Yield model development project implementation plan

NASA Technical Reports Server (NTRS)

Ambroziak, R. A.

1982-01-01

Tasks remaining to be completed are summarized for the following major project elements: (1) evaluation of crop yield models; (2) crop yield model research and development; (3) data acquisition processing, and storage; (4) related yield research: defining spectral and/or remote sensing data requirements; developing input for driving and testing crop growth/yield models; real time testing of wheat plant process models) and (5) project management and support.

Evaluation of Precipitation Indices for Global Crop Modeling and Definition of Drought Response Function to Yields

NASA Astrophysics Data System (ADS)

Kaneko, D.

2017-12-01

Climate change initiates abnormal meteorological disasters. Drought causes climate instability, thus producing poor harvests because of low rates of photosynthesis and sterile pollination. This research evaluates drought indices regarding precipitation and includes this data in global geophysical crop models that concern with evaporation, stomata opening, advection-effects from sea surface temperature anomalies, photosynthesis, carbon partitioning, crop yields, and crop production. Standard precipitation index (SPI) is a useful tool because of related variable not used in the stomata model. However, SPI is not an adequate tool for drought in irrigated fields. Contrary to expectations, the global comparisons of spatial characteristics between stomata opening/evapotranspiration and SPI for monitoring continental crop extremes produced serious defects and obvious differences between evapotranspiration and the small stomata-opening phenomena. The reason for this is that SPI does not include surface air temperature in its analysis. The Penman equation (Epen) describes potential evaporation better than SPI for recent hot droughts caused by climate change. However, the distribution of precipitation is a necessary condition for crop monitoring because it affirms the trend of the dry results computed by crop models. Consequently, the author uses global precipitation data observed by microwave passive sensors on TRMM and GCOM-W satellites. This remote sensing data conveniently supplies spatial distributions of global and seasonal precipitation. The author has designed a model to measure the effects of drought on crop yield and the degree of stomata closure related to the photosynthesis rate. To determine yield effects, the drought injury function is defined by integrating stomata closure during the two seasons from flowering to pollination. The stomata, defined by ratio between Epen and Eac, reflect the effects of drought and irrigation. Stomata-closure model includes the

Optimization of processing parameters of UAV integral structural components based on yield response

NASA Astrophysics Data System (ADS)

Chen, Yunsheng

2018-05-01

In order to improve the overall strength of unmanned aerial vehicle (UAV), it is necessary to optimize the processing parameters of UAV structural components, which is affected by initial residual stress in the process of UAV structural components processing. Because machining errors are easy to occur, an optimization model for machining parameters of UAV integral structural components based on yield response is proposed. The finite element method is used to simulate the machining parameters of UAV integral structural components. The prediction model of workpiece surface machining error is established, and the influence of the path of walking knife on residual stress of UAV integral structure is studied, according to the stress of UAV integral component. The yield response of the time-varying stiffness is analyzed, and the yield response and the stress evolution mechanism of the UAV integral structure are analyzed. The simulation results show that this method is used to optimize the machining parameters of UAV integral structural components and improve the precision of UAV milling processing. The machining error is reduced, and the deformation prediction and error compensation of UAV integral structural parts are realized, thus improving the quality of machining.

Argentina soybean yield model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

A model based on multiple regression was developed to estimate soybean yields for the country of Argentina. A meteorological data set was obtained for the country by averaging data for stations within the soybean growing area. Predictor variables for the model were derived from monthly total precipitation and monthly average temperature. A trend variable was included for the years 1969 to 1978 since an increasing trend in yields due to technology was observed between these years.

Argentina wheat yield model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

Five models based on multiple regression were developed to estimate wheat yields for the five wheat growing provinces of Argentina. Meteorological data sets were obtained for each province by averaging data for stations within each province. Predictor variables for the models were derived from monthly total precipitation, average monthly mean temperature, and average monthly maximum temperature. Buenos Aires was the only province for which a trend variable was included because of increasing trend in yield due to technology from 1950 to 1963.

Argentina corn yield model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

A model based on multiple regression was developed to estimate corn yields for the country of Argentina. A meteorological data set was obtained for the country by averaging data for stations within the corn-growing area. Predictor variables for the model were derived from monthly total precipitation, average monthly mean temperature, and average monthly maximum temperature. A trend variable was included for the years 1965 to 1980 since an increasing trend in yields due to technology was observed between these years.

Yield Model Development (YMD) implementation plan for fiscal years 1981 and 1982

NASA Technical Reports Server (NTRS)

Ambroziak, R. A. (Principal Investigator)

1981-01-01

A plan is described for supporting USDA crop production forecasting and estimation by (1) testing, evaluating, and selecting crop yield models for application testing; (2) identifying areas of feasible research for improvement of models; and (3) conducting research to modify existing models and to develop new crop yield assessment methods. Tasks to be performed for each of these efforts are described as well as for project management and support. The responsibilities of USDA, USDC, USDI, and NASA are delineated as well as problem areas to be addressed.

Simulating the yield impacts of organ-level quantitative trait loci associated with drought response in maize: a "gene-to-phenotype" modeling approach.

PubMed

Chenu, Karine; Chapman, Scott C; Tardieu, François; McLean, Greg; Welcker, Claude; Hammer, Graeme L

2009-12-01

Under drought, substantial genotype-environment (G x E) interactions impede breeding progress for yield. Identifying genetic controls associated with yield response is confounded by poor genetic correlations across testing environments. Part of this problem is related to our inability to account for the interplay of genetic controls, physiological traits, and environmental conditions throughout the crop cycle. We propose a modeling approach to bridge this "gene-to-phenotype" gap. For maize under drought, we simulated the impact of quantitative trait loci (QTL) controlling two key processes (leaf and silk elongation) that influence crop growth, water use, and grain yield. Substantial G x E interaction for yield was simulated for hypothetical recombinant inbred lines (RILs) across different seasonal patterns of drought. QTL that accelerated leaf elongation caused an increase in crop leaf area and yield in well-watered or preflowering water deficit conditions, but a reduction in yield under terminal stresses (as such "leafy" genotypes prematurely exhausted the water supply). The QTL impact on yield was substantially enhanced by including pleiotropic effects of these QTL on silk elongation and on consequent grain set. The simulations obtained illustrated the difficulty of interpreting the genetic control of yield for genotypes influenced only by the additive effects of QTL associated with leaf and silk growth. The results highlight the potential of integrative simulation modeling for gene-to-phenotype prediction and for exploiting G x E interactions for complex traits such as drought tolerance.

Evaluation of the CEAS model for barley yields in North Dakota and Minnesota

NASA Technical Reports Server (NTRS)

Barnett, T. L. (Principal Investigator)

1981-01-01

The CEAS yield model is based upon multiple regression analysis at the CRD and state levels. For the historical time series, yield is regressed on a set of variables derived from monthly mean temperature and monthly precipitation. Technological trend is represented by piecewise linear and/or quadriatic functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test (1970-79) demonstrated that biases are small and performance as indicated by the root mean square errors are acceptable for intended application, however, model response for individual years particularly unusual years, is not very reliable and shows some large errors. The model is objective, adequate, timely, simple and not costly. It considers scientific knowledge on a broad scale but not in detail, and does not provide a good current measure of modeled yield reliability.

Modeling survival, yield, volume partitioning and their response to thinning for longleaf pine plantations

Treesearch

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

Multi-Wheat-Model Ensemble Responses to Interannual Climate Variability

NASA Technical Reports Server (NTRS)

Ruane, Alex C.; Hudson, Nicholas I.; Asseng, Senthold; Camarrano, Davide; Ewert, Frank; Martre, Pierre; Boote, Kenneth J.; Thorburn, Peter J.; Aggarwal, Pramod K.; Angulo, Carlos

2016-01-01

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981e2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2 0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-termwarming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts.

Quantifying long-term responses of crop yield and nitrate leaching in an intensive farmland using agro-eco-environmental model.

PubMed

Sun, Mei; Huo, Zailin; Zheng, Yanxia; Dai, Xiaoqin; Feng, Shaoyuan; Mao, Xiaomin

2018-02-01

Quantitatively ascertaining and analyzing long-term responses of crop yield and nitrate leaching on varying irrigation and fertilization treatments are focal points for guaranteeing crop yield and reducing nitrogen loss. The calibrated agricultural-hydrological RZWQM2 model was used to explore the long-term (2003-2013) transport processes of water and nitrogen and the nitrate leaching amount into groundwater in summer maize and winter wheat rotation field in typical intensive plant area in the North China Plain, Daxing district of Beijing. Simulation results showed that application rates of irrigation and nitrogen fertilizer have couple effects on crop yields and nitrogen leaching of root zone. When both the irrigation and fertilizer for summer maize and winter wheat were 400mm and 400kgNha -1 , respectively, nitrate leaching into groundwater accounted for 47.9% of application amount of nitrogen fertilizer. When application amount of irrigation is 200mm and fertilization is 200kgNha -1 , NUPE (nitrogen uptake efficiency), NUE (nitrogen use efficiency), NPFP (nitrogen partial factor productivity), and W pi (irrigation water productive efficiency) were in general higher than that under other irrigation and fertilization condition (irrigation from 104-400mm, fertilizer 104-400kgNha -1 ). Irrigation bigger than 200mm could shorten the response time of nitrate leaching in deeper soil layer in different irrigation treatment. Copyright © 2017 Elsevier B.V. All rights reserved.

Brazil soybean yield covariance model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

A model based on multiple regression was developed to estimate soybean yields for the seven soybean-growing states of Brazil. The meteorological data of these seven states were pooled and the years 1975 to 1980 were used to model since there was no technological trend in the yields during these years. Predictor variables were derived from monthly total precipitation and monthly average temperature.

Second Generation Crop Yield Models Review

NASA Technical Reports Server (NTRS)

Hodges, T. (Principal Investigator)

1982-01-01

Second generation yield models, including crop growth simulation models and plant process models, may be suitable for large area crop yield forecasting in the yield model development project. Subjective and objective criteria for model selection are defined and models which might be selected are reviewed. Models may be selected to provide submodels as input to other models; for further development and testing; or for immediate testing as forecasting tools. A plant process model may range in complexity from several dozen submodels simulating (1) energy, carbohydrates, and minerals; (2) change in biomass of various organs; and (3) initiation and development of plant organs, to a few submodels simulating key physiological processes. The most complex models cannot be used directly in large area forecasting but may provide submodels which can be simplified for inclusion into simpler plant process models. Both published and unpublished models which may be used for development or testing are reviewed. Several other models, currently under development, may become available at a later date.

Venetoclax Yields Strong Responses in CLL.

PubMed

2016-02-01

Results from an international phase II study show that the investigational BCL2 inhibitor venetoclax is effective in patients with chronic lymphocytic leukemia and the chromosome 17p deletion, whose prognosis is particularly poor. Venetoclax yielded high and durable responses in this population, including several complete remissions. ©2016 American Association for Cancer Research.

Simulating the Yield Impacts of Organ-Level Quantitative Trait Loci Associated With Drought Response in Maize: A “Gene-to-Phenotype” Modeling Approach

PubMed Central

Chenu, Karine; Chapman, Scott C.; Tardieu, François; McLean, Greg; Welcker, Claude; Hammer, Graeme L.

2009-01-01

Under drought, substantial genotype–environment (G × E) interactions impede breeding progress for yield. Identifying genetic controls associated with yield response is confounded by poor genetic correlations across testing environments. Part of this problem is related to our inability to account for the interplay of genetic controls, physiological traits, and environmental conditions throughout the crop cycle. We propose a modeling approach to bridge this “gene-to-phenotype” gap. For maize under drought, we simulated the impact of quantitative trait loci (QTL) controlling two key processes (leaf and silk elongation) that influence crop growth, water use, and grain yield. Substantial G × E interaction for yield was simulated for hypothetical recombinant inbred lines (RILs) across different seasonal patterns of drought. QTL that accelerated leaf elongation caused an increase in crop leaf area and yield in well-watered or preflowering water deficit conditions, but a reduction in yield under terminal stresses (as such “leafy” genotypes prematurely exhausted the water supply). The QTL impact on yield was substantially enhanced by including pleiotropic effects of these QTL on silk elongation and on consequent grain set. The simulations obtained illustrated the difficulty of interpreting the genetic control of yield for genotypes influenced only by the additive effects of QTL associated with leaf and silk growth. The results highlight the potential of integrative simulation modeling for gene-to-phenotype prediction and for exploiting G × E interactions for complex traits such as drought tolerance. PMID:19786622

Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models

DOE PAGES

Blanc, Élodie

2017-01-26

This study provides statistical emulators of crop yields based on global gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project Fast Track project. The ensemble of simulations is used to build a panel of annual crop yields from five crop models and corresponding monthly summer weather variables for over a century at the grid cell level globally. This dataset is then used to estimate, for each crop and gridded crop model, the statistical relationship between yields, temperature, precipitation and carbon dioxide. This study considers a new functional form to better capture the non-linear response of yields to weather,more » especially for extreme temperature and precipitation events, and now accounts for the effect of soil type. In- and out-of-sample validations show that the statistical emulators are able to replicate spatial patterns of yields crop levels and changes overtime projected by crop models reasonably well, although the accuracy of the emulators varies by model and by region. This study therefore provides a reliable and accessible alternative to global gridded crop yield models. By emulating crop yields for several models using parsimonious equations, the tools provide a computationally efficient method to account for uncertainty in climate change impact assessments.« less

Statistical emulators of maize, rice, soybean and wheat yields from global gridded crop models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Blanc, Élodie

This study provides statistical emulators of crop yields based on global gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project Fast Track project. The ensemble of simulations is used to build a panel of annual crop yields from five crop models and corresponding monthly summer weather variables for over a century at the grid cell level globally. This dataset is then used to estimate, for each crop and gridded crop model, the statistical relationship between yields, temperature, precipitation and carbon dioxide. This study considers a new functional form to better capture the non-linear response of yields to weather,more » especially for extreme temperature and precipitation events, and now accounts for the effect of soil type. In- and out-of-sample validations show that the statistical emulators are able to replicate spatial patterns of yields crop levels and changes overtime projected by crop models reasonably well, although the accuracy of the emulators varies by model and by region. This study therefore provides a reliable and accessible alternative to global gridded crop yield models. By emulating crop yields for several models using parsimonious equations, the tools provide a computationally efficient method to account for uncertainty in climate change impact assessments.« less

Brazil wheat yield covariance model

NASA Technical Reports Server (NTRS)

Callis, S. L.; Sakamoto, C.

1984-01-01

A model based on multiple regression was developed to estimate wheat yields for the wheat growing states of Rio Grande do Sul, Parana, and Santa Catarina in Brazil. The meteorological data of these three states were pooled and the years 1972 to 1979 were used to develop the model since there was no technological trend in the yields during these years. Predictor variables were derived from monthly total precipitation, average monthly mean temperature, and average monthly maximum temperature.

Response of wheat growth, grain yield and water use to elevated CO2 under a Free-Air CO2 Enrichment (FACE) experiment and modelling in a semi-arid environment.

PubMed

O'Leary, Garry J; Christy, Brendan; Nuttall, James; Huth, Neil; Cammarano, Davide; Stöckle, Claudio; Basso, Bruno; Shcherbak, Iurii; Fitzgerald, Glenn; Luo, Qunying; Farre-Codina, Immaculada; Palta, Jairo; Asseng, Senthold

2014-12-05

The response of wheat crops to elevated CO 2 (eCO 2 ) was measured and modelled with the Australian Grains Free-Air CO 2 Enrichment experiment, located at Horsham, Australia. Treatments included CO 2 by water, N and temperature. The location represents a semi-arid environment with a seasonal VPD of around 0.5 kPa. Over 3 years, the observed mean biomass at anthesis and grain yield ranged from 4200 to 10 200 kg ha -1 and 1600 to 3900 kg ha -1 , respectively, over various sowing times and irrigation regimes. The mean observed response to daytime eCO 2 (from 365 to 550 μmol mol -1 CO 2 ) was relatively consistent for biomass at stem elongation and at anthesis and LAI at anthesis and grain yield with 21%, 23%, 21% and 26%, respectively. Seasonal water use was decreased from 320 to 301 mm (P = 0.10) by eCO 2 , increasing water use efficiency for biomass and yield, 36% and 31%, respectively. The performance of six models (APSIM-Wheat, APSIM-Nwheat, CAT-Wheat, CROPSYST, OLEARY-CONNOR and SALUS) in simulating crop responses to eCO 2 was similar and within or close to the experimental error for accumulated biomass, yield and water use response, despite some variations in early growth and LAI. The primary mechanism of biomass accumulation via radiation use efficiency (RUE) or transpiration efficiency (TE) was not critical to define the overall response to eCO 2 . However, under irrigation, the effect of late sowing on response to eCO 2 to biomass accumulation at DC65 was substantial in the observed data (~40%), but the simulated response was smaller, ranging from 17% to 28%. Simulated response from all six models under no water or nitrogen stress showed similar response to eCO 2 under irrigation, but the differences compared to the dryland treatment were small. Further experimental work on the interactive effects of eCO 2 , water and temperature is required to resolve these model discrepancies. © 2014 John Wiley & Sons Ltd.

Simple agrometeorological models for estimating Guineagrass yield in Southeast Brazil.

PubMed

Pezzopane, José Ricardo Macedo; da Cruz, Pedro Gomes; Santos, Patricia Menezes; Bosi, Cristiam; de Araujo, Leandro Coelho

2014-09-01

The objective of this work was to develop and evaluate agrometeorological models to simulate the production of Guineagrass. For this purpose, we used forage yield from 54 growing periods between December 2004-January 2007 and April 2010-March 2012 in irrigated and non-irrigated pastures in São Carlos, São Paulo state, Brazil (latitude 21°57'42″ S, longitude 47°50'28″ W and altitude 860 m). Initially we performed linear regressions between the agrometeorological variables and the average dry matter accumulation rate for irrigated conditions. Then we determined the effect of soil water availability on the relative forage yield considering irrigated and non-irrigated pastures, by means of segmented linear regression among water balance and relative production variables (dry matter accumulation rates with and without irrigation). The models generated were evaluated with independent data related to 21 growing periods without irrigation in the same location, from eight growing periods in 2000 and 13 growing periods between December 2004-January 2007 and April 2010-March 2012. The results obtained show the satisfactory predictive capacity of the agrometeorological models under irrigated conditions based on univariate regression (mean temperature, minimum temperature and potential evapotranspiration or degreedays) or multivariate regression. The response of irrigation on production was well correlated with the climatological water balance variables (ratio between actual and potential evapotranspiration or between actual and maximum soil water storage). The models that performed best for estimating Guineagrass yield without irrigation were based on minimum temperature corrected by relative soil water storage, determined by the ratio between the actual soil water storage and the soil water holding capacity.irrigation in the same location, in 2000, 2010 and 2011. The results obtained show the satisfactory predictive capacity of the agrometeorological models under

Yield and Economic Responses of Peanut to Crop Rotation Sequence

USDA-ARS?s Scientific Manuscript database

National Peanut Research Laboratory, Dawson, GA 39842. Proper crop rotation is essential to maintaining high peanut yield and quality. However, the economic considerations of maintaining or altering crop rotation sequences must incorporate the commodity prices, production costs, and yield responses...

Yield estimation of sugarcane based on agrometeorological-spectral models

NASA Technical Reports Server (NTRS)

Rudorff, Bernardo Friedrich Theodor; Batista, Getulio Teixeira

1990-01-01

This work has the objective to assess the performance of a yield estimation model for sugarcane (Succharum officinarum). The model uses orbital gathered spectral data along with yield estimated from an agrometeorological model. The test site includes the sugarcane plantations of the Barra Grande Plant located in Lencois Paulista municipality in Sao Paulo State. Production data of four crop years were analyzed. Yield data observed in the first crop year (1983/84) were regressed against spectral and agrometeorological data of that same year. This provided the model to predict the yield for the following crop year i.e., 1984/85. The model to predict the yield of subsequent years (up to 1987/88) were developed similarly, incorporating all previous years data. The yield estimations obtained from these models explained 69, 54, and 50 percent of the yield variation in the 1984/85, 1985/86, and 1986/87 crop years, respectively. The accuracy of yield estimations based on spectral data only (vegetation index model) and on agrometeorological data only (agrometeorological model) were also investigated.

Yield and Economic Responses of Peanut to Crop Rotation Sequence

USDA-ARS?s Scientific Manuscript database

Proper crop rotation is essential to maintaining high peanut yield and quality. However, the economic considerations of maintaining or altering crop rotation sequences must incorporate the commodity prices, production costs, and yield responses of all crops in, or potentially in, the crop rotation ...

Evaluation of trends in wheat yield models

NASA Technical Reports Server (NTRS)

Ferguson, M. C.

1982-01-01

Trend terms in models for wheat yield in the U.S. Great Plains for the years 1932 to 1976 are evaluated. The subset of meteorological variables yielding the largest adjusted R(2) is selected using the method of leaps and bounds. Latent root regression is used to eliminate multicollinearities, and generalized ridge regression is used to introduce bias to provide stability in the data matrix. The regression model used provides for two trends in each of two models: a dependent model in which the trend line is piece-wise continuous, and an independent model in which the trend line is discontinuous at the year of the slope change. It was found that the trend lines best describing the wheat yields consisted of combinations of increasing, decreasing, and constant trend: four combinations for the dependent model and seven for the independent model.

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

NASA Astrophysics Data System (ADS)

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

Simulation of crop yield variability by improved root-soil-interaction modelling

NASA Astrophysics Data System (ADS)

Duan, X.; Gayler, S.; Priesack, E.

2009-04-01

Understanding the processes and factors that govern the within-field variability in crop yield has attached great importance due to applications in precision agriculture. Crop response to environment at field scale is a complex dynamic process involving the interactions of soil characteristics, weather conditions and crop management. The numerous static factors combined with temporal variations make it very difficult to identify and manage the variability pattern. Therefore, crop simulation models are considered to be useful tools in analyzing separately the effects of change in soil or weather conditions on the spatial variability, in order to identify the cause of yield variability and to quantify the spatial and temporal variation. However, tests showed that usual crop models such as CERES-Wheat and CERES-Maize were not able to quantify the observed within-field yield variability, while their performance on crop growth simulation under more homogeneous and mainly non-limiting conditions was sufficent to simulate average yields at the field-scale. On a study site in South Germany, within-field variability in crop growth has been documented since years. After detailed analysis and classification of the soil patterns, two site specific factors, the plant-available-water and the O2 deficiency, were considered as the main causes of the crop growth variability in this field. Based on our measurement of root distribution in the soil profile, we hypothesize that in our case the insufficiency of the applied crop models to simulate the yield variability can be due to the oversimplification of the involved root models which fail to be sensitive to different soil conditions. In this study, the root growth model described by Jones et al. (1991) was adapted by using data of root distributions in the field and linking the adapted root model to the CERES crop model. The ability of the new root model to increase the sensitivity of the CERES crop models to different enviromental

Efficient SRAM yield optimization with mixture surrogate modeling

NASA Astrophysics Data System (ADS)

Zhongjian, Jiang; Zuochang, Ye; Yan, Wang

2016-12-01

Largely repeated cells such as SRAM cells usually require extremely low failure-rate to ensure a moderate chi yield. Though fast Monte Carlo methods such as importance sampling and its variants can be used for yield estimation, they are still very expensive if one needs to perform optimization based on such estimations. Typically the process of yield calculation requires a lot of SPICE simulation. The circuit SPICE simulation analysis accounted for the largest proportion of time in the process yield calculation. In the paper, a new method is proposed to address this issue. The key idea is to establish an efficient mixture surrogate model. The surrogate model is based on the design variables and process variables. This model construction method is based on the SPICE simulation to get a certain amount of sample points, these points are trained for mixture surrogate model by the lasso algorithm. Experimental results show that the proposed model is able to calculate accurate yield successfully and it brings significant speed ups to the calculation of failure rate. Based on the model, we made a further accelerated algorithm to further enhance the speed of the yield calculation. It is suitable for high-dimensional process variables and multi-performance applications.

How Do Various Maize Crop Models Vary in Their Responses to Climate Change Factors?

NASA Technical Reports Server (NTRS)

Bassu, Simona; Brisson, Nadine; Grassini, Patricio; Durand, Jean-Louis; Boote, Kenneth; Lizaso, Jon; Jones, James W.; Rosenzweig, Cynthia; Ruane, Alex C.; Adam, Myriam;

How do various maize crop models vary in their responses to climate change factors?

PubMed

Bassu, Simona; Brisson, Nadine; Durand, Jean-Louis; Boote, Kenneth; Lizaso, Jon; Jones, James W; Rosenzweig, Cynthia; Ruane, Alex C; Adam, Myriam; Baron, Christian; Basso, Bruno; Biernath, Christian; Boogaard, Hendrik; Conijn, Sjaak; Corbeels, Marc; Deryng, Delphine; De Sanctis, Giacomo; Gayler, Sebastian; Grassini, Patricio; Hatfield, Jerry; Hoek, Steven; Izaurralde, Cesar; Jongschaap, Raymond; Kemanian, Armen R; Kersebaum, K Christian; Kim, Soo-Hyung; Kumar, Naresh S; Makowski, David; Müller, Christoph; Nendel, Claas; Priesack, Eckart; Pravia, Maria Virginia; Sau, Federico; Shcherbak, Iurii; Tao, Fulu; Teixeira, Edmar; Timlin, Dennis; Waha, Katharina

2014-07-01

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 μmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information. © 2014 John Wiley & Sons Ltd.

Olive response to water availability: yield response functions, soil water content indicators and evaluation of adaptability to climate change

NASA Astrophysics Data System (ADS)

Riccardi, Maria; Alfieri, Silvia Maria; Basile, Angelo; Bonfante, Antonello; Menenti, Massimo; Monaco, Eugenia; De Lorenzi, Francesca

2013-04-01

Climate evolution, with the foreseen increase of temperature and frequency of drought events during the summer, could cause significant changes in the availability of water resources specially in the Mediterranean region. European countries need to encourage sustainable agriculture practices, reducing inputs, especially of water, and minimizing any negative impact on crop quantity and quality. Olive is an important crop in the Mediterranean region that has traditionally been cultivated with no irrigation and is known to attain acceptable production under dry farming. Therefore this crop will not compete for foreseen reduced water resources. However, a good quantitative knowledge must be available about effects of reduced precipitation and water availability on yield. Yield response functions, coupled with indicators of soil water availability, provide a quantitative description of the cultivar- specific behavior in relation to hydrological conditions. Yield response functions of 11 olive cultivars, typical of Mediterranean environment, were determined using experimental data (unpublished or reported in scientific literature). The yield was expressed as relative yield (Yr); the soil water availability was described by means of different indicators: relative soil water deficit (RSWD), relative evapotranspiration (RED) and transpiration deficit (RTD). Crops can respond nonlinearly to changes in their growing conditions and exhibit threshold responses, so for the yield functions of each olive cultivar both linear regression and threshold-slope models were considered to evaluate the best fit. The level of relative yield attained in rain-fed conditions was identified and defined as the acceptable yield level (Yrrainfed). The value of the indicator (RSWD, RED and RTD) corresponding to Yrrainfed was determined for each cultivar and indicated as the critical value of water availability. The error in the determination of the critical value was estimated. By means of a

A Remote Sensing-Derived Corn Yield Assessment Model

NASA Astrophysics Data System (ADS)

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

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

NASA Technical Reports Server (NTRS)

Li, Tao; Hasegawa, Toshihiro; Yin, Xinyou; Zhu, Yan; Boote, Kenneth; Adam, Myriam; Bregaglio, Simone; Buis, Samuel; Confalonieri, Roberto; Fumoto, Tamon;

Predicting paddlefish roe yields using an extension of the Beverton–Holt equilibrium yield-per-recruit model

USGS Publications Warehouse

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.

Rooting traits of peanut genotypes with different yield responses to terminal drought

USDA-ARS?s Scientific Manuscript database

Drought at pod filling can severely reduce yield of peanut. Better root systems can reduce yield loss from drought. However, the relationship of root characters with yield under terminal drought is not well understood. The objective of this study was to investigate the responses of peanut genotyp...

Process gg{yields}h{sub 0}{yields}{gamma}{gamma} in the Lee-Wick standard model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Krauss, F.; Underwood, T. E. J.; Zwicky, R.

2008-01-01

The process gg{yields}h{sub 0}{yields}{gamma}{gamma} is studied in the Lee-Wick extension of the standard model (LWSM) proposed by Grinstein, O'Connell, and Wise. In this model, negative norm partners for each SM field are introduced with the aim to cancel quadratic divergences in the Higgs mass. All sectors of the model relevant to gg{yields}h{sub 0}{yields}{gamma}{gamma} are diagonalized and results are commented on from the perspective of both the Lee-Wick and higher-derivative formalisms. Deviations from the SM rate for gg{yields}h{sub 0} are found to be of the order of 15%-5% for Lee-Wick masses in the range 500-1000 GeV. Effects on the rate formore » h{sub 0}{yields}{gamma}{gamma} are smaller, of the order of 5%-1% for Lee-Wick masses in the same range. These comparatively small changes may well provide a means of distinguishing the LWSM from other models such as universal extra dimensions where same-spin partners to standard model fields also appear. Corrections to determinations of Cabibbo-Kobayashi-Maskawa (CKM) elements |V{sub t(b,s,d)}| are also considered and are shown to be positive, allowing the possibility of measuring a CKM element larger than unity, a characteristic signature of the ghostlike nature of the Lee-Wick fields.« less

How model and input uncertainty impact maize yield simulations in West Africa

NASA Astrophysics Data System (ADS)

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.

Yield modeling of acoustic charge transport transversal filters

NASA Technical Reports Server (NTRS)

Kenney, J. S.; May, G. S.; Hunt, W. D.

1995-01-01

This paper presents a yield model for acoustic charge transport transversal filters. This model differs from previous IC yield models in that it does not assume that individual failures of the nondestructive sensing taps necessarily cause a device failure. A redundancy in the number of taps included in the design is explained. Poisson statistics are used to describe the tap failures, weighted over a uniform defect density distribution. A representative design example is presented. The minimum number of taps needed to realize the filter is calculated, and tap weights for various numbers of redundant taps are calculated. The critical area for device failure is calculated for each level of redundancy. Yield is predicted for a range of defect densities and redundancies. To verify the model, a Monte Carlo simulation is performed on an equivalent circuit model of the device. The results of the yield model are then compared to the Monte Carlo simulation. Better than 95% agreement was obtained for the Poisson model with redundant taps ranging from 30% to 150% over the minimum.

Development of a comprehensive watershed model applied to study stream yield under drought conditions

USGS Publications Warehouse

Perkins, S.P.; Sophocleous, M.

1999-01-01

We developed a model code to simulate a watershed's hydrology and the hydraulic response of an interconnected stream-aquifer system, and applied the model code to the Lower Republican River Basin in Kansas. The model code links two well-known computer programs: MODFLOW (modular 3-D flow model), which simulates ground water flow and stream-aquifer interaction; and SWAT (soil water assessment tool), a soil water budget simulator for an agricultural watershed. SWAT represents a basin as a collection of subbasins in terms of soil, land use, and weather data, and simulates each subbasin on a daily basis to determine runoff, percolation, evaporation, irrigation, pond seepages and crop growth. Because SWAT applies a lumped hydrologic model to each subbasin, spatial heterogeneities with respect to factors such as soil type and land use are not resolved geographically, but can instead be represented statistically. For the Republican River Basin model, each combination of six soil types and three land uses, referred to as a hydrologic response unit (HRU), was simulated with a separate execution of SWAT. A spatially weighted average was then taken over these results for each hydrologic flux and time step by a separate program, SWBAVG. We wrote a package for MOD-FLOW to associate each subbasin with a subset of aquifer grid cells and stream reaches, and to distribute the hydrologic fluxes given for each subbasin by SWAT and SWBAVG over MODFLOW's stream-aquifer grid to represent tributary flow, surface and ground water diversions, ground water recharge, and evapotranspiration from ground water. The Lower Republican River Basin model was calibrated with respect to measured ground water levels, streamflow, and reported irrigation water use. The model was used to examine the relative contributions of stream yield components and the impact on stream yield and base flow of administrative measures to restrict irrigation water use during droughts. Model results indicate that tributary

Comparison of statistical models for analyzing wheat yield time series.

PubMed

Michel, Lucie; Makowski, David

2013-01-01

The world's population is predicted to exceed nine billion by 2050 and there is increasing concern about the capability of agriculture to feed such a large population. Foresight studies on food security are frequently based on crop yield trends estimated from yield time series provided by national and regional statistical agencies. Various types of statistical models have been proposed for the analysis of yield time series, but the predictive performances of these models have not yet been evaluated in detail. In this study, we present eight statistical models for analyzing yield time series and compare their ability to predict wheat yield at the national and regional scales, using data provided by the Food and Agriculture Organization of the United Nations and by the French Ministry of Agriculture. The Holt-Winters and dynamic linear models performed equally well, giving the most accurate predictions of wheat yield. However, dynamic linear models have two advantages over Holt-Winters models: they can be used to reconstruct past yield trends retrospectively and to analyze uncertainty. The results obtained with dynamic linear models indicated a stagnation of wheat yields in many countries, but the estimated rate of increase of wheat yield remained above 0.06 t ha⁻¹ year⁻¹ in several countries in Europe, Asia, Africa and America, and the estimated values were highly uncertain for several major wheat producing countries. The rate of yield increase differed considerably between French regions, suggesting that efforts to identify the main causes of yield stagnation should focus on a subnational scale.

Comparison of Statistical Models for Analyzing Wheat Yield Time Series

PubMed Central

Michel, Lucie; Makowski, David

2013-01-01

The world's population is predicted to exceed nine billion by 2050 and there is increasing concern about the capability of agriculture to feed such a large population. Foresight studies on food security are frequently based on crop yield trends estimated from yield time series provided by national and regional statistical agencies. Various types of statistical models have been proposed for the analysis of yield time series, but the predictive performances of these models have not yet been evaluated in detail. In this study, we present eight statistical models for analyzing yield time series and compare their ability to predict wheat yield at the national and regional scales, using data provided by the Food and Agriculture Organization of the United Nations and by the French Ministry of Agriculture. The Holt-Winters and dynamic linear models performed equally well, giving the most accurate predictions of wheat yield. However, dynamic linear models have two advantages over Holt-Winters models: they can be used to reconstruct past yield trends retrospectively and to analyze uncertainty. The results obtained with dynamic linear models indicated a stagnation of wheat yields in many countries, but the estimated rate of increase of wheat yield remained above 0.06 t ha−1 year−1 in several countries in Europe, Asia, Africa and America, and the estimated values were highly uncertain for several major wheat producing countries. The rate of yield increase differed considerably between French regions, suggesting that efforts to identify the main causes of yield stagnation should focus on a subnational scale. PMID:24205280

Response of wheat yield in Spain to large-scale patterns

NASA Astrophysics Data System (ADS)

Hernandez-Barrera, Sara; Rodriguez-Puebla, Concepcion

2016-04-01

Crops are vulnerable to extreme climate conditions as drought, heat stress and frost risk. In previous study we have quantified the influence of these climate conditions for winter wheat in Spain (Hernandez-Barrera et al. 2015). The climate extremes respond to large-scale atmospheric and oceanic patterns. Therefore, a question emerges in our investigation: How large-scale patterns affect wheat yield? Obtaining and understanding these relationships require different approaches. In this study, we first obtained the leading mode of observed wheat yield variability to characterize the common variability over different provinces in Spain. Then, the wheat variability is related to different modes of mean sea level pressure, jet stream and sea surface temperature by using Partial Least-Squares, which captures the relevant climate drivers accounting for variations in wheat yield from sowing to harvesting. We used the ERA-Interim reanalysis data and the Extended Reconstructed Sea Surface Temperature (SST) (ERSST v3b). The derived model provides insight about the teleconnections between wheat yield and atmospheric and oceanic circulations, which is considered to project the wheat yield trend under global warming using outputs of twelve climate models corresponding to the Coupled Models Intercomparison Project phase 5 (CMIP5). Hernandez-Barrera S., C. Rodríguez-Puebla and A.J. Challinor. Effects of diurnal temperature range and drought on wheat yield in Spain. Theoretical and Applied Climatology (submitted)

[Response of water yield function of ecosystem to land use change in Nansi Lake Basin based on CLUE-S model and InVEST model .

PubMed

Guo, Hong Wei; Sun, Xiao Yin; Lian, Li Shu; Zhang, Da Zhi; Xu, Yan

2016-09-01

Land use change has an important role in hydrological processes and utilization of water resources, and is the main driving force of water yield function of ecosystem. This paper analyzed the change of land use from 1990 to 2013 in Nansi Lake Basin, Shandong Province. The future land use in 2030 was also predicted and simulated by CLUE-S model. Based on land use scenarios, we analyzed the influence of land use change on ecosystem function of water yield in nearly 25 years through InVEST water yield model and spatial mapping. The results showed that the area of construction land increased by 3.5% in 2013 because of burgeoning urbanization process, but farmland area decreased by 2.4% which was conversed to construction land mostly. The simulated result of InVEST model suggested that water yield level of whole basin decreased firstly and increased subsequently during last 25 years and peaked at 232.1 mm in 2013. The construction land area would increase by 6.7% in 2030 based on the land use scenarios of fast urbanization, which would lead to a remarkable growth for water yield and risk of flowing flooding. However, the water yield level of whole basin would decrease by 1.2 % in 2013 if 300 meter-wide forest buffer strips around Nansi Lake were built up.

THE IMPACTS OF CLIMATE CHANGE ON RICE YIELD: A COMPARISON OF FOUR MODEL PERFORMANCES

EPA Science Inventory

Increasing concentrations of carbon dioxide (CO2) and other greenhouse gases are expected to modify temperature and rainfall the next 50-100 years. echanisms and hypotheses of plant response to these changes could be incorporated in models predicting crop yield estimates to bette...

Crop yield responses to a hardwood biochar across varied soils and climate conditions

USDA-ARS?s Scientific Manuscript database

Biochars applied to soil for crop yield improvements have produced mixed results. The assorted crop yield responses may be linked to employing biochars with diverse chemical and physical characteristics. To clarify if biochars can improve crop yields, it may be prudent to evaluate one biochar type...

Quantification of ozone exposure- and stomatal uptake-yield response relationships for soybean in Northeast China.

PubMed

Zhang, Weiwei; Feng, Zhaozhong; Wang, Xiaoke; Liu, Xiaobing; Hu, Enzhu

2017-12-01

High ground-level O 3 is a new threat to agricultural production in Northeast China with the increasing ambient O 3 concentration. Little is known about its impacts on soybean production in this key agricultural region. Accumulated O 3 exposure-response and stomatal O 3 flux-response relationships were developed during two continuous growing seasons to evaluate O 3 -induced yield reduction of four typical soybean cultivars in Northeast China. Results showed that critical levels of AOT40 (accumulated hourly O 3 concentrations over a threshold of 40nmol·mol -1 ), SUM06 (sum of all hourly average O 3 concentrations over 0.06μmol·mol -1 ) and W126 (sum of O 3 concentrations weighted by a sigmoidal function) in relation to 5% reduction in relative seed yield were 4.2, 7.6 and 6.8μmol·mol -1 ·h, respectively. The effect of O 3 on plants was influenced by leaf position in canopy. An improved Jarvis stomatal conductance model including leaf (node) position fitted well with field measurements. The best linear relationship between stomatal O 3 flux and relative soybean yield was obtained when phytotoxic ozone dose was integrated over a threshold of 9.6nmol·m -2 ·s -1 (POD 9.6 ) to represent the detoxification capacity of soybean. POD 9.6 and the commonly used POD 6 in relation to 5% reduction in relative seed yield of soybean were 0.9mmol·m -2 and 1.8mmol·m -2 , respectively. O 3 concentrations above ~38nmol·mol -1 contributed to POD 9.6 and caused seed yield loss in soybean. Current annual yield loss of soybean at ambient O 3 was estimated to range between 23.4% and 30.2%. The O 3 dose-response relationships and corresponding thresholds obtained here will benefit regional O 3 risk assessment on soybean production in Northeast China. Copyright © 2017 Elsevier B.V. All rights reserved.

Building a statistical emulator for prediction of crop yield response to climate change: a global gridded panel data set approach

NASA Astrophysics Data System (ADS)

Mistry, Malcolm; De Cian, Enrica; Wing, Ian Sue

2015-04-01

There is widespread concern that trends and variability in weather induced by climate change will detrimentally affect global agricultural productivity and food supplies. Reliable quantification of the risks of negative impacts at regional and global scales is a critical research need, which has so far been met by forcing state-of-the-art global gridded crop models with outputs of global climate model (GCM) simulations in exercises such as the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP)-Fastrack. Notwithstanding such progress, it remains challenging to use these simulation-based projections to assess agricultural risk because their gridded fields of crop yields are fundamentally denominated as discrete combinations of warming scenarios, GCMs and crop models, and not as model-specific or model-averaged yield response functions of meteorological shifts, which may have their own independent probability of occurrence. By contrast, the empirical climate economics literature has adeptly represented agricultural responses to meteorological variables as reduced-form statistical response surfaces which identify the crop productivity impacts of additional exposure to different intervals of temperature and precipitation [cf Schlenker and Roberts, 2009]. This raises several important questions: (1) what do the equivalent reduced-form statistical response surfaces look like for crop model outputs, (2) do they exhibit systematic variation over space (e.g., crop suitability zones) or across crop models with different characteristics, (3) how do they compare to estimates based on historical observations, and (4) what are the implications for the characterization of climate risks? We address these questions by estimating statistical yield response functions for four major crops (maize, rice, wheat and soybeans) over the historical period (1971-2004) as well as future climate change scenarios (2005-2099) using ISIMIP-Fastrack data for five GCMs and seven crop models

Intracranial baroreflex yielding an early cushing response in human.

PubMed

Schmidt, E A; Czosnyka, Z; Momjian, S; Czosnyka, M; Bech, R A; Pickard, J D

2005-01-01

The Cushing response is a pre-terminal sympatho-adrenal systemic response to very high ICP. Animal studies have demonstrated that a moderate rise of ICP yields a reversible pressure-mediated systemic response. Infusion studies are routine procedures to investigate, by infusing CSF space with saline, the cerebrospinal fluid (CSF) biophysics in patients suspected of hydrocephalus. Our study aims at assessing systemic and cerebral haemodynamic changes during moderate rise of ICP in human. Infusion studies were performed in 34 patients. This is a routine test perform in patients presenting with symptoms of NPH during their pre-shunting assessment. Arterial blood pressure (ABP) and cerebral blood flow velocity (FV) were non-invasively monitored with photoplethysmography and transcranial Doppler. The rise in ICP (8.2 +/- 5.1 mmHg to 25 +/- 8.3 mmHg) was followed by a significant rise in ABP (106.6 +/- 29.7 mmHg to 115.2 +/- 30.1 mmHg), drop in CPP (98.3 +/- 29 mmHg to 90.2 +/- 30.7 mmHg) and decrease in FV (55.6 +/- 17 cm/s to 51.1 +/- 16.3 cm/s). Increasing ICP did not alter heart rate (70.4 +/- 10.4/min to 70.3 +/- 9.1/min) but augmented the heart rate variance (0.046 +/- 0.058 to 0.067 +/- 0.075/min). In a population suspected of hydrocephalus, our study demonstrated that a moderate rise of ICP yields a reversible pressure-mediated systemic response, demonstrating an early Cushing response in human and a putative intracranial baroreflex.

Rooting traits of peanut genotypes with different yield response to terminal drought

USDA-ARS?s Scientific Manuscript database

Drought at pod filling and maturity stages can severely reduce yield of peanut. Better root systems can reduce yield loss from drought. The goal of this study was to investigate the responses to terminal drought of peanut genotypes for root dry weight and root length density. A field experiment was ...

Bioenergy Sorghum Crop Model Predicts VPD-Limited Transpiration Traits Enhance Biomass Yield in Water-Limited Environments

PubMed Central

Truong, Sandra K.; McCormick, Ryan F.; Mullet, John E.

2017-01-01

Bioenergy sorghum is targeted for production in water-limited annual cropland therefore traits that improve plant water capture, water use efficiency, and resilience to water deficit are necessary to maximize productivity. A crop modeling framework, APSIM, was adapted to predict the growth and biomass yield of energy sorghum and to identify potentially useful traits for crop improvement. APSIM simulations of energy sorghum development and biomass accumulation replicated results from field experiments across multiple years, patterns of rainfall, and irrigation schemes. Modeling showed that energy sorghum's long duration of vegetative growth increased water capture and biomass yield by ~30% compared to short season crops in a water-limited production region. Additionally, APSIM was extended to enable modeling of VPD-limited transpiration traits that reduce crop water use under high vapor pressure deficits (VPDs). The response of transpiration rate to increasing VPD was modeled as a linear response until a VPD threshold was reached, at which the slope of the response decreases, representing a range of responses to VPD observed in sorghum germplasm. Simulation results indicated that the VPD-limited transpiration trait is most beneficial in hot and dry regions of production where crops are exposed to extended periods without rainfall during the season or to a terminal drought. In these environments, slower but more efficient transpiration increases biomass yield and prevents or delays the exhaustion of soil water and onset of leaf senescence. The VPD-limited transpiration responses observed in sorghum germplasm increased biomass accumulation by 20% in years with lower summer rainfall, and the ability to drastically reduce transpiration under high VPD conditions could increase biomass by 6% on average across all years. This work indicates that the productivity and resilience of bioenergy sorghum grown in water-limited environments could be further enhanced by development

Bioenergy Sorghum Crop Model Predicts VPD-Limited Transpiration Traits Enhance Biomass Yield in Water-Limited Environments.

PubMed

Truong, Sandra K; McCormick, Ryan F; Mullet, John E

2017-01-01

Bioenergy sorghum is targeted for production in water-limited annual cropland therefore traits that improve plant water capture, water use efficiency, and resilience to water deficit are necessary to maximize productivity. A crop modeling framework, APSIM, was adapted to predict the growth and biomass yield of energy sorghum and to identify potentially useful traits for crop improvement. APSIM simulations of energy sorghum development and biomass accumulation replicated results from field experiments across multiple years, patterns of rainfall, and irrigation schemes. Modeling showed that energy sorghum's long duration of vegetative growth increased water capture and biomass yield by ~30% compared to short season crops in a water-limited production region. Additionally, APSIM was extended to enable modeling of VPD-limited transpiration traits that reduce crop water use under high vapor pressure deficits (VPDs). The response of transpiration rate to increasing VPD was modeled as a linear response until a VPD threshold was reached, at which the slope of the response decreases, representing a range of responses to VPD observed in sorghum germplasm. Simulation results indicated that the VPD-limited transpiration trait is most beneficial in hot and dry regions of production where crops are exposed to extended periods without rainfall during the season or to a terminal drought. In these environments, slower but more efficient transpiration increases biomass yield and prevents or delays the exhaustion of soil water and onset of leaf senescence. The VPD-limited transpiration responses observed in sorghum germplasm increased biomass accumulation by 20% in years with lower summer rainfall, and the ability to drastically reduce transpiration under high VPD conditions could increase biomass by 6% on average across all years. This work indicates that the productivity and resilience of bioenergy sorghum grown in water-limited environments could be further enhanced by development

Bioenergy sorghum crop model predicts VPD-limited transpiration traits enhance biomass yield in water-limited environments

DOE PAGES

Truong, Sandra K.; McCormick, Ryan F.; Mullet, John E.

2017-03-21

Bioenergy sorghum is targeted for production in water-limited annual cropland therefore traits that improve plant water capture, water use efficiency, and resilience to water deficit are necessary to maximize productivity. A crop modeling framework, APSIM, was adapted to predict the growth and biomass yield of energy sorghum and to identify potentially useful traits for crop improvement. APSIM simulations of energy sorghum development and biomass accumulation replicated results from field experiments across multiple years, patterns of rainfall, and irrigation schemes. Modeling showed that energy sorghum’s long duration of vegetative growth increased water capture and biomass yield by ~30% compared to shortmore » season crops in a water-limited production region. Additionally, APSIM was extended to enable modeling of VPD-limited transpiration traits that reduce crop water use under high vapor pressure deficits (VPDs). The response of transpiration rate to increasing VPD was modeled as a linear response until a VPD threshold was reached, at which the slope of the response decreases, representing a range of responses to VPD observed in sorghum germplasm. Simulation results indicated that the VPD-limited transpiration trait is most beneficial in hot and dry regions of production where crops are exposed to extended periods without rainfall during the season or to a terminal drought. In these environments, slower but more efficient transpiration increases biomass yield and prevents or delays the exhaustion of soil water and onset of leaf senescence. The VPD-limited transpiration responses observed in sorghum germplasm increased biomass accumulation by 20% in years with lower summer rainfall, and the ability to drastically reduce transpiration under high VPD conditions could increase biomass by 6% on average across all years. This work indicates that the productivity and resilience of bioenergy sorghum grown in water-limited environments could be further enhanced by

Bioenergy sorghum crop model predicts VPD-limited transpiration traits enhance biomass yield in water-limited environments

DOE Office of Scientific and Technical Information (OSTI.GOV)

Truong, Sandra K.; McCormick, Ryan F.; Mullet, John E.

Bioenergy sorghum is targeted for production in water-limited annual cropland therefore traits that improve plant water capture, water use efficiency, and resilience to water deficit are necessary to maximize productivity. A crop modeling framework, APSIM, was adapted to predict the growth and biomass yield of energy sorghum and to identify potentially useful traits for crop improvement. APSIM simulations of energy sorghum development and biomass accumulation replicated results from field experiments across multiple years, patterns of rainfall, and irrigation schemes. Modeling showed that energy sorghum’s long duration of vegetative growth increased water capture and biomass yield by ~30% compared to shortmore » season crops in a water-limited production region. Additionally, APSIM was extended to enable modeling of VPD-limited transpiration traits that reduce crop water use under high vapor pressure deficits (VPDs). The response of transpiration rate to increasing VPD was modeled as a linear response until a VPD threshold was reached, at which the slope of the response decreases, representing a range of responses to VPD observed in sorghum germplasm. Simulation results indicated that the VPD-limited transpiration trait is most beneficial in hot and dry regions of production where crops are exposed to extended periods without rainfall during the season or to a terminal drought. In these environments, slower but more efficient transpiration increases biomass yield and prevents or delays the exhaustion of soil water and onset of leaf senescence. The VPD-limited transpiration responses observed in sorghum germplasm increased biomass accumulation by 20% in years with lower summer rainfall, and the ability to drastically reduce transpiration under high VPD conditions could increase biomass by 6% on average across all years. This work indicates that the productivity and resilience of bioenergy sorghum grown in water-limited environments could be further enhanced by

Optimizing rice yields while minimizing yield-scaled global warming potential.

PubMed

Pittelkow, Cameron M; Adviento-Borbe, Maria A; van Kessel, Chris; Hill, James E; Linquist, Bruce A

2014-05-01

To meet growing global food demand with limited land and reduced environmental impact, agricultural greenhouse gas (GHG) emissions are increasingly evaluated with respect to crop productivity, i.e., on a yield-scaled as opposed to area basis. Here, we compiled available field data on CH4 and N2 O emissions from rice production systems to test the hypothesis that in response to fertilizer nitrogen (N) addition, yield-scaled global warming potential (GWP) will be minimized at N rates that maximize yields. Within each study, yield N surplus was calculated to estimate deficit or excess N application rates with respect to the optimal N rate (defined as the N rate at which maximum yield was achieved). Relationships between yield N surplus and GHG emissions were assessed using linear and nonlinear mixed-effects models. Results indicate that yields increased in response to increasing N surplus when moving from deficit to optimal N rates. At N rates contributing to a yield N surplus, N2 O and yield-scaled N2 O emissions increased exponentially. In contrast, CH4 emissions were not impacted by N inputs. Accordingly, yield-scaled CH4 emissions decreased with N addition. Overall, yield-scaled GWP was minimized at optimal N rates, decreasing by 21% compared to treatments without N addition. These results are unique compared to aerobic cropping systems in which N2 O emissions are the primary contributor to GWP, meaning yield-scaled GWP may not necessarily decrease for aerobic crops when yields are optimized by N fertilizer addition. Balancing gains in agricultural productivity with climate change concerns, this work supports the concept that high rice yields can be achieved with minimal yield-scaled GWP through optimal N application rates. Moreover, additional improvements in N use efficiency may further reduce yield-scaled GWP, thereby strengthening the economic and environmental sustainability of rice systems. © 2013 John Wiley & Sons Ltd.

Applying the age-shift approach to model responses to midrotation fertilization

Treesearch

Colleen A. Carlson; Thomas R. Fox; H. Lee Allen; Timothy J. Albaugh

2010-01-01

Growth and yield models used to evaluate midrotation fertilization economics require adjustments to account for the typically observed responses. This study investigated the use of age-shift models to predict midrotation fertilizer responses. Age-shift prediction models were constructed from a regional study consisting of 43 installations of a nitrogen (N) by...

Crop yields response to water pressures in the Ebro basin in Spain: risk and water policy implications

NASA Astrophysics Data System (ADS)

Quiroga, S.; Fernández-Haddad, Z.; Iglesias, A.

2011-02-01

The increasing pressure on water systems in the Mediterranean enhances existing water conflicts and threatens water supply for agriculture. In this context, one of the main priorities for agricultural research and public policy is the adaptation of crop yields to water pressures. This paper focuses on the evaluation of hydrological risk and water policy implications for food production. Our methodological approach includes four steps. For the first step, we estimate the impacts of rainfall and irrigation water on crop yields. However, this study is not limited to general crop production functions since it also considers the linkages between those economic and biophysical aspects which may have an important effect on crop productivity. We use statistical models of yield response to address how hydrological variables affect the yield of the main Mediterranean crops in the Ebro river basin. In the second step, this study takes into consideration the effects of those interactions and analyzes gross value added sensitivity to crop production changes. We then use Montecarlo simulations to characterize crop yield risk to water variability. Finally we evaluate some policy scenarios with irrigated area adjustments that could cope in a context of increased water scarcity. A substantial decrease in irrigated land, of up to 30% of total, results in only moderate losses of crop productivity. The response is crop and region specific and may serve to prioritise adaptation strategies.

IMPACTS OF CLIMATE CHANGE ON RICE YIELD: EVALUATION OF THE EFFICACITY OF DIFFERENT MODELING APPROACHES

EPA Science Inventory

Increasing concentrations of carbon dioxide (CO2) and other greenhouse gases are expected to modify the climate of the earth in the next 50-100 years. echanisms of plant response to these changes need to be incorporated in models that predict crop yield to obtain an understanding...

Local yield stress statistics in model amorphous solids

NASA Astrophysics Data System (ADS)

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.

Influence of yield surface curvature on the macroscopic yielding and ductile failure of isotropic porous plastic materials

NASA Astrophysics Data System (ADS)

Dæhli, Lars Edvard Bryhni; Morin, David; Børvik, Tore; Hopperstad, Odd Sture

2017-10-01

Numerical unit cell models of an approximative representative volume element for a porous ductile solid are utilized to investigate differences in the mechanical response between a quadratic and a non-quadratic matrix yield surface. A Hershey equivalent stress measure with two distinct values of the yield surface exponent is employed as the matrix description. Results from the unit cell calculations are further used to calibrate a heuristic extension of the Gurson model which incorporates effects of the third deviatoric stress invariant. An assessment of the porous plasticity model reveals its ability to describe the unit cell response to some extent, however underestimating the effect of the Lode parameter for the lower triaxiality ratios imposed in this study when compared to unit cell simulations. Ductile failure predictions by means of finite element simulations using a unit cell model that resembles an imperfection band are then conducted to examine how the non-quadratic matrix yield surface influences the failure strain as compared to the quadratic matrix yield surface. Further, strain localization predictions based on bifurcation analyses and imperfection band analyses are undertaken using the calibrated porous plasticity model. These simulations are then compared to the unit cell calculations in order to elucidate the differences between the various modelling strategies. The current study reveals that strain localization analyses using an imperfection band model and a spatially discretized unit cell are in reasonable agreement, while the bifurcation analyses predict higher strain levels at localization. Imperfection band analyses are finally used to calculate failure loci for the quadratic and the non-quadratic matrix yield surface under a wide range of loading conditions. The underlying matrix yield surface is demonstrated to have a pronounced influence on the onset of strain localization.

Evidence for compensatory photosynthetic and yield response of soybeans to aphid herbivory

DOE PAGES

Kucharik, Christopher J.; Mork, Amelia C.; Meehan, Timothy D.; ...

2016-04-13

The soybean aphid, Aphis glycines Matsumura, an exotic species in North America that has been detected in 21 U.S. states and Canada, is a major pest for soybean that can reduce maximum photosynthetic capacity and yields. Our existing knowledge is based on relatively few studies that do not span a wide variety of environmental conditions, and often focus on relatively high and damaging population pressure. We examined the effects of varied populations and duration of soybean aphids on soybean photosynthetic rates and yield in two experiments. In a 2011 field study, we found that plants with low cumulative aphid daysmore » (CAD, less than 2,300) had higher yields than plants not experiencing significant aphid pressure, suggesting a compensatory growth response to low aphid pressure. This response did not hold at higher CAD, and yields declined. In a 2013 controlled-environment greenhouse study, soybean plants were well-watered and fertilized with nitrogen (N), and aphid populations were manipulated to reach moderate to high levels (8,000–50,000 CAD). Plants tolerated these population levels when aphids were introduced during the vegetative or reproductive phenological stages of the plant, showing no significant reduction in yield. Leaf N concentration and CAD were positively and significantly correlated with increasing ambient photosynthetic rates. Our findings suggest that, given the right environmental conditions, modern soybean plants can withstand higher aphid pressure than previously assumed. Moreover, soybean plants also responded positively through a compensatory photosynthetic effect to moderate population pressure, contributing to stable or increased yield.« less

Forest Growth and Yield Models Viewed From a Different Perspective

Treesearch

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...

Application of wheat yield model to United States and India. [Great Plains

NASA Technical Reports Server (NTRS)

Feyerherm, A. M. (Principal Investigator)

1977-01-01

The author has identified the following significant results. The wheat yield model was applied to the major wheat-growing areas of the US and India. In the US Great Plains, estimates from the winter and spring wheat models agreed closely with USDA-SRS values in years with the lowest yields, but underestimated in years with the highest yields. Application to the Eastern Plains and Northwest indicated the importance of cultural factors, as well as meteorological ones in the model. It also demonstrated that the model could be used, in conjunction with USDA-SRRS estimates, to estimate yield losses due to factors not included in the model, particularly diseases and freezes. A fixed crop calendar for India was built from a limited amount of available plot data from that country. Application of the yield model gave measurable evidence that yield variation from state to state was due to different mixes of levels of meteorological and cultural factors.

Plausible rice yield losses under future climate warming.

PubMed

Zhao, Chuang; Piao, Shilong; Wang, Xuhui; Huang, Yao; Ciais, Philippe; Elliott, Joshua; Huang, Mengtian; Janssens, Ivan A; Li, Tao; Lian, Xu; Liu, Yongwen; Müller, Christoph; Peng, Shushi; Wang, Tao; Zeng, Zhenzhong; Peñuelas, Josep

2016-12-19

Rice is the staple food for more than 50% of the world's population 1-3 . Reliable prediction of changes in rice yield is thus central for maintaining global food security. This is an extraordinary challenge. Here, we compare the sensitivity of rice yield to temperature increase derived from field warming experiments and three modelling approaches: statistical models, local crop models and global gridded crop models. Field warming experiments produce a substantial rice yield loss under warming, with an average temperature sensitivity of -5.2 ± 1.4% K -1 . Local crop models give a similar sensitivity (-6.3 ± 0.4% K -1 ), but statistical and global gridded crop models both suggest less negative impacts of warming on yields (-0.8 ± 0.3% and -2.4 ± 3.7% K -1 , respectively). Using data from field warming experiments, we further propose a conditional probability approach to constrain the large range of global gridded crop model results for the future yield changes in response to warming by the end of the century (from -1.3% to -9.3% K -1 ). The constraint implies a more negative response to warming (-8.3 ± 1.4% K -1 ) and reduces the spread of the model ensemble by 33%. This yield reduction exceeds that estimated by the International Food Policy Research Institute assessment (-4.2 to -6.4% K -1 ) (ref. 4). Our study suggests that without CO 2 fertilization, effective adaptation and genetic improvement, severe rice yield losses are plausible under intensive climate warming scenarios.

Modeling the effects of ozone on soybean growth and yield.

PubMed

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.

Recent changes in county-level corn yield variability in the United States from observations and crop models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leng, Guoyong

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,more » 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

SCS-CN based time-distributed sediment yield model

NASA Astrophysics Data System (ADS)

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.

Crop yield response to climate change varies with crop spatial distribution pattern

DOE PAGES

Leng, Guoyong; Huang, Maoyi

2017-05-03

The linkage between crop yield and climate variability has been confirmed in numerous studies using statistical approaches. A crucial assumption in these studies is that crop spatial distribution pattern is constant over time. Here, we explore how changes in county-level corn spatial distribution pattern modulate the response of its yields to climate change at the state level over the Contiguous United States. Our results show that corn yield response to climate change varies with crop spatial distribution pattern, with distinct impacts on the magnitude and even the direction at the state level. Corn yield is predicted to decrease by 20~40%more » by 2050s when considering crop spatial distribution pattern changes, which is 6~12% less than the estimates with fixed cropping pattern. The beneficial effects are mainly achieved by reducing the negative impacts of daily maximum temperature and strengthening the positive impacts of precipitation. Our results indicate that previous empirical studies could be biased in assessing climate change impacts by ignoring the changes in crop spatial distribution pattern. As a result, this has great implications for understanding the increasing debates on whether climate change will be a net gain or loss for regional agriculture.« less

Crop yield response to climate change varies with crop spatial distribution pattern

DOE Office of Scientific and Technical Information (OSTI.GOV)

Leng, Guoyong; Huang, Maoyi

The linkage between crop yield and climate variability has been confirmed in numerous studies using statistical approaches. A crucial assumption in these studies is that crop spatial distribution pattern is constant over time. Here, we explore how changes in county-level corn spatial distribution pattern modulate the response of its yields to climate change at the state level over the Contiguous United States. Our results show that corn yield response to climate change varies with crop spatial distribution pattern, with distinct impacts on the magnitude and even the direction at the state level. Corn yield is predicted to decrease by 20~40%more » by 2050s when considering crop spatial distribution pattern changes, which is 6~12% less than the estimates with fixed cropping pattern. The beneficial effects are mainly achieved by reducing the negative impacts of daily maximum temperature and strengthening the positive impacts of precipitation. Our results indicate that previous empirical studies could be biased in assessing climate change impacts by ignoring the changes in crop spatial distribution pattern. As a result, this has great implications for understanding the increasing debates on whether climate change will be a net gain or loss for regional agriculture.« less

Fission yield calculation using toy model based on Monte Carlo simulation

NASA Astrophysics Data System (ADS)

Jubaidah, Kurniadi, Rizal

2015-09-01

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 (Rc), mean of left curve (μL) and mean of right curve (μR), deviation of left curve (σL) and deviation of right curve (σR). 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

Models of subjective response to in-flight motion data

NASA Technical Reports Server (NTRS)

Rudrapatna, A. N.; Jacobson, I. D.

1973-01-01

Mathematical relationships between subjective comfort and environmental variables in an air transportation system are investigated. As a first step in model building, only the motion variables are incorporated and sensitivities are obtained using stepwise multiple regression analysis. The data for these models have been collected from commercial passenger flights. Two models are considered. In the first, subjective comfort is assumed to depend on rms values of the six-degrees-of-freedom accelerations. The second assumes a Rustenburg type human response function in obtaining frequency weighted rms accelerations, which are used in a linear model. The form of the human response function is examined and the results yield a human response weighting function for different degrees of freedom.

Responses of inulin content and inulin yield of Jerusalem artichoke genotypes to seasonal environments

USDA-ARS?s Scientific Manuscript database

Seasonal variation (e.g. temperature and photoperiod) between growing seasons might affect inulin content and inulin yield of Jerusalem artichoke. However, there is limited information on genotypic response to seasons for inulin content and inulin yield. The objective of this study was to investig...

The summer flow and water yield response to timber harvest

Treesearch

Elizabeth T. Keppeler

1998-01-01

Continuous measurement of streamflow at the Caspar Creek watersheds has led to several analyses of the effects of two harvest methods (selection and clearcut) on summer flows and annual yield. Although all Caspar Creek analyses have indicated an increase in runoff after timber removal, the magnitude and duration of the response depend on the nature and extent of the...

Fission yield calculation using toy model based on Monte Carlo simulation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jubaidah, E-mail: jubaidah@student.itb.ac.id; Physics Department, Faculty of Mathematics and Natural Science – State University of Medan. Jl. Willem Iskandar Pasar V Medan Estate – North Sumatera, Indonesia 20221; Kurniadi, Rizal, E-mail: rijalk@fi.itb.ac.id

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. Theremore » 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 90« less

Multivariate Statistical Models for Predicting Sediment Yields from Southern California Watersheds

USGS Publications Warehouse

Gartner, Joseph E.; Cannon, Susan H.; Helsel, Dennis R.; Bandurraga, Mark

2009-01-01

Debris-retention basins in Southern California are frequently used to protect communities and infrastructure from the hazards of flooding and debris flow. Empirical models that predict sediment yields are used to determine the size of the basins. Such models have been developed using analyses of records of the amount of material removed from debris retention basins, associated rainfall amounts, measures of watershed characteristics, and wildfire extent and history. In this study we used multiple linear regression methods to develop two updated empirical models to predict sediment yields for watersheds located in Southern California. The models are based on both new and existing measures of volume of sediment removed from debris retention basins, measures of watershed morphology, and characterization of burn severity distributions for watersheds located in Ventura, Los Angeles, and San Bernardino Counties. The first model presented reflects conditions in watersheds located throughout the Transverse Ranges of Southern California and is based on volumes of sediment measured following single storm events with known rainfall conditions. The second model presented is specific to conditions in Ventura County watersheds and was developed using volumes of sediment measured following multiple storm events. To relate sediment volumes to triggering storm rainfall, a rainfall threshold was developed to identify storms likely to have caused sediment deposition. A measured volume of sediment deposited by numerous storms was parsed among the threshold-exceeding storms based on relative storm rainfall totals. The predictive strength of the two models developed here, and of previously-published models, was evaluated using a test dataset consisting of 65 volumes of sediment yields measured in Southern California. The evaluation indicated that the model developed using information from single storm events in the Transverse Ranges best predicted sediment yields for watersheds in San

Random Forests for Global and Regional Crop Yield Predictions.

PubMed

Jeong, Jig Han; Resop, Jonathan P; Mueller, Nathaniel D; Fleisher, David H; Yun, Kyungdahm; Butler, Ethan E; Timlin, Dennis J; Shim, Kyo-Moon; Gerber, James S; Reddy, Vangimalla R; Kim, Soo-Hyung

2016-01-01

Accurate predictions of crop yield are critical for developing effective agricultural and food policies at the regional and global scales. We evaluated a machine-learning method, Random Forests (RF), for its ability to predict crop yield responses to climate and biophysical variables at global and regional scales in wheat, maize, and potato in comparison with multiple linear regressions (MLR) serving as a benchmark. We used crop yield data from various sources and regions for model training and testing: 1) gridded global wheat grain yield, 2) maize grain yield from US counties over thirty years, and 3) potato tuber and maize silage yield from the northeastern seaboard region. RF was found highly capable of predicting crop yields and outperformed MLR benchmarks in all performance statistics that were compared. For example, the root mean square errors (RMSE) ranged between 6 and 14% of the average observed yield with RF models in all test cases whereas these values ranged from 14% to 49% for MLR models. Our results show that RF is an effective and versatile machine-learning method for crop yield predictions at regional and global scales for its high accuracy and precision, ease of use, and utility in data analysis. RF may result in a loss of accuracy when predicting the extreme ends or responses beyond the boundaries of the training data.

Separating heat stress from moisture stress: analyzing yield response to high temperature in irrigated maize

NASA Astrophysics Data System (ADS)

Carter, Elizabeth K.; Melkonian, Jeff; Riha, Susan J.; Shaw, Stephen B.

2016-09-01

Several recent studies have indicated that high air temperatures are limiting maize (Zea mays L.) yields in the US Corn Belt and project significant yield losses with expected increases in growing season temperatures. Further work has suggested that high air temperatures are indicative of high evaporative demand, and that decreases in maize yields which correlate to high temperatures and vapor pressure deficits (VPD) likely reflect underlying soil moisture limitations. It remains unclear whether direct high temperature impacts on yields, independent of moisture stress, can be observed under current temperature regimes. Given that projected high temperature and moisture may not co-vary the same way as they have historically, quantitative analyzes of direct temperature impacts are critical for accurate yield projections and targeted mitigation strategies under shifting temperature regimes. To evaluate yield response to above optimum temperatures independent of soil moisture stress, we analyzed climate impacts on irrigated maize yields obtained from the National Corn Growers Association (NCGA) corn yield contests for Nebraska, Kansas and Missouri. In irrigated maize, we found no evidence of a direct negative impact on yield by daytime air temperature, calculated canopy temperature, or VPD when analyzed seasonally. Solar radiation was the primary yield-limiting climate variable. Our analyses suggested that elevated night temperature impacted yield by increasing rates of phenological development. High temperatures during grain-fill significantly interacted with yields, but this effect was often beneficial and included evidence of acquired thermo-tolerance. Furthermore, genetics and management—information uniquely available in the NCGA contest data—explained more yield variability than climate, and significantly modified crop response to climate. Thermo-acclimation, improved genetics and changes to management practices have the potential to partially or completely

Comparison of Response Surface and Kriging Models for Multidisciplinary Design Optimization

NASA Technical Reports Server (NTRS)

Simpson, Timothy W.; Korte, John J.; Mauery, Timothy M.; Mistree, Farrokh

1998-01-01

In this paper, we compare and contrast the use of second-order response surface models and kriging models for approximating non-random, deterministic computer analyses. After reviewing the response surface method for constructing polynomial approximations, kriging is presented as an alternative approximation method for the design and analysis of computer experiments. Both methods are applied to the multidisciplinary design of an aerospike nozzle which consists of a computational fluid dynamics model and a finite-element model. Error analysis of the response surface and kriging models is performed along with a graphical comparison of the approximations, and four optimization problems m formulated and solved using both sets of approximation models. The second-order response surface models and kriging models-using a constant underlying global model and a Gaussian correlation function-yield comparable results.

An efficient energy response model for liquid scintillator detectors

NASA Astrophysics Data System (ADS)

Lebanowski, Logan; Wan, Linyan; Ji, Xiangpan; Wang, Zhe; Chen, Shaomin

2018-05-01

Liquid scintillator detectors are playing an increasingly important role in low-energy neutrino experiments. In this article, we describe a generic energy response model of liquid scintillator detectors that provides energy estimations of sub-percent accuracy. This model fits a minimal set of physically-motivated parameters that capture the essential characteristics of scintillator response and that can naturally account for changes in scintillator over time, helping to avoid associated biases or systematic uncertainties. The model employs a one-step calculation and look-up tables, yielding an immediate estimation of energy and an efficient framework for quantifying systematic uncertainties and correlations.

Modelling drought-related yield losses in Iberia using remote sensing and multiscalar indices

NASA Astrophysics Data System (ADS)

Ribeiro, Andreia F. S.; Russo, Ana; Gouveia, Célia M.; Páscoa, Patrícia

2018-04-01

The response of two rainfed winter cereal yields (wheat and barley) to drought conditions in the Iberian Peninsula (IP) was investigated for a long period (1986-2012). Drought hazard was evaluated based on the multiscalar Standardized Precipitation Evapotranspiration Index (SPEI) and three remote sensing indices, namely the Vegetation Condition (VCI), the Temperature Condition (TCI), and the Vegetation Health (VHI) Indices. A correlation analysis between the yield and the drought indicators was conducted, and multiple linear regression (MLR) and artificial neural network (ANN) models were established to estimate yield at the regional level. The correlation values suggested that yield reduces with moisture depletion (low values of VCI) during early-spring and with too high temperatures (low values of TCI) close to the harvest time. Generally, all drought indicators displayed greatest influence during the plant stages in which the crop is photosynthetically more active (spring and summer), rather than the earlier moments of plants life cycle (autumn/winter). Our results suggested that SPEI is more relevant in the southern sector of the IP, while remote sensing indices are rather good in estimating cereal yield in the northern sector of the IP. The strength of the statistical relationships found by MLR and ANN methods is quite similar, with some improvements found by the ANN. A great number of true positives (hits) of occurrence of yield-losses exhibiting hit rate (HR) values higher than 69% was obtained.

Statistics-based model for prediction of chemical biosynthesis yield from Saccharomyces cerevisiae

PubMed Central

2011-01-01

Background The robustness of Saccharomyces cerevisiae in facilitating industrial-scale production of ethanol extends its utilization as a platform to synthesize other metabolites. Metabolic engineering strategies, typically via pathway overexpression and deletion, continue to play a key role for optimizing the conversion efficiency of substrates into the desired products. However, chemical production titer or yield remains difficult to predict based on reaction stoichiometry and mass balance. We sampled a large space of data of chemical production from S. cerevisiae, and developed a statistics-based model to calculate production yield using input variables that represent the number of enzymatic steps in the key biosynthetic pathway of interest, metabolic modifications, cultivation modes, nutrition and oxygen availability. Results Based on the production data of about 40 chemicals produced from S. cerevisiae, metabolic engineering methods, nutrient supplementation, and fermentation conditions described therein, we generated mathematical models with numerical and categorical variables to predict production yield. Statistically, the models showed that: 1. Chemical production from central metabolic precursors decreased exponentially with increasing number of enzymatic steps for biosynthesis (>30% loss of yield per enzymatic step, P-value = 0); 2. Categorical variables of gene overexpression and knockout improved product yield by 2~4 folds (P-value < 0.1); 3. Addition of notable amount of intermediate precursors or nutrients improved product yield by over five folds (P-value < 0.05); 4. Performing the cultivation in a well-controlled bioreactor enhanced the yield of product by three folds (P-value < 0.05); 5. Contribution of oxygen to product yield was not statistically significant. Yield calculations for various chemicals using the linear model were in fairly good agreement with the experimental values. The model generally underestimated the ethanol production as

Integrated model for predicting rice yield with climate change

NASA Astrophysics Data System (ADS)

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.

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

NASA Astrophysics Data System (ADS)

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

Response of barley to grasshopper defoliation in interior Alaska: dry matter and grain yield.

PubMed

Begna, Sultan H; Fielding, Dennis J

2005-12-01

Barley, Hordeum vulgare L., is well adapted to subarctic Alaska growing conditions, but little is known about its response to grasshopper defoliation. A field experiment was conducted to study dry matter and grain yield in response to a combination of grasshopper defoliation and weeds in 2002 and 2003 near Delta Junction, AK (63 degrees 55' N, 145 degrees 20' W). Barley plants at third to fourth leaf stage were exposed to a combination of two levels of weeds (present or absent) and four densities of grasshoppers (equivalent to 0, 25, 50, and 75 grasshoppers per m2) of third to fourth instars of Melanoplus sanguinipes (F). Dry matter accumulation by the barley plants was determined at three times during the growing seasons: approximately 10 d after introduction of the grasshoppers, shortly after anthesis, and at maturity. Dry matter accumulation and grain yield were much lower in 2003 than in 2002, probably due to very low levels of soil moisture early in the growing season of 2003. Head clipping accounted for a greater portion of yield loss in 2003 than in 2002. The percentage of reduction in harvestable yield due to grasshoppers remained fairly constant between years (1.9 and 1.4 g per grasshopper per m2 in 2002 and 2003, respectively) despite a large difference in overall yield. Examination of the yield components suggest that yields were reduced by the early season drought in 2003 primarily through fewer seeds per head, whereas grasshoppers in both years reduced average seed weight, but not numbers of seeds.

Estimating the responses of winter wheat yields to moisture variations in the past 35 years in Jiangsu Province of China

PubMed Central

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. PMID:29329353

Estimating the responses of winter wheat yields to moisture variations in the past 35 years in Jiangsu Province of China.

PubMed

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.

Development of LACIE CCEA-1 weather/wheat yield models. [regression analysis

NASA Technical Reports Server (NTRS)

Strommen, N. D.; Sakamoto, C. M.; Leduc, S. K.; Umberger, D. E. (Principal Investigator)

1979-01-01

The advantages and disadvantages of the casual (phenological, dynamic, physiological), statistical regression, and analog approaches to modeling for grain yield are examined. Given LACIE's primary goal of estimating wheat production for the large areas of eight major wheat-growing regions, the statistical regression approach of correlating historical yield and climate data offered the Center for Climatic and Environmental Assessment the greatest potential return within the constraints of time and data sources. The basic equation for the first generation wheat-yield model is given. Topics discussed include truncation, trend variable, selection of weather variables, episodic events, strata selection, operational data flow, weighting, and model results.

Response of winter and spring wheat grain yields to meteorological variation

NASA Technical Reports Server (NTRS)

Feyerherm, A. M.; Kanemasu, E. T.; Paulsen, G. M.

1977-01-01

Mathematical models which quantify the relation of wheat yield to selected weather-related variables are presented. Other sources of variation (amount of applied nitrogen, improved varieties, cultural practices) have been incorporated in the models to explain yield variation both singly and in combination with weather-related variables. Separate models were developed for fall-planted (winter) and spring-planted (spring) wheats. Meteorological variation is observed, basically, by daily measurements of minimum and maximum temperatures, precipitation, and tabled values of solar radiation at the edge of the atmosphere and daylength. Two different soil moisture budgets are suggested to compute simulated values of evapotranspiration; one uses the above-mentioned inputs, the other uses the measured temperatures and precipitation but replaces the tabled values (solar radiation and daylength) by measured solar radiation and satellite-derived multispectral scanner data to estimate leaf area index. Weather-related variables are defined by phenological stages, rather than calendar periods, to make the models more universally applicable.

A meteorologically-driven yield reduction model for spring and winter wheat

NASA Technical Reports Server (NTRS)

Ravet, F. W.; Cremins, W. J.; Taylor, T. W.; Ashburn, P.; Smika, D.; Aaronson, A. (Principal Investigator)

1983-01-01

A yield reduction model for spring and winter wheat was developed for large-area crop condition assessment. Reductions are expressed in percentage from a base yield and are calculated on a daily basis. The algorithm contains two integral components: a two-layer soil water budget model and a crop calendar routine. Yield reductions associated with hot, dry winds (Sukhovey) and soil moisture stress are determined. Input variables include evapotranspiration, maximum temperature and precipitation; subsequently crop-stage, available water holding percentage and stress duration are evaluated. No specific base yield is required and may be selected by the user; however, it may be generally characterized as the maximum likely to be produced commercially at a location.

Data-driven reduced order models for effective yield strength and partitioning of strain in multiphase materials

NASA Astrophysics Data System (ADS)

Latypov, Marat I.; Kalidindi, Surya R.

2017-10-01

There is a critical need for the development and verification of practically useful multiscale modeling strategies for simulating the mechanical response of multiphase metallic materials with heterogeneous microstructures. In this contribution, we present data-driven reduced order models for effective yield strength and strain partitioning in such microstructures. These models are built employing the recently developed framework of Materials Knowledge Systems that employ 2-point spatial correlations (or 2-point statistics) for the quantification of the heterostructures and principal component analyses for their low-dimensional representation. The models are calibrated to a large collection of finite element (FE) results obtained for a diverse range of microstructures with various sizes, shapes, and volume fractions of the phases. The performance of the models is evaluated by comparing the predictions of yield strength and strain partitioning in two-phase materials with the corresponding predictions from a classical self-consistent model as well as results of full-field FE simulations. The reduced-order models developed in this work show an excellent combination of accuracy and computational efficiency, and therefore present an important advance towards computationally efficient microstructure-sensitive multiscale modeling frameworks.

National Variation in Crop Yield Production Functions

NASA Astrophysics Data System (ADS)

Devineni, N.; Rising, J. A.

2017-12-01

A new multilevel model for yield prediction at the county scale using regional climate covariates is presented in this paper. A new crop specific water deficit index, growing degree days, extreme degree days, and time-trend as an approximation of technology improvements are used as predictors to estimate annual crop yields for each county from 1949 to 2009. Every county in the United States is allowed to have unique parameters describing how these weather predictors are related to yield outcomes. County-specific parameters are further modeled as varying according to climatic characteristics, allowing the prediction of parameters in regions where crops are not currently grown and into the future. The structural relationships between crop yield and regional climate as well as trends are estimated simultaneously. All counties are modeled in a single multilevel model with partial pooling to automatically group and reduce estimation uncertainties. The model captures up to 60% of the variability in crop yields after removing the effect of technology, does well in out of sample predictions and is useful in relating the climate responses to local bioclimatic factors. We apply the predicted growing models in a cost-benefit analysis to identify the most economically productive crop in each county.

Improving Rice Modeling Success Rate with Ternary Non-structural Fertilizer Response Model.

PubMed

Li, Juan; Zhang, Mingqing; Chen, Fang; Yao, Baoquan

2018-06-13

Fertilizer response modelling is an important technical approach to realize metrological fertilization on rice. With the goal of solving the problems of a low success rate of a ternary quadratic polynomial model (TPFM) and to expand the model's applicability, this paper established a ternary non-structural fertilizer response model (TNFM) based on the experimental results from N, P and K fertilized rice fields. Our research results showed that the TNFM significantly improved the modelling success rate by addressing problems arising from setting the bias and multicollinearity in a TPFM. The results from 88 rice field trials in China indicated that the proportion of typical TNFMs that satisfy the general fertilizer response law of plant nutrition was 40.9%, while the analogous proportion of TPFMs was only 26.1%. The recommended fertilization showed a significant positive linear correlation between the two models, and the parameters N 0 , P 0 and K 0 that estimated the value of soil supplying nutrient equivalents can be used as better indicators of yield potential in plots where no N or P or K fertilizer was applied. The theoretical analysis showed that the new model has a higher fitting accuracy and a wider application range.

Simulating evapotranspiration (ET) yield response of selected corn varieties under full and limited irrigation in the Texas High Plains using DSSAT-CERES-Maize

USDA-ARS?s Scientific Manuscript database

Water scarcity due to drought and groundwater depletion has led to increased interest in deficit irrigation strategies that reduce irrigation requirements while maintaining profitable yields. This has resulted in an increase in the number modeling studies aimed at evaluating crop response to limite...

Top ten models constrained by b {yields} s{gamma}

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hewett, J.L.

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.

Biogas and methane yield in response to co- and separate digestion of biomass wastes.

PubMed

Adelard, Laetitia; Poulsen, Tjalfe G; Rakotoniaina, Volana

2015-01-01

The impact of co-digestion as opposed to separate digestion, on biogas and methane yield (apparent synergetic effects) was investigated for three biomass materials (pig manure, cow manure and food waste) under mesophilic conditions over a 36 day period. In addition to the three biomass materials (digested separately), 13 biomass mixtures (co-digested) were used. Two approaches for modelling biogas and methane yield during co-digestion, based on volatile solids concentration and ultimate gas and methane potentials, were evaluated. The dependency of apparent synergetic effects on digestion time and biomass mixture composition was further assessed using measured cumulative biogas and methane yields and specific biogas and methane generation rates. Results indicated that it is possible, based on known volatile solids concentration and ultimate biogas or methane yields for a set of biomass materials digested separately, to accurately estimate gas yields for biomass mixtures made from these materials using calibrated models. For the biomass materials considered here, modelling indicated that the addition of pig manure is the main cause of synergetic effects. Co-digestion generally resulted in improved ultimate biogas and methane yields compared to separate digestion. Biogas and methane production was furthermore significantly higher early (0-7 days) and to some degree also late (above 20 days) in the digestion process during co-digestion. © The Author(s) 2014.

Impacts of extreme heat and drought on crop yields in China: an assessment by using the DLEM-AG2 model

NASA Astrophysics Data System (ADS)

Zhang, J.; Yang, J.; Pan, S.; Tian, H.

2016-12-01

China is not only one of the major agricultural production countries with the largest population in the world, but it is also the most susceptible to climate change and extreme events. Much concern has been raised about how extreme climate has affected crop yield, which is crucial for China's food supply security. However, the quantitative assessment of extreme heat and drought impacts on crop yield in China has rarely been investigated. By using the Dynamic Land Ecosystem Model (DLEM-AG2), a highly integrated process-based ecosystem model with crop-specific simulation, here we quantified spatial and temporal patterns of extreme climatic heat and drought stress and their impacts on the yields of major food crops (rice, wheat, maize, and soybean) across China during 1981-2015, and further investigated the underlying mechanisms. Simulated results showed that extreme heat and drought stress significantly reduced national cereal production and increased the yield gaps between potential yield and rain-fed yield. The drought stress was the primary factor to reduce crop yields in the semi-arid and arid regions, and extreme heat stress slightly aggravated the yield loss. The yield gap between potential yield and rain-fed yield was larger at locations with lower precipitation. Our results suggest that a large exploitable yield gap in response to extreme climatic heat-drought stress offers an opportunity to increase productivity in China by optimizing agronomic practices, such as irrigation, fertilizer use, sowing density, and sowing date.

Optimization of MR fluid Yield stress using Taguchi Method and Response Surface Methodology Techniques

NASA Astrophysics Data System (ADS)

Mangal, S. K.; Sharma, Vivek

2018-02-01

Magneto rheological fluids belong to a class of smart materials whose rheological characteristics such as yield stress, viscosity etc. changes in the presence of applied magnetic field. In this paper, optimization of MR fluid constituents is obtained with on-state yield stress as response parameter. For this, 18 samples of MR fluids are prepared using L-18 Orthogonal Array. These samples are experimentally tested on a developed & fabricated electromagnet setup. It has been found that the yield stress of MR fluid mainly depends on the volume fraction of the iron particles and type of carrier fluid used in it. The optimal combination of the input parameters for the fluid are found to be as Mineral oil with a volume percentage of 67%, iron powder of 300 mesh size with a volume percentage of 32%, oleic acid with a volume percentage of 0.5% and tetra-methyl-ammonium-hydroxide with a volume percentage of 0.7%. This optimal combination of input parameters has given the on-state yield stress as 48.197 kPa numerically. An experimental confirmation test on the optimized MR fluid sample has been then carried out and the response parameter thus obtained has found matching quite well (less than 1% error) with the numerically obtained values.

A spectral-spatial-dynamic hierarchical Bayesian (SSD-HB) model for estimating soybean yield

NASA Astrophysics Data System (ADS)

Kazama, Yoriko; Kujirai, Toshihiro

2014-10-01

A method called a "spectral-spatial-dynamic hierarchical-Bayesian (SSD-HB) model," which can deal with many parameters (such as spectral and weather information all together) by reducing the occurrence of multicollinearity, is proposed. Experiments conducted on soybean yields in Brazil fields with a RapidEye satellite image indicate that the proposed SSD-HB model can predict soybean yield with a higher degree of accuracy than other estimation methods commonly used in remote-sensing applications. In the case of the SSD-HB model, the mean absolute error between estimated yield of the target area and actual yield is 0.28 t/ha, compared to 0.34 t/ha when conventional PLS regression was applied, showing the potential effectiveness of the proposed model.

Field warming experiments shed light on the wheat yield response to temperature in China

PubMed Central

Zhao, Chuang; Piao, Shilong; Huang, Yao; Wang, Xuhui; Ciais, Philippe; Huang, Mengtian; Zeng, Zhenzhong; Peng, Shushi

2016-01-01

Wheat growth is sensitive to temperature, but the effect of future warming on yield is uncertain. Here, focusing on China, we compiled 46 observations of the sensitivity of wheat yield to temperature change (SY,T, yield change per °C) from field warming experiments and 102 SY,T estimates from local process-based and statistical models. The average SY,T from field warming experiments, local process-based models and statistical models is −0.7±7.8(±s.d.)% per °C, −5.7±6.5% per °C and 0.4±4.4% per °C, respectively. Moreover, SY,T is different across regions and warming experiments indicate positive SY,T values in regions where growing-season mean temperature is low, and water supply is not limiting, and negative values elsewhere. Gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project appear to capture the spatial pattern of SY,T deduced from warming observations. These results from local manipulative experiments could be used to improve crop models in the future. PMID:27853151

Modelling Bambara Groundnut Yield in Southern Africa: Towards a Climate-Resilient Future

NASA Technical Reports Server (NTRS)

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.

Modeling the impact of climate change on watershed discharge and sediment yield in the black soil region, northeastern China

NASA Astrophysics Data System (ADS)

Li, Zhiying; Fang, Haiyan

2017-09-01

Climate change is expected to impact discharge and sediment yield in watersheds. The purpose of this paper is to assess the potential impacts of climate change on water discharge and sediment yield for the Yi'an watershed of the black soil region, northeastern China, based on the newly released Representative Concentration Pathways (RCPs) during 2071-2099. For this purpose, the TETIS model was implemented to simulate the hydrological and sedimentological responses to climate change. The model calibration (1971-1977) and validation (1978-1987) performances were rated as satisfactory. The modeling results for the four RCP scenarios relative to the control scenario under the same land use configuration indicated an increase in discharge of 16.3% (RCP 2.6), 14.3% (RCP 4.5), 36.7% (RCP 6.0) and 71.4% (RCP 8.5) and an increase in the sediment yield of 16.5% (RCP 2.6), 32.4% (RCP 4.5), 81.8% (RCP 6.0) and 170% (RCP 8.5). This implies that the negative impact of climate change on sediment yield is generally greater than that on discharge. At the monthly scale, both discharge and sediment yield increased dramatically in April to June and August to September. A more vigorous hydrological cycle and an increase in high values of sediment yield are also expected. These changes in annual discharge and sediment yield were closely linked with changes in precipitation, whereas monthly changes in late spring and autumn were mainly related to temperature. This study highlights the possible adverse impact of climate change on discharge and sediment yield in the black soil region of northeastern China and could provide scientific basis for adaptive management.

Analyzing and modelling the effect of long-term fertilizer management on crop yield and soil organic carbon in China.

PubMed

Zhang, Jie; Balkovič, Juraj; Azevedo, Ligia B; Skalský, Rastislav; Bouwman, Alexander F; Xu, Guang; Wang, Jinzhou; Xu, Minggang; Yu, Chaoqing

2018-06-15

This study analyzes the influence of various fertilizer management practices on crop yield and soil organic carbon (SOC) based on the long-term field observations and modelling. Data covering 11 years from 8 long-term field trials were included, representing a range of typical soil, climate, and agro-ecosystems in China. The process-based model EPIC (Environmental Policy Integrated Climate model) was used to simulate the response of crop yield and SOC to various fertilization regimes. The results showed that the yield and SOC under additional manure application treatment were the highest while the yield under control treatment was the lowest (30%-50% of NPK yield) at all sites. The SOC in northern sites appeared more dynamic than that in southern sites. The variance partitioning analysis (VPA) showed more variance of crop yield could be explained by the fertilization factor (42%), including synthetic nitrogen (N), phosphorus (P), potassium (K) fertilizers, and fertilizer NPK combined with manure. The interactive influence of soil (total N, P, K, and available N, P, K) and climate factors (mean annual temperature and precipitation) determine the largest part of the SOC variance (32%). EPIC performs well in simulating both the dynamics of crop yield (NRMSE = 32% and 31% for yield calibration and validation) and SOC (NRMSE = 13% and 19% for SOC calibration and validation) under diverse fertilization practices in China. EPIC can assist in predicting the impacts of different fertilization regimes on crop growth and soil carbon dynamics, and contribute to the optimization of fertilizer management for different areas in China. Copyright © 2018. Published by Elsevier B.V.

Model-assisted forest yield estimation with light detection and ranging

Treesearch

Jacob L. Strunk; Stephen E. Reutebuch; Hans-Erik Andersen; Peter J. Gould; Robert J. McGaughey

2012-01-01

Previous studies have demonstrated that light detection and ranging (LiDAR)-derived variables can be used to model forest yield variables, such as biomass, volume, and number of stems. However, the next step is underrepresented in the literature: estimation of forest yield with appropriate confidence intervals. It is of great importance that the procedures required for...

From field to region yield predictions in response to pedo-climatic variations in Eastern Canada

NASA Astrophysics Data System (ADS)

JÉGO, G.; Pattey, E.; Liu, J.

2013-12-01

to improve regional crop modelling. Although several aspects of the coupling between crop models and RS data have been investigated (e.g., inversion of radiative transfer models and neural networks), extensive verification using multiple years and sites is required prior to implementation of the RS assimilation at the regional scale, in order to evaluate the model robustness and consistency in response to climatic variations. STICS performance was evaluated over several years for spring wheat, soybean and corn yield predictions over two small rainfed agricultural region of Eastern Canada exhibiting a high degree of pedodiversity when LAI derived from EO data was assimilated. Averaged yield predictions of the three crops compared well with measurements (10%model performance decreased significantly with the distance of the weather data station. The next steps of this work will be to evaluate alternative optimization algorithms, optimize key parameters controlling N budget, use high-spatial-resolution gridded weather data as input data, and updated soil databases using EO.

Modeling global yield growth of major crops under multiple socioeconomic pathways

NASA Astrophysics Data System (ADS)

Iizumi, T.; Kim, W.; Zhihong, S.; Nishimori, M.

2016-12-01

Global gridded crop models (GGCMs) are a key tool in deriving global food security scenarios under climate change. However, it is difficult for GGCMs to reproduce the reported yield growth patterns—rapid growth, yield stagnation and yield collapse. Here, we propose a set of parameterizations for GGCMs to capture the contributions to yield from technological improvements at the national and multi-decadal scales. These include country annual per capita gross domestic product (GDP)-based parameterizations for the nitrogen application rate and crop tolerance to stresses associated with high temperature, low temperature, water deficit and water excess. Using a GGCM combined with the parameterizations, we present global 140-year (1961-2100) yield growth simulations for maize, soybean, rice and wheat under multiple shared socioeconomic pathways (SSPs) and no climate change. The model reproduces the major characteristics of reported global and country yield growth patterns over the 1961-2013 period. Under the most rapid developmental pathway SSP5, the simulated global yields for 2091-2100, relative to 2001-2010, are the highest (1.21-1.82 times as high, with variations across the crops), followed by SSP1 (1.14-1.56 times as high), SSP2 (1.12-1.49 times as high), SSP4 (1.08-1.38 times as high) and SSP3 (1.08-1.36 times as high). Future country yield growth varies substantially by income level as well as by crop and by SSP. These yield pathways offer a new baseline for addressing the interdisciplinary questions related to global agricultural development, food security and climate change.

Atmospheric CO2 concentration impacts on maize yield performance under dry conditions: do crop model simulate it right ?

NASA Astrophysics Data System (ADS)

Durand, Jean-Louis; Delusca, Kénel; Boote, Ken; Lizaso, Jon; Manderscheid, Remy; Jochaim Weigel, Hans; Ruane, Alex C.; Rosenzweig, Cynthia; Jones, Jim; Ahuja, Laj; Anapalli, Saseendran; Basso, Bruno; Baron, Christian; Bertuzzi, Patrick; Biernath, Christian; Deryng, Delphine; Ewert, Frank; Gaiser, Thomas; Gayler, Sebastian; Heinlein, Florian; Kersebaum, Kurt Christian; Kim, Soo-Hyung; Müller, Christoph; Nendel, Claas; Olioso, Albert; Priesack, Eckhart; Ramirez-Villegas, Julian; Ripoche, Dominique; Rötter, Reimund; Seidel, Sabine; Srivastava, Amit; Tao, Fulu; Timlin, Dennis; Twine, Tracy; Wang, Enli; Webber, Heidi; Zhao, Shigan

2017-04-01

In most regions of the world, maize yields are at risk of be reduced due to rising temperatures and reduced water availability. Rising temperature tends to reduce the length of the growth cycle and the amount of intercepted solar energy. Water deficits reduce the leaf area expansion, photosynthesis and sometimes, with an even more pronounced impact, severely reduce the efficiency of kernel set. In maize, the major consequence of atmospheric CO2 concentration ([CO2]) is the stomatal closure-induced reduction of leaf transpiration rate, which tends to mitigate those negative impacts. Indeed FACE studies report significant positive responses to CO2 of maize yields (and other C4 crops) under dry conditions only. Given the projections by climatologists (typically doubling of [CO2] by the end of this century) projected impacts must take that climate variable into account. However, several studies show a large incertitude in estimating the impact of increasing [CO2] on maize remains using the main crop models. The aim of this work was to compare the simulations of different models using input data from a FACE experiment conducted in Braunschweig during 2 years under limiting and non-limiting water conditions. Twenty modelling groups using different maize models were given the same instructions and input data. Following calibration of cultivar parameters under non-limiting water conditions and under ambient [CO2] treatments of both years, simulations were undertaken for the other treatments: High [ CO2 ] (550 ppm) 2007 and 2008 in both irrigation regimes, and DRY AMBIENT 2007 and 2008. Only under severe water deficits did models simulate an increase in yield for CO2 enrichment, which was associated with higher harvest index and, for those models which simulated it, higher grain number. However, the CO2 enhancement under water deficit simulated by the 20 models was 20 % at most and 10 % on average only, i.e. twice less than observed in that experiment. As in the experiment

What is the Best Model Specification and Earth Observation Product for Predicting Regional Grain Yields in Food Insecure Countries?

NASA Astrophysics Data System (ADS)

Davenport, F., IV; Harrison, L.; Shukla, S.; Husak, G. J.; Funk, C. C.

2017-12-01

We evaluate the predictive accuracy of an ensemble of empirical model specifications that use earth observation data to predict sub-national grain yields in Mexico and East Africa. Products that are actively used for seasonal drought monitoring are tested as yield predictors. Our research is driven by the fact that East Africa is a region where decisions regarding agricultural production are critical to preventing the loss of economic livelihoods and human life. Regional grain yield forecasts can be used to anticipate availability and prices of key staples, which can turn can inform decisions about targeting humanitarian response such as food aid. Our objective is to identify-for a given region, grain, and time year- what type of model and/or earth observation can most accurately predict end of season yields. We fit a set of models to county level panel data from Mexico, Kenya, Sudan, South Sudan, and Somalia. We then examine out of sample predicative accuracy using various linear and non-linear models that incorporate spatial and time varying coefficients. We compare accuracy within and across models that use predictor variables from remotely sensed measures of precipitation, temperature, soil moisture, and other land surface processes. We also examine at what point in the season a given model or product is most useful for determining predictive accuracy. Finally we compare predictive accuracy across a variety of agricultural regimes including high intensity irrigated commercial agricultural and rain fed subsistence level farms.

Statistical modeling of SRAM yield performance and circuit variability

NASA Astrophysics Data System (ADS)

Cheng, Qi; Chen, Yijian

2015-03-01

In this paper, we develop statistical models to investigate SRAM yield performance and circuit variability in the presence of self-aligned multiple patterning (SAMP) process. It is assumed that SRAM fins are fabricated by a positivetone (spacer is line) self-aligned sextuple patterning (SASP) process which accommodates two types of spacers, while gates are fabricated by a more pitch-relaxed self-aligned quadruple patterning (SAQP) process which only allows one type of spacer. A number of possible inverter and SRAM structures are identified and the related circuit multi-modality is studied using the developed failure-probability and yield models. It is shown that SRAM circuit yield is significantly impacted by the multi-modality of fins' spatial variations in a SRAM cell. The sensitivity of 6-transistor SRAM read/write failure probability to SASP process variations is calculated and the specific circuit type with the highest probability to fail in the reading/writing operation is identified. Our study suggests that the 6-transistor SRAM configuration may not be scalable to 7-nm half pitch and more robust SRAM circuit design needs to be researched.

Universality and depinning models for plastic yield in amorphous materials

NASA Astrophysics Data System (ADS)

Budrikis, Zoe; Fernandez Castellano, David; Sandfeld, Stefan; Zaiser, Michael; Zapperi, Stefano

Plastic yield in amorphous materials occurs as a result of complex collective dynamics of local reorganizations, which gives rise to rich phenomena such as strain localization, intermittent dynamics and power-law distributed avalanches. While such systems have received considerable attention, both theoretical and experimental, controversy remains over the nature of the yielding transition. We present a new fully-tensorial coarsegrained model in 2D and 3D, and demonstrate that the exponents describing avalanche distributions are universal under a variety of loading conditions, system dimensionality and size, and boundary conditions. Our results show that while depinning-type models in general are apt to describe the system, mean field depinning models are not.

Economic impacts of climate change on agriculture: a comparison of process-based and statistical yield models

NASA Astrophysics Data System (ADS)

Moore, Frances C.; Baldos, Uris Lantz C.; Hertel, Thomas

2017-06-01

A large number of studies have been published examining the implications of climate change for agricultural productivity that, broadly speaking, can be divided into process-based modeling and statistical approaches. Despite a general perception that results from these methods differ substantially, there have been few direct comparisons. Here we use a data-base of yield impact studies compiled for the IPCC Fifth Assessment Report (Porter et al 2014) to systematically compare results from process-based and empirical studies. Controlling for differences in representation of CO2 fertilization between the two methods, we find little evidence for differences in the yield response to warming. The magnitude of CO2 fertilization is instead a much larger source of uncertainty. Based on this set of impact results, we find a very limited potential for on-farm adaptation to reduce yield impacts. We use the Global Trade Analysis Project (GTAP) global economic model to estimate welfare consequences of yield changes and find negligible welfare changes for warming of 1 °C-2 °C if CO2 fertilization is included and large negative effects on welfare without CO2. Uncertainty bounds on welfare changes are highly asymmetric, showing substantial probability of large declines in welfare for warming of 2 °C-3 °C even including the CO2 fertilization effect.

A dose-response relationship for marketable yield reduction of two lettuce (Lactuca sativa L.) cultivars exposed to tropospheric ozone in Southern Europe.

PubMed

Marzuoli, Riccardo; Finco, Angelo; Chiesa, Maria; Gerosa, Giacomo

2017-12-01

The present study investigated the response to ozone (O 3 ) of two cultivars (cv.'Romana' and cv. 'Canasta') of irrigated lettuce grown in an open-top chamber (OTC) experiment in Mediterranean conditions. Two different levels of O 3 were applied, ambient O 3 in non-filtered OTCs (NF-OTCs) and -40% of ambient O 3 in charcoal-filtered OTCs (CF-OTCs), during four consecutive growing cycles. At the end of each growing cycle, the marketable yield (fresh biomass) was assessed while during the growing periods, measurements of the stomatal conductance at leaf level were performed and used to define a stomatal conductance model for calculation of the phytotoxic ozone dose (POD) absorbed by the plants.Results showed that O 3 caused statistically significant yield reductions in the first and in the last growing cycle. In general, the marketable yield of the NF-OTC plants was always lower than the CF-OTC plants for both cultivars, with mean reductions of -18.5 and -14.5% for 'Romana' and 'Canasta', respectively. On the contrary, there was no statistically significant difference in marketable yield due to the cultivar factor or to the interaction between O 3 and cultivar in any of the growing cycle performed.Dose-response relationships for the marketable relative yield based on the POD values were calculated according to different flux threshold values (Y). The best regression fit was obtained using an instantaneous flux threshold of 6 nmol O 3 m -2  s -1 (POD 6 ); the same value was obtained also for other crops. According to the generic lettuce dose-response relationship, an O 3 critical level of 1 mmol O 3 m -2 of POD 6 for a 15% of marketable yield loss was found.

Evaluation of weather-based rice yield models in India.

PubMed

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.

Drought mitigation in perennial crops by fertilization and adjustments of regional yield models for future climate variability

NASA Astrophysics Data System (ADS)

Kantola, I. B.; Blanc-Betes, E.; Gomez-Casanovas, N.; Masters, M. D.; Bernacchi, C.; DeLucia, E. H.

2017-12-01

Increased variability and intensity of precipitation in the Midwest agricultural belt due to climate change is a major concern. The success of perennial bioenergy crops in replacing maize for bioethanol production is dependent on sustained yields that exceed maize, and the marketing of perennial crops often emphasizes the resilience of perennial agriculture to climate stressors. Land conversion from maize for bioethanol to Miscanthus x giganteus (miscanthus) increases yields and annual evapotranspiration rates (ET). However, establishment of miscanthus also increases biome water use efficiency (the ratio between net ecosystem productivity after harvest and ET), due to greater belowground biomass in miscanthus than in maize or soybean. In 2012, a widespread drought reduced the yield of 5-year-old miscanthus plots in central Illinois by 36% compared to the previous two years. Eddy covariance data indicated continued soil water deficit during the hydrologically-normal growing season in 2013 and miscanthus yield failed to rebound as expected, lagging behind pre-drought yields by an average of 53% over the next three years. In early 2014, nitrogen fertilizer was applied to half of mature (7-year-old) miscanthus plots in an effort to improve yields. In plots with annual post-emergence application of 60 kg ha-1 of urea, peak biomass was 29% greater than unfertilized miscanthus in 2014, and 113% greater in 2015, achieving statistically similar yields to the pre-drought average. Regional-scale models of perennial crop productivity use 30-year climate averages that are inadequate for predicting long-term effects of short-term extremes on perennial crops. Modeled predictions of perennial crop productivity incorporating repeated extreme weather events, observed crop response, and the use of management practices to mitigate water deficit demonstrate divergent effects on predicted yields.

A generalized self-consistent polycrystal model for the yield strength of nanocrystalline materials

NASA Astrophysics Data System (ADS)

Jiang, B.; Weng, G. J.

2004-05-01

Inspired by recent molecular dynamic simulations of nanocrystalline solids, a generalized self-consistent polycrystal model is proposed to study the transition of yield strength of polycrystalline metals as the grain size decreases from the traditional coarse grain to the nanometer scale. These atomic simulations revealed that a significant portion of atoms resides in the grain boundaries and the plastic flow of the grain-boundary region is responsible for the unique characteristics displayed by such materials. The proposed model takes each oriented grain and its immediate grain boundary to form a pair, which in turn is embedded in the infinite effective medium with a property representing the orientational average of all these pairs. We make use of the linear comparison composite to determine the nonlinear behavior of the nanocrystalline polycrystal through the concept of secant moduli. To this end an auxiliary problem of Christensen and Lo (J. Mech. Phys. Solids 27 (1979) 315) superimposed on the eigenstrain field of Luo and Weng (Mech. Mater. 6 (1987) 347) is first considered, and then the nonlinear elastoplastic polycrystal problem is addressed. The plastic flow of each grain is calculated from its crystallographic slips, but the plastic behavior of the grain-boundary phase is modeled as that of an amorphous material. The calculated yield stress for Cu is found to follow the classic Hall-Petch relation initially, but as the gain size decreases it begins to depart from it. The yield strength eventually attains a maximum at a critical grain size and then the Hall-Petch slope turns negative in the nano-range. It is also found that, when the Hall-Petch relation is observed, the plastic behavior of the polycrystal is governed by crystallographic slips in the grains, but when the slope is negative it is governed by the grain boundaries. During the transition both grains and grain boundaries contribute competitively.

Modeling contribution of shallow groundwater to evapotranspiration and yield of maize in an arid area

NASA Astrophysics Data System (ADS)

Gao, Xiaoyu; Huo, Zailin; Qu, Zhongyi; Xu, Xu; Huang, Guanhua; Steenhuis, Tammo S.

2017-02-01

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/m3. 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.

Modeling contribution of shallow groundwater to evapotranspiration and yield of maize in an arid area

PubMed Central

Gao, Xiaoyu; Huo, Zailin; Qu, Zhongyi; Xu, Xu; Huang, Guanhua; Steenhuis, Tammo S.

2017-01-01

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/m3. 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. PMID:28220874

Modeling contribution of shallow groundwater to evapotranspiration and yield of maize in an arid area.

PubMed

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.

Modeling in Real Time During the Ebola Response.

PubMed

Meltzer, Martin I; Santibanez, Scott; Fischer, Leah S; Merlin, Toby L; Adhikari, Bishwa B; Atkins, Charisma Y; Campbell, Caresse; Fung, Isaac Chun-Hai; Gambhir, Manoj; Gift, Thomas; Greening, Bradford; Gu, Weidong; Jacobson, Evin U; Kahn, Emily B; Carias, Cristina; Nerlander, Lina; Rainisch, Gabriel; Shankar, Manjunath; Wong, Karen; Washington, Michael L

2016-07-08

To aid decision-making during CDC's response to the 2014-2016 Ebola virus disease (Ebola) epidemic in West Africa, CDC activated a Modeling Task Force to generate estimates on various topics related to the response in West Africa and the risk for importation of cases into the United States. Analysis of eight Ebola response modeling projects conducted during August 2014-July 2015 provided insight into the types of questions addressed by modeling, the impact of the estimates generated, and the difficulties encountered during the modeling. This time frame was selected to cover the three phases of the West African epidemic curve. Questions posed to the Modeling Task Force changed as the epidemic progressed. Initially, the task force was asked to estimate the number of cases that might occur if no interventions were implemented compared with cases that might occur if interventions were implemented; however, at the peak of the epidemic, the focus shifted to estimating resource needs for Ebola treatment units. Then, as the epidemic decelerated, requests for modeling changed to generating estimates of the potential number of sexually transmitted Ebola cases. Modeling to provide information for decision-making during the CDC Ebola response involved limited data, a short turnaround time, and difficulty communicating the modeling process, including assumptions and interpretation of results. Despite these challenges, modeling yielded estimates and projections that public health officials used to make key decisions regarding response strategy and resources required. The impact of modeling during the Ebola response demonstrates the usefulness of modeling in future responses, particularly in the early stages and when data are scarce. Future modeling can be enhanced by planning ahead for data needs and data sharing, and by open communication among modelers, scientists, and others to ensure that modeling and its limitations are more clearly understood. The activities summarized in

Growth and yield responses of field-grown sweetpotato to elevated carbon dioxide

DOE Office of Scientific and Technical Information (OSTI.GOV)

Biswas, P.K.; Hileman, D.R.; Ghosh, P.P.

1996-09-01

Root crops are important in developing countries, where food supplies are frequently marginal. Increases in atmospheric CO{sub 2} usually lead to increases in plant growth and yield, but little is known about the response of root crops to CO{sub 2} enrichment under field conditions. This experiment was conducted to investigate the effects of CO{sub 2} enrichment on growth and yield of field-grown sweetpotato. Plants were grown in open-top chambers in the field at four CO{sub 2} levels ranging from 354 (ambient) to 665 {mu}mol mol{sup {minus}1} in two growing seasons. Shoot growth was not affected significantly by elevated CO{sub 2}.more » Yield of storage roots increased 46 and 75% at the highest CO{sub 2} level in the 2 yr. The yield enhancement occurred through increases in the number of storage roots in the second year. Storage-root/shoot ratios increased 44% and leaf nitrogen concentrations decreased by 24% at the highest CO{sub 2} level. A comparison of plants grown in the open field to plants grown in open-top chambers at ambient CO{sub 2} concentrations indicated that open-top chambers reduced shoot growth in the first year and storage-root yield in both years. These results are consistent with the majority of CO{sub 2}-enrichment studies done on pot-grown sweetpotato. 37 refs., 2 figs., 5 tabs.« less

How do various maize crop models vary in their responses to climate change factors?

USDA-ARS?s Scientific Manuscript database

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models give similar grain yield responses to changes in climatic factors, or whether they agr...

Primary and Secondary Yield Losses Caused by Pests and Diseases: Assessment and Modeling in Coffee

PubMed Central

Gary, Christian; Tixier, Philippe; Lechevallier, Esther

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. PMID:28046054

Primary and Secondary Yield Losses Caused by Pests and Diseases: Assessment and Modeling in Coffee.

PubMed

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.

Modelling predicts that tolerance to drought during reproductive development will be required for high yield potential and stability of wheat in Europe

NASA Astrophysics Data System (ADS)

Semenov, Mikhail A.; Stratonovitch, Pierre; Paul, Matthew J.

2017-04-01

Short periods of extreme weather, such as a spell of high temperature or drought during a sensitive stage of development, could result in substantial yield losses due to reduction in grain number and grain size. In a modelling study (Stratonovitch & Semenov 2015), heat tolerance around flowering in wheat was identified as a key trait for increased yield potential in Europe under climate change. Ji et all (Ji et al. 2010) demonstrated cultivar specific responses of yield to drought stress around flowering in wheat. They hypothesised that carbohydrate supply to anthers may be the key in maintaining pollen fertility and grain number in wheat. It was shown in (Nuccio et al. 2015) that genetically modified varieties of maize that increase the concentration of sucrose in ear spikelets, performed better under non-drought and drought conditions in field experiments. The objective of this modelling study was to assess potential benefits of tolerance to drought during reproductive development for wheat yield potential and yield stability across Europe. We used the Sirius wheat model to optimise wheat ideotypes for 2050 (HadGEM2, RCP8.5) climate scenarios at selected European sites. Eight cultivar parameters were optimised to maximise mean yields, including parameters controlling phenology, canopy growth and water limitation. At those sites where water could be limited, ideotypes sensitive to drought produced substantially lower mean yields and higher yield variability compare with tolerant ideotypes. Therefore, tolerance to drought during reproductive development is likely to be required for wheat cultivars optimised for the future climate in Europe in order to achieve high yield potential and yield stability.

Genetic Basis for Variation in Wheat Grain Yield in Response to Varying Nitrogen Application.

PubMed

Mahjourimajd, Saba; Taylor, Julian; Sznajder, Beata; Timmins, Andy; Shahinnia, Fahimeh; Rengel, Zed; Khabaz-Saberi, Hossein; Kuchel, Haydn; Okamoto, Mamoru; Langridge, Peter

2016-01-01

Nitrogen (N) is a major nutrient needed to attain optimal grain yield (GY) in all environments. Nitrogen fertilisers represent a significant production cost, in both monetary and environmental terms. Developing genotypes capable of taking up N early during development while limiting biomass production after establishment and showing high N-use efficiency (NUE) would be economically beneficial. Genetic variation in NUE has been shown previously. Here we describe the genetic characterisation of NUE and identify genetic loci underlying N response under different N fertiliser regimes in a bread wheat population of doubled-haploid lines derived from a cross between two Australian genotypes (RAC875 × Kukri) bred for a similar production environment. NUE field trials were carried out at four sites in South Australia and two in Western Australia across three seasons. There was genotype-by-environment-by-treatment interaction across the sites and also good transgressive segregation for yield under different N supply in the population. We detected some significant Quantitative Trait Loci (QTL) associated with NUE and N response at different rates of N application across the sites and years. It was also possible to identify lines showing positive N response based on the rankings of their Best Linear Unbiased Predictions (BLUPs) within a trial. Dissecting the complexity of the N effect on yield through QTL analysis is a key step towards elucidating the molecular and physiological basis of NUE in wheat.

Genetic Basis for Variation in Wheat Grain Yield in Response to Varying Nitrogen Application

PubMed Central

Mahjourimajd, Saba; Taylor, Julian; Sznajder, Beata; Timmins, Andy; Shahinnia, Fahimeh; Rengel, Zed; Khabaz-Saberi, Hossein; Kuchel, Haydn; Okamoto, Mamoru

2016-01-01

Nitrogen (N) is a major nutrient needed to attain optimal grain yield (GY) in all environments. Nitrogen fertilisers represent a significant production cost, in both monetary and environmental terms. Developing genotypes capable of taking up N early during development while limiting biomass production after establishment and showing high N-use efficiency (NUE) would be economically beneficial. Genetic variation in NUE has been shown previously. Here we describe the genetic characterisation of NUE and identify genetic loci underlying N response under different N fertiliser regimes in a bread wheat population of doubled-haploid lines derived from a cross between two Australian genotypes (RAC875 × Kukri) bred for a similar production environment. NUE field trials were carried out at four sites in South Australia and two in Western Australia across three seasons. There was genotype-by-environment-by-treatment interaction across the sites and also good transgressive segregation for yield under different N supply in the population. We detected some significant Quantitative Trait Loci (QTL) associated with NUE and N response at different rates of N application across the sites and years. It was also possible to identify lines showing positive N response based on the rankings of their Best Linear Unbiased Predictions (BLUPs) within a trial. Dissecting the complexity of the N effect on yield through QTL analysis is a key step towards elucidating the molecular and physiological basis of NUE in wheat. PMID:27459317

A Multidimensional Ideal Point Item Response Theory Model for Binary Data.

PubMed

Maydeu-Olivares, Albert; Hernández, Adolfo; McDonald, Roderick P

2006-12-01

We introduce a multidimensional item response theory (IRT) model for binary data based on a proximity response mechanism. Under the model, a respondent at the mode of the item response function (IRF) endorses the item with probability one. The mode of the IRF is the ideal point, or in the multidimensional case, an ideal hyperplane. The model yields closed form expressions for the cell probabilities. We estimate and test the goodness of fit of the model using only information contained in the univariate and bivariate moments of the data. Also, we pit the new model against the multidimensional normal ogive model estimated using NOHARM in four applications involving (a) attitudes toward censorship, (b) satisfaction with life, (c) attitudes of morality and equality, and (d) political efficacy. The normal PDF model is not invariant to simple operations such as reverse scoring. Thus, when there is no natural category to be modeled, as in many personality applications, it should be fit separately with and without reverse scoring for comparisons.

Inflow, Outflow, Yields, and Stellar Population Mixing in Chemical Evolution Models

NASA Astrophysics Data System (ADS)

Andrews, Brett H.; Weinberg, David H.; Schönrich, Ralph; Johnson, Jennifer A.

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.

Modelling and validation of magnetorheological brake responses using parametric approach

NASA Astrophysics Data System (ADS)

Z, Zainordin A.; A, Abdullah M.; K, Hudha

2013-12-01

Magnetorheological brake (MR Brake) is one x-by-wire systems which performs better than conventional brake systems. MR brake consists of a rotating disc that is immersed with Magnetorheological Fluid (MR Fluid) in an enclosure of an electromagnetic coil. The applied magnetic field will increase the yield strength of the MR fluid where this fluid was used to decrease the speed of the rotating shaft. The purpose of this paper is to develop a mathematical model to represent MR brake with a test rig. The MR brake model is developed based on actual torque characteristic which is coupled with motion of a test rig. Next, the experimental are performed using MR brake test rig and obtained three output responses known as angular velocity response, torque response and load displacement response. Furthermore, the MR brake was subjected to various current. Finally, the simulation results of MR brake model are then verified with experimental results.

An adapted yield criterion for the evolution of subsequent yield surfaces

NASA Astrophysics Data System (ADS)

Küsters, N.; Brosius, A.

2017-09-01

In numerical analysis of sheet metal forming processes, the anisotropic material behaviour is often modelled with isotropic work hardening and an average Lankford coefficient. In contrast, experimental observations show an evolution of the Lankford coefficients, which can be associated with a yield surface change due to kinematic and distortional hardening. Commonly, extensive efforts are carried out to describe these phenomena. In this paper an isotropic material model based on the Yld2000-2d criterion is adapted with an evolving yield exponent in order to change the yield surface shape. The yield exponent is linked to the accumulative plastic strain. This change has the effect of a rotating yield surface normal. As the normal is directly related to the Lankford coefficient, the change can be used to model the evolution of the Lankford coefficient during yielding. The paper will focus on the numerical implementation of the adapted material model for the FE-code LS-Dyna, mpi-version R7.1.2-d. A recently introduced identification scheme [1] is used to obtain the parameters for the evolving yield surface and will be briefly described for the proposed model. The suitability for numerical analysis will be discussed for deep drawing processes in general. Efforts for material characterization and modelling will be compared to other common yield surface descriptions. Besides experimental efforts and achieved accuracy, the potential of flexibility in material models and the risk of ambiguity during identification are of major interest in this paper.

A Theoretical Model for Estimation of Yield Strength of Fiber Metal Laminate

NASA Astrophysics Data System (ADS)

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.

Evaluation of weather-based rice yield models in India

NASA Astrophysics Data System (ADS)

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.

MODIS Data Assimilation in the CROPGRO model for improving soybean yield estimations

NASA Astrophysics Data System (ADS)

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.

Adopting adequate leaching requirement for practical response models of basil to salinity

NASA Astrophysics Data System (ADS)

Babazadeh, Hossein; Tabrizi, Mahdi Sarai; Darvishi, Hossein Hassanpour

2016-07-01

Several mathematical models are being used for assessing plant response to salinity of the root zone. Objectives of this study included quantifying the yield salinity threshold value of basil plants to irrigation water salinity and investigating the possibilities of using irrigation water salinity instead of saturated extract salinity in the available mathematical models for estimating yield. To achieve the above objectives, an extensive greenhouse experiment was conducted with 13 irrigation water salinity levels, namely 1.175 dS m-1 (control treatment) and 1.8 to 10 dS m-1. The result indicated that, among these models, the modified discount model (one of the most famous root water uptake model which is based on statistics) produced more accurate results in simulating the basil yield reduction function using irrigation water salinities. Overall the statistical model of Steppuhn et al. on the modified discount model and the math-empirical model of van Genuchten and Hoffman provided the best results. In general, all of the statistical models produced very similar results and their results were better than math-empirical models. It was also concluded that if enough leaching was present, there was no significant difference between the soil salinity saturated extract models and the models using irrigation water salinity.

Yield Stress Model for Molten Composition B-3

NASA Astrophysics Data System (ADS)

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.

Statistical rice yield modeling using blended MODIS-Landsat based crop phenology metrics in Taiwan

NASA Astrophysics Data System (ADS)

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 < 0.001). The comparison results between the estimated yields and the government's yield statistics for the first and second crops indicated a close significant relationship between the two datasets (R2 > 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

System dynamics approach for modeling of sugar beet yield considering the effects of climatic variables.

PubMed

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.

Inflow, Outflow, Yields, and Stellar Population Mixing in Chemical Evolution Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Andrews, Brett H.; Weinberg, David H.; Schönrich, Ralph

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 tracksmore » 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.« less

Characterizing bias correction uncertainty in wheat yield predictions

NASA Astrophysics Data System (ADS)

Ortiz, Andrea Monica; Jones, Julie; Freckleton, Robert; Scaife, Adam

2017-04-01

Farming systems are under increased pressure due to current and future climate change, variability and extremes. Research on the impacts of climate change on crop production typically rely on the output of complex Global and Regional Climate Models, which are used as input to crop impact models. Yield predictions from these top-down approaches can have high uncertainty for several reasons, including diverse model construction and parameterization, future emissions scenarios, and inherent or response uncertainty. These uncertainties propagate down each step of the 'cascade of uncertainty' that flows from climate input to impact predictions, leading to yield predictions that may be too complex for their intended use in practical adaptation options. In addition to uncertainty from impact models, uncertainty can also stem from the intermediate steps that are used in impact studies to adjust climate model simulations to become more realistic when compared to observations, or to correct the spatial or temporal resolution of climate simulations, which are often not directly applicable as input into impact models. These important steps of bias correction or calibration also add uncertainty to final yield predictions, given the various approaches that exist to correct climate model simulations. In order to address how much uncertainty the choice of bias correction method can add to yield predictions, we use several evaluation runs from Regional Climate Models from the Coordinated Regional Downscaling Experiment over Europe (EURO-CORDEX) at different resolutions together with different bias correction methods (linear and variance scaling, power transformation, quantile-quantile mapping) as input to a statistical crop model for wheat, a staple European food crop. The objective of our work is to compare the resulting simulation-driven hindcasted wheat yields to climate observation-driven wheat yield hindcasts from the UK and Germany in order to determine ranges of yield

Operational modelling: the mechanisms influencing TB diagnostic yield in an Xpert® MTB/RIF-based algorithm.

PubMed

Dunbar, R; Naidoo, P; Beyers, N; Langley, I

2017-04-01

Cape Town, South Africa. To compare the diagnostic yield for smear/culture and Xpert® MTB/RIF algorithms and to investigate the mechanisms influencing tuberculosis (TB) yield. We developed and validated an operational model of the TB diagnostic process, first with the smear/culture algorithm and then with the Xpert algorithm. We modelled scenarios by varying TB prevalence, adherence to diagnostic algorithms and human immunodeficiency virus (HIV) status. This enabled direct comparisons of diagnostic yield in the two algorithms to be made. Routine data showed that diagnostic yield had decreased over the period of the Xpert algorithm roll-out compared to the yield when the smear/culture algorithm was in place. However, modelling yield under identical conditions indicated a 13.3% increase in diagnostic yield from the Xpert algorithm compared to smear/culture. The model demonstrated that the extensive use of culture in the smear/culture algorithm and the decline in TB prevalence are the main factors contributing to not finding an increase in diagnostic yield in the routine data. We demonstrate the benefits of an operational model to determine the effect of scale-up of a new diagnostic algorithm, and recommend that policy makers use operational modelling to make appropriate decisions before new diagnostic algorithms are scaled up.

Multivariate regression model for predicting yields of grade lumber from yellow birch sawlogs

Treesearch

Andrew F. Howard; Daniel A. Yaussy

1986-01-01

A multivariate regression model was developed to predict green board-foot yields for the common grades of factory lumber processed from yellow birch factory-grade logs. The model incorporates the standard log measurements of scaling diameter, length, proportion of scalable defects, and the assigned USDA Forest Service log grade. Differences in yields between band and...

NEST: a comprehensive model for scintillation yield in liquid xenon

DOE PAGES

Szydagis, M.; Barry, N.; Kazkaz, K.; ...

2011-10-03

Here, 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 bemore » 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).« less

Growth models for ponderosa pine: I. Yield of unthinned plantations in northern California.

Treesearch

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...

[Evaluating the response of yield and evapotranspiration of winter wheat and the adaptation by adjusting crop variety to climate change in Huang-Huai-Hai Plain].

PubMed

Hu, Shi; Mo, Xing-guo; Lin, Zhong-hui

2015-04-01

Based on the multi-model datasets of three representative concentration pathway (RCP) emission scenarios from IPCC5, the response of yield and accumulative evapotranspiration (ET) of winter wheat to climate change in the future were assessed by VIP model. The results showed that if effects of CO2 enrichment were excluded, temperature rise would lead to a reduction in the length of the growing period for wheat under the three climate change scenarios, and the wheat yield and ET presented a decrease tendency. The positive effect of atmospheric CO2 enrichment could offset most negative effect introduced by temperature rising, indicating that atmospheric CO2 enrichment would be the prime reason of the wheat yield rising in future. In 2050s, wheat yield would increase 14.8% (decrease 2.5% without CO2 fertilization) , and ET would decrease 2.1% under RCP4.5. By adoption of new crop variety with enhanced requirement on accumulative temperature, the wheat yield would increase more significantly with CO2 fertilization, but the water consumption would also increase. Therefore, cultivar breeding new irrigation techniques and agronomical management should be explored under the challenges of climate change in the future.

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

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

NASA Astrophysics Data System (ADS)

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

2014-03-01

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

A mathematical framework for yield (vs. rate) optimization in constraint-based modeling and applications in metabolic engineering.

PubMed

Klamt, Steffen; Müller, Stefan; Regensburger, Georg; Zanghellini, Jürgen

2018-05-01

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

DANSS Neutrino Spectrometer: Detector Calibration, Response Stability, and Light Yield

NASA Astrophysics Data System (ADS)

Alekseev, I. G.; Belov, V. V.; Danilov, M. V.; Zhitnikov, I. V.; Kobyakin, A. S.; Kuznetsov, A. S.; Machikhiliyan, I. V.; Medvedev, D. V.; Rusinov, V. Yu.; Svirida, D. N.; Skrobova, N. A.; Starostin, A. S.; Tarkovsky, E. I.; Fomina, M. V.; Shevchik, E. A.; Shirchenko, M. V.

2018-05-01

Apart from monitoring nuclear reactor parameters, the DANSS neutrino experiment is aimed at searching for sterile neutrinos through a detailed analysis of the ratio of reactor antineutrino spectra measured at different distances from the reactor core. The light collection system of the detector is dual, comprising both the vacuum photomultiplier tubes (PMTs) and silicon photomultipliers (SiPMs). In this paper, the techniques developed to calibrate the responses of these photodetectors are discussed in detail. The long-term stability of the key parameters of the detector and their dependences on the ambient temperature are investigated. The results of detector light yield measurements, performed independently with PMTs and SiPMs are reported.

Corn grain yield response to stover removal under variable nitrogen, irrigation, and carbon amendments

USDA-ARS?s Scientific Manuscript database

Demand for corn (Zea mays L.) stover either for livestock or cellulosic ethanol production have increased the importance of determining stover removal effects on biomass production. The objectives of this study was to evaluate yield response and N use from continuous stover removal under two irriga...

Evaluating accuracy of DSSAT model for soybean yield estimation using satellite weather data

NASA Astrophysics Data System (ADS)

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.

Development of a Coupled Hydrological/Sediment Yield Model for a Watershed at Regional Level

NASA Technical Reports Server (NTRS)

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.

Growth and yield models for central hardwoods

Treesearch

Martin E. Dale; Donald E. Hilt

1989-01-01

Over the last 20 years computers have become an efficient tool to estimate growth and yield. Computerized yield estimates vary from simple approximation or interpolation of traditional normal yield tables to highly sophisticated programs that simulate the growth and yield of each individual tree.

Evaluation of the Williams-type model for barley yields in North Dakota and Minnesota

NASA Technical Reports Server (NTRS)

Barnett, T. L. (Principal Investigator)

1981-01-01

The Williams-type yield model is based on multiple regression analysis of historial time series data at CRD level pooled to regional level (groups of similar CRDs). Basic variables considered in the analysis include USDA yield, monthly mean temperature, monthly precipitation, soil texture and topographic information, and variables derived from these. Technologic trend is represented by piecewise linear and/or quadratic functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test (1970-1979) demonstrate that biases are small and performance based on root mean square appears to be acceptable for the intended AgRISTARS large area applications. The model is objective, adequate, timely, simple, and not costly. It consideres scientific knowledge on a broad scale but not in detail, and does not provide a good current measure of modeled yield reliability.

Updated stomatal flux and flux-effect models for wheat for quantifying effects of ozone on grain yield, grain mass and protein yield.

PubMed

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.

Simple nonlinear modelling of earthquake response in torsionally coupled R/C structures: A preliminary study

NASA Astrophysics Data System (ADS)

Saiidi, M.

1982-07-01

The equivalent of a single degree of freedom (SDOF) nonlinear model, the Q-model-13, was examined. The study intended to: (1) determine the seismic response of a torsionally coupled building based on the multidegree of freedom (MDOF) and (SDOF) nonlinear models; and (2) develop a simple SDOF nonlinear model to calculate displacement history of structures with eccentric centers of mass and stiffness. It is shown that planar models are able to yield qualitative estimates of the response of the building. The model is used to estimate the response of a hypothetical six-story frame wall reinforced concrete building with torsional coupling, using two different earthquake intensities. It is shown that the Q-Model-13 can lead to a satisfactory estimate of the response of the structure in both cases.

Winter wheat response to irrigation, nitrogen fertilization, and cold hazards in the Community Land Model 5

NASA Astrophysics Data System (ADS)

Lu, Y.

2017-12-01

Winter wheat is a staple crop for global food security, and is the dominant vegetation cover for a significant fraction of earth's croplands. As such, it plays an important role in soil carbon balance, and land-atmosphere interactions in these key regions. Accurate simulation of winter wheat growth is not only crucial for future yield prediction under changing climate, but also for understanding the energy and water cycles for winter wheat dominated regions. A winter wheat growth model has been developed in the Community Land Model 4.5 (CLM4.5), but its responses to irrigation and nitrogen fertilization have not been validated. In this study, I will validate winter wheat growth response to irrigation and nitrogen fertilization at five winter wheat field sites (TXLU, KSMA, NESA, NDMA, and ABLE) in North America, which were originally designed to understand winter wheat response to nitrogen fertilization and water treatments (4 nitrogen levels and 3 irrigation regimes). I also plan to further update the linkages between winter wheat yield and cold hazards. The previous cold damage function only indirectly affects yield through reduction on leaf area index (LAI) and hence photosynthesis, such approach could sometimes produce an unwanted higher yield when the reduced LAI saved more nutrient in the grain fill stage.

Investigation of optimal conditions for production of highly crystalline nanocellulose with increased yield via novel Cr(III)-catalyzed hydrolysis: Response surface methodology.

PubMed

Chen, You Wei; Lee, Hwei Voon; Abd Hamid, Sharifah Bee

2017-12-15

For the first time, a highly efficient Cr(NO 3 ) 3 catalysis system was proposed for optimization the yield and crystallinity of nanocellulose end product. A five-level three-factor central composite design coupled with response surface methodology was employed to elucidate parameters interactions between three design factors, namely reaction temperature (x 1 ), reaction time (x 2 ) and concentration of Cr(NO 3 ) 3 (x 3 ) over a broad range of process conditions and determine the effect on crystallinity index and product yield. The developed models predicted the maximum nanocellulose yield of 87% at optimum process conditions of 70.6°C, 1.48h, and 0.48M Cr(NO 3 ) 3 . At these conditions, the obtained nanocellulose presented high crystallinity index (75.3%), spider-web-like interconnected network morphology with the average width of 31.2±14.3nm. In addition, the yielded nanocellulose rendered a higher thermal stability than that of original cellulosic source and expected to be widely used as reinforcement agent in bio-nanocomposites materials. Copyright © 2017 Elsevier Ltd. All rights reserved.

Climatically driven yield variability of major crops in Khakassia (South Siberia)

NASA Astrophysics Data System (ADS)

Babushkina, Elena A.; Belokopytova, Liliana V.; Zhirnova, Dina F.; Shah, Santosh K.; Kostyakova, Tatiana V.

2018-06-01

We investigated the variability of yield of the three main crop cultures in the Khakassia Republic: spring wheat, spring barley, and oats. In terms of yield values, variability characteristics, and climatic response, the agricultural territory of Khakassia can be divided into three zones: (1) the Northern Zone, where crops yield has a high positive response to the amount of precipitation, May-July, and a moderately negative one to the temperatures of the same period; (2) the Central Zone, where crops yield depends mainly on temperatures; and (3) the Southern Zone, where climate has the least expressed impact on yield. The dominant pattern in the crops yield is caused by water stress during periods of high temperatures and low moisture supply with heat stress as additional reason. Differences between zones are due to combinations of temperature latitudinal gradient, precipitation altitudinal gradient, and the presence of a well-developed hydrological network and the irrigational system as moisture sources in the Central Zone. More detailed analysis shows differences in the climatic sensitivity of crops during phases of their vegetative growth and grain development and, to a lesser extent, during harvesting period. Multifactor linear regression models were constructed to estimate climate- and autocorrelation-induced variability of the crops yield. These models allowed prediction of the possibility of yield decreasing by at least 2-11% in the next decade due to increasing of the regional summer temperatures.

Climatically driven yield variability of major crops in Khakassia (South Siberia)

NASA Astrophysics Data System (ADS)

Babushkina, Elena A.; Belokopytova, Liliana V.; Zhirnova, Dina F.; Shah, Santosh K.; Kostyakova, Tatiana V.

2017-12-01

We investigated the variability of yield of the three main crop cultures in the Khakassia Republic: spring wheat, spring barley, and oats. In terms of yield values, variability characteristics, and climatic response, the agricultural territory of Khakassia can be divided into three zones: (1) the Northern Zone, where crops yield has a high positive response to the amount of precipitation, May-July, and a moderately negative one to the temperatures of the same period; (2) the Central Zone, where crops yield depends mainly on temperatures; and (3) the Southern Zone, where climate has the least expressed impact on yield. The dominant pattern in the crops yield is caused by water stress during periods of high temperatures and low moisture supply with heat stress as additional reason. Differences between zones are due to combinations of temperature latitudinal gradient, precipitation altitudinal gradient, and the presence of a well-developed hydrological network and the irrigational system as moisture sources in the Central Zone. More detailed analysis shows differences in the climatic sensitivity of crops during phases of their vegetative growth and grain development and, to a lesser extent, during harvesting period. Multifactor linear regression models were constructed to estimate climate- and autocorrelation-induced variability of the crops yield. These models allowed prediction of the possibility of yield decreasing by at least 2-11% in the next decade due to increasing of the regional summer temperatures.

Modeling human vestibular responses during eccentric rotation and off vertical axis rotation

NASA Technical Reports Server (NTRS)

Merfeld, D. M.; Paloski, W. H. (Principal Investigator)

1995-01-01

A mathematical model has been developed to help explain human multi-sensory interactions. The most important constituent of the model is the hypothesis that the nervous system incorporates knowledge of sensory dynamics into an "internal model" of these dynamics. This internal model allows the nervous system to integrate the sensory information from many different sensors into a coherent estimate of self-motion. The essence of the model is unchanged from a previously published model of monkey eye movement responses; only a few variables have been adjusted to yield the prediction of human responses. During eccentric rotation, the model predicts that the axis of eye rotation shifts slightly toward alignment with gravito-inertial force. The model also predicts that the time course of the perception of tilt following the acceleration phase of eccentric rotation is much slower than that during deceleration. During off vertical axis rotation (OVAR) the model predicts a small horizontal bias along with small horizontal, vertical, and torsional oscillations. Following OVAR stimulation, when stopped right- or left-side down, a small vertical component is predicted that decays with the horizontal post-rotatory response. All of the predictions are consistent with measurements of human responses.

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

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lobell, D; Field, C; Cahill, K

2006-01-10

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

A multivariate model and statistical method for validating tree grade lumber yield equations

Treesearch

Donald W. Seegrist

1975-01-01

Lumber yields within lumber grades can be described by a multivariate linear model. A method for validating lumber yield prediction equations when there are several tree grades is presented. The method is based on multivariate simultaneous test procedures.

Estimation of regional material yield from coastal landslides based on historical digital terrain modelling

USGS Publications Warehouse

Hapke, C.J.

2005-01-01

High-resolution historical (1942) and recent (1994) digital terrain models were derived from aerial photographs along the Big Sur coastline in central California to measure the long-term volume of material that enters the nearshore environment. During the 52-year measurement time period, an average of 21 000 ?? 3100 m3 km-1 a-1 of material was eroded from nine study sections distributed along the coast, with a low yield of 1000 ?? 240 m3 km-1 a-1 and a high of 46 700 ?? 7300 m3 km-1 a-1. The results compare well with known volumes from several deep-seated landslides in the area and suggest that the processes by which material is delivered to the coast are episodic in nature. In addition, a number of parameters are investigated to determine what influences the substantial variation in yield along the coast. It is found that the magnitude of regional coastal landslide sediment yield is primarily related to the physical strength of the slope-forming material. Coastal Highway 1 runs along the lower portion of the slope along this stretch of coastline, and winter storms frequently damage the highway. The California Department of Transportation is responsible for maintaining this scenic highway while minimizing the impacts to the coastal ecosystems that are part of the Monterey Bay National Marine Sanctuary. This study provides environmental managers with critical background data on the volumes of material that historically enter the nearshore from landslides, as well as demonstrating the application of deriving historical digital terrain data to model landscape evolution. Published in 2005 by John Wiley & Sons, Ltd.

Comparison of CEAS and Williams-type models for spring wheat yields in North Dakota and Minnesota

NASA Technical Reports Server (NTRS)

Barnett, T. L. (Principal Investigator)

1982-01-01

The CEAS and Williams-type yield models are both based on multiple regression analysis of historical time series data at CRD level. The CEAS model develops a separate relation for each CRD; the Williams-type model pools CRD data to regional level (groups of similar CRDs). Basic variables considered in the analyses are USDA yield, monthly mean temperature, monthly precipitation, and variables derived from these. The Williams-type model also used soil texture and topographic information. Technological trend is represented in both by piecewise linear functions of year. Indicators of yield reliability obtained from a ten-year bootstrap test of each model (1970-1979) demonstrate that the models are very similar in performance in all respects. Both models are about equally objective, adequate, timely, simple, and inexpensive. Both consider scientific knowledge on a broad scale but not in detail. Neither provides a good current measure of modeled yield reliability. The CEAS model is considered very slightly preferable for AgRISTARS applications.

Effects of stage of pregnancy on variance components, daily milk yields and 305-day milk yield in Holstein cows, as estimated by using a test-day model.

PubMed

Yamazaki, T; Hagiya, K; Takeda, H; Osawa, T; Yamaguchi, S; Nagamine, Y

2016-08-01

Pregnancy and calving are elements indispensable for dairy production, but the daily milk yield of cows decline as pregnancy progresses, especially during the late stages. Therefore, the effect of stage of pregnancy on daily milk yield must be clarified to accurately estimate the breeding values and lifetime productivity of cows. To improve the genetic evaluation model for daily milk yield and determine the effect of the timing of pregnancy on productivity, we used a test-day model to assess the effects of stage of pregnancy on variance component estimates, daily milk yields and 305-day milk yield during the first three lactations of Holstein cows. Data were 10 646 333 test-day records for the first lactation; 8 222 661 records for the second; and 5 513 039 records for the third. The data were analyzed within each lactation by using three single-trait random regression animal models: one model that did not account for the stage of pregnancy effect and two models that did. The effect of stage of pregnancy on test-day milk yield was included in the model by applying a regression on days pregnant or fitting a separate lactation curve for each days open (days from calving to pregnancy) class (eight levels). Stage of pregnancy did not affect the heritability estimates of daily milk yield, although the additive genetic and permanent environmental variances in late lactation were decreased by accounting for the stage of pregnancy effect. The effects of days pregnant on daily milk yield during late lactation were larger in the second and third lactations than in the first lactation. The rates of reduction of the 305-day milk yield of cows that conceived fewer than 90 days after the second or third calving were significantly (P<0.05) greater than that after the first calving. Therefore, we conclude that differences between the negative effects of early pregnancy in the first, compared with later, lactations should be included when determining the optimal number of days open

An alternative approach for modeling strength differential effect in sheet metals with symmetric yield functions

NASA Astrophysics Data System (ADS)

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.

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

NASA Astrophysics Data System (ADS)

Setiyono, T. D.

2014-12-01

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

Estimation efficiency of usage satellite derived and modelled biophysical products for yield forecasting

NASA Astrophysics Data System (ADS)

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.

Modeling precipitation-runoff relationships to determine water yield from a ponderosa pine forest watershed

Treesearch

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...

Yield estimation of corn based on multitemporal LANDSAT-TM data as input for an agrometeorological model

NASA Astrophysics Data System (ADS)

Bach, Heike

1998-07-01

In order to test remote sensing data with advanced yield formation models for accuracy and timeliness of yield estimation of corn, a project was conducted for the State Ministry for Rural Environment, Food, and Forestry of Baden-Württemberg (Germany). This project was carried out during the course of the `Special Yield Estimation', a regular procedure conducted for the European Union, to more accurately estimate agricultural yield. The methodology employed uses field-based plant parameter estimation from atmospherically corrected multitemporal/multispectral LANDSAT-TM data. An agrometeorological plant-production-model is used for yield prediction. Based solely on four LANDSAT-derived estimates (between May and August) and daily meteorological data, the grain yield of corn fields was determined for 1995. The modelled yields were compared with results gathered independently within the Special Yield Estimation for 23 test fields in the upper Rhine valley. The agreement between LANDSAT-based estimates (six weeks before harvest) and Special Yield Estimation (at harvest) shows a relative error of 2.3%. The comparison of the results for single fields shows that six weeks before harvest, the grain yield of corn was estimated with a mean relative accuracy of 13% using satellite information. The presented methodology can be transferred to other crops and geographical regions. For future applications hyperspectral sensors show great potential to further enhance the results for yield prediction with remote sensing.

Use of selection indices to model the functional response of predators

USGS Publications Warehouse

Joly, D.O.; Patterson, B.R.

2003-01-01

The functional response of a predator to changing prey density is an important determinant of stability of predatora??prey systems. We show how Manly's selection indices can be used to distinguish between hyperbolic and sigmoidal models of a predator functional response to primary prey density in the presence of alternative prey. Specifically, an inverse relationship between prey density and preference for that prey results in a hyperbolic functional response while a positive relationship can yield either a hyperbolic or sigmoidal functional response, depending on the form and relative magnitudes of the density-dependent preference model, attack rate, and handling time. As an example, we examine wolf (Canis lupus) functional response to moose (Alces alces) density in the presence of caribou (Rangifer tarandus). The use of selection indices to evaluate the form of the functional response has significant advantages over previous attempts to fit Holling's functional response curves to killing-rate data directly, including increased sensitivity, use of relatively easily collected data, and consideration of other explanatory factors (e.g., weather, seasons, productivity).

Properties of model-averaged BMDLs: a study of model averaging in dichotomous response risk estimation.

PubMed

Wheeler, Matthew W; Bailer, A John

2007-06-01

Model averaging (MA) has been proposed as a method of accounting for model uncertainty in benchmark dose (BMD) estimation. The technique has been used to average BMD dose estimates derived from dichotomous dose-response experiments, microbial dose-response experiments, as well as observational epidemiological studies. While MA is a promising tool for the risk assessor, a previous study suggested that the simple strategy of averaging individual models' BMD lower limits did not yield interval estimators that met nominal coverage levels in certain situations, and this performance was very sensitive to the underlying model space chosen. We present a different, more computationally intensive, approach in which the BMD is estimated using the average dose-response model and the corresponding benchmark dose lower bound (BMDL) is computed by bootstrapping. This method is illustrated with TiO(2) dose-response rat lung cancer data, and then systematically studied through an extensive Monte Carlo simulation. The results of this study suggest that the MA-BMD, estimated using this technique, performs better, in terms of bias and coverage, than the previous MA methodology. Further, the MA-BMDL achieves nominal coverage in most cases, and is superior to picking the "best fitting model" when estimating the benchmark dose. Although these results show utility of MA for benchmark dose risk estimation, they continue to highlight the importance of choosing an adequate model space as well as proper model fit diagnostics.

Improved partition equilibrium model for predicting analyte response in electrospray ionization mass spectrometry.

PubMed

Du, Lihong; White, Robert L

2009-02-01

A previously proposed partition equilibrium model for quantitative prediction of analyte response in electrospray ionization mass spectrometry is modified to yield an improved linear relationship. Analyte mass spectrometer response is modeled by a competition mechanism between analyte and background electrolytes that is based on partition equilibrium considerations. The correlation between analyte response and solution composition is described by the linear model over a wide concentration range and the improved model is shown to be valid for a wide range of experimental conditions. The behavior of an analyte in a salt solution, which could not be explained by the original model, is correctly predicted. The ion suppression effects of 16:0 lysophosphatidylcholine (LPC) on analyte signals are attributed to a combination of competition for excess charge and reduction of total charge due to surface tension effects. In contrast to the complicated mathematical forms that comprise the original model, the simplified model described here can more easily be employed to predict analyte mass spectrometer responses for solutions containing multiple components. Copyright (c) 2008 John Wiley & Sons, Ltd.

Crop Yield Predictions - High Resolution Statistical Model for Intra-season Forecasts Applied to Corn in the US

NASA Astrophysics Data System (ADS)

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.

Whole season compared to growth-stage resolved temperature trends: implications for US maize yield

NASA Astrophysics Data System (ADS)

Butler, E. E.; Mueller, N. D.; Huybers, P. J.

2014-12-01

The effect of temperature on maize yield has generally been considered using a single value for the entire growing season. We compare the effect of temperature trends on yield between two distinct models: a single temperature sensitivity for the whole season and a variable sensitivity across four distinct agronomic development stages. The more resolved variable-sensitivity model indicates roughly a factor of two greater influence of temperature on yield than that implied by the single-sensitivity model. The largest discrepancies occur in silking, which is demonstrated to be the most sensitive stage in the variable-sensitivity model. For instance, whereas median yields are observed to be only 53% of typical values during the hottest 1% of silking-stage temperatures, the single-sensitivity model over predicts median yields of 68% whereas the variable-sensitivity model more correctly predicts median yields of 61%. That the variable sensitivity model is also not capable of capturing the full extent of yield losses suggests that further refinement to represent the non-linear response would be useful. Results from the variable sensitivity model also indicate that management decisions regarding planting times, which have generally shifted toward earlier dates, have led to greater yield benefit than that implied by the single-sensitivity model. Together, the variation of both temperature trends and yield variability within growing stages calls for closer attention to how changes in management interact with changes in climate to ultimately affect yields.

Evaluation of Thompson-type trend and monthly weather data models for corn yields in Iowa, Illinois, and Indiana

NASA Technical Reports Server (NTRS)

French, V. (Principal Investigator)

1982-01-01

An evaluation was made of Thompson-Type models which use trend terms (as a surrogate for technology), meteorological variables based on monthly average temperature, and total precipitation to forecast and estimate corn yields in Iowa, Illinois, and Indiana. Pooled and unpooled Thompson-type models were compared. Neither was found to be consistently superior to the other. Yield reliability indicators show that the models are of limited use for large area yield estimation. The models are objective and consistent with scientific knowledge. Timely yield forecasts and estimates can be made during the growing season by using normals or long range weather forecasts. The models are not costly to operate and are easy to use and understand. The model standard errors of prediction do not provide a useful current measure of modeled yield reliability.

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

NASA Astrophysics Data System (ADS)

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.

Non-invasive indicators associated with the milk yield response after anthelmintic treatment at calving in dairy cows.

PubMed

Verschave, Sien H; Vercruysse, Jozef; Forbes, Andrew; Opsomer, Geert; Hostens, Miel; Duchateau, Luc; Charlier, Johannes

2014-11-14

Gastrointestinal nematodes are an important cause of reduced performance in cattle. Previous studies in Europe showed that after anthelmintic treatment an average gain in milk production of around 1 kg per day/cow can be expected. However, (1) these studies have mainly evaluated group-based anthelmintic treatments during the grazing season or at housing and (2) little is known about parameters affecting variations in the treatment response amongst cows. A better knowledge of such parameters could help to select animals that benefit most from treatment and thus lead to a more rational use of anthelmintics. Therefore, a randomized, non-blinded, controlled clinical trial was performed on 11 commercial dairy farms (477 animals) in Belgium, aiming (1) to study the effect of eprinomectin treatment at calving on milk production and (2) to investigate whether the milk yield response was related to non-invasive animal parameters such that these could be used to inform targeted selective treatment decisions. Analyses show that eprinomectin treatment around calving resulted in an average (± standard error) increase of 0.97 (±0.41) kg in daily milk yield that was followed up over 274 days on average. Milk yield responses were higher in multiparous compared to primiparous cows and in cows with a high (4(th) quartile) anti-O. ostertagi antibody level in a milk sample from the previous lactation. Nonetheless, high responses were also seen in animals with a low (1(st) quartile) anti-O. ostertagi antibody level. In addition, positive treatment responses were associated with higher faecal egg counts and a moderate body condition score at calving (2(nd) quartile). In conclusion, this study provides novel insights into the production response after anthelmintic treatment at calving and factors which influence this. The data could be used to support the development of evidence-based targeted selective anthelmintic treatment strategies in dairy cattle.

Influence of Different Yield Loci on Failure Prediction with Damage Models

NASA Astrophysics Data System (ADS)

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.

Modeling lateral geniculate nucleus response with contrast gain control. Part 2: Analysis

PubMed Central

Cope, Davis; Blakeslee, Barbara; McCourt, Mark E.

2014-01-01

Cope, Blakeslee and McCourt (2013) proposed a class of models for LGN ON-cell behavior consisting of a linear response with divisive normalization by local stimulus contrast. Here we analyze a specific model with the linear response defined by a difference-of-Gaussians filter and a circular Gaussian for the gain pool weighting function. For sinusoidal grating stimuli, the parameter region for band-pass behavior of the linear response is determined, the gain control response is shown to act as a switch (changing from “off” to “on” with increasing spatial frequency), and it is shown that large gain pools stabilize the optimal spatial frequency of the total nonlinear response at a fixed value independent of contrast and stimulus magnitude. Under- and super-saturation as well as contrast saturation occur as typical effects of stimulus magnitude. For circular spot stimuli, it is shown that large gain pools stabilize the spot size that yields the maximum response. PMID:24562034

Infrasound Propagation Modeling for Explosive Yield Estimation

NASA Astrophysics Data System (ADS)

Howard, J. E.; Golden, P.; Negraru, P.

2013-12-01

This study focuses on developing methods of estimating the size or yield of HE surface explosions from local and regional infrasound measurements in the southwestern United States. A munitions disposal facility near Mina, Nevada provides a repeating ground-truth source for this study, with charge weights ranging from 870 - 3800 lbs. Detonation logs and GPS synchronized videos were obtained for a sample of shots representing the full range of weights. These are used to calibrate a relationship between charge weight and spectral level from seismic waveforms recorded at the Nevada Seismic Array (NVAR) at a distance of 36 km. Origin times and yields for the remaining shots are inferred from the seismic recordings at NVAR. Infrasound arrivals from the detonations have been continuously recorded on three four-element, small aperture infrasound arrays since late 2009. NVIAR is collocated with NVAR at a range of approximately 36 km to the northeast. FALN and DNIAR are located at ranges of 154 km to the north, and 293 km to the southeast respectively. Travel times and amplitudes for stratospheric arrivals at DNIAR show strong seasonal variability with the largest amplitudes and celerities occurring during the winter months when the stratospheric winds are favorable. Stratospheric celerities for FNIAR to the north are more consistent as they are not strongly affected by the predominantly meridional stratospheric winds. Tropospheric arrivals at all three arrays show considerable variability that does not appear to be a seasonal effect. Naval Research Laboratory Ground to Space (NRL-G2S) Mesoscale models are used to specify the atmosphere along the propagation path for each detonation. Ray-tracing is performed for each source/receiver pair to identify events for which the models closely match the travel-time observations. This subset of events is used to establish preliminary wind correction formulas using wind values from the G2S profile for the entire propagation path. These

Behavior and Milk Yield Responses of Dairy Cattle to Simulated Jet Aircraft Noise.

DTIC Science & Technology

1992-01-01

dairy cows and release of prolactin (Prl) and Cortisol (Gc) in response to the milking stimuli. Thirty-six lactating Holstein dairy cows were...assigned to experiment when between 79 and 155 days in milk (DIM). Experiment was an incomplete block design with three treatments. Cows were... Cows were exposed to noise on 10-12 d/period with a frequency of 1-4 times/d. Milk yields, milk composition and residual milk were measured

Variation of yield loci in finite element analysis by considering texture evolution for AA5042 aluminum sheets

NASA Astrophysics Data System (ADS)

Yoon, Jonghun; Kim, Kyungjin; Yoon, Jeong Whan

2013-12-01

Yield function has various material parameters that describe how materials respond plastically in given conditions. However, a significant number of mechanical tests are required to identify the many material parameters for yield function. In this study, an effective method using crystal plasticity through a virtual experiment is introduced to develop the anisotropic yield function for AA5042. The crystal plasticity approach was used to predict the anisotropic response of the material in order to consider a number of stress or strain modes that would not otherwise be evident through mechanical testing. A rate-independent crystal plasticity model based on a smooth single crystal yield surface, which removes the innate ambiguity problem within the rate-independent model and Taylor model for polycrystalline deformation behavior were employed to predict the material's response in the balanced biaxial stress, pure shear, and plane strain states to identify the parameters for the anisotropic yield function of AA5042.

Water yield responses to climate change and variability across the North–South Transect of Eastern China (NSTEC)

Treesearch

Nan Lu; Ge Sun; Xiaoming Feng; Bojie Fu

2013-01-01

China is facing a growing water crisis due to climate and land use change, and rise in human water demand across this rapidly developing country. Understanding the spatial and temporal ecohydrologic responses to climate change is critical to sustainable water resource management. We investigated water yield (WY) responses to historical (1981–2000) and projected...

Evaluation of the CEAS trend and monthly weather data models for soybean yields in Iowa, Illinois, and Indiana

NASA Technical Reports Server (NTRS)

French, V. (Principal Investigator)

1982-01-01

The CEAS models evaluated use historic trend and meteorological and agroclimatic variables to forecast soybean yields in Iowa, Illinois, and Indiana. Indicators of yield reliability and current measures of modeled yield reliability were obtained from bootstrap tests on the end of season models. Indicators of yield reliability show that the state models are consistently better than the crop reporting district (CRD) models. One CRD model is especially poor. At the state level, the bias of each model is less than one half quintal/hectare. The standard deviation is between one and two quintals/hectare. The models are adequate in terms of coverage and are to a certain extent consistent with scientific knowledge. Timely yield estimates can be made during the growing season using truncated models. The models are easy to understand and use and are not costly to operate. Other than the specification of values used to determine evapotranspiration, the models are objective. Because the method of variable selection used in the model development is adequately documented, no evaluation can be made of the objectivity and cost of redevelopment of the model.

Crop Yield Simulations Using Multiple Regional Climate Models in the Southwestern United States

NASA Astrophysics Data System (ADS)

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.

Optimization of microwave-assisted extraction of total extract, stevioside and rebaudioside-A from Stevia rebaudiana (Bertoni) leaves, using response surface methodology (RSM) and artificial neural network (ANN) modelling.

PubMed

Ameer, Kashif; Bae, Seong-Woo; Jo, Yunhee; Lee, Hyun-Gyu; Ameer, Asif; Kwon, Joong-Ho

2017-08-15

Stevia rebaudiana (Bertoni) consists of stevioside and rebaudioside-A (Reb-A). We compared response surface methodology (RSM) and artificial neural network (ANN) modelling for their estimation and predictive capabilities in building effective models with maximum responses. A 5-level 3-factor central composite design was used to optimize microwave-assisted extraction (MAE) to obtain maximum yield of target responses as a function of extraction time (X 1 : 1-5min), ethanol concentration, (X 2 : 0-100%) and microwave power (X 3 : 40-200W). Maximum values of the three output parameters: 7.67% total extract yield, 19.58mg/g stevioside yield, and 15.3mg/g Reb-A yield, were obtained under optimum extraction conditions of 4min X 1 , 75% X 2 , and 160W X 3 . The ANN model demonstrated higher efficiency than did the RSM model. Hence, RSM can demonstrate interaction effects of inherent MAE parameters on target responses, whereas ANN can reliably model the MAE process with better predictive and estimation capabilities. Copyright © 2017. Published by Elsevier Ltd.

Differential responses in yield of pumpkin (Cucurbita maxima L.) and nightshade (Solanum retroflexum Dun.) to the application of three animal manures.

PubMed

Azeez, J O; Van Averbeke, W; Okorogbona, A O M

2010-04-01

Crop responses to different manures differs considerably, however, the factors responsible for it have not been conclusively elucidated. Consequently, this study examined the biomass response of Cucurbita maxima and Solanum retroflexum to application rates of chicken and kraal manures of cattle and goat, and soil factors related to salinity. The crops' biomass yield increased linearly with increase in application rates of kraal and chicken manures, but steeper in the latter. Results showed that significant decline in biomass yield in chicken manure at rates above 8.5 tons ha(-1) were not due to salinity. The crops' response to cattle and goat kraal manures was linear but polynomial (cubic) in layer chicken manure. It was concluded that the yield decline in chicken manure was due to other manure factors except salinity, probably toxicity effect of the manure fatty acids. Further research was however, recommended to elucidate this claim. Copyright 2009 Elsevier Ltd. All rights reserved.

Model Identification and FE Simulations: Effect of Different Yield Loci and Hardening Laws in Sheet Forming

NASA Astrophysics Data System (ADS)

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.

Refinement and evaluation of the Massachusetts firm-yield estimator model version 2.0

USGS Publications Warehouse

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

Robust features of future climate change impacts on sorghum yields in West Africa

NASA Astrophysics Data System (ADS)

Sultan, B.; Guan, K.; Kouressy, M.; Biasutti, M.; Piani, C.; Hammer, G. L.; McLean, G.; Lobell, D. B.

2014-10-01

West Africa is highly vulnerable to climate hazards and better quantification and understanding of the impact of climate change on crop yields are urgently needed. Here we provide an assessment of near-term climate change impacts on sorghum yields in West Africa and account for uncertainties both in future climate scenarios and in crop models. Towards this goal, we use simulations of nine bias-corrected CMIP5 climate models and two crop models (SARRA-H and APSIM) to evaluate the robustness of projected crop yield impacts in this area. In broad agreement with the full CMIP5 ensemble, our subset of bias-corrected climate models projects a mean warming of +2.8 °C in the decades of 2031-2060 compared to a baseline of 1961-1990 and a robust change in rainfall in West Africa with less rain in the Western part of the Sahel (Senegal, South-West Mali) and more rain in Central Sahel (Burkina Faso, South-West Niger). Projected rainfall deficits are concentrated in early monsoon season in the Western part of the Sahel while positive rainfall changes are found in late monsoon season all over the Sahel, suggesting a shift in the seasonality of the monsoon. In response to such climate change, but without accounting for direct crop responses to CO2, mean crop yield decreases by about 16-20% and year-to-year variability increases in the Western part of the Sahel, while the eastern domain sees much milder impacts. Such differences in climate and impacts projections between the Western and Eastern parts of the Sahel are highly consistent across the climate and crop models used in this study. We investigate the robustness of impacts for different choices of cultivars, nutrient treatments, and crop responses to CO2. Adverse impacts on mean yield and yield variability are lowest for modern cultivars, as their short and nearly fixed growth cycle appears to be more resilient to the seasonality shift of the monsoon, thus suggesting shorter season varieties could be considered a potential

Intervertebral disc response to cyclic loading--an animal model.

PubMed

Ekström, L; Kaigle, A; Hult, E; Holm, S; Rostedt, M; Hansson, T

1996-01-01

The viscoelastic response of a lumbar motion segment loaded in cyclic compression was studied in an in vivo porcine model (N = 7). Using surgical techniques, a miniaturized servohydraulic exciter was attached to the L2-L3 motion segment via pedicle fixation. A dynamic loading scheme was implemented, which consisted of one hour of sinusoidal vibration at 5 Hz, 50 N peak load, followed by one hour of restitution at zero load and one hour of sinusoidal vibration at 5 Hz, 100 N peak load. The force and displacement responses of the motion segment were sampled at 25 Hz. The experimental data were used for evaluating the parameters of two viscoelastic models: a standard linear solid model (three-parameter) and a linear Burger's fluid model (four-parameter). In this study, the creep behaviour under sinusoidal vibration at 5 Hz closely resembled the creep behaviour under static loading observed in previous studies. Expanding the three-parameter solid model into a four-parameter fluid model made it possible to separate out a progressive linear displacement term. This deformation was not fully recovered during restitution and is therefore an indication of a specific effect caused by the cyclic loading. High variability was observed in the parameters determined from the 50 N experimental data, particularly for the elastic modulus E1. However, at the 100 N load level, significant differences between the models were found. Both models accurately predicted the creep response under the first 800 s of 100 N loading, as displayed by mean absolute errors for the calculated deformation data from the experimental data of 1.26 and 0.97 percent for the solid and fluid models respectively. The linear Burger's fluid model, however, yielded superior predictions particularly for the initial elastic response.

Simulating and Predicting Cereal Crop Yields in Ethiopia: Model Calibration and Verification

NASA Astrophysics Data System (ADS)

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.

AgRISTARS: Yield model development/soil moisture. Interface control document

NASA Technical Reports Server (NTRS)

1980-01-01

The interactions and support functions required between the crop Yield Model Development (YMD) Project and Soil Moisture (SM) Project are defined. The requirements for YMD support of SM and vice-versa are outlined. Specific tasks in support of these interfaces are defined for development of support functions.

Estimation of dew yield from radiative condensers by means of an energy balance model

NASA Astrophysics Data System (ADS)

Maestre-Valero, J. F.; Ragab, R.; Martínez-Alvarez, V.; Baille, A.

2012-08-01

SummaryThis paper presents an energy balance modelling approach to predict the nightly water yield and the surface temperature (Tf) of two passive radiative dew condensers (RDCs) tilted 30° from horizontal. One was fitted with a white hydrophilic polyethylene foil recommended for dew harvest and the other with a black polyethylene foil widely used in horticulture. The model was validated in south-eastern Spain by comparing the simulation outputs with field measurements of Tf and dew yield. The results indicate that the model is robust and accurate in reproducing the behaviour of the two RDCs, especially in what refers to Tf, whose estimates were very close to the observations. The results were somewhat less precise for dew yield, with a larger scatter around the 1:1 relationship. A sensitivity analysis showed that the simulated dew yield was highly sensitive to changes in relative humidity and downward longwave radiation. The proposed approach provides a useful tool to water managers for quantifying the amount of dew that could be harvested as a valuable water resource in arid, semiarid and water stressed regions.

Random regression models using different functions to model test-day milk yield of Brazilian Holstein cows.

PubMed

Bignardi, A B; El Faro, L; Torres Júnior, R A A; Cardoso, V L; Machado, P F; Albuquerque, L G

2011-10-31

We analyzed 152,145 test-day records from 7317 first lactations of Holstein cows recorded from 1995 to 2003. Our objective was to model variations in test-day milk yield during the first lactation of Holstein cows by random regression model (RRM), using various functions in order to obtain adequate and parsimonious models for the estimation of genetic parameters. Test-day milk yields were grouped into weekly classes of days in milk, ranging from 1 to 44 weeks. The contemporary groups were defined as herd-test-day. The analyses were performed using a single-trait RRM, including the direct additive, permanent environmental and residual random effects. In addition, contemporary group and linear and quadratic effects of the age of cow at calving were included as fixed effects. The mean trend of milk yield was modeled with a fourth-order orthogonal Legendre polynomial. The additive genetic and permanent environmental covariance functions were estimated by random regression on two parametric functions, Ali and Schaeffer and Wilmink, and on B-spline functions of days in milk. The covariance components and the genetic parameters were estimated by the restricted maximum likelihood method. Results from RRM parametric and B-spline functions were compared to RRM on Legendre polynomials and with a multi-trait analysis, using the same data set. Heritability estimates presented similar trends during mid-lactation (13 to 31 weeks) and between week 37 and the end of lactation, for all RRM. Heritabilities obtained by multi-trait analysis were of a lower magnitude than those estimated by RRM. The RRMs with a higher number of parameters were more useful to describe the genetic variation of test-day milk yield throughout the lactation. RRM using B-spline and Legendre polynomials as base functions appears to be the most adequate to describe the covariance structure of the data.

Maize yield response to nitrogen as influenced by spatio-temporal variations of soil-water-topography dynamics

USDA-ARS?s Scientific Manuscript database

Reducing N loss from agricultural lands and applying N fertilizer at rates that satisfy both economic and environmental objectives is critical for sustainable agricultural management. This study investigated spatial variability in maize yield response to N and its controlling factors along a typical...

Positive yield response of eggplant (Solanum melongena L.) to inoculation with AM fungi produced on-farm

USDA-ARS?s Scientific Manuscript database

Inoculation of vegetable seedlings with arbuscular mycorrhizal [AM] fungi during the greenhouse growth phase is both biologically and economically practical. Inocula may be purchased or produced on-farm. This work contrasted the fruit yield response of eggplant seedlings with and without inoculati...

Non-randomized response model for sensitive survey with noncompliance.

PubMed

Wu, Qin; Tang, Man-Lai

2016-12-01

Collecting representative data on sensitive issues has long been problematic and challenging in public health prevalence investigation (e.g. non-suicidal self-injury), medical research (e.g. drug habits), social issue studies (e.g. history of child abuse), and their interdisciplinary studies (e.g. premarital sexual intercourse). Alternative data collection techniques that can be adopted to study sensitive questions validly become more important and necessary. As an alternative to the famous Warner randomized response model, non-randomized response triangular model has recently been developed to encourage participants to provide truthful responses in surveys involving sensitive questions. Unfortunately, both randomized and non-randomized response models could underestimate the proportion of subjects with the sensitive characteristic as some respondents do not believe that these techniques can protect their anonymity. As a result, some authors hypothesized that lack of trust and noncompliance should be highest among those who have the most to lose and the least to use for the anonymity provided by using these techniques. Some researchers noticed the existence of noncompliance and proposed new models to measure noncompliance in order to get reliable information. However, all proposed methods were based on randomized response models which require randomizing devices, restrict the survey to only face-to-face interview and are lack of reproductivity. Taking the noncompliance into consideration, we introduce new non-randomized response techniques in which no covariate is required. Asymptotic properties of the proposed estimates for sensitive characteristic as well as noncompliance probabilities are developed. Our proposed techniques are empirically shown to yield accurate estimates for both sensitive and noncompliance probabilities. A real example about premarital sex among university students is used to demonstrate our methodologies. © The Author(s) 2014.

Spatial variability of climate change impacts on yield of rice and wheat in the Indian Ganga Basin.

PubMed

Mishra, Ashok; Singh, R; Raghuwanshi, N S; Chatterjee, C; Froebrich, Jochen

2013-12-01

Indian Ganga Basin (IGB), one of the most densely populated areas in the world, is facing a significant threat to food grain production, besides increased yield gap between actual and potential production, due to climate change. We have analyzed the spatial variability of climate change impacts on rice and wheat yields at three different locations representing the upper, middle and lower IGB. The DSSAT model is used to simulate the effects of climate variability and climate change on rice and wheat yields by analyzing: (i) spatial crop yield response to current climate, and (ii) impact of a changing climate as projected by two regional climate models, REMO and HadRM3, based on SRES A1B emission scenarios for the period 2011-2040. Results for current climate demonstrate a significant gap between actual and potential yield for upper, middle and lower IGB stations. The analysis based on RCM projections shows that during 2011-2040, the largest reduction in rice and wheat yields will occur in the upper IGB (reduction of potential rice and wheat yield respectively by 43.2% and 20.9% by REMO, and 24.8% and 17.2% by HadRM3). In the lower IGB, however, contrasting results are obtained, with HadRM3 based projections showing an increase in the potential rice and wheat yields, whereas, REMO based projections show decreased potential yields. We discuss the influence of agro-climatic factors; variation in temperature, length of maturity period and leaf area index which are responsible for modeled spatial variability in crop yield response within the IGB. Copyright © 2013 Elsevier B.V. All rights reserved.

Data-Driven Haptic Modeling and Rendering of Viscoelastic and Frictional Responses of Deformable Objects.

PubMed

Yim, Sunghoon; Jeon, Seokhee; Choi, Seungmoon

2016-01-01

In this paper, we present an extended data-driven haptic rendering method capable of reproducing force responses during pushing and sliding interaction on a large surface area. The main part of the approach is a novel input variable set for the training of an interpolation model, which incorporates the position of a proxy - an imaginary contact point on the undeformed surface. This allows us to estimate friction in both sliding and sticking states in a unified framework. Estimating the proxy position is done in real-time based on simulation using a sliding yield surface - a surface defining a border between the sliding and sticking regions in the external force space. During modeling, the sliding yield surface is first identified via an automated palpation procedure. Then, through manual palpation on a target surface, input data and resultant force data are acquired. The data are used to build a radial basis interpolation model. During rendering, this input-output mapping interpolation model is used to estimate force responses in real-time in accordance with the interaction input. Physical performance evaluation demonstrates that our approach achieves reasonably high estimation accuracy. A user study also shows plausible perceptual realism under diverse and extensive exploration.

High-resolution, regional-scale crop yield simulations for the Southwestern United States

NASA Astrophysics Data System (ADS)

Stack, D. H.; Kafatos, M.; Medvigy, D.; El-Askary, H. M.; Hatzopoulos, N.; Kim, J.; Kim, S.; Prasad, A. K.; Tremback, C.; Walko, R. L.; Asrar, G. R.

2012-12-01

Over the past few decades, there have been many process-based crop models developed with the goal of better understanding the impacts of climate, soils, and management decisions on crop yields. These models simulate the growth and development of crops in response to environmental drivers. Traditionally, process-based crop models have been run at the individual farm level for yield optimization and management scenario testing. Few previous studies have used these models over broader geographic regions, largely due to the lack of gridded high-resolution meteorological and soil datasets required as inputs for these data intensive process-based models. In particular, assessment of regional-scale yield variability due to climate change requires high-resolution, regional-scale, climate projections, and such projections have been unavailable until recently. The goal of this study was to create a framework for extending the Agricultural Production Systems sIMulator (APSIM) crop model for use at regional scales and analyze spatial and temporal yield changes in the Southwestern United States (CA, AZ, and NV). Using the scripting language Python, an automated pipeline was developed to link Regional Climate Model (RCM) output with the APSIM crop model, thus creating a one-way nested modeling framework. This framework was used to combine climate, soil, land use, and agricultural management datasets in order to better understand the relationship between climate variability and crop yield at the regional-scale. Three different RCMs were used to drive APSIM: OLAM, RAMS, and WRF. Preliminary results suggest that, depending on the model inputs, there is some variability between simulated RCM driven maize yields and historical yields obtained from the United States Department of Agriculture (USDA). Furthermore, these simulations showed strong non-linear correlations between yield and meteorological drivers, with critical threshold values for some of the inputs (e.g. minimum and

A Generalized Measurement Model to Quantify Health: The Multi-Attribute Preference Response Model

PubMed Central

Krabbe, Paul F. M.

2013-01-01

After 40 years of deriving metric values for health status or health-related quality of life, the effective quantification of subjective health outcomes is still a challenge. Here, two of the best measurement tools, the discrete choice and the Rasch model, are combined to create a new model for deriving health values. First, existing techniques to value health states are briefly discussed followed by a reflection on the recent revival of interest in patients’ experience with regard to their possible role in health measurement. Subsequently, three basic principles for valid health measurement are reviewed, namely unidimensionality, interval level, and invariance. In the main section, the basic operation of measurement is then discussed in the framework of probabilistic discrete choice analysis (random utility model) and the psychometric Rasch model. It is then shown how combining the main features of these two models yields an integrated measurement model, called the multi-attribute preference response (MAPR) model, which is introduced here. This new model transforms subjective individual rank data into a metric scale using responses from patients who have experienced certain health states. Its measurement mechanism largely prevents biases such as adaptation and coping. Several extensions of the MAPR model are presented. The MAPR model can be applied to a wide range of research problems. If extended with the self-selection of relevant health domains for the individual patient, this model will be more valid than existing valuation techniques. PMID:24278141

Superparasitism, immune response and optimum progeny yield in the gregarious parasitoid Palmistichus elaeisis.

PubMed

de S Pereira, Kleber; Guedes, Nelsa Maria P; Serrão, José E; Zanuncio, José C; Guedes, Raul Narciso C

2017-06-01

The subsequent deposition of an egg clutch by a female parasitoid into a host already parasitised either by itself or a conspecific (i.e. superparasitism) is a counterintuitive adaptive strategy, particularly considering the female parasitoid's ability to recognise the parasitised hosts. Such a scenario suggests that the adaptive value of superparasitism depends on the number of clutches laid in the same host, with consequences for parasitoid progeny yield. Here, we tested whether such is the case for the gregarious parasitoid Palmistichus elaeisis and explored its underlying basis. Allowing female parasitoids to lay multiple egg clutches in a single melonworm host pupa, parasitoid progeny and fitness exhibited a peak or optimum at three egg clutches laid per host pupa. In addition, haemocyte count, encapsulation and melanisation decreased with the number of egg clutches laid per host pupa. An optimum number of three clutches laid per host pupa was detected for P. elaeisis. As immune response via haemocyte production, encapsulation and melanisation decreased with the number of clutches laid per host, the higher parasitoid yield and fitness observed is the likely consequence of a compromised immune response coupled with an accommodative (i.e. scramble) larval competitive strategy allowing enough resources for optimum balance of parasitoid number and quality produced. © 2017 Society of Chemical Industry. © 2017 Society of Chemical Industry.

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models.

PubMed

Bignardi, A B; El Faro, L; Cardoso, V L; Machado, P F; Albuquerque, L G

2009-09-01

The objective of the present study was to estimate milk yield genetic parameters applying random regression models and parametric correlation functions combined with a variance function to model animal permanent environmental effects. A total of 152,145 test-day milk yields from 7,317 first lactations of Holstein cows belonging to herds located in the southeastern region of Brazil were analyzed. Test-day milk yields were divided into 44 weekly classes of days in milk. Contemporary groups were defined by herd-test-day comprising a total of 2,539 classes. The model included direct additive genetic, permanent environmental, and residual random effects. The following fixed effects were considered: contemporary group, age of cow at calving (linear and quadratic regressions), and the population average lactation curve modeled by fourth-order orthogonal Legendre polynomial. Additive genetic effects were modeled by random regression on orthogonal Legendre polynomials of days in milk, whereas permanent environmental effects were estimated using a stationary or nonstationary parametric correlation function combined with a variance function of different orders. The structure of residual variances was modeled using a step function containing 6 variance classes. The genetic parameter estimates obtained with the model using a stationary correlation function associated with a variance function to model permanent environmental effects were similar to those obtained with models employing orthogonal Legendre polynomials for the same effect. A model using a sixth-order polynomial for additive effects and a stationary parametric correlation function associated with a seventh-order variance function to model permanent environmental effects would be sufficient for data fitting.

Statistical Analysis of Large Simulated Yield Datasets for Studying Climate Effects

NASA Technical Reports Server (NTRS)

Makowski, David; Asseng, Senthold; Ewert, Frank; Bassu, Simona; Durand, Jean-Louis; Martre, Pierre; Adam, Myriam; Aggarwal, Pramod K.; Angulo, Carlos; Baron, Chritian;

Climate change and maize yield in southern Africa: what can farm management do?

PubMed

Rurinda, Jairos; van Wijk, Mark T; Mapfumo, Paul; Descheemaeker, Katrien; Supit, Iwan; Giller, Ken E

2015-12-01

There is concern that food insecurity will increase in southern Africa due to climate change. We quantified the response of maize yield to projected climate change and to three key management options - planting date, fertilizer use and cultivar choice - using the crop simulation model, agricultural production systems simulator (APSIM), at two contrasting sites in Zimbabwe. Three climate periods up to 2100 were selected to cover both near- and long-term climates. Future climate data under two radiative forcing scenarios were generated from five global circulation models. The temperature is projected to increase significantly in Zimbabwe by 2100 with no significant change in mean annual total rainfall. When planting before mid-December with a high fertilizer rate, the simulated average grain yield for all three maize cultivars declined by 13% for the periods 2010-2039 and 2040-2069 and by 20% for 2070-2099 compared with the baseline climate, under low radiative forcing. Larger declines in yield of up to 32% were predicted for 2070-2099 with high radiative forcing. Despite differences in annual rainfall, similar trends in yield changes were observed for the two sites studied, Hwedza and Makoni. The yield response to delay in planting was nonlinear. Fertilizer increased yield significantly under both baseline and future climates. The response of maize to mineral nitrogen decreased with progressing climate change, implying a decrease in the optimal fertilizer rate in the future. Our results suggest that in the near future, improved crop and soil fertility management will remain important for enhanced maize yield. Towards the end of the 21st century, however, none of the farm management options tested in the study can avoid large yield losses in southern Africa due to climate change. There is a need to transform the current cropping systems of southern Africa to offset the negative impacts of climate change. © 2015 John Wiley & Sons Ltd.

Potential water yield response following clearcut harvesting on north and south slopes in northern Idaho

Treesearch

Richard G. Cline; Harold F. Haupt; Gaylon S. Campbell

1977-01-01

The hydrologic response of small clearcuts on north and south slopes in northern Idaho was investigated. On the north slope, substantial gains (27 to 35 cm) in potential water yield per year resulted from (a) removal of transpiring surfaces associated with plant cover, (b) elimination of snow interception by a closed-canopied forest, and (C) delayed reoccupation of the...

MODELING UNCERTAINTY OF RUNOFF AND SEDIMENT YIELD IN TWO EXPERIMENTAL WATERSHEDS

EPA Science Inventory

Sediment loading from agriculture is adversely impacting surface water quality and ecological conditions. In this regard, the use of distributed hydrologic models has gained acceptance in management of soil erosion and sediment yield from agricultural watersheds. Soil infiltrati...

Uncertainty in Simulating Wheat Yields Under Climate Change

NASA Technical Reports Server (NTRS)

Asseng, S.; Ewert, F.; Rosenzweig, Cynthia; Jones, J. W.; Hatfield, J. W.; Ruane, A. C.; Boote, K. J.; Thornburn, P. J.; Rotter, R. P.; Cammarano, D.;

Uncertainty in simulating wheat yields under climate change

NASA Astrophysics Data System (ADS)

Asseng, S.; Ewert, F.; Rosenzweig, C.; Jones, J. W.; Hatfield, J. L.; Ruane, A. C.; Boote, K. J.; Thorburn, P. J.; Rötter, R. P.; Cammarano, D.; Brisson, N.; Basso, B.; Martre, P.; Aggarwal, P. K.; Angulo, C.; Bertuzzi, P.; Biernath, C.; Challinor, A. J.; Doltra, J.; Gayler, S.; Goldberg, R.; Grant, R.; Heng, L.; Hooker, J.; Hunt, L. A.; Ingwersen, J.; Izaurralde, R. C.; Kersebaum, K. C.; Müller, C.; Naresh Kumar, S.; Nendel, C.; O'Leary, G.; Olesen, J. E.; Osborne, T. M.; Palosuo, T.; Priesack, E.; Ripoche, D.; Semenov, M. A.; Shcherbak, I.; Steduto, P.; Stöckle, C.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; Wallach, D.; White, J. W.; Williams, J. R.; Wolf, J.

2013-09-01

Projections of climate change impacts on crop yields are inherently uncertain. Uncertainty is often quantified when projecting future greenhouse gas emissions and their influence on climate. However, multi-model uncertainty analysis of crop responses to climate change is rare because systematic and objective comparisons among process-based crop simulation models are difficult. Here we present the largest standardized model intercomparison for climate change impacts so far. We found that individual crop models are able to simulate measured wheat grain yields accurately under a range of environments, particularly if the input information is sufficient. However, simulated climate change impacts vary across models owing to differences in model structures and parameter values. A greater proportion of the uncertainty in climate change impact projections was due to variations among crop models than to variations among downscaled general circulation models. Uncertainties in simulated impacts increased with CO2 concentrations and associated warming. These impact uncertainties can be reduced by improving temperature and CO2 relationships in models and better quantified through use of multi-model ensembles. Less uncertainty in describing how climate change may affect agricultural productivity will aid adaptation strategy development andpolicymaking.

Fusion yield: Guderley model and Tsallis statistics

NASA Astrophysics Data System (ADS)

Haubold, H. J.; Kumar, D.

2011-02-01

The reaction rate probability integral is extended from Maxwell-Boltzmann approach to a more general approach by using the pathway model introduced by Mathai in 2005 (A pathway to matrix-variate gamma and normal densities. Linear Algebr. Appl. 396, 317-328). The extended thermonuclear reaction rate is obtained in the closed form via a Meijer's G-function and the so-obtained G-function is represented as a solution of a homogeneous linear differential equation. A physical model for the hydrodynamical process in a fusion plasma-compressed and laser-driven spherical shock wave is used for evaluating the fusion energy integral by integrating the extended thermonuclear reaction rate integral over the temperature. The result obtained is compared with the standard fusion yield obtained by Haubold and John in 1981 (Analytical representation of the thermonuclear reaction rate and fusion energy production in a spherical plasma shock wave. Plasma Phys. 23, 399-411). An interpretation for the pathway parameter is also given.

Unitary Response Regression Models

ERIC Educational Resources Information Center

Lipovetsky, S.

2007-01-01

The dependent variable in a regular linear regression is a numerical variable, and in a logistic regression it is a binary or categorical variable. In these models the dependent variable has varying values. However, there are problems yielding an identity output of a constant value which can also be modelled in a linear or logistic regression with…

How Accurately Do Maize Crop Models Simulate the Interactions of Atmospheric CO2 Concentration Levels With Limited Water Supply on Water Use and Yield?

NASA Technical Reports Server (NTRS)

Durand, Jean-Louis; Delusca, Kenel; Boote, Ken; Lizaso, Jon; Manderscheid, Remy; Weigel, Hans Johachim; Ruane, Alexander Clark; Rosenzweig, Cynthia E.; Jones, Jim; Ahuja, Laj;

BioSTAR, a New Biomass and Yield Modeling Software

NASA Astrophysics Data System (ADS)

Kappas, M.; Degener, J.; Bauboeck, R.

2013-12-01

, sorghum, sunflower and, sugar beet. Calibrations for rye grass, cup plant, poplar and willow still need to be performed. A Comparison of simulated and observed biomass yields for sites in Lower Saxony has rendered good results with errors (RMSE) ranging from below 10% (winter wheat, n= 102) and 18.6 % (sunflower, n=8) (Bauböck, unpublished). Because simulations can be made with limited soil data (soil type or texture class) and a limited climate data set (smallest set can be either monthly means of precipitation, temperature and, radiation or precipitation, temperature and, humidity) and the software is capable of processing large datasets, the model appears to be a promising tool for mid or large scale biomass and yield predictions. Up to now the model has only been used for yield predictions with current state climate and climate change scenarios in Lower Saxony, but comparisons with output data of the model AquaCrop (Steduto, et al., 2009) have shown good performance in arid and semi-arid climates (Bauböck, 2013).

Correlations between the modelled potato crop yield and the general atmospheric circulation

NASA Astrophysics Data System (ADS)

Sepp, Mait; Saue, Triin

2012-07-01

Biology-related indicators do not usually depend on just one meteorological element but on a combination of several weather indicators. One way to establish such integral indicators is to classify the general atmospheric circulation into a small number of circulation types. The aim of present study is to analyse connections between general atmospheric circulation and potato crop yield in Estonia. Meteorologically possible yield (MPY), calculated by the model POMOD, is used to characterise potato crop yield. Data of three meteorological stations and the biological parameters of two potato sorts were applied to the model, and 73 different classifications of atmospheric circulation from catalogue 1.2 of COST 733, domain 05 are used to qualify circulation conditions. Correlation analysis showed that there is at least one circulation type in each of the classifications with at least one statistically significant (99%) correlation with potato crop yield, whether in Kuressaare, Tallinn or Tartu. However, no classifications with circulation types correlating with MPY in all three stations at the same time were revealed. Circulation types inducing a decrease in the potato crop yield are more clearly represented. Clear differences occurred between the observed geographical locations as well as between the seasons: derived from the number of significant circulation types, summer and Kuressaare stand out. Of potato varieties, late 'Anti' is more influenced by circulation. Analysis of MSLP maps of circulation types revealed that the seaside stations (Tallinn, Kuressaare) suffer from negative effects of anti-cyclonic conditions (drought), while Tartu suffers from the cyclonic activity (excessive water).

Modelling and Forecasting of Rice Yield in support of Crop Insurance

NASA Astrophysics Data System (ADS)

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 .

Microplume model of spatial-yield spectra. [applying to electron gas degradation in molecular nitrogen gas

NASA Technical Reports Server (NTRS)

Green, A. E. S.; Singhal, R. P.

1979-01-01

An analytic representation for the spatial (radial and longitudinal) yield spectra is developed in terms of a model containing three simple 'microplumes'. The model is applied to electron energy degradation in molecular nitrogen gas for 0.1 to 5 keV incident electrons. From the nature of the cross section input to this model it is expected that the scaled spatial yield spectra for other gases will be quite similar. The model indicates that each excitation, ionization, etc. plume should have its individual spatial and energy dependence. Extensions and aeronomical and radiological applications of the model are discussed.

Regional-scale yield simulations using crop and climate models: assessing uncertainties, sensitivity to temperature and adaptation options

NASA Astrophysics Data System (ADS)

Challinor, A. J.

2010-12-01

Recent progress in assessing the impacts of climate variability and change on crops using multiple regional-scale simulations of crop and climate (i.e. ensembles) is presented. Simulations for India and China used perturbed responses to elevated carbon dioxide constrained using observations from FACE studies and controlled environments. Simulations with crop parameter sets representing existing and potential future adapted varieties were also carried out. The results for India are compared to sensitivity tests on two other crop models. For China, a parallel approach used socio-economic data to account for autonomous farmer adaptation. Results for the USA analysed cardinal temperatures under a range of local warming scenarios for 2711 varieties of spring wheat. The results are as follows: 1. Quantifying and reducing uncertainty. The relative contribution of uncertainty in crop and climate simulation to the total uncertainty in projected yield changes is examined. The observational constraints from FACE and controlled environment studies are shown to be the likely critical factor in maintaining relatively low crop parameter uncertainty. Without these constraints, crop simulation uncertainty in a doubled CO2 environment would likely be greater than uncertainty in simulating climate. However, consensus across crop models in India varied across different biophysical processes. 2. The response of yield to changes in local mean temperature was examined and compared to that found in the literature. No consistent response to temperature change was found across studies. 3. Implications for adaptation. China. The simulations of spring wheat in China show the relative importance of tolerance to water and heat stress in avoiding future crop failures. The greatest potential for reducing the number of harvests less than one standard deviation below the baseline mean yield value comes from alleviating water stress; the greatest potential for reducing harvests less than two

A Model for Simulating the Response of Aluminum Honeycomb Structure to Transverse Loading

NASA Technical Reports Server (NTRS)

Ratcliffe, James G.; Czabaj, Michael W.; Jackson, Wade C.

2012-01-01

A 1-dimensional material model was developed for simulating the transverse (thickness-direction) loading and unloading response of aluminum honeycomb structure. The model was implemented as a user-defined material subroutine (UMAT) in the commercial finite element analysis code, ABAQUS(Registered TradeMark)/Standard. The UMAT has been applied to analyses for simulating quasi-static indentation tests on aluminum honeycomb-based sandwich plates. Comparison of analysis results with data from these experiments shows overall good agreement. Specifically, analyses of quasi-static indentation tests yielded accurate global specimen responses. Predicted residual indentation was also in reasonable agreement with measured values. Overall, this simple model does not involve a significant computational burden, which makes it more tractable to simulate other damage mechanisms in the same analysis.

Quantifying potential yield and water-limited yield of summer maize in the North China Plain

NASA Astrophysics Data System (ADS)

Jiang, Mingnuo; Liu, Chaoshun; Chen, Maosi

2017-09-01

The North China Plain is a major food producing region in China, and climate change could pose a threat to food production in the region. Based on China Meteorological Forcing Dataset, simulating the growth of summer maize in North China Plain from 1979 to 2015 with the regional implementation of crop growth model WOFOST. The results showed that the model can reflect the potential yield and water-limited yield of Summer Maize in North China Plain through the calibration and validation of WOFOST model. After the regional implementation of model, combined with the reanalysis data, the model can better reproduce the regional history of summer maize yield in the North China Plain. The yield gap in Southeastern Beijing, southern Tianjin, southern Hebei province, Northwestern Shandong province is significant, these means the water condition is the main factor to summer maize yield in these regions.

Genetic Parameters And Selection Response For Yield Traits In Bread Wheat Under Irrigated And Rainfed Environments

NASA Astrophysics Data System (ADS)

Khalil, Iftikhar Hussain; at-ur-Rahman, Hiday; Khan, Imran

2008-01-01

A set of 22 F5:7 experimental wheat lines along with four check cultivars (Dera-98, Fakhr-e-Sarhad, Ghaznavi-98 and Tatara) were evaluated as independent experiments under irrigated and rainfed environments using a randomized complete block design at NWFP Agricultural University, Peshawar during 2004-05. The two environments were statistically different for days to heading and spike length only. Highly significant genetic variability existed among the wheat lines (P<0.01) in the combined analysis across environments for all traits. Genotype×environment interactions were non-significant for all traits except 1000-grain weight indicating consistent performance of wheat genotypes across the two environments. Wheat lines and check cultivars were 2 to 5 days early in heading under rainfed environment compared to the irrigated. Plant height, spike length, 1000-grain weight, biological and grain yields were generally reduced under rainfed environment. Genetic variances were of greater magnitude than environmental variances for most of the traits in both environments. The heritability estimates were of higher magnitude (0.74 to 0.96) for days to heading, plant height, spike length, biological and grain yield, while medium (0.31 to 0.51) for 1000-grain weight. Selection differentials were negative for heading (-7.3 days in irrigated vs -9.4 days in rainfed) and plant height (-9.0 cm in irrigated vs -8.7 cm in rainfed) indicating possibility of selecting wheat genotypes with early heading and short plant stature. Positive selection differentials of 1.3 vs 1.6 cm for spike length, 3.8 vs 3.4 g for 1000-grain weight, 2488.2 vs 3139.7 kg ha-1 for biological yield and 691.6 vs 565.4 kg ha-1 for grain yield at 20% selection intensity were observed under irrigated and rainfed environments, respectively. Expected selection responses were 7.98 vs 8.91 days for heading, 8.20 vs 9.52 cm for plant height, 1.01 vs 1.61 cm for spike length, 2.12 vs 1.15 g for 1000-grain weight, 1655

Annual Corn Yield Estimation through Multi-temporal MODIS Data

NASA Astrophysics Data System (ADS)

Shao, Y.; Zheng, B.; Campbell, J. B.

2013-12-01

This research employed 13 years of the Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate annual corn yield for the Midwest of the United States. The overall objective of this study was to examine if annual corn yield could be accurately predicted using MODIS time-series NDVI (Normalized Difference Vegetation Index) and ancillary data such monthly precipitation and temperature. MODIS-NDVI 16-Day composite images were acquired from the USGS EROS Data Center for calendar years 2000 to 2012. For the same time-period, county level corn yield statistics were obtained from the National Agricultural Statistics Service (NASS). The monthly precipitation and temperature measures were derived from Precipitation-Elevation Regressions on Independent Slopes Model (PRISM) climate data. A cropland mask was derived using 2006 National Land Cover Database. For each county and within the cropland mask, the MODIS-NDVI time-series data and PRISM climate data were spatially averaged, at their respective time steps. We developed a random forest predictive model with the MODIS-NDVI and climate data as predictors and corn yield as response. To assess the model accuracy, we used twelve years of data as training and the remaining year as hold-out testing set. The training and testing procedures were repeated 13 times. The R2 ranged from 0.72 to 0.83 for testing years. It was also found that the inclusion of climate data did not improve the model predictive performance. MODIS-NDVI time-series data alone might provide sufficient information for county level corn yield prediction.

Modeling heterogeneous responsiveness of intrinsic apoptosis pathway

PubMed Central

2013-01-01

Background Apoptosis is a cell suicide mechanism that enables multicellular organisms to maintain homeostasis and to eliminate individual cells that threaten the organism’s survival. Dependent on the type of stimulus, apoptosis can be propagated by extrinsic pathway or intrinsic pathway. The comprehensive understanding of the molecular mechanism of apoptotic signaling allows for development of mathematical models, aiming to elucidate dynamical and systems properties of apoptotic signaling networks. There have been extensive efforts in modeling deterministic apoptosis network accounting for average behavior of a population of cells. Cellular networks, however, are inherently stochastic and significant cell-to-cell variability in apoptosis response has been observed at single cell level. Results To address the inevitable randomness in the intrinsic apoptosis mechanism, we develop a theoretical and computational modeling framework of intrinsic apoptosis pathway at single-cell level, accounting for both deterministic and stochastic behavior. Our deterministic model, adapted from the well-accepted Fussenegger model, shows that an additional positive feedback between the executioner caspase and the initiator caspase plays a fundamental role in yielding the desired property of bistability. We then examine the impact of intrinsic fluctuations of biochemical reactions, viewed as intrinsic noise, and natural variation of protein concentrations, viewed as extrinsic noise, on behavior of the intrinsic apoptosis network. Histograms of the steady-state output at varying input levels show that the intrinsic noise could elicit a wider region of bistability over that of the deterministic model. However, the system stochasticity due to intrinsic fluctuations, such as the noise of steady-state response and the randomness of response delay, shows that the intrinsic noise in general is insufficient to produce significant cell-to-cell variations at physiologically relevant level of

Climate-based statistical regression models for crop yield forecasting of coffee in humid tropical Kerala, India

NASA Astrophysics Data System (ADS)

Jayakumar, M.; Rajavel, M.; Surendran, U.

2016-12-01

A study on the variability of coffee yield of both Coffea arabica and Coffea canephora as influenced by climate parameters (rainfall (RF), maximum temperature (Tmax), minimum temperature (Tmin), and mean relative humidity (RH)) was undertaken at Regional Coffee Research Station, Chundale, Wayanad, Kerala State, India. The result on the coffee yield data of 30 years (1980 to 2009) revealed that the yield of coffee is fluctuating with the variations in climatic parameters. Among the species, productivity was higher for C. canephora coffee than C. arabica in most of the years. Maximum yield of C. canephora (2040 kg ha-1) was recorded in 2003-2004 and there was declining trend of yield noticed in the recent years. Similarly, the maximum yield of C. arabica (1745 kg ha-1) was recorded in 1988-1989 and decreased yield was noticed in the subsequent years till 1997-1998 due to year to year variability in climate. The highest correlation coefficient was found between the yield of C. arabica coffee and maximum temperature during January (0.7) and between C. arabica coffee yield and RH during July (0.4). Yield of C. canephora coffee had highest correlation with maximum temperature, RH and rainfall during February. Statistical regression model between selected climatic parameters and yield of C. arabica and C. canephora coffee was developed to forecast the yield of coffee in Wayanad district in Kerala. The model was validated for years 2010, 2011, and 2012 with the coffee yield data obtained during the years and the prediction was found to be good.

Brazilian Soybean Yields and Yield Gaps Vary with Farm Size

NASA Astrophysics Data System (ADS)

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.

Spectral behavior of wheat yield variety trials

NASA Technical Reports Server (NTRS)

Hatfield, J. L.

1981-01-01

Little variation between varieties is seen at jointing, but the variability is found to increase during grain filling and decline again at maturity. No relationship is found between spectral response and yield, and when yields are segregated into various classes the spectral response is the same. Spring and winter nurseries are found to separate during the reproductive stage because of differences in dates of heading and maturity, but they exhibit similar spectral responses. The transformed normalized difference is at a minimum after the maximum grain weight occurs and the leaves begin to brown and fall off. These data of 100% ground cover demonstrate that it is not possible to predict grain yield from only spectral data. This, however, may not apply when reduced yields are caused by less-than-full ground cover

[Effect of near infrared spectrum on the precision of PLS model for oil yield from oil shale].

PubMed

Wang, Zhi-Hong; Liu, Jie; Chen, Xiao-Chao; Sun, Yu-Yang; Yu, Yang; Lin, Jun

2012-10-01

It is impossible to use present measurement methods for the oil yield of oil shale to realize in-situ detection and these methods unable to meet the requirements of the oil shale resources exploration and exploitation. But in-situ oil yield analysis of oil shale can be achieved by the portable near infrared spectroscopy technique. There are different correlativities of NIR spectrum data formats and contents of sample components, and the different absorption specialities of sample components shows in different NIR spectral regions. So with the proportioning samples, the PLS modeling experiments were done by 3 formats (reflectance, absorbance and K-M function) and 4 regions of modeling spectrum, and the effect of NIR spectral format and region to the precision of PLS model for oil yield from oil shale was studied. The results show that the best data format is reflectance and the best modeling region is combination spectral range by PLS model method and proportioning samples. Therefore, the appropriate data format and the proper characteristic spectral region can increase the precision of PLS model for oil yield form oil shale.

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

NASA Astrophysics Data System (ADS)

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.

Functional modeling of the human auditory brainstem response to broadband stimulationa)

PubMed Central

Verhulst, Sarah; Bharadwaj, Hari M.; Mehraei, Golbarg; Shera, Christopher A.; Shinn-Cunningham, Barbara G.

2015-01-01

Population responses such as the auditory brainstem response (ABR) are commonly used for hearing screening, but the relationship between single-unit physiology and scalp-recorded population responses are not well understood. Computational models that integrate physiologically realistic models of single-unit auditory-nerve (AN), cochlear nucleus (CN) and inferior colliculus (IC) cells with models of broadband peripheral excitation can be used to simulate ABRs and thereby link detailed knowledge of animal physiology to human applications. Existing functional ABR models fail to capture the empirically observed 1.2–2 ms ABR wave-V latency-vs-intensity decrease that is thought to arise from level-dependent changes in cochlear excitation and firing synchrony across different tonotopic sections. This paper proposes an approach where level-dependent cochlear excitation patterns, which reflect human cochlear filter tuning parameters, drive AN fibers to yield realistic level-dependent properties of the ABR wave-V. The number of free model parameters is minimal, producing a model in which various sources of hearing-impairment can easily be simulated on an individualized and frequency-dependent basis. The model fits latency-vs-intensity functions observed in human ABRs and otoacoustic emissions while maintaining rate-level and threshold characteristics of single-unit AN fibers. The simulations help to reveal which tonotopic regions dominate ABR waveform peaks at different stimulus intensities. PMID:26428802

Spectral considerations for modeling yield of canola

USDA-ARS?s Scientific Manuscript database

Conspicuous yellow flowers that are present in a Brassica oilseed crop such as canola require careful consideration when selecting a spectral index for yield estimation. This study evaluated spectral indices for multispectral sensors that correlate with the seed yield of Brassica oilseed crops. A ...

Impacts of aerosol pollutant mitigation on lowland rice yields in China

NASA Astrophysics Data System (ADS)

Zhang, Tianyi; Li, Tao; Yue, Xu; Yang, Xiaoguang

2017-10-01

Aerosol pollution in China is significantly altering radiative transfer processes and is thereby potentially affecting rice photosynthesis and yields. However, the response of rice photosynthesis to aerosol-induced radiative perturbations is still not well understood. Here, we employ a process-based modelling approach to simulate changes in incoming radiation (RAD) and the diffuse radiation fraction (DF) with aerosol mitigation in China and their associated impacts on rice yields. Aerosol reduction has the positive effect of increasing RAD and the negative effect of decreasing DF on rice photosynthesis and yields. In rice production areas where the average RAD during the growing season is lower than 250 W m-2, aerosol reduction is beneficial for higher rice yields, whereas in areas with RAD>250 W m-2, aerosol mitigation causes yield declines due to the associated reduction in the DF, which decreases the light use efficiency. As a net effect, rice yields were estimated to significantly increase by 0.8%-2.6% with aerosol concentrations reductions from 20 to 100%, which is lower than the estimates obtained in earlier studies that only considered the effects of RAD. This finding suggests that both RAD and DF are important processes influencing rice yields and should be incorporated into future assessments of agricultural responses to variations in aerosol-induced radiation under climate change.

A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change.

PubMed

Ashraf, M Irfan; Meng, Fan-Rui; Bourque, Charles P-A; MacLean, David A

2015-01-01

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 technology

A Novel Modelling Approach for Predicting Forest Growth and Yield under Climate Change

PubMed Central

Ashraf, M. Irfan; Meng, Fan-Rui; Bourque, Charles P.-A.; MacLean, David A.

2015-01-01

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 cm2 5-year-1 and volume: 0.0008 m3 5-year-1). Model variability described by root mean squared error (RMSE) in basal area prediction was 40.53 cm2 5-year-1 and 0.0393 m3 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 technology has substantial

Interrelations between glucose-induced insulin response, metabolic indicators, and time of first ovulation in high-yielding dairy cows.

PubMed

Bossaert, P; Leroy, J L M R; De Vliegher, S; Opsomer, G

2008-09-01

High-yielding dairy cows are more susceptible to metabolic and reproductive disorders than low-yielding cows. Insulin plays a pivotal role in the development of both problems. In the present study, we aimed to assess the glucose-induced insulin responses of dairy cows at different time points relative to calving and to relate this to the metabolic status and the time of first ovulation. Twenty-three healthy, multiparous Holstein-Friesian cows with a high genetic merit for milk yield were studied from 14 d prepartum to 42 d postpartum. Intravenous glucose tolerance tests were performed on -14, 14, and 42 d relative to calving to evaluate the plasma insulin and glucose responses to a glucose load, as estimated by the peak concentration, the area under the curve (AUC), and the clearance rates of insulin and glucose. Blood samples were obtained at 3-d intervals and analyzed for glucose, insulin, and nonesterified fatty acids (NEFA). The time of first ovulation was defined by transrectal ultrasonography and plasma progesterone analysis. Glucose-induced insulin AUC and peak concentration decreased and glucose clearance increased during lactation compared with the dry period. Plasma NEFA concentrations were negatively related to insulin AUC and peak concentrations. Fourteen cows ovulated within 42 d postpartum, and the remaining 9 cows suffered from delayed resumption of ovarian function. Survival analysis demonstrated that cows with lower NEFA concentrations during the dry period tended to have earlier resumption of ovarian activity. In conclusion, our data suggest a decreased plasma insulin response to glucose postpartum in high-yielding dairy cows, possibly contributing to metabolic stress during the early postpartum period. It is hypothesized that NEFA impair glucose-induced insulin secretion in dairy cows. Additionally, our results suggest the importance of lipolysis during the transition period as a risk factor for delayed ovulation.

Advanced model for the prediction of the neutron-rich fission product yields

NASA Astrophysics Data System (ADS)

Rubchenya, V. A.; Gorelov, D.; Jokinen, A.; Penttilä, H.; Äystö, J.

2013-12-01

The consistent models for the description of the independent fission product formation cross sections in the spontaneous fission and in the neutron and proton induced fission at the energies up to 100 MeV is developed. This model is a combination of new version of the two-component exciton model and a time-dependent statistical model for fusion-fission process with inclusion of dynamical effects for accurate calculations of nucleon composition and excitation energy of the fissioning nucleus at the scission point. For each member of the compound nucleus ensemble at the scission point, the primary fission fragment characteristics: kinetic and excitation energies and their yields are calculated using the scission-point fission model with inclusion of the nuclear shell and pairing effects, and multimodal approach. The charge distribution of the primary fragment isobaric chains was considered as a result of the frozen quantal fluctuations of the isovector nuclear matter density at the scission point with the finite neck radius. Model parameters were obtained from the comparison of the predicted independent product fission yields with the experimental results and with the neutron-rich fission product data measured with a Penning trap at the Accelerator Laboratory of the University of Jyväskylä (JYFLTRAP).

Modeling agricultural commodity prices and volatility in response to anticipated climate change

NASA Astrophysics Data System (ADS)

Lobell, D. B.; Tran, N.; Welch, J.; Roberts, M.; Schlenker, W.

2012-12-01

Food prices have shown a positive trend in the past decade, with episodes of rapid increases in 2008 and 2011. These increases pose a threat to food security in many regions of the world, where the poor are generally net consumers of food, and are also thought to increase risks of social and political unrest. The role of global warming in these price reversals have been debated, but little quantitative work has been done. A particular challenge in modeling these effects is that they require understanding links between climate and food supply, as well as between food supply and prices. Here we combine the anticipated effects of climate change on yield levels and volatility with an empirical competitive storage model to examine how expected climate change might affect prices and social welfare in the international food commodity market. We show that price level and volatility do increase over time in response to decreasing yield, and increasing yield variability. Land supply and storage demand both increase, but production and consumption continue to fall leading to a decrease in consumer surplus, and a corresponding though smaller increase in producer surplus.

Noise exposure-response relationships established from repeated binary observations: Modeling approaches and applications.

PubMed

Schäffer, Beat; Pieren, Reto; Mendolia, Franco; Basner, Mathias; Brink, Mark

2017-05-01

Noise exposure-response relationships are used to estimate the effects of noise on individuals or a population. Such relationships may be derived from independent or repeated binary observations, and modeled by different statistical methods. Depending on the method by which they were established, their application in population risk assessment or estimation of individual responses may yield different results, i.e., predict "weaker" or "stronger" effects. As far as the present body of literature on noise effect studies is concerned, however, the underlying statistical methodology to establish exposure-response relationships has not always been paid sufficient attention. This paper gives an overview on two statistical approaches (subject-specific and population-averaged logistic regression analysis) to establish noise exposure-response relationships from repeated binary observations, and their appropriate applications. The considerations are illustrated with data from three noise effect studies, estimating also the magnitude of differences in results when applying exposure-response relationships derived from the two statistical approaches. Depending on the underlying data set and the probability range of the binary variable it covers, the two approaches yield similar to very different results. The adequate choice of a specific statistical approach and its application in subsequent studies, both depending on the research question, are therefore crucial.

Effects of geoengineering on crop yields

NASA Astrophysics Data System (ADS)

Pongratz, J.; Lobell, D. B.; Cao, L.; Caldeira, K.

2011-12-01

The potential of "solar radiation management" (SRM) to reduce future climate change and associated risks has been receiving significant attention in scientific and policy circles. SRM schemes aim to reduce global warming despite increasing atmospheric CO2 concentrations by diminishing the amount of solar insolation absorbed by the Earth, for example, by injecting scattering aerosols into the atmosphere. Climate models predict that SRM could fully compensate warming at the global mean in a high-CO2 world. While reduction of global warming may offset a part of the predicted negative effects of future climate change on crop yields, SRM schemes are expected to alter regional climate and to have substantial effects on climate variables other than temperature, such as precipitation. It has therefore been warned that, overall, SRM may pose a risk to food security. Assessments of benefits and risks of geoengineering are imperative, yet such assessments are only beginning to emerge; in particular, effects on global food security have not previously been assessed. Here, for the first time, we combine climate model simulations with models of crop yield responses to climate to assess large-scale changes in yields and food production under SRM. In most crop-growing regions, we find that yield losses caused by climate changes are substantially reduced under SRM as compared with a non-geoengineered doubling of atmospheric CO2. Substantial yield losses with SRM are only found for rice in high latitudes, where the limits of low temperatures are no longer alleviated. At the same time, the beneficial effect of CO2-fertilization on plant productivity remains active. Overall therefore, SRM in our models causes global crop yields to increase. We estimate the direct effects of climate and CO2 changes on crop production, and do not quantify effects of market dynamics and management changes. We note, however, that an SRM deployment would be unlikely to maintain the economic status quo, as

Application of a CROPWAT Model to Analyze Crop Yields in Nicaragua

NASA Astrophysics Data System (ADS)

Doria, R.; Byrne, J. M.

2013-12-01

ABSTRACT Changes in climate are likely to influence crop yields due to varying evapotranspiration and precipitation over agricultural regions. In Nicaragua, agriculture is extensive, with new areas of land brought into production as the population increases. Nicaraguan staple food items (maize and beans) are produced mostly by small scale farmers with less than 10 hectares, but they are critical for income generation and food security for rural communities. Given that the majority of these farmers are dependent on rain for crop irrigation, and that maize and beans are sensitive to variations in temperature and rainfall patterns, the present study was undertaken to assess the impact of climate change on these crop yields. Climate data were generated per municipio representing the three major climatic zones of the country: the wet Pacific lowland, the cooler Central highland, and the Caribbean lowland. Historical normal climate data from 1970-2000 (baseline period) were used as input to CROPWAT model to analyze the potential and actual evapotranspiration (ETo and ETa, respectively) that affects crop yields. Further, generated local climatic data of future years (2030-2099) under various scenarios were inputted to the CROPWAT to determine changes in ETo and ETa from the baseline period. Spatial variability maps of both ETo and ETa as well as crop yields were created. Results indicated significant variation in seasonal rainfall depth during the baseline period and predicted decreasing trend in the future years that eventually affects yields. These maps enable us to generate appropriate adaptation measures and best management practices for small scale farmers under future climate change scenarios. KEY WORDS: Climate change, evapotranspiration, CROPWAT, yield, Nicaragua

On use of the multistage dose-response model for assessing laboratory animal carcinogenicity

PubMed Central

Nitcheva, Daniella; Piegorsch, Walter W.; West, R. Webster

2007-01-01

We explore how well a statistical multistage model describes dose-response patterns in laboratory animal carcinogenicity experiments from a large database of quantal response data. The data are collected from the U.S. EPA’s publicly available IRIS data warehouse and examined statistically to determine how often higher-order values in the multistage predictor yield significant improvements in explanatory power over lower-order values. Our results suggest that the addition of a second-order parameter to the model only improves the fit about 20% of the time, while adding even higher-order terms apparently does not contribute to the fit at all, at least with the study designs we captured in the IRIS database. Also included is an examination of statistical tests for assessing significance of higher-order terms in a multistage dose-response model. It is noted that bootstrap testing methodology appears to offer greater stability for performing the hypothesis tests than a more-common, but possibly unstable, “Wald” test. PMID:17490794

A hierarchical spatial model for well yield in complex aquifers

NASA Astrophysics Data System (ADS)

Montgomery, J.; O'sullivan, F.

2017-12-01

Efficiently siting and managing groundwater wells requires reliable estimates of the amount of water that can be produced, or the well yield. This can be challenging to predict in highly complex, heterogeneous fractured aquifers due to the uncertainty around local hydraulic properties. Promising statistical approaches have been advanced in recent years. For instance, kriging and multivariate regression analysis have been applied to well test data with limited but encouraging levels of prediction accuracy. Additionally, some analytical solutions to diffusion in homogeneous porous media have been used to infer "effective" properties consistent with observed flow rates or drawdown. However, this is an under-specified inverse problem with substantial and irreducible uncertainty. We describe a flexible machine learning approach capable of combining diverse datasets with constraining physical and geostatistical models for improved well yield prediction accuracy and uncertainty quantification. Our approach can be implemented within a hierarchical Bayesian framework using Markov Chain Monte Carlo, which allows for additional sources of information to be incorporated in priors to further constrain and improve predictions and reduce the model order. We demonstrate the usefulness of this approach using data from over 7,000 wells in a fractured bedrock aquifer.

Crop weather models of barley and spring wheat yield for agrophysical units in North Dakota

NASA Technical Reports Server (NTRS)

Leduc, S. (Principal Investigator)

1982-01-01

Models based on multiple regression were developed to estimate barley yield and spring wheat yield from weather data for Agrophysical units(APU) in North Dakota. The predictor variables are derived from monthly average temperature and monthly total precipitation data at meteorological stations in the cooperative network. The models are similar in form to the previous models developed for Crop Reporting Districts (CRD). The trends and derived variables were the same and the approach to select the significant predictors was similar to that used in developing the CRD models. The APU models show sight improvements in some of the statistics of the models, e.g., explained variation. These models are to be independently evaluated and compared to the previously evaluated CRD models. The comparison will indicate the preferred model area for this application, i.e., APU or CRD.

Prediction of winter wheat high yield from remote sensing based model: application in United States and Ukraine

NASA Astrophysics Data System (ADS)

Franch, B.; Vermote, E.; Roger, J. C.; Skakun, S.; Becker-Reshef, I.; Justice, C. O.

2017-12-01

Accurate and timely crop yield forecasts are critical for making informed agricultural policies and investments, as well as increasing market efficiency and stability. In Becker-Reshef et al. (2010) and Franch et al. (2015) we developed an empirical generalized model for forecasting winter wheat yield. It is based on the relationship between the Normalized Difference Vegetation Index (NDVI) at the peak of the growing season and the Growing Degree Day (GDD) information extracted from NCEP/NCAR reanalysis data. These methods were applied to MODIS CMG data in Ukraine, the US and China with errors around 10%. However, the NDVI is saturated for yield values higher than 4 MT/ha. As a consequence, the model had to be re-calibrated in each country and the validation of the national yields showed low correlation coefficients. In this study we present a new model based on the extrapolation of the pure wheat signal (100% of wheat within the pixel) from MODIS data at 1km resolution and using the Difference Vegetation Index (DVI). The model has been applied to monitor the national yield of winter wheat in the United States and Ukraine from 2001 to 2016.

Hidden Markov Item Response Theory Models for Responses and Response Times.

PubMed

Molenaar, Dylan; Oberski, Daniel; Vermunt, Jeroen; De Boeck, Paul

2016-01-01

Current approaches to model responses and response times to psychometric tests solely focus on between-subject differences in speed and ability. Within subjects, speed and ability are assumed to be constants. Violations of this assumption are generally absorbed in the residual of the model. As a result, within-subject departures from the between-subject speed and ability level remain undetected. These departures may be of interest to the researcher as they reflect differences in the response processes adopted on the items of a test. In this article, we propose a dynamic approach for responses and response times based on hidden Markov modeling to account for within-subject differences in responses and response times. A simulation study is conducted to demonstrate acceptable parameter recovery and acceptable performance of various fit indices in distinguishing between different models. In addition, both a confirmatory and an exploratory application are presented to demonstrate the practical value of the modeling approach.

Modeling Response Signal and Response Time Data

ERIC Educational Resources Information Center

Ratcliff, Roger

2006-01-01

The diffusion model (Ratcliff, 1978) and the leaky competing accumulator model (LCA, Usher & McClelland, 2001) were tested against two-choice data collected from the same subjects with the standard response time procedure and the response signal procedure. In the response signal procedure, a stimulus is presented and then, at one of a number of…

Incorporating uncertainty into the ranking of SPARROW model nutrient yields from Mississippi/Atchafalaya River basin watersheds

USGS Publications Warehouse

Robertson, Dale M.; Schwarz, Gregory E.; Saad, David A.; Alexander, Richard B.

2009-01-01

Excessive loads of nutrients transported by tributary rivers have been linked to hypoxia in the Gulf of Mexico. Management efforts to reduce the hypoxic zone in the Gulf of Mexico and improve the water quality of rivers and streams could benefit from targeting nutrient reductions toward watersheds with the highest nutrient yields delivered to sensitive downstream waters. One challenge is that most conventional watershed modeling approaches (e.g., mechanistic models) used in these management decisions do not consider uncertainties in the predictions of nutrient yields and their downstream delivery. The increasing use of parameter estimation procedures to statistically estimate model coefficients, however, allows uncertainties in these predictions to be reliably estimated. Here, we use a robust bootstrapping procedure applied to the results of a previous application of the hybrid statistical/mechanistic watershed model SPARROW (Spatially Referenced Regression On Watershed attributes) to develop a statistically reliable method for identifying “high priority” areas for management, based on a probabilistic ranking of delivered nutrient yields from watersheds throughout a basin. The method is designed to be used by managers to prioritize watersheds where additional stream monitoring and evaluations of nutrient-reduction strategies could be undertaken. Our ranking procedure incorporates information on the confidence intervals of model predictions and the corresponding watershed rankings of the delivered nutrient yields. From this quantified uncertainty, we estimate the probability that individual watersheds are among a collection of watersheds that have the highest delivered nutrient yields. We illustrate the application of the procedure to 818 eight-digit Hydrologic Unit Code watersheds in the Mississippi/Atchafalaya River basin by identifying 150 watersheds having the highest delivered nutrient yields to the Gulf of Mexico. Highest delivered yields were from

The strain path dependence of plastic deformation response of AA5754: Experiment and modeling

NASA Astrophysics Data System (ADS)

Pham, Minh-Son; Hu, Lin; Iadicola, Mark; Creuziger, Adam; Rollett, Anthony D.

2013-12-01

This work presents modeling of experiments on a balanced biaxial (BB) pre-strained AA5754 alloy, subsequently reloaded uniaxially along the rolling direction and transverse direction. The material exhibits a complex plastic deformation response during the change in strain path due to 1) crystallographic texture, 2) aging (interactions between dislocations and Mg atoms) and 3) recovery (annihilation and re-arrangement of dislocations). With a BB prestrain of about 5 %, the aging process is dominant, and the yield strength for uniaxially deformed samples is observed to be higher than the flow stress during BB straining. The strain hardening rate after changing path is, however, lower than that for pre-straining. Higher degrees of pre-straining make the dynamic recovery more active. The dynamic recovery at higher strain levels compensates for the aging effect, and results in: 1) a reduction of the yield strength, and 2) an increase in the hardening rate of re-strained specimens along other directions. The yield strength of deformed samples is further reduced if these samples are left at room temperature to let static recovery occur. The synergistic influences of texture condition, aging and recovery processes on the material response make the modeling of strain path dependence of mechanical behavior of AA5754 challenging. In this study, the influence of crystallographic texture is taken into account by incorporating the latent hardening into a visco-plastic self-consistent model. Different strengths of dislocation glide interaction models in 24 slip systems are used to represent the latent hardening. Moreover, the aging and recovery effects are also included into the latent hardening model by considering strong interactions between dislocations and dissolved atom Mg and the microstructural evolution. These microstructural considerations provide a powerful capability to successfully describe the strain path dependence of plastic deformation behavior of AA5754.

Maize Yield Response to Water Supply and Fertilizer Input in a Semi-Arid Environment of Northeast China

PubMed Central

Yin, Guanghua; Gu, Jian; Zhang, Fasheng; Hao, Liang; Cong, Peifei; Liu, Zuoxin

2014-01-01

Maize grain yield varies highly with water availability as well as with fertilization and relevant agricultural management practices. With a 311-A optimized saturation design, field experiments were conducted between 2006 and 2009 to examine the yield response of spring maize (Zhengdan 958, Zea mays L) to irrigation (I), nitrogen fertilization (total nitrogen, urea-46% nitrogen,) and phosphorus fertilization (P2O5, calcium superphosphate-13% P2O5) in a semi-arid area environment of Northeast China. According to our estimated yield function, the results showed that N is the dominant factor in determining maize grain yield followed by I, while P plays a relatively minor role. The strength of interaction effects among I, N and P on maize grain yield follows the sequence N+I >P+I>N+P. Individually, the interaction effects of N+I and N+P on maize grain yield are positive, whereas that of P+I is negative. To achieve maximum grain yield (10506.0 kg·ha−1) for spring maize in the study area, the optimum application rates of I, N and P are 930.4 m3·ha−1, 304.9 kg·ha−1 and 133.2 kg·ha−1 respectively that leads to a possible economic profit (EP) of 10548.4 CNY·ha−1 (CNY, Chinese Yuan). Alternately, to obtain the best EP (10827.3 CNY·ha−1), the optimum application rates of I, N and P are 682.4 m3·ha−1, 241.0 kg·ha−1 and 111.7 kg·ha−1 respectively that produces a potential grain yield of 10289.5 kg·ha−1. PMID:24465896

Collaborative simulations and experiments for a novel yield model of coal devolatilization in oxy-coal combustion conditions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Iavarone, Salvatore; Smith, Sean T.; Smith, Philip J.

Oxy-coal combustion is an emerging low-cost “clean coal” technology for emissions reduction and Carbon Capture and Sequestration (CCS). The use of Computational Fluid Dynamics (CFD) tools is crucial for the development of cost-effective oxy-fuel technologies and the minimization of environmental concerns at industrial scale. The coupling of detailed chemistry models and CFD simulations is still challenging, especially for large-scale plants, because of the high computational efforts required. The development of scale-bridging models is therefore necessary, to find a good compromise between computational efforts and the physical-chemical modeling precision. This paper presents a procedure for scale-bridging modeling of coal devolatilization, inmore » the presence of experimental error, that puts emphasis on the thermodynamic aspect of devolatilization, namely the final volatile yield of coal, rather than kinetics. The procedure consists of an engineering approach based on dataset consistency and Bayesian methodology including Gaussian-Process Regression (GPR). Experimental data from devolatilization tests carried out in an oxy-coal entrained flow reactor were considered and CFD simulations of the reactor were performed. Jointly evaluating experiments and simulations, a novel yield model was validated against the data via consistency analysis. In parallel, a Gaussian-Process Regression was performed, to improve the understanding of the uncertainty associated to the devolatilization, based on the experimental measurements. Potential model forms that could predict yield during devolatilization were obtained. The set of model forms obtained via GPR includes the yield model that was proven to be consistent with the data. Finally, the overall procedure has resulted in a novel yield model for coal devolatilization and in a valuable evaluation of uncertainty in the data, in the model form, and in the model parameters.« less

Collaborative simulations and experiments for a novel yield model of coal devolatilization in oxy-coal combustion conditions

DOE PAGES

Iavarone, Salvatore; Smith, Sean T.; Smith, Philip J.; ...

2017-06-03

Oxy-coal combustion is an emerging low-cost “clean coal” technology for emissions reduction and Carbon Capture and Sequestration (CCS). The use of Computational Fluid Dynamics (CFD) tools is crucial for the development of cost-effective oxy-fuel technologies and the minimization of environmental concerns at industrial scale. The coupling of detailed chemistry models and CFD simulations is still challenging, especially for large-scale plants, because of the high computational efforts required. The development of scale-bridging models is therefore necessary, to find a good compromise between computational efforts and the physical-chemical modeling precision. This paper presents a procedure for scale-bridging modeling of coal devolatilization, inmore » the presence of experimental error, that puts emphasis on the thermodynamic aspect of devolatilization, namely the final volatile yield of coal, rather than kinetics. The procedure consists of an engineering approach based on dataset consistency and Bayesian methodology including Gaussian-Process Regression (GPR). Experimental data from devolatilization tests carried out in an oxy-coal entrained flow reactor were considered and CFD simulations of the reactor were performed. Jointly evaluating experiments and simulations, a novel yield model was validated against the data via consistency analysis. In parallel, a Gaussian-Process Regression was performed, to improve the understanding of the uncertainty associated to the devolatilization, based on the experimental measurements. Potential model forms that could predict yield during devolatilization were obtained. The set of model forms obtained via GPR includes the yield model that was proven to be consistent with the data. Finally, the overall procedure has resulted in a novel yield model for coal devolatilization and in a valuable evaluation of uncertainty in the data, in the model form, and in the model parameters.« less

Statistical modelling of grapevine yield in the Port Wine region under present and future climate conditions

NASA Astrophysics Data System (ADS)

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.

Response moderation models for conditional dependence between response time and response accuracy.

PubMed

Bolsinova, Maria; Tijmstra, Jesper; Molenaar, Dylan

2017-05-01

It is becoming more feasible and common to register response times in the application of psychometric tests. Researchers thus have the opportunity to jointly model response accuracy and response time, which provides users with more relevant information. The most common choice is to use the hierarchical model (van der Linden, 2007, Psychometrika, 72, 287), which assumes conditional independence between response time and accuracy, given a person's speed and ability. However, this assumption may be violated in practice if, for example, persons vary their speed or differ in their response strategies, leading to conditional dependence between response time and accuracy and confounding measurement. We propose six nested hierarchical models for response time and accuracy that allow for conditional dependence, and discuss their relationship to existing models. Unlike existing approaches, the proposed hierarchical models allow for various forms of conditional dependence in the model and allow the effect of continuous residual response time on response accuracy to be item-specific, person-specific, or both. Estimation procedures for the models are proposed, as well as two information criteria that can be used for model selection. Parameter recovery and usefulness of the information criteria are investigated using simulation, indicating that the procedure works well and is likely to select the appropriate model. Two empirical applications are discussed to illustrate the different types of conditional dependence that may occur in practice and how these can be captured using the proposed hierarchical models. © 2016 The British Psychological Society.

Modeling of yield surface evolution in uniaxial and biaxial loading conditions using a prestrained large scale specimen

NASA Astrophysics Data System (ADS)

Zaman, Shakil Bin; Barlat, Frédéric; Kim, Jin Hwan

2018-05-01

Large-scale advanced high strength steel (AHSS) sheet specimens were deformed in uniaxial tension, using a novel grip system mounted on a MTS universal tension machine. After pre-strain, they were used as a pre-strained material to examine the anisotropic response in the biaxial tension tests with various load ratios, and orthogonal tension tests at 45° and 90° from the pre-strain axis. The flow curve and the instantaneous r-value of the pre-strained steel in each of the aforementioned uniaxial testing conditions were also measured and compared with those of the undeformed steel. Furthermore, an exhaustive analysis of the yield surface was also conducted and the results, prior and post-prestrain were represented and compared. The homogeneous anisotropic hardening (HAH) model [1] was employed to predict the behavior of the pre-strained material. It was found that the HAH-predicted flow curves after non-linear strain path change and the yield loci after uniaxial pre-strain were in good agreement with the experiments, while the r-value evolution after strain path change was qualitatively well predicted.

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

NASA Astrophysics Data System (ADS)

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

2017-11-01

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

Slope Controls Grain Yield and Climatic Yield in Mountainous Yunnan province, China

NASA Astrophysics Data System (ADS)

Duan, X.; Rong, L.; Gu, Z.; Feng, D.

2017-12-01

Mountainous regions are increasingly vulnerable to food insecurity because of limited arable land, growing population pressure, and climate change. Development of sustainable mountain agriculture will require an increased understanding of the effects of environmental factors on grain and climatic yields. The objective of this study was to explore the relationships between actual grain yield, climatic yield, and environmental factors in a mountainous region in China. We collected data on the average grain yield per unit area in 119 counties in Yunnan province from 1985 to 2012, and chose 17 environmental factors for the same period. Our results showed that actual grain yield ranged from 1.43 to 6.92 t·ha-1, and the climatic yield ranged from -0.15 to -0.01 t·ha-1. Lower climatic yield but higher grain yield was generally found in central areas and at lower slopes and elevations in the western and southwestern counties of Yunnan province. Higher climatic yield but lower grain yield were found in northwestern parts of Yunnan province on steep slopes. Annual precipation and temperature had a weak influence on the climatic yield. Slope explained 44.62 and 26.29% of the variation in grain yield and climatic yield. The effects of topography on grain and climatic yields were greater than climatic factors. Slope was the most important environmental variable for the variability in climatic and grain yields in the mountainous Yunnan province due to the highly heterogeneous topographic conditions. Conversion of slopes to terraces in areas with higher climatic yields is an effective way to maintain grain production in response to climate variability. Additionally, soil amendments and soil and water conservation measures should be considered to maintain soil fertility and aid in sustainable development in central areas, and in counties at lower slopes and elevations in western and southwestern Yunnan province.

The yield and post-yield behavior of high-density polyethylene

NASA Technical Reports Server (NTRS)

Semeliss, M. A.; Wong, R.; Tuttle, M. E.

1990-01-01

An experimental and analytical evaluation was made of the yield and post-yield behavior of high-density polyethylene, a semi-crystalline thermoplastic. Polyethylene was selected for study because it is very inexpensive and readily available in the form of thin-walled tubes. Thin-walled tubular specimens were subjected to axial loads and internal pressures, such that the specimens were subjected to a known biaxial loading. A constant octahederal shear stress rate was imposed during all tests. The measured yield and post-yield behavior was compared with predictions based on both isotropic and anisotropic models. Of particular interest was whether inelastic behavior was sensitive to the hydrostatic stress level. The major achievements and conclusions reached are discussed.

Dissection of niche competition between introduced and indigenous arbuscular mycorrhizal fungi with respect to soybean yield responses.

PubMed

Niwa, Rieko; Koyama, Takuya; Sato, Takumi; Adachi, Katsuki; Tawaraya, Keitaro; Sato, Shusei; Hirakawa, Hideki; Yoshida, Shigenobu; Ezawa, Tatsuhiro

2018-05-09

Arbuscular mycorrhizal (AM) fungi associate with most land plants and deliver phosphorus to the host. Identification of biotic/abiotic factors that determine crop responses to AM fungal inoculation is an essential step for successful application of the fungi in sustainable agriculture. We conducted three field trials on soybean with a commercial inoculum and developed a new molecular tool to dissect interactions between the inoculum and indigenous fungi on the MiSeq sequencing platform. Regression analysis indicated that sequence read abundance of the inoculum fungus was the most significant factor that determined soybean yield responses to the inoculation, suggesting that dominance of the inoculum fungus is a necessary condition for positive yield responses. Agricultural practices (fallow/cropping in the previous year) greatly affected the colonization levels (i.e. read abundances) of the inoculum fungus via altering the propagule density of indigenous AM fungi. Analysis of niche competition revealed that the inoculum fungus competed mainly with the indigenous fungi that are commonly distributed in the trial sites, probably because their life-history strategy is the same as that of the inoculum fungus. In conclusion, we provide a new framework for evaluating the significance of environmental factors towards successful application of AM fungi in agriculture.

Effects of diurnal temperature range and drought on wheat yield in Spain

NASA Astrophysics Data System (ADS)

Hernandez-Barrera, S.; Rodriguez-Puebla, C.; Challinor, A. J.

2017-07-01

This study aims to provide new insight on the wheat yield historical response to climate processes throughout Spain by using statistical methods. Our data includes observed wheat yield, pseudo-observations E-OBS for the period 1979 to 2014, and outputs of general circulation models in phase 5 of the Coupled Models Inter-comparison Project (CMIP5) for the period 1901 to 2099. In investigating the relationship between climate and wheat variability, we have applied the approach known as the partial least-square regression, which captures the relevant climate drivers accounting for variations in wheat yield. We found that drought occurring in autumn and spring and the diurnal range of temperature experienced during the winter are major processes to characterize the wheat yield variability in Spain. These observable climate processes are used for an empirical model that is utilized in assessing the wheat yield trends in Spain under different climate conditions. To isolate the trend within the wheat time series, we implemented the adaptive approach known as Ensemble Empirical Mode Decomposition. Wheat yields in the twenty-first century are experiencing a downward trend that we claim is a consequence of widespread drought over the Iberian Peninsula and an increase in the diurnal range of temperature. These results are important to inform about the wheat vulnerability in this region to coming changes and to develop adaptation strategies.

A Technique of Fuzzy C-Mean in Multiple Linear Regression Model toward Paddy Yield

NASA Astrophysics Data System (ADS)

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.

A toy model for the yield of a tamped fission bomb

NASA Astrophysics Data System (ADS)

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.

Crop status evaluations and yield predictions

NASA Technical Reports Server (NTRS)

Haun, J. R.

1976-01-01

One phase of the large area crop inventory project is presented. Wheat yield models based on the input of environmental variables potentially obtainable through the use of space remote sensing were developed and demonstrated. By the use of a unique method for visually qualifying daily plant development and subsequent multifactor computer analyses, it was possible to develop practical models for predicting crop development and yield. Development of wheat yield prediction models was based on the discovery that morphological changes in plants are detected and quantified on a daily basis, and that this change during a portion of the season was proportional to yield.

Hydrostatic Stress Effect On the Yield Behavior of Inconel 100

NASA Technical Reports Server (NTRS)

Allen, Phillip A.; Wilson, Christopher D.

2002-01-01

Classical metal plasticity theory assumes that hydrostatic stress has no effect on the yield and postyield behavior of metals. Recent reexaminations of classical theory have revealed a significant effect of hydrostatic stress on the yield behavior of notched geometries. New experiments and nonlinear finite element analyses (FEA) of Inconel 100 (IN 100) equal-arm bend and double-edge notch tension (DENT) test specimens have revealed the effect of internal hydrostatic tensile stresses on yielding. Nonlinear FEA using the von Mises (yielding is independent of hydrostatic stress) and the Drucker-Prager (yielding is linearly dependent on hydrostatic stress) yield functions was performed. In all test cases, the von Mises constitutive model, which is independent of hydrostatic pressure, overestimated the load for a given displacement or strain. Considering the failure displacements or strains, the Drucker-Prager FEMs predicted loads that were 3% to 5% lower than the von Mises values. For the failure loads, the Drucker Prager FEMs predicted strains that were 20% to 35% greater than the von Mises values. The Drucker-Prager yield function seems to more accurately predict the overall specimen response of geometries with significant internal hydrostatic stress influence.

Progress and Challenges in Predicting Crop Responses to Atmospheric [CO2

NASA Astrophysics Data System (ADS)

Kent, J.; Paustian, K.

2017-12-01

Increasing atmospheric [CO2] directly accelerates photosynthesis in C3 crops, and indirectly promotes yields by reducing stomatal conductance and associated water losses in C3 and C4 crops. Several decades of experiments have exposed crops to eCO2 in greenhouses and other enclosures and observed yield increases on the order of 33%. FACE systems were developed in the early 1990s to better replicate open-field growing conditions. Some authors contend that FACE results indicate lower crop yield responses than enclosure studies, while others maintain no significant difference or attribute differences to various methodological factors. The crop CO2 response processes in many crop models were developed using results from enclosure experiments. This work tested the ability of one such model, DayCent, to reproduce crop responses to CO2 enrichment from several FACE experiments. DayCent performed well at simulating yield and transpiration responses in C4 crops, but significantly overestimated yield responses in C3 crops. After adjustment of CO2-response parameters, DayCent was able to reproduce mean yield responses for specific crops. However, crop yield responses from FACE experiments vary widely across years and sites, and likely reflect complex interactions between conditions such as weather, soils, cultivars, and biotic stressors. Further experimental work is needed to identify the secondary variables that explain this variability so that models can more reliably forecast crop yields under climate change. Likewise, CO2 impacts on crop outcomes such as belowground biomass allocation and grain N content have implications for agricultural C fluxes and human nutrition, respectively, but are poorly understood and thus difficult to simulate with confidence.

Sustainable fisheries in shallow lakes: an independent empirical test of the Chinese mitten crab yield model

NASA Astrophysics Data System (ADS)

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.

Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress.

PubMed

Gu, Junfei; Yin, Xinyou; Zhang, Chengwei; Wang, Huaqi; Struik, Paul C

2014-09-01

Genetic markers can be used in combination with ecophysiological crop models to predict the performance of genotypes. Crop models can estimate the contribution of individual markers to crop performance in given environments. The objectives of this study were to explore the use of crop models to design markers and virtual ideotypes for improving yields of rice (Oryza sativa) under drought stress. Using the model GECROS, crop yield was dissected into seven easily measured parameters. Loci for these parameters were identified for a rice population of 94 introgression lines (ILs) derived from two parents differing in drought tolerance. Marker-based values of ILs for each of these parameters were estimated from additive allele effects of the loci, and were fed to the model in order to simulate yields of the ILs grown under well-watered and drought conditions and in order to design virtual ideotypes for those conditions. To account for genotypic yield differences, it was necessary to parameterize the model for differences in an additional trait 'total crop nitrogen uptake' (Nmax) among the ILs. Genetic variation in Nmax had the most significant effect on yield; five other parameters also significantly influenced yield, but seed weight and leaf photosynthesis did not. Using the marker-based parameter values, GECROS also simulated yield variation among 251 recombinant inbred lines of the same parents. The model-based dissection approach detected more markers than the analysis using only yield per se. Model-based sensitivity analysis ranked all markers for their importance in determining yield differences among the ILs. Virtual ideotypes based on markers identified by modelling had 10-36 % more yield than those based on markers for yield per se. This study outlines a genotype-to-phenotype approach that exploits the potential value of marker-based crop modelling in developing new plant types with high yields. The approach can provide more markers for selection programmes for

Linking ecophysiological modelling with quantitative genetics to support marker-assisted crop design for improved yields of rice (Oryza sativa) under drought stress

PubMed Central

Gu, Junfei; Yin, Xinyou; Zhang, Chengwei; Wang, Huaqi; Struik, Paul C.

2014-01-01

Background and Aims Genetic markers can be used in combination with ecophysiological crop models to predict the performance of genotypes. Crop models can estimate the contribution of individual markers to crop performance in given environments. The objectives of this study were to explore the use of crop models to design markers and virtual ideotypes for improving yields of rice (Oryza sativa) under drought stress. Methods Using the model GECROS, crop yield was dissected into seven easily measured parameters. Loci for these parameters were identified for a rice population of 94 introgression lines (ILs) derived from two parents differing in drought tolerance. Marker-based values of ILs for each of these parameters were estimated from additive allele effects of the loci, and were fed to the model in order to simulate yields of the ILs grown under well-watered and drought conditions and in order to design virtual ideotypes for those conditions. Key Results To account for genotypic yield differences, it was necessary to parameterize the model for differences in an additional trait ‘total crop nitrogen uptake’ (Nmax) among the ILs. Genetic variation in Nmax had the most significant effect on yield; five other parameters also significantly influenced yield, but seed weight and leaf photosynthesis did not. Using the marker-based parameter values, GECROS also simulated yield variation among 251 recombinant inbred lines of the same parents. The model-based dissection approach detected more markers than the analysis using only yield per se. Model-based sensitivity analysis ranked all markers for their importance in determining yield differences among the ILs. Virtual ideotypes based on markers identified by modelling had 10–36 % more yield than those based on markers for yield per se. Conclusions This study outlines a genotype-to-phenotype approach that exploits the potential value of marker-based crop modelling in developing new plant types with high yields. The

Modelling crop yield, soil organic C and P under variable long-term fertilizer management in China

NASA Astrophysics Data System (ADS)

Zhang, Jie; Xu, Guang; Xu, Minggang; Balkovič, Juraj; Azevedo, Ligia B.; Skalský, Rastislav; Wang, Jinzhou; Yu, Chaoqing

2016-04-01

Phosphorus (P) is a major limiting nutrient for plant growth. P, as a nonrenewable resource and the controlling factor of aquatic entrophication, is critical for food security and human future, and concerns sustainable resource use and environmental impacts. It is thus essential to find an integrated and effective approach to optimize phosphorus fertilizer application in the agro-ecosystem while maintaining crop yield and minimizing environmental risk. Crop P models have been used to simulate plant-soil interactions but are rarely validated with scattered long-term fertilizer control field experiments. We employed a process-based model named Environmental Policy Integrated Climate model (EPIC) to simulate grain yield, soil organic carbon (SOC) and soil available P based upon 8 field experiments in China with 11 years dataset, representing the typical Chinese soil types and agro-ecosystems of different regions. 4 treatments, including N, P, and K fertilizer (NPK), no fertilizer (CK), N and K fertilizer (NK) and N, P, K and manure (NPKM) were measured and modelled. A series of sensitivity tests were conducted to analyze the sensitivity of grain yields and soil available P to sequential fertilizer rates in typical humid, normal and drought years. Our results indicated that the EPIC model showed a significant agreement for simulating grain yields with R2=0.72, index of agreement (d)=0.87, modeling efficiency (EF)=0.68, p<0.01 and SOC with R2=0.70, d=0.86, EF=0.59, and p<0.01. EPIC can well simulate soil available P moderately and capture the temporal changes in soil P reservoirs. Both of Crop yields and soil available were found more sensitive to the fertilizer P rates in humid than drought year and soil available P is closely linked to concentrated rainfall. This study concludes that EPIC model has great potential to simulate the P cycle in croplands in China and can explore the optimum management practices.

Study of the strong {sigma}{sub c}{yields}{lambda}{sub c}{pi},{sigma}{sub c}*{yields}{lambda}{sub c}{pi} and {xi}{sub c}*{yields}{xi}{sub c}{pi} decays in a nonrelativistic quark model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Albertus, C.; Nieves, J.; Hernandez, E.

We present results for the strong widths corresponding to the {sigma}{sub c}{yields}{lambda}{sub c}{pi}, {sigma}{sub c}*{yields}{lambda}{sub c}{pi} and {xi}{sub c}*{yields}{xi}{sub c}{pi} decays. The calculations have been done in a nonrelativistic constituent quark model with wave functions that take advantage of the constraints imposed by heavy quark symmetry. Partial conservation of axial current hypothesis allows us to determine the strong vertices from an analysis of the axial current matrix elements. Our results {gamma}({sigma}{sub c}{sup ++}{yields}{lambda}{sub c}{sup +}{pi}{sup +})=2.41{+-}0.07{+-}0.02 MeV, {gamma}({sigma}{sub c}{sup +}{yields}{lambda}{sub c}{sup +}{pi}{sup 0})=2.79{+-}0.08{+-}0.02 MeV, {gamma}({sigma}{sub c}{sup 0}{yields}{lambda}{sub c}{sup +}{pi}{sup -})=2.37{+-}0.07{+-}0.02 MeV, {gamma}({sigma}{sub c}*{sup ++}{yields}{lambda}{sub c}{sup +}{pi}{sup +})=17.52{+-}0.74{+-}0.12 MeV, {gamma}({sigma}{sub c}*{supmore » +}{yields}{lambda}{sub c}{sup +}{pi}{sup 0})=17.31{+-}0.73{+-}0.12 MeV, {gamma}({sigma}{sub c}*{sup 0}{yields}{lambda}{sub c}{sup +}{pi}{sup -})=16.90{+-}0.71{+-}0.12 MeV, {gamma}({xi}{sub c}*{sup +}{yields}{xi}{sub c}{sup 0}{pi}{sup +}+{xi}{sub c}{sup +}{pi}{sup 0})=3.18{+-}0.10{+-}0.01 MeV, and {gamma}({xi}{sub c}*{sup 0}{yields}{xi}{sub c}{sup +}{pi}{sup -}+{xi}{sub c}{sup 0}{pi}{sup 0})=3.03{+-}0.10{+-}0.01 MeV are in good agreement with experimental determinations.« less

Hot spots of wheat yield decline with rising temperatures.

PubMed

Asseng, Senthold; Cammarano, Davide; Basso, Bruno; Chung, Uran; Alderman, Phillip D; Sonder, Kai; Reynolds, Matthew; Lobell, David B

2017-06-01

Many of the irrigated spring wheat regions in the world are also regions with high poverty. The impacts of temperature increase on wheat yield in regions of high poverty are uncertain. A grain yield-temperature response function combined with a quantification of model uncertainty was constructed using a multimodel ensemble from two key irrigated spring wheat areas (India and Sudan) and applied to all irrigated spring wheat regions in the world. Southern Indian and southern Pakistani wheat-growing regions with large yield reductions from increasing temperatures coincided with high poverty headcounts, indicating these areas as future food security 'hot spots'. The multimodel simulations produced a linear absolute decline of yields with increasing temperature, with uncertainty varying with reference temperature at a location. As a consequence of the linear absolute yield decline, the relative yield reductions are larger in low-yielding environments (e.g., high reference temperature areas in southern India, southern Pakistan and all Sudan wheat-growing regions) and farmers in these regions will be hit hardest by increasing temperatures. However, as absolute yield declines are about the same in low- and high-yielding regions, the contributed deficit to national production caused by increasing temperatures is higher in high-yielding environments (e.g., northern India) because these environments contribute more to national wheat production. Although Sudan could potentially grow more wheat if irrigation is available, grain yields would be low due to high reference temperatures, with future increases in temperature further limiting production. © 2016 John Wiley & Sons Ltd.

Impacts of climate change on peanut yield in China simulated by CMIP5 multi-model ensemble projections

NASA Astrophysics Data System (ADS)

Xu, Hanqing; Tian, Zhan; Zhong, Honglin; Fan, Dongli; Shi, Runhe; Niu, Yilong; He, Xiaogang; Chen, Maosi

2017-09-01

Peanut is one of the major edible vegetable oil crops in China, whose growth and yield are very sensitive to climate change. In addition, agriculture climate resources are expected to be redistributed under climate change, which will further influence the growth, development, cropping patterns, distribution and production of peanut. In this study, we used the DSSAT-Peanut model to examine the climate change impacts on peanut production, oil industry and oil food security in China. This model is first calibrated using site observations including 31 years' (1981-2011) climate, soil and agronomy data. This calibrated model is then employed to simulate the future peanut yield based on 20 climate scenarios from 5 Global Circulation Models (GCMs) developed by the InterSectoral Impact Model Intercomparison Project (ISIMIP) driven by 4 Representative Concentration Pathways (RCPs). Results indicate that the irrigated peanut yield will decrease 2.6% under the RCP 2.6 scenario, 9.9% under the RCP 4.5 scenario and 29% under the RCP 8.5 scenario, respectively. Similarly, the rain-fed peanut yield will also decrease, with a 2.5% reduction under the RCP 2.6 scenario, 11.5% reduction under the RCP 4.5 scenario and 30% reduction under the RCP 8.5 scenario, respectively.

Dynamic response analysis of structure under time-variant interval process model

NASA Astrophysics Data System (ADS)

Xia, Baizhan; Qin, Yuan; Yu, Dejie; Jiang, Chao

2016-10-01

Due to the aggressiveness of the environmental factor, the variation of the dynamic load, the degeneration of the material property and the wear of the machine surface, parameters related with the structure are distinctly time-variant. Typical model for time-variant uncertainties is the random process model which is constructed on the basis of a large number of samples. In this work, we propose a time-variant interval process model which can be effectively used to deal with time-variant uncertainties with limit information. And then two methods are presented for the dynamic response analysis of the structure under the time-variant interval process model. The first one is the direct Monte Carlo method (DMCM) whose computational burden is relative high. The second one is the Monte Carlo method based on the Chebyshev polynomial expansion (MCM-CPE) whose computational efficiency is high. In MCM-CPE, the dynamic response of the structure is approximated by the Chebyshev polynomials which can be efficiently calculated, and then the variational range of the dynamic response is estimated according to the samples yielded by the Monte Carlo method. To solve the dependency phenomenon of the interval operation, the affine arithmetic is integrated into the Chebyshev polynomial expansion. The computational effectiveness and efficiency of MCM-CPE is verified by two numerical examples, including a spring-mass-damper system and a shell structure.

Estimation of a Ramsay-Curve Item Response Theory Model by the Metropolis-Hastings Robbins-Monro Algorithm

ERIC Educational Resources Information Center

Monroe, Scott; Cai, Li

2014-01-01

In Ramsay curve item response theory (RC-IRT) modeling, the shape of the latent trait distribution is estimated simultaneously with the item parameters. In its original implementation, RC-IRT is estimated via Bock and Aitkin's EM algorithm, which yields maximum marginal likelihood estimates. This method, however, does not produce the…

Accelerating yield ramp through design and manufacturing collaboration

NASA Astrophysics Data System (ADS)

Sarma, Robin C.; Dai, Huixiong; Smayling, Michael C.; Duane, Michael P.

2004-12-01

Ramping an integrated circuit from first silicon bring-up to production yield levels is a challenge for all semiconductor products on the path to profitable market entry. Two approaches to accelerating yield ramp are presented. The first is the use of laser mask writers for fast throughput, high yield, and cost effective pattern transfer. The second is the use of electrical test to find a defect and identify the physical region to probe in failure analysis that is most likely to uncover the root cause. This provides feedback to the design team on modifications to make to the design to avoid the yield issue in a future tape-out revision. Additionally, the process parameter responsible for the root cause of the defect is forward annotated through the design, mask and wafer coordinate systems so it can be monitored in-line on subsequent lots of the manufacturing run. This results in an improved recipe for the manufacturing equipment to potentially prevent the recurrence of the defect and raise yield levels on the following material. The test diagnostics approach is enabled by the seamless traceability of a feature across the design, photomask and wafer, made possible by a common data model for design, mask pattern generation and wafer fabrication.

SPATIO-TEMPORAL MODELING OF AGRICULTURAL YIELD DATA WITH AN APPLICATION TO PRICING CROP INSURANCE CONTRACTS

PubMed Central

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

Ion Yields in the Coupled Chemical and Physical Dynamics Model of Matrix-Assisted Laser Desorption/Ionization

NASA Astrophysics Data System (ADS)

Knochenmuss, Richard

2015-08-01

The Coupled Chemical and Physical Dynamics (CPCD) model of matrix assisted laser desorption ionization has been restricted to relative rather than absolute yield comparisons because the rate constant for one step in the model was not accurately known. Recent measurements are used to constrain this constant, leading to good agreement with experimental yield versus fluence data for 2,5-dihydroxybenzoic acid. Parameters for alpha-cyano-4-hydroxycinnamic acid are also estimated, including contributions from a possible triplet state. The results are compared with the polar fluid model, the CPCD is found to give better agreement with the data.

Multitrait, Random Regression, or Simple Repeatability Model in High-Throughput Phenotyping Data Improve Genomic Prediction for Wheat Grain Yield.

PubMed

Sun, Jin; Rutkoski, Jessica E; Poland, Jesse A; Crossa, José; Jannink, Jean-Luc; Sorrells, Mark E

2017-07-01

High-throughput phenotyping (HTP) platforms can be used to measure traits that are genetically correlated with wheat ( L.) grain yield across time. Incorporating such secondary traits in the multivariate pedigree and genomic prediction models would be desirable to improve indirect selection for grain yield. In this study, we evaluated three statistical models, simple repeatability (SR), multitrait (MT), and random regression (RR), for the longitudinal data of secondary traits and compared the impact of the proposed models for secondary traits on their predictive abilities for grain yield. Grain yield and secondary traits, canopy temperature (CT) and normalized difference vegetation index (NDVI), were collected in five diverse environments for 557 wheat lines with available pedigree and genomic information. A two-stage analysis was applied for pedigree and genomic selection (GS). First, secondary traits were fitted by SR, MT, or RR models, separately, within each environment. Then, best linear unbiased predictions (BLUPs) of secondary traits from the above models were used in the multivariate prediction models to compare predictive abilities for grain yield. Predictive ability was substantially improved by 70%, on average, from multivariate pedigree and genomic models when including secondary traits in both training and test populations. Additionally, (i) predictive abilities slightly varied for MT, RR, or SR models in this data set, (ii) results indicated that including BLUPs of secondary traits from the MT model was the best in severe drought, and (iii) the RR model was slightly better than SR and MT models under drought environment. Copyright © 2017 Crop Science Society of America.

Growth and yield of shortleaf pine

Treesearch

Paul A. Murphy

1986-01-01

A survey of available growth and yield information for shortleaf pine (Pinus echinata Mill.) is given. The kinds of studies and data sources that produce this information are also evaluated, and an example of how a growth and yield model can be used to answer management questions is illustrated. Guidelines are given for using growth and yield models, and needs for...

Electron-induced electron yields of uncharged insulating materials

NASA Astrophysics Data System (ADS)

Hoffmann, Ryan Carl

Presented here are electron-induced electron yield measurements from high-resistivity, high-yield materials to support a model for the yield of uncharged insulators. These measurements are made using a low-fluence, pulsed electron beam and charge neutralization to minimize charge accumulation. They show charging induced changes in the total yield, as much as 75%, even for incident electron fluences of <3 fC/mm2, when compared to an uncharged yield. The evolution of the yield as charge accumulates in the material is described in terms of electron recapture, based on the extended Chung and Everhart model of the electron emission spectrum and the dual dynamic layer model for internal charge distribution. This model is used to explain charge-induced total yield modification measured in high-yield ceramics, and to provide a method for determining electron yield of uncharged, highly insulating, high-yield materials. A sequence of materials with progressively greater charge susceptibility is presented. This series starts with low-yield Kapton derivative called CP1, then considers a moderate-yield material, Kapton HN, and ends with a high-yield ceramic, polycrystalline aluminum oxide. Applicability of conductivity (both radiation induced conductivity (RIC) and dark current conductivity) to the yield is addressed. Relevance of these results to spacecraft charging is also discussed.

Remote sensing and modelling of vegetation dynamics for early estimation and spatial analysis of grain yields in semiarid context in central Tunisia

NASA Astrophysics Data System (ADS)

Chahbi, Aicha; Zribi, Mehrez; Lili-Chabaane, Zohra

2016-04-01

In arid and semi-arid areas, population growth, urbanization, food security and climate change have an impact on agriculture in general and particular on the cereal production. Therefore to improve food security in arid countries, crop canopy monitoring and yield forecasting cereals are needed. Many models, based on the use of remote sensing or agro-meteorological models, have been developed to estimate the biomass and grain yield of cereals. Through the use of a rich database, acquired over a period of two years for more than 80 test fields, and from optical satellite SPOT/HRV images, the aim of the present study is to evaluate the feasibility of two yield prediction approaches. The first approach is based on the application of the semi-empirical growth model SAFY, developed to simulate the dynamics of the LAI and the grain yield, at the field scale. The model is able to reproduce the time evolution of the leaf area index of all fields with acceptable error. However, an inter-comparison between ground yield measurements and SAFY model simulations reveals that the yields are under-estimated by this model. We can explain the limits of the semi-empirical model SAFY by its simplicity and also by various factors that were not considered (fertilization, irrigation,...). To improve the yield estimation, a new approach is proposed: the grain yield is estimated in function of the LAI in the growth period between 25 March and 5 April. The LAI of this period is estimated by SAFY model. A linear relationship is developed between the measured grain yield and the LAI area of the maximum growth period.This approach is robust, the measured and estimated grain yields are well correlated. Following the validation of this approach, yield estimations are proposed for the entire studied site using the SPOT/HRV images.

Simulating large-scale crop yield by using perturbed-parameter ensemble method

NASA Astrophysics Data System (ADS)

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

2010-12-01

is still high in most grids regardless of the countries. However, the model showed comparatively low reproducibility in the slope areas, such as around the Rocky Mountains in South Dakota, around the Great Xing'anling Mountains in Heilongjiang, and around the Brazilian Plateau. As there is a wide-ranging local climate conditions in the complex terrain, such as the slope of mountain, the GCM grid-scale weather inputs is likely one of major sources of error. The results of this study highlight the benefits of the perturbed-parameter ensemble method in simulating crop yield on a GCM grid scale: (1) the posterior PDF of parameter could quantify the uncertainty of parameter value of the crop model associated with the local crop production aspects; (2) the method can explicitly account for the uncertainty of parameter value in the crop model simulations; (3) the method achieve a Monte Carlo approximation of probability of sub-grid scale yield, accounting for the nonlinear response of crop yield to weather and management; (4) the method is therefore appropriate to aggregate the simulated sub-grid scale yields to a grid-scale yield and it may be a reason for high performance of the model in capturing inter-annual variation of yield.

A data-oriented semi-process model for evaluating the yields of major crops at global scale (PRYSBI-2)

NASA Astrophysics Data System (ADS)

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

2013-12-01

Demand for major cereal crops will double by 2050 compared to the amount in 2005 due to the population growth, dietary change, and increase in biofuel use. This requires substantial efforts to increase crop yields under changing climate, water resources, and land use. In order to explore possible paths to meet the supply target, global crop modeling is a useful approach. To that end, we developed a process-based large-area crop model (called PRYSBIE-2) for major crops, including soybean. This model consisted of the enzyme kinetics model for photosynthetic carbon assimilation and soil water balance model from SWAT. The parameter values on water stress, nitrogen stress were calibrated over global croplands from one grid cell to another (1.125° in latitude and longitude) using Markov Chain Monte Carlo (MCMC) methods. The historical yield data collected from major crop-producing countries on a state, county, or prefecture scale were used as the calibration data. Then we obtained the model parameter sets that can give high correlation coefficients between the historical and estimated yield time series for the period 1980-2006. We analyzed the impacts on soybean yields in the three top soybean-producing countries (the USA, China, and Brazil) associated with the changes in climate and CO2 during the period 1980-2006, using the model. We found that, given the simulated yields and reported harvested areas, the estimated average net benefit from the CO2 fertilization effect (with one standard deviation) in the USA, Brazil, and China in the years was 42.70×32.52 Mt, 35.30×28.55 Mt, and 12.52×15.11 Mt, respectively. Results suggest that the CO2-induced increases in soybean yields in the USA and China likely offset a part of the negative impacts on yields due to the historical temperature rise. In contrast, the net effect of the past change in climate and CO2 in Brazil appeared to be positive. This study demonstrates a quantitative estimation of the impacts of the changes

What aspects of future rainfall changes matter for crop yields in West Africa?

NASA Astrophysics Data System (ADS)

Guan, Kaiyu; Sultan, Benjamin; Biasutti, Michela; Baron, Christian; Lobell, David B.

2015-10-01

How rainfall arrives, in terms of its frequency, intensity, the timing and duration of rainy season, may have a large influence on rainfed agriculture. However, a thorough assessment of these effects is largely missing. This study combines a new synthetic rainfall model and two independently validated crop models (APSIM and SARRA-H) to assess sorghum yield response to possible shifts in seasonal rainfall characteristics in West Africa. We find that shifts in total rainfall amount primarily drive the rainfall-related crop yield change, with less relevance to intraseasonal rainfall features. However, dry regions (total annual rainfall below 500 mm/yr) have a high sensitivity to rainfall frequency and intensity, and more intense rainfall events have greater benefits for crop yield than more frequent rainfall. Delayed monsoon onset may negatively impact yields. Our study implies that future changes in seasonal rainfall characteristics should be considered in designing specific crop adaptations in West Africa.

Sensitivity analysis of the Aquacrop and SAFYE crop models for the assessment of water limited winter wheat yield in regional scale applications.

PubMed

Silvestro, Paolo Cosmo; Pignatti, Stefano; Yang, Hao; Yang, Guijun; Pascucci, Simone; Castaldi, Fabio; Casa, Raffaele

2017-01-01

Process-based models can be usefully employed for the assessment of field and regional-scale impact of drought on crop yields. However, in many instances, especially when they are used at the regional scale, it is necessary to identify the parameters and input variables that most influence the outputs and to assess how their influence varies when climatic and environmental conditions change. In this work, two different crop models, able to represent yield response to water, Aquacrop and SAFYE, were compared, with the aim to quantify their complexity and plasticity through Global Sensitivity Analysis (GSA), using Morris and EFAST (Extended Fourier Amplitude Sensitivity Test) techniques, for moderate to strong water limited climate scenarios. Although the rankings of the sensitivity indices was influenced by the scenarios used, the correlation among the rankings, higher for SAFYE than for Aquacrop, assessed by the top-down correlation coefficient (TDCC), revealed clear patterns. Parameters and input variables related to phenology and to water stress physiological processes were found to be the most influential for Aquacrop. For SAFYE, it was found that the water stress could be inferred indirectly from the processes regulating leaf growth, described in the original SAFY model. SAFYE has a lower complexity and plasticity than Aquacrop, making it more suitable to less data demanding regional scale applications, in case the only objective is the assessment of crop yield and no detailed information is sought on the mechanisms of the stress factors affecting its limitations.

Sensitivity analysis of the Aquacrop and SAFYE crop models for the assessment of water limited winter wheat yield in regional scale applications

PubMed Central

Pignatti, Stefano; Yang, Hao; Yang, Guijun; Pascucci, Simone; Castaldi, Fabio

2017-01-01

Process-based models can be usefully employed for the assessment of field and regional-scale impact of drought on crop yields. However, in many instances, especially when they are used at the regional scale, it is necessary to identify the parameters and input variables that most influence the outputs and to assess how their influence varies when climatic and environmental conditions change. In this work, two different crop models, able to represent yield response to water, Aquacrop and SAFYE, were compared, with the aim to quantify their complexity and plasticity through Global Sensitivity Analysis (GSA), using Morris and EFAST (Extended Fourier Amplitude Sensitivity Test) techniques, for moderate to strong water limited climate scenarios. Although the rankings of the sensitivity indices was influenced by the scenarios used, the correlation among the rankings, higher for SAFYE than for Aquacrop, assessed by the top-down correlation coefficient (TDCC), revealed clear patterns. Parameters and input variables related to phenology and to water stress physiological processes were found to be the most influential for Aquacrop. For SAFYE, it was found that the water stress could be inferred indirectly from the processes regulating leaf growth, described in the original SAFY model. SAFYE has a lower complexity and plasticity than Aquacrop, making it more suitable to less data demanding regional scale applications, in case the only objective is the assessment of crop yield and no detailed information is sought on the mechanisms of the stress factors affecting its limitations. PMID:29107963

Forecasting of cereals yields in a semi-arid area using the agrometeorological model «SAFY» combined to optical SPOT/HRV images

NASA Astrophysics Data System (ADS)

Chahbi, Aicha; Zribi, Mehrez; Lili-Chabaane, Zohra; Mougenot, Bernard

2015-10-01

In semi-arid areas, an operational grain yield forecasting system, which could help decision-makers to plan annual imports, is needed. It can be challenging to monitor the crop canopy and production capacity of plants, especially cereals. Many models, based on the use of remote sensing or agro-meteorological models, have been developed to estimate the biomass and grain yield of cereals. Remote sensing has demonstrated its strong potential for the monitoring of the vegetation's dynamics and temporal variations. Through the use of a rich database, acquired over a period of two years for more than 60 test fields, and from 20 optical satellite SPOT/HRV images, the aim of the present study is to evaluate the feasibility of two approaches to estimate the dynamics and yields of cereals in the context of semi-arid, low productivity regions in North Africa. The first approach is based on the application of the semi-empirical growth model SAFY "Simple Algorithm For Yield estimation", developed to simulate the dynamics of the leaf area index and the grain yield, at the field scale. The model is able to reproduce the time evolution of the LAI of all fields. However, the yields are under-estimated. Therefore, we developed a new approach to improve the SAFY model. The grain yield is function of LAI area in the growth period between 25 March and 5 April. This approach is robust, the measured and estimated grain yield are well correlated. Finally, this model is used in combination with remotely sensed LAI measurements to estimate yield for the entire studied site.

Changing forest water yields in response to climate warming: results from long-term experimental watershed sites across North America.

PubMed

Creed, Irena F; Spargo, Adam T; Jones, Julia A; Buttle, Jim M; Adams, Mary B; Beall, Fred D; Booth, Eric G; Campbell, John L; Clow, Dave; Elder, Kelly; Green, Mark B; Grimm, Nancy B; Miniat, Chelcy; Ramlal, Patricia; Saha, Amartya; Sebestyen, Stephen; Spittlehouse, Dave; Sterling, Shannon; Williams, Mark W; Winkler, Rita; Yao, Huaxia

2014-10-01

Climate warming is projected to affect forest water yields but the effects are expected to vary. We investigated how forest type and age affect water yield resilience to climate warming. To answer this question, we examined the variability in historical water yields at long-term experimental catchments across Canada and the United States over 5-year cool and warm periods. Using the theoretical framework of the Budyko curve, we calculated the effects of climate warming on the annual partitioning of precipitation (P) into evapotranspiration (ET) and water yield. Deviation (d) was defined as a catchment's change in actual ET divided by P [AET/P; evaporative index (EI)] coincident with a shift from a cool to a warm period - a positive d indicates an upward shift in EI and smaller than expected water yields, and a negative d indicates a downward shift in EI and larger than expected water yields. Elasticity was defined as the ratio of interannual variation in potential ET divided by P (PET/P; dryness index) to interannual variation in the EI - high elasticity indicates low d despite large range in drying index (i.e., resilient water yields), low elasticity indicates high d despite small range in drying index (i.e., nonresilient water yields). Although the data needed to fully evaluate ecosystems based on these metrics are limited, we were able to identify some characteristics of response among forest types. Alpine sites showed the greatest sensitivity to climate warming with any warming leading to increased water yields. Conifer forests included catchments with lowest elasticity and stable to larger water yields. Deciduous forests included catchments with intermediate elasticity and stable to smaller water yields. Mixed coniferous/deciduous forests included catchments with highest elasticity and stable water yields. Forest type appeared to influence the resilience of catchment water yields to climate warming, with conifer and deciduous catchments more susceptible to

Changing forest water yields in response to climate warming: results from long-term experimental watershed sites across North America

PubMed Central

Creed, Irena F; Spargo, Adam T; Jones, Julia A; Buttle, Jim M; Adams, Mary B; Beall, Fred D; Booth, Eric G; Campbell, John L; Clow, Dave; Elder, Kelly; Green, Mark B; Grimm, Nancy B; Miniat, Chelcy; Ramlal, Patricia; Saha, Amartya; Sebestyen, Stephen; Spittlehouse, Dave; Sterling, Shannon; Williams, Mark W; Winkler, Rita; Yao, Huaxia

2014-01-01

Climate warming is projected to affect forest water yields but the effects are expected to vary. We investigated how forest type and age affect water yield resilience to climate warming. To answer this question, we examined the variability in historical water yields at long-term experimental catchments across Canada and the United States over 5-year cool and warm periods. Using the theoretical framework of the Budyko curve, we calculated the effects of climate warming on the annual partitioning of precipitation (P) into evapotranspiration (ET) and water yield. Deviation (d) was defined as a catchment's change in actual ET divided by P [AET/P; evaporative index (EI)] coincident with a shift from a cool to a warm period – a positive d indicates an upward shift in EI and smaller than expected water yields, and a negative d indicates a downward shift in EI and larger than expected water yields. Elasticity was defined as the ratio of interannual variation in potential ET divided by P (PET/P; dryness index) to interannual variation in the EI – high elasticity indicates low d despite large range in drying index (i.e., resilient water yields), low elasticity indicates high d despite small range in drying index (i.e., nonresilient water yields). Although the data needed to fully evaluate ecosystems based on these metrics are limited, we were able to identify some characteristics of response among forest types. Alpine sites showed the greatest sensitivity to climate warming with any warming leading to increased water yields. Conifer forests included catchments with lowest elasticity and stable to larger water yields. Deciduous forests included catchments with intermediate elasticity and stable to smaller water yields. Mixed coniferous/deciduous forests included catchments with highest elasticity and stable water yields. Forest type appeared to influence the resilience of catchment water yields to climate warming, with conifer and deciduous catchments more susceptible to

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

PubMed

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.

Fourier modeling of the BOLD response to a breath-hold task: Optimization and reproducibility.

PubMed

Pinto, Joana; Jorge, João; Sousa, Inês; Vilela, Pedro; Figueiredo, Patrícia

2016-07-15

Cerebrovascular reactivity (CVR) reflects the capacity of blood vessels to adjust their caliber in order to maintain a steady supply of brain perfusion, and it may provide a sensitive disease biomarker. Measurement of the blood oxygen level dependent (BOLD) response to a hypercapnia-inducing breath-hold (BH) task has been frequently used to map CVR noninvasively using functional magnetic resonance imaging (fMRI). However, the best modeling approach for the accurate quantification of CVR maps remains an open issue. Here, we compare and optimize Fourier models of the BOLD response to a BH task with a preparatory inspiration, and assess the test-retest reproducibility of the associated CVR measurements, in a group of 10 healthy volunteers studied over two fMRI sessions. Linear combinations of sine-cosine pairs at the BH task frequency and its successive harmonics were added sequentially in a nested models approach, and were compared in terms of the adjusted coefficient of determination and corresponding variance explained (VE) of the BOLD signal, as well as the number of voxels exhibiting significant BOLD responses, the estimated CVR values, and their test-retest reproducibility. The brain average VE increased significantly with the Fourier model order, up to the 3rd order. However, the number of responsive voxels increased significantly only up to the 2nd order, and started to decrease from the 3rd order onwards. Moreover, no significant relative underestimation of CVR values was observed beyond the 2nd order. Hence, the 2nd order model was concluded to be the optimal choice for the studied paradigm. This model also yielded the best test-retest reproducibility results, with intra-subject coefficients of variation of 12 and 16% and an intra-class correlation coefficient of 0.74. In conclusion, our results indicate that a Fourier series set consisting of a sine-cosine pair at the BH task frequency and its two harmonics is a suitable model for BOLD-fMRI CVR measurements

Maize (Zea mays L.) yield response to nitrogen as influenced by spatio-temporal variations of soil-water-topography dynamics

USDA-ARS?s Scientific Manuscript database

Reducing nitrogen (N) loss from agricultural lands and applying N fertilizer at rates that satisfy both economic and environmental objectives is critical for sustainable agricultural management. This study investigated spatial variability in maize yield response to N and its controlling factors alon...

Rice grain yield and quality responses to free-air CO2 enrichment combined with soil and water warming.

PubMed

Usui, Yasuhiro; Sakai, Hidemitsu; Tokida, Takeshi; Nakamura, Hirofumi; Nakagawa, Hiroshi; Hasegawa, Toshihiro

2016-03-01

Rising air temperatures are projected to reduce rice yield and quality, whereas increasing atmospheric CO2 concentrations ([CO2 ]) can increase grain yield. For irrigated rice, ponded water is an important temperature environment, but few open-field evaluations are available on the combined effects of temperature and [CO2 ], which limits our ability to predict future rice production. We conducted free-air CO2 enrichment and soil and water warming experiments, for three growing seasons to determine the yield and quality response to elevated [CO2 ] (+200 μmol mol(-1) , E-[CO2 ]) and soil and water temperatures (+2 °C, E-T). E-[CO2 ] significantly increased biomass and grain yield by approximately 14% averaged over 3 years, mainly because of increased panicle and spikelet density. E-T significantly increased biomass but had no significant effect on the grain yield. E-T decreased days from transplanting to heading by approximately 1%, but days to the maximum tiller number (MTN) stage were reduced by approximately 8%, which limited the panicle density and therefore sink capacity. On the other hand, E-[CO2 ] increased days to the MTN stage by approximately 4%, leading to a greater number of tillers. Grain appearance quality was decreased by both treatments, but E-[CO2 ] showed a much larger effect than did E-T. The significant decrease in undamaged grains (UDG) by E-[CO2 ] was mainly the result of an increased percentage of white-base grains (WBSG), which were negatively correlated with grain protein content. A significant decrease in grain protein content by E-[CO2 ] accounted in part for the increased WBSG. The dependence of WBSG on grain protein content, however, was different among years; the slope and intercept of the relationship were positively correlated with a heat dose above 26 °C. Year-to-year variation in the response of grain appearance quality demonstrated that E-[CO2 ] and rising air temperatures synergistically reduce grain appearance quality of

Climate driven crop planting date in the ACME Land Model (ALM): Impacts on productivity and yield

NASA Astrophysics Data System (ADS)

Drewniak, B.

2017-12-01

Climate is one of the key drivers of crop suitability and productivity in a region. The influence of climate and weather on the growing season determine the amount of time crops spend in each growth phase, which in turn impacts productivity and, more importantly, yields. Planting date can have a strong influence on yields with earlier planting generally resulting in higher yields, a sensitivity that is also present in some crop models. Furthermore, planting date is already changing and may continue, especially if longer growing seasons caused by future climate change drive early (or late) planting decisions. Crop models need an accurate method to predict plant date to allow these models to: 1) capture changes in crop management to adapt to climate change, 2) accurately model the timing of crop phenology, and 3) improve crop simulated influences on carbon, nutrient, energy, and water cycles. Previous studies have used climate as a predictor for planting date. Climate as a plant date predictor has more advantages than fixed plant dates. For example, crop expansion and other changes in land use (e.g., due to changing temperature conditions), can be accommodated without additional model inputs. As such, a new methodology to implement a predictive planting date based on climate inputs is added to the Accelerated Climate Model for Energy (ACME) Land Model (ALM). The model considers two main sources of climate data important for planting: precipitation and temperature. This method expands the current temperature threshold planting trigger and improves the estimated plant date in ALM. Furthermore, the precipitation metric for planting, which synchronizes the crop growing season with the wettest months, allows tropical crops to be introduced to the model. This presentation will demonstrate how the improved model enhances the ability of ALM to capture planting date compared with observations. More importantly, the impact of changing the planting date and introducing tropical

A model-based approach to investigating the pathophysiological mechanisms of hypertension and response to antihypertensive therapies: extending the Guyton model.

PubMed

Hallow, K Melissa; Lo, Arthur; Beh, Jeni; Rodrigo, Manoj; Ermakov, Sergey; Friedman, Stuart; de Leon, Hector; Sarkar, Anamika; Xiong, Yuan; Sarangapani, Ramesh; Schmidt, Henning; Webb, Randy; Kondic, Anna Georgieva

2014-05-01

Reproducibly differential responses to different classes of antihypertensive agents are observed among hypertensive patients and may be due to interindividual differences in hypertension pathology. Computational models provide a tool for investigating the impact of underlying disease mechanisms on the response to antihypertensive therapies with different mechanisms of action. We present the development, calibration, validation, and application of an extension of the Guyton/Karaaslan model of blood pressure regulation. The model incorporates a detailed submodel of the renin-angiotensin-aldosterone system (RAAS), allowing therapies that target different parts of this pathway to be distinguished. Literature data on RAAS biomarker and blood pressure responses to different classes of therapies were used to refine the physiological actions of ANG II and aldosterone on renin secretion, renal vascular resistance, and sodium reabsorption. The calibrated model was able to accurately reproduce the RAAS biomarker and blood pressure responses to combinations of dual-RAAS agents, as well as RAAS therapies in combination with diuretics or calcium channel blockers. The final model was used to explore the impact of underlying mechanisms of hypertension on the blood pressure response to different classes of antihypertensive agents. Simulations indicate that the underlying etiology of hypertension can impact the magnitude of response to a given class of therapy, making a patient more sensitive to one class and less sensitive others. Given that hypertension is usually the result of multiple mechanisms, rather than a single factor, these findings yield insight into why combination therapy is often required to adequately control blood pressure.

Computational model of chromosome aberration yield induced by high- and low-LET radiation exposures.

PubMed

Ponomarev, Artem L; George, Kerry; Cucinotta, Francis A

2012-06-01

We present a computational model for calculating the yield of radiation-induced chromosomal aberrations in human cells based on a stochastic Monte Carlo approach and calibrated using the relative frequencies and distributions of chromosomal aberrations reported in the literature. A previously developed DNA-fragmentation model for high- and low-LET radiation called the NASARadiationTrackImage model was enhanced to simulate a stochastic process of the formation of chromosomal aberrations from DNA fragments. The current version of the model gives predictions of the yields and sizes of translocations, dicentrics, rings, and more complex-type aberrations formed in the G(0)/G(1) cell cycle phase during the first cell division after irradiation. As the model can predict smaller-sized deletions and rings (<3 Mbp) that are below the resolution limits of current cytogenetic analysis techniques, we present predictions of hypothesized small deletions that may be produced as a byproduct of properly repaired DNA double-strand breaks (DSB) by nonhomologous end-joining. Additionally, the model was used to scale chromosomal exchanges in two or three chromosomes that were obtained from whole-chromosome FISH painting analysis techniques to whole-genome equivalent values.

Testing the accuracy of growth and yield models for southern hardwood forests

Treesearch

H. Michael Rauscher; Michael J. Young; Charles D. Webb; Daniel J. Robison

2000-01-01

The accuracy of ten growth and yield models for Southern Appalachian upland hardwood forests and southern bottomland forests was evaluated. In technical applications, accuracy is the composite of both bias (average error) and precision. Results indicate that GHAT, NATPIS, and a locally calibrated version of NETWIGS may be regarded as being operationally valid...

Variation of inulin content, inulin yield and water use efficiency for inulin yield in Jerusalem artichoke genotypes under different water regimes

USDA-ARS?s Scientific Manuscript database

The information on genotypic variation for inulin content, inulin yield and water use efficiency of inulin yield (WUEi) in response to drought is limited. This study was to investigate the genetic variability in inulin content, inulin yield and WUEi of Jerusalem artichoke (Helianthus tuberosus L.) ...

Stand-level growth and yield component models for red oak-sweetgum forests on Mid-South minor stream bottoms

Treesearch

Emily B. Schultz; J. Clint Iles; Thomas G. Matney; Andrew W. Ezell; James S. Meadows; Theodor D. Leininger; al. et.

2010-01-01

Greater emphasis is being placed on Southern bottomland hardwood management, but relatively few growth and yield prediction systems exist that are based on sufficient measurements. We present the aggregate stand-level expected yield and structural component equations for a red oak (Quercus section Lobatae)-sweetgum (Liquidambar styraciflua L.) growth and yield model....

Influence of Yield Stress Determination in Anisotropic Hardening Model on Springback Prediction in Dual-Phase Steel

NASA Astrophysics Data System (ADS)

Lee, J.; Bong, H. J.; Ha, J.; Choi, J.; Barlat, F.; Lee, M.-G.

2018-05-01

In this study, a numerical sensitivity analysis of the springback prediction was performed using advanced strain hardening models. In particular, the springback in U-draw bending for dual-phase 780 steel sheets was investigated while focusing on the effect of the initial yield stress determined from the cyclic loading tests. The anisotropic hardening models could reproduce the flow stress behavior under the non-proportional loading condition for the considered parametric cases. However, various identification schemes for determining the yield stress of the anisotropic hardening models significantly influenced the springback prediction. The deviations from the measured springback varied from 4% to 13.5% depending on the identification method.

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

NASA Astrophysics Data System (ADS)

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

Yield response to variable rate irrigation in corn

USDA-ARS?s Scientific Manuscript database

To investigate the impact of variable rate irrigation on corn yield, twenty plots of corn were laid out under a center pivot variable rate irrigation (VRI) system in an experimental field near Stoneville, MS. The VRI system is equipped with five VRI zone control units, a global positioning system (G...

Assessing disease stress and modeling yield losses in alfalfa

NASA Astrophysics Data System (ADS)

Guan, Jie

weight, percentage reflectance (810 nm), and green leaf area index (GLAI). Percentage reflectance (810 nm) assessments had a stronger relationship with dry weight and green leaf area index than percentage defoliation assessments. Our research conclusively demonstrates that percentage reflectance measurements can be used to nondestructively assess green leaf area index which is a direct measure of plant health and an indirect measure of productivity. This research conclusively demonstrates that remote sensing is superior to visual assessment method to assess alfalfa stress and to model yield and GLAI in the alfalfa foliar disease pathosystem.

A Measurement Model for Likert Responses that Incorporates Response Time

ERIC Educational Resources Information Center

Ferrando, Pere J.; Lorenzo-Seva, Urbano

2007-01-01

This article describes a model for response times that is proposed as a supplement to the usual factor-analytic model for responses to graded or more continuous typical-response items. The use of the proposed model together with the factor model provides additional information about the respondent and can potentially increase the accuracy of the…

EXPERIMENTAL DESIGN STRATEGY FOR THE WEIBULL DOSE RESPONSE MODEL (JOURNAL VERSION)

EPA Science Inventory

The objective of the research was to determine optimum design point allocation for estimation of relative yield losses from ozone pollution when the true and fitted yield-ozone dose response relationship follows the Weibull. The optimum design is dependent on the values of the We...

A sub-canopy structure for simulating oil palm in the Community Land Model: phenology, allocation and yield

NASA Astrophysics Data System (ADS)

Fan, Y.; Roupsard, O.; Bernoux, M.; Le Maire, G.; Panferov, O.; Kotowska, M. M.; Knohl, A.

2015-06-01

Land surface modelling has been widely used to characterize the two-way interactions between climate and human activities in terrestrial ecosystems such as deforestation, agricultural expansion, and urbanization. Towards an effort to quantify the effects of forests to oil palm conversion occurring in the tropics on land-atmosphere carbon, water and energy fluxes, we introduce a new perennial crop plant functional type (PFT) for oil palm. Due to the modular and sequential nature of oil palm growth (around 40 stacked phytomers) and yield (fruit bunches axillated on each phytomer), we developed a specific sub-canopy structure for simulating palm's growth and yield within the framework of the Community Land Model (CLM4.5). In this structure each phytomer has its own prognostic leaf growth and fruit yield capacity like a PFT but with shared stem and root components among all phytomers. Phenology and carbon and nitrogen allocation operate on the different phytomers in parallel but at unsynchronized steps, so that multiple fruit yields per annum are enabled in terms of carbon and nitrogen outputs. An important phenological phase is identified for the palm PFT - the storage growth period of bud and "spear" leaves which are photosynthetically inactive before expansion. Agricultural practices such as transplanting, fertilization, and leaf pruning are represented. Parameters introduced for the new PFT were calibrated and validated with field measurements of leaf area index (LAI) and yield from Sumatra, Indonesia. In calibration with a mature oil palm plantation, the cumulative yields from 2005 to 2014 matched perfectly between simulation and observation (mean percentage error = 4 %). Simulated inter-annual dynamics of PFT-level and phytomer-level LAI were both within the range of field measurements. Validation from eight independent oil palm sites shows the ability of the model to adequately predict the average leaf growth and fruit yield across sites but also indicates that

Analytic model to estimate thermonuclear neutron yield in z-pinches using the magnetic Noh problem

NASA Astrophysics Data System (ADS)

Allen, Robert C.

The objective was to build a model which could be used to estimate neutron yield in pulsed z-pinch experiments, benchmark future z-pinch simulation tools and to assist scaling for breakeven systems. To accomplish this, a recent solution to the magnetic Noh problem was utilized which incorporates a self-similar solution with cylindrical symmetry and azimuthal magnetic field (Velikovich, 2012). The self-similar solution provides the conditions needed to calculate the time dependent implosion dynamics from which batch burn is assumed and used to calculate neutron yield. The solution to the model is presented. The ion densities and time scales fix the initial mass and implosion velocity, providing estimates of the experimental results given specific initial conditions. Agreement is shown with experimental data (Coverdale, 2007). A parameter sweep was done to find the neutron yield, implosion velocity and gain for a range of densities and time scales for DD reactions and a curve fit was done to predict the scaling as a function of preshock conditions.

African crop yield reductions due to increasingly unbalanced Nitrogen and Phosphorus consumption

NASA Astrophysics Data System (ADS)

van der Velde, Marijn; Folberth, Christian; Balkovič, Juraj; Ciais, Philippe; Fritz, Steffen; Janssens, Ivan A.; Obersteiner, Michael; See, Linda; Skalský, Rastislav; Xiong, Wei; Peñuealas, Josep

2014-05-01

The impact of soil nutrient depletion on crop production has been known for decades, but robust assessments of the impact of increasingly unbalanced nitrogen (N) and phosphorus (P) application rates on crop production are lacking. Here, we use crop response functions based on 741 FAO maize crop trials and EPIC crop modeling across Africa to examine maize yield deficits resulting from unbalanced N:P applications under low, medium, and high input scenarios, for past (1975), current, and future N:P mass ratios of respectively, 1:0.29, 1:0.15, and 1:0.05. At low N inputs (10 kg/ha), current yield deficits amount to 10% but will increase up to 27% under the assumed future N:P ratio, while at medium N inputs (50 kg N/ha), future yield losses could amount to over 40%. The EPIC crop model was then used to simulate maize yields across Africa. The model results showed relative median future yield reductions at low N inputs of 40%, and 50% at medium and high inputs, albeit with large spatial variability. Dominant low-quality soils such as Ferralsols, which are strongly adsorbing P, and Arenosols with a low nutrient retention capacity, are associated with a strong yield decline, although Arenosols show very variable crop yield losses at low inputs. Optimal N:P ratios, i.e. those where the lowest amount of applied P produces the highest yield (given N input) where calculated with EPIC to be as low as 1:0.5. Finally, we estimated the additional P required given current N inputs, and given N inputs that would allow Africa to close yield gaps (ca. 70%). At current N inputs, P consumption would have to increase 2.3-fold to be optimal, and to increase 11.7-fold to close yield gaps. The P demand to overcome these yield deficits would provide a significant additional pressure on current global extraction of P resources.

Identifying critical nitrogen application rate for maize yield and nitrate leaching in a Haplic Luvisol soil using the DNDC model.

PubMed

Zhang, Yitao; Wang, Hongyuan; Liu, Shen; Lei, Qiuliang; Liu, Jian; He, Jianqiang; Zhai, Limei; Ren, Tianzhi; Liu, Hongbin

2015-05-01

Identification of critical nitrogen (N) application rate can provide management supports for ensuring grain yield and reducing amount of nitrate leaching to ground water. A five-year (2008-2012) field lysimeter (1 m × 2 m × 1.2 m) experiment with three N treatments (0, 180 and 240 kg Nha(-1)) was conducted to quantify maize yields and amount of nitrate leaching from a Haplic Luvisol soil in the North China Plain. The experimental data were used to calibrate and validate the process-based model of Denitrification-Decomposition (DNDC). After this, the model was used to simulate maize yield production and amount of nitrate leaching under a series of N application rates and to identify critical N application rate based on acceptable yield and amount of nitrate leaching for this cropping system. The results of model calibration and validation indicated that the model could correctly simulate maize yield and amount of nitrate leaching, with satisfactory values of RMSE-observation standard deviation ratio, model efficiency and determination coefficient. The model simulations confirmed the measurements that N application increased maize yield compared with the control, but the high N rate (240 kg Nha(-1)) did not produce more yield than the low one (120 kg Nha(-1)), and that the amount of nitrate leaching increased with increasing N application rate. The simulation results suggested that the optimal N application rate was in a range between 150 and 240 kg ha(-1), which would keep the amount of nitrate leaching below 18.4 kg NO₃(-)-Nha(-1) and meanwhile maintain acceptable maize yield above 9410 kg ha(-1). Furthermore, 180 kg Nha(-1) produced the highest yields (9837 kg ha(-1)) and comparatively lower amount of nitrate leaching (10.0 kg NO₃(-)-Nha(-1)). This study will provide a valuable reference for determining optimal N application rate (or range) in other crop systems and regions in China. Copyright © 2015 Elsevier B.V. All rights reserved.

Introducing a New Experimental Islet Transplantation Model using Biomimetic Hydrogel and a Simple High Yield Islet Isolation Technique.

PubMed

Mohammadi Ayenehdeh, Jamal; Niknam, Bahareh; Hashemi, Seyed Mahmoud; Rahavi, Hossein; Rezaei, Nima; Soleimani, Masoud; Tajik, Nader

2017-07-01

Islet transplantation could be an ideal alternative treatment to insulin therapy for type 1 diabetes Mellitus (T1DM). This clinical and experimental field requires a model that covers problems such as requiring a large number of functional and viable islets, the optimal transplantation site, and the prevention of islet dispersion. Hence, the methods of choice for isolation of functional islets and transplantation are crucial. The present study has introduced an experimental model that overcomes some critical issues in islet transplantation, including in situ pancreas perfusion by digestive enzymes through common bile duct. In comparison with conventional methods, we inflated the pancreas in Petri dishes with only 1 ml collagenase type XI solution, which was followed by hand-picking isolation or Ficoll gradient separation to purify the islets. Then we used a hydrogel composite in which the islets were embedded and transplanted into the peritoneal cavity of the streptozotocin-induced diabetic C57BL/6 mice. As compared to the yield of the classical methods, in our modified technique, the mean yield of isolation was about 130-200 viable islets/mouse pancreas. In vitro glucose-mediated insulin secretion assay indicated an appropriate response in isolated islets. In addition, data from in vivo experiments revealed that the allograft remarkably maintained blood glucose levels under 400 mg/dl and hydrogel composite prevents the passage of immune cells. In the model presented here, the rapid islet isolation technique and the application of biomimetic hydrogel wrapping of islets could facilitate islet transplantation procedures.

A Global Analysis of Light and Charge Yields in Liquid Xenon

DOE PAGES

Lenardo, Brian; Kazkaz, Kareem; Manalaysay, Aaron; ...

2015-11-04

Here, we present an updated model of light and charge yields from nuclear recoils in liquid xenon with a simultaneously constrained parameter set. A global analysis is performed using measurements of electron and photon yields compiled from all available historical data, as well as measurements of the ratio of the two. These data sweep over energies from keV and external applied electric fields from V/cm. The model is constrained by constructing global cost functions and using a simulated annealing algorithm and a Markov Chain Monte Carlo approach to optimize and find confidence intervals on all free parameters in the model.more » This analysis contrasts with previous work in that we do not unnecessarily exclude datasets nor impose artificially conservative assumptions, do not use spline functions, and reduce the number of parameters used in NEST v 0.98. Here, we report our results and the calculated best-fit charge and light yields. These quantities are crucial to understanding the response of liquid xenon detectors in the energy regime important for rare event searches such as the direct detection of dark matter particles.« less

Growth and yield in Eucalyptus globulus

Treesearch

James A. Rinehart; Richard B. Standiford

1983-01-01

A study of the major Eucalyptus globulus stands throughout California conducted by Woodbridge Metcalf in 1924 provides a complete and accurate data set for generating variable site-density yield models. Two models were developed using linear regression techniques. Model I depicts a linear relationship between age and yield best used for stands between five and fifteen...

An advanced constitutive model in the sheet metal forming simulation: the Teodosiu microstructural model and the Cazacu Barlat yield criterion

NASA Astrophysics Data System (ADS)

Alves, J. L.; Oliveira, M. C.; Menezes, L. F.

2004-06-01

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.

Comparison of the CEAS and Williams-type barley yield models for North Dakota and Minnesota

NASA Technical Reports Server (NTRS)

Leduc, S. (Principal Investigator)

1982-01-01

The CEAS and Williams type models were compared based on specified selection criteria which includes a ten year bootstrap test (1970-1979). Based on this, the models were quite comparable; however, the CEAS model was slightly better overall. The Williams type model seemed better for the 1974 estimates. Because that year spring wheat yield was particularly low, the Williams type model should not be excluded from further consideration.

Effect of nanoconfinement on the sputter yield in ultrathin polymeric films: Experiments and model

NASA Astrophysics Data System (ADS)

Cristaudo, Vanina; Poleunis, Claude; Delcorte, Arnaud

2018-06-01

This fundamental contribution on secondary ion mass spectrometry (SIMS) polymer depth-profiling by large argon clusters investigates the dependence of the sputter yield volume (Y) on the thickness (d) of ultrathin films as a function of the substrate nature, i.e. hard vs soft. For this purpose, thin films of polystyrene (PS) oligomers (∼4,000 amu) are spin-coated, respectively, onto silicon and poly (methyl methacrylate) supports and, then, bombarded by 10 keV Ar3000+ ions. The investigated thickness ranges from 15 to 230 nm. Additionally, the influence of the polymer molecular weight on Y(d) for PS thin films on Si is explored. The sputtering efficiency is found to be strongly dependent on the overlayer thickness, only in the case of the silicon substrate. A simple phenomenological model is proposed for the description of the thickness influence on the sputtering yield. Molecular dynamics (MD) simulations conducted on amorphous films of polyethylene-like oligomers of increasing thickness (from 2 to 20 nm), under comparable cluster bombardment conditions, predict a significant increase of the sputtering yield for ultrathin layers on hard substrates, induced by energy confinement in the polymer, and support our phenomenological model.

Do maize models capture the impacts of heat and drought stresses on yield? Using algorithm ensembles to identify successful approaches.

PubMed

Jin, Zhenong; Zhuang, Qianlai; Tan, Zeli; Dukes, Jeffrey S; Zheng, Bangyou; Melillo, Jerry M

2016-09-01

Stresses from heat and drought are expected to increasingly suppress crop yields, but the degree to which current models can represent these effects is uncertain. Here we evaluate the algorithms that determine impacts of heat and drought stress on maize in 16 major maize models by incorporating these algorithms into a standard model, the Agricultural Production Systems sIMulator (APSIM), and running an ensemble of simulations. Although both daily mean temperature and daylight temperature are common choice of forcing heat stress algorithms, current parameterizations in most models favor the use of daylight temperature even though the algorithm was designed for daily mean temperature. Different drought algorithms (i.e., a function of soil water content, of soil water supply to demand ratio, and of actual to potential transpiration ratio) simulated considerably different patterns of water shortage over the growing season, but nonetheless predicted similar decreases in annual yield. Using the selected combination of algorithms, our simulations show that maize yield reduction was more sensitive to drought stress than to heat stress for the US Midwest since the 1980s, and this pattern will continue under future scenarios; the influence of excessive heat will become increasingly prominent by the late 21st century. Our review of algorithms in 16 crop models suggests that the impacts of heat and drought stress on plant yield can be best described by crop models that: (i) incorporate event-based descriptions of heat and drought stress, (ii) consider the effects of nighttime warming, and (iii) coordinate the interactions among multiple stresses. Our study identifies the proficiency with which different model formulations capture the impacts of heat and drought stress on maize biomass and yield production. The framework presented here can be applied to other modeled processes and used to improve yield predictions of other crops with a wide variety of crop models. © 2016 John

Biological profiling and dose-response modeling tools ...

EPA Pesticide Factsheets

Through its ToxCast project, the U.S. EPA has developed a battery of in vitro high throughput screening (HTS) assays designed to assess the potential toxicity of environmental chemicals. At present, over 1800 chemicals have been tested in up to 600 assays, yielding a large number of concentration-response data sets. Standard processing of these data sets involves finding a best fitting mathematical model and set of model parameters that specify this model. The model parameters include quantities such as the half-maximal activity concentration (or “AC50”) that have biological significance and can be used to inform the efficacy or potency of a given chemical with respect to a given assay. All of this data is processed and stored in an online-accessible database and website: http://actor.epa.gov/dashboard2. Results from these in vitro assays are used in a multitude of ways. New pathways and targets can be identified and incorporated into new or existing adverse outcome pathways (AOPs). Pharmacokinetic models such as those implemented EPA’s HTTK R package can be used to translate an in vitro concentration into an in vivo dose; i.e., one can predict the oral equivalent dose that might be expected to activate a specific biological pathway. Such predicted values can then be compared with estimated actual human exposures prioritize chemicals for further testing.Any quantitative examination should be accompanied by estimation of uncertainty. We are developing met

Comparison Between the Use of SAR and Optical Data for Wheat Yield Estimations Using Crop Model Assimilation

NASA Astrophysics Data System (ADS)

Silvestro, Paolo Cosmo; Yang, Hao; Jin, X. L.; Yang, Guijun; Casa, Raffaele; Pignatti, Stefano

2016-08-01

The ultimate aim of this work is to develop methods for the assimilation of the biophysical variables estimated by remote sensing in a suitable crop growth model. Two strategies were followed, one based on the use of Leaf Area Index (LAI) estimated by optical data, and the other based on the use of biomass estimated by SAR. The first one estimates LAI from the reflectance measured by the optical sensors on board of HJ1A, HJ1B and Landsat, using a method based on the training of artificial neural networks (ANN) with PROSAIL model simulations. The retrieved LAI is used to improve wheat yield estimation, using assimilation methods based on the Ensemble Kalman Filter, which assimilate the biophysical variables into growth crop model. The second strategy estimates biomass from SAR imagery. Polarimetric decomposition methods were used based on multi-temporal fully polarimetric Radarsat-2 data during the entire growing season. The estimated biomass was assimilating to FAO Aqua crop model for improving the winter wheat yield estimation, with the Particle Swarm Optimization (PSO) method. These procedures were used in a spatial application with data collected in the rural area of Yangling (Shaanxi Province) in 2014 and were validated for a number of wheat fields for which ground yield data had been recorded and according to statistical yield data for the area.

Benefits of seasonal forecasts of crop yields

NASA Astrophysics Data System (ADS)

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

2017-12-01

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

Criteria for Yielding of Dispersion-Strengthened Alloys

NASA Technical Reports Server (NTRS)

Ansell, G. S.; Lenel, F. V.

1960-01-01

A dislocation model is presented in order to account for the yield behavior of alloys with a finely dispersed second-phase. The criteria for yielding used in the model, is that appreciable yielding occurs in these alloys when the shear stress due to piled-up groups of dislocations is sufficient to fracture or plastically deform the dispersed second-phase particles, relieving the back stress on the dislocation sources. Equations derived on the basis of this model, predict that the yield stress of the alloys varies as the reciprocal square root of the mean free path between dispersed particles. Experimental data is presented for several SAP-Type alloys, precipitation-hardened alloys and steels which are in good agreement with the yield strength variation as a function of dispersion spacing predicted by this theoretical treatment.

New Model for Europa's Tidal Response Based after Laboratory Measurements

NASA Astrophysics Data System (ADS)

Castillo, J. C.; McCarthy, C.; Choukroun, M.; Rambaux, N.

2009-12-01

We explore the application of the Andrade model to the modeling of Europa’s tidal response at the orbital period and for different librations. Previous models have generally assumed that the satellite behaves as a Maxwell body. However, at the frequencies exciting Europa’s tides and librations, material anelasticity tends to dominate the satellite’s response for a wide range of temperatures, a feature that is not accounted for by the Maxwell model. Many experimental studies on the anelasticity of rocks, ice, and hydrates, suggest that the Andrade model usually provides a good fit to the dissipation spectra obtained for a wide range of frequencies, encompassing the tidal frequencies of most icy satellites. These data indicate that, at Europa’s orbital frequency, the Maxwell model overestimates water ice attenuation at temperature warmer than ~240 K, while it tends to significantly underestimate it at lower temperatures. Based on the available data we suggest an educated extrapolation of available data to Europa’s conditions. We compute the tidal response of a model of Europa differentiated in a rocky core and a water-rich shell. We assume various degrees of stratification of the core involving hydrated and anhydrous silicates, as well as an iron core. The water-rich shell of Europa is assumed to be fully frozen, or to have preserved a deep liquid layer. In both cases we consider a range of thermal structures, based on existing models. These structures take into account the presence of non-ice materials, especially hydrated salts. This new approach yields a greater tidal response (amplitude and phase lag) than previously expected. This is due to the fact that a greater volume of material dissipates tidal energy in comparison to models assuming a Maxwell body. Another feature of interest is that the tidal stress expected in Europa is at about the threshold between a linear and non-linear mechanical response of water ice as a function of stress. Increased

Bayesian Inference of Baseline Fertility and Treatment Effects via a Crop Yield-Fertility Model

PubMed Central

Chen, Hungyen; Yamagishi, Junko; Kishino, Hirohisa

2014-01-01

To effectively manage soil fertility, knowledge is needed of how a crop uses nutrients from fertilizer applied to the soil. Soil quality is a combination of biological, chemical and physical properties and is hard to assess directly because of collective and multiple functional effects. In this paper, we focus on the application of these concepts to agriculture. We define the baseline fertility of soil as the level of fertility that a crop can acquire for growth from the soil. With this strict definition, we propose a new crop yield-fertility model that enables quantification of the process of improving baseline fertility and the effects of treatments solely from the time series of crop yields. The model was modified from Michaelis-Menten kinetics and measured the additional effects of the treatments given the baseline fertility. Using more than 30 years of experimental data, we used the Bayesian framework to estimate the improvements in baseline fertility and the effects of fertilizer and farmyard manure (FYM) on maize (Zea mays), barley (Hordeum vulgare), and soybean (Glycine max) yields. Fertilizer contributed the most to the barley yield and FYM contributed the most to the soybean yield among the three crops. The baseline fertility of the subsurface soil was very low for maize and barley prior to fertilization. In contrast, the baseline fertility in this soil approximated half-saturated fertility for the soybean crop. The long-term soil fertility was increased by adding FYM, but the effect of FYM addition was reduced by the addition of fertilizer. Our results provide evidence that long-term soil fertility under continuous farming was maintained, or increased, by the application of natural nutrients compared with the application of synthetic fertilizer. PMID:25405353

Food for Thought: Crop Yields in the Columbia River Basin in an Altered Future

NASA Astrophysics Data System (ADS)

Rajagopalan, K.; Chinnayakanahalli, K.; Nelson, R.; Stockle, C.; Kruger, C.; Brady, M.; Adam, J. C.

2013-12-01

Growth of global population and food consumption in the next several decades is expected to result in a food security challenge. Strategies to address this challenge, such as enhancing agricultural productivity and resiliency, need to be considered within the context of a full range of plausible consequences so as to identify investments that create win-win-win scenarios for the environment, economy, and society. Regional earth systems models can provide the necessary scale-appropriate framework to inform the decision making context for adaptation strategies, especially in the context of global change. In an altered future, changes to climate, technology and socioeconomics affect regional agriculture both directly and indirectly. These effects are not independent and an integrated process-based model may better capture unanticipated non-linear and non-monotonic responses and feedbacks over time . BioEarth is a research initiative designed to explore the coupling of multiple stand-alone earth systems models to generate usable information for agricultural and natural resource decision making at the regional scale at decadal time-steps. This project focuses on the U.S. Pacific Northwest (PNW) region and is a framework that integrates atmospheric, terrestrial, aquatic, and economic models. We apply component models of BioEarth to the Columbia River basin in the PNW to study the direct and indirect impacts of climate change on regional irrigated and dryland crop yields for a variety of annual and perennial crops. Results indicate that the net effect of climate change on crop yields is dependent on the crop type. There is a negative effect of temperature on yields for most crops. Dryland winter wheat is a notable exception. With warming, although the available growing season increases, faster thermal accumulation results in a shorter time to maturity. Precipitation changes in the region have a positive impact on dryland agriculture. Carbon dioxide (CO2) fertilization has

[Light response characteristics of photosynthesis and model comparison of Distylium chinense in different flooding durations].

PubMed

Liu, Ze-bin; Cheng, Rui-mei; Xiao, Wen-fa; Guo, Quan-shui; Wang, Na

2015-04-01

The light responses of photosynthesis of two-year-old Distytum chinense seedlings subjected to a simulated reservoir flooding environment in autumn and winter seasons were measured by using a Li-6400 XT portable photosynthesis system, and the light response curves were fitted and analyzed by three models of the rectangular hyperbola, non-rectangular hyperbola and modified rectangular hyperbola to investigate the applicability of different light response models for the D. chinense in different flooding durations and the adaption regulation of light response parameters to flooding stress. The results showed that the fitting effect of the non-rectangular hyperbola model for light response process of D. chinense under normal growth condition and under short-term flooding (15 days of flooding) was better than that of the other two models, while the fitting effect of the modified rectangular hyperbola model for light response process of D. chinense under longer-term flooding (30, 45 and 60 days of flooding) was better than that of the other two models. The modified rectangular hyperbola model gave the best fitted results of light compensation point (LCP) , maximum net photosynthetic rate (P(n max)) and light saturation point (LSP), and the non-rectangular hyperbola model gave the best fitted result of dark respiration rate (R(d)). The apparent quantum yield (Φ), P(n max) and LSP of D. chinense gradually decreased, and the LCP and R(d) of D. chinense gradually increased in early flooding (30 days), but D. chinense gradually produced adaptability for flooding as the flooding duration continued to increase, and various physiological indexes were gradually stabilized. Thus, this species has adaptability to some degree to the flooding environment.

Modeling sediment yield in small catchments at event scale: Model comparison, development and evaluation

NASA Astrophysics Data System (ADS)

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.

The Z {yields} cc-bar {yields} {gamma}{gamma}*, Z {yields} bb-bar {yields} {gamma}{gamma}* triangle diagrams and the Z {yields} {gamma}{psi}, Z {yields} {gamma}Y decays

DOE Office of Scientific and Technical Information (OSTI.GOV)

Achasov, N. N., E-mail: achasov@math.nsc.ru

2011-03-15

The approach to the Z {yields} {gamma}{psi} and Z {yields} {gamma}Y decay study is presented in detail, based on the sum rules for the Z {yields} cc-bar {yields} {gamma}{gamma}* and Z {yields} bb-bar {yields} {gamma}{gamma}* amplitudes and their derivatives. The branching ratios of the Z {yields} {gamma}{psi} and Z {yields} {gamma}Y decays are calculated for different hypotheses on saturation of the sum rules. The lower bounds of {Sigma}{sub {psi}} BR(Z {yields} {gamma}{psi}) = 1.95 Multiplication-Sign 10{sup -7} and {Sigma}{sub {upsilon}} BR(Z {yields} {gamma}Y) = 7.23 Multiplication-Sign 10{sup -7} are found. Deviations from the lower bounds are discussed, including the possibilitymore » of BR(Z {yields} {gamma}J/{psi}(1S)) {approx} BR(Z {yields} {gamma}Y(1S)) {approx} 10{sup -6}, that could be probably measured in LHC. The angular distributions in the Z {yields} {gamma}{psi} and Z {yields} {gamma}Y decays are also calculated.« less

Integrated remote sensing imagery and two-dimensional hydraulic modeling approach for impact evaluation of flood on crop yields

NASA Astrophysics Data System (ADS)

Chen, Huili; Liang, Zhongyao; Liu, Yong; Liang, Qiuhua; Xie, Shuguang

2017-10-01

The projected frequent occurrences of extreme flood events will cause significant losses to crops and will threaten food security. To reduce the potential risk and provide support for agricultural flood management, prevention, and mitigation, it is important to account for flood damage to crop production and to understand the relationship between flood characteristics and crop losses. A quantitative and effective evaluation tool is therefore essential to explore what and how flood characteristics will affect the associated crop loss, based on accurately understanding the spatiotemporal dynamics of flood evolution and crop growth. Current evaluation methods are generally integrally or qualitatively based on statistic data or ex-post survey with less diagnosis into the process and dynamics of historical flood events. Therefore, a quantitative and spatial evaluation framework is presented in this study that integrates remote sensing imagery and hydraulic model simulation to facilitate the identification of historical flood characteristics that influence crop losses. Remote sensing imagery can capture the spatial variation of crop yields and yield losses from floods on a grid scale over large areas; however, it is incapable of providing spatial information regarding flood progress. Two-dimensional hydraulic model can simulate the dynamics of surface runoff and accomplish spatial and temporal quantification of flood characteristics on a grid scale over watersheds, i.e., flow velocity and flood duration. The methodological framework developed herein includes the following: (a) Vegetation indices for the critical period of crop growth from mid-high temporal and spatial remote sensing imagery in association with agricultural statistics data were used to develop empirical models to monitor the crop yield and evaluate yield losses from flood; (b) The two-dimensional hydraulic model coupled with the SCS-CN hydrologic model was employed to simulate the flood evolution process

COBRA ATD multispectral camera response model

NASA Astrophysics Data System (ADS)

Holmes, V. Todd; Kenton, Arthur C.; Hilton, Russell J.; Witherspoon, Ned H.; Holloway, John H., Jr.

2000-08-01

A new multispectral camera response model has been developed in support of the US Marine Corps (USMC) Coastal Battlefield Reconnaissance and Analysis (COBRA) Advanced Technology Demonstration (ATD) Program. This analytical model accurately estimates response form five Xybion intensified IMC 201 multispectral cameras used for COBRA ATD airborne minefield detection. The camera model design is based on a series of camera response curves which were generated through optical laboratory test performed by the Naval Surface Warfare Center, Dahlgren Division, Coastal Systems Station (CSS). Data fitting techniques were applied to these measured response curves to obtain nonlinear expressions which estimates digitized camera output as a function of irradiance, intensifier gain, and exposure. This COBRA Camera Response Model was proven to be very accurate, stable over a wide range of parameters, analytically invertible, and relatively simple. This practical camera model was subsequently incorporated into the COBRA sensor performance evaluation and computational tools for research analysis modeling toolbox in order to enhance COBRA modeling and simulation capabilities. Details of the camera model design and comparisons of modeled response to measured experimental data are presented.