between laboratory experiments, ranging over 4 orders of magnitude from 5 × 10-7 to 5 × 10-3 molecules/eV for different particles and energies. We revisit decades of laboratory data to solve this long-standing puzzle, finding an inverse projectile range dependence in the O2 yields, due to preferential O2 formation from an 30 Å thick oxygenated surface layer. Highly penetrating projectile ions and electrons with ranges ≳30 Å are therefore less efficient at producing O2 than slow/heavy ions and low-energy electrons (≲ 400 eV) which deposit most energy near the surface. Unlike O2, the H2O2 yields from penetrating projectiles fall within a comparatively narrow range of (0.1-6) × 10-3 molecules/eV and do not depend on range, suggesting that H2O2 forms deep in the ice uniformly along the projectile track, e.g., by reactions of OH radicals. We develop an analytical model for O2, H2, and H2O2 yields from pure water ice for electrons and singly charged ions of any mass and energy and apply the model to estimate possible O2 source rates on several icy satellites. The yields are upper limits for icy bodies on which surface impurities may be present.
NONLINEAR PLANT PIECEWISE-CONTINUOUS MODEL MATRIX PARAMETERS ESTIMATION
Directory of Open Access Journals (Sweden)
Roman L. Leibov
2017-09-01
Full Text Available This paper presents a nonlinear plant piecewise-continuous model matrix parameters estimation technique using nonlinear model time responses and random search method. One of piecewise-continuous model application areas is defined. The results of proposed approach application for aircraft turbofan engine piecewisecontinuous model formation are presented
Estimation methods for nonlinear state-space models in ecology
DEFF Research Database (Denmark)
Pedersen, Martin Wæver; Berg, Casper Willestofte; Thygesen, Uffe Høgsbro
2011-01-01
The use of nonlinear state-space models for analyzing ecological systems is increasing. A wide range of estimation methods for such models are available to ecologists, however it is not always clear, which is the appropriate method to choose. To this end, three approaches to estimation in the theta...... logistic model for population dynamics were benchmarked by Wang (2007). Similarly, we examine and compare the estimation performance of three alternative methods using simulated data. The first approach is to partition the state-space into a finite number of states and formulate the problem as a hidden...... Markov model (HMM). The second method uses the mixed effects modeling and fast numerical integration framework of the AD Model Builder (ADMB) open-source software. The third alternative is to use the popular Bayesian framework of BUGS. The study showed that state and parameter estimation performance...