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Author |
Mansouri, M.; Destain, M.-F. |
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Title  |
Predicting biomass and grain protein content using Bayesian methods |
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Journal Article |
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Year |
2015 |
Publication |
Stochastic Environmental Research and Risk Assessment |
Abbreviated Journal |
Stoch. Environ. Res. Risk Assess. |
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Volume |
29 |
Issue |
4 |
Pages |
1167-1177 |
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Keywords |
crop model; particle filter; prediction; ensemble kalman filter; parameter-estimation; particle filters; decision-support; state estimation; model; nitrogen; navigation; tracking; systems |
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Abstract |
This paper deals with the problem of predicting biomass and grain protein content using improved particle filtering (IPF) based on minimizing the Kullback-Leibler divergence. The performances of IPF are compared with those of the conventional particle filtering (PF) in two comparative studies. In the first one, we apply IPF and PF at a simple dynamic crop model with the aim to predict a single state variable, namely the winter wheat biomass, and to estimate several model parameters. In the second study, the proposed IPF and the PF are applied to a complex crop model (AZODYN) to predict a winter-wheat quality criterion, namely the grain protein content. The results of both comparative studies reveal that the IPF method provides a better estimation accuracy than the PF method. The benefit of the IPF method lies in its ability to provide accuracy related advantages over the PF method since, unlike the PF which depends on the choice of the sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of this sampling distribution, which also utilizes the observed data. The performance of the proposed method is evaluated in terms of estimation accuracy, root mean square error, mean absolute error and execution times. |
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1436-3240 1436-3259 |
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CropM |
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MA @ admin @ |
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4664 |
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Webber, H.; White, J.W.; Kimball, B.A.; Ewert, F.; Asseng, S.; Rezaei, E.E.; Pinter, P.J., Jr.; Hatfield, J.L.; Reynolds, M.P.; Ababaei, B.; Bindi, M.; Doltra, J.; Ferrise, R.; Kage, H.; Kassie, B.T.; Kersebaum, K.-C.; Luig, A.; Olesen, J.E.; Semenov, M.A.; Stratonovitch, P.; Ratjen, A.M.; LaMorte, R.L.; Leavitt, S.W.; Hunsaker, D.J.; Wall, G.W.; Martre, P. |
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Title |
Physical robustness of canopy temperature models for crop heat stress simulation across environments and production conditions |
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Journal Article |
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Year |
2018 |
Publication |
Field Crops Research |
Abbreviated Journal |
Field Crops Research |
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Volume |
216 |
Issue |
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Pages |
75-88 |
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Keywords |
Heat stress; Crop model improvement; Heat and drought interactions; Climate change impact assessments; Canopy temperature; Wheat; Air CO2 Enrichment; Elevated Carbon-Dioxide; Water-Use Efficiency; Climate-Change; Wheat Evapotranspiration; Stomatal Conductance; Multimodel Ensembles; Farming Systems; Drought-Stress; Spring Wheat |
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Despite widespread application in studying climate change impacts, most crop models ignore complex interactions among air temperature, crop and soil water status, CO2 concentration and atmospheric conditions that influence crop canopy temperature. The current study extended previous studies by evaluating Tc simulations from nine crop models at six locations across environmental and production conditions. Each crop model implemented one of an empirical (EMP), an energy balance assuming neutral stability (EBN) or an energy balance correcting for atmospheric stability conditions (EBSC) approach to simulate Tc. Model performance in predicting Tc was evaluated for two experiments in continental North America with various water, nitrogen and CO2 treatments. An empirical model fit to one dataset had the best performance, followed by the EBSC models. Stability conditions explained much of the differences between modeling approaches. More accurate simulation of heat stress will likely require use of energy balance approaches that consider atmospheric stability conditions. |
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2018-02-19 |
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0378-4290 |
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CropM, ft_macsur |
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MA @ admin @ |
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5189 |
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Yin, X.G.; Kersebaum, K.C.; Kollas, C.; Manevski, K.; Baby, S.; Beaudoin, N.; Ozturk, I.; Gaiser, T.; Wu, L.H.; Hoffmann, M.; Charfeddine, M.; Conradt, T.; Constantin, J.; Ewert, F.; de Cortazar-Atauri, I.G.; Giglio, L.; Hlavinka, P.; Hoffmann, H.; Launay, M.; Louarn, G.; Manderscheid, R.; Mary, B.; Mirschel, W.; Nende, C.; Pacholskin, A.; Palosuo, T.; Ripoche-Wachter, D.; Rotter, R.P.; Ruget, F.; Sharif, B.; Trnka, M.; Ventrella, D.; Weigel, H.J.; Olesen, J.E.; Yin, X.; Kersebaum, K.C.; Kollas, C.; Manevski, K.; Baby, S.; Beaudoin, N.; Ozturk, I.; Gaiser, T.; Wu, L.; Hoffmann, M.; Charfeddine, M.; Conradt, T.; Constantin, J.; Ewert, F.; de Cortazar-Atauri, I.G.; Giglio, L.; Hlavinka, P.; Hoffmann, H.; Launay, M.; Louarn, G.; Manderscheid, R.; Mary, B.; Mirschel, W.; Nende, C.; Pacholskin, A.; Palosuo, T.; Ripoche-Wachter, D.; Roetter, R.P.; Ruget, F.; Sharif, B.; Trnka, M.; Ventrella, D.; Weigel, H.-J.; Olesen, J.E. |
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Title |
Performance of process-based models for simulation of grain N in crop rotations across Europe |
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Journal Article |
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Year |
2017 |
Publication |
Agricultural Systems |
Abbreviated Journal |
Agric. Syst. |
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Volume |
154 |
Issue |
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Pages |
63-77 |
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Keywords |
Calibration, Crop model, Crop rotation, Grain N content, Model evaluation, Model initialization; Climate-Change; Winter-Wheat; Nitrogen-Fertilization; Agroecosystem; Models; Multimodel Ensembles; Yield Response; Use Efficiency; Soil-Moisture; Oilseed Rape; Elevated Co2 |
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Abstract |
The accurate estimation of crop grain nitrogen (N; N in grain yield) is crucial for optimizing agricultural N management, especially in crop rotations. In the present study, 12 process-based models were applied to simulate the grain N of i) seven crops in rotations, ii) across various pedo-climatic and agro-management conditions in Europe, under both continuous simulation and single year simulation, and for iv) two calibration levels, namely minimal and detailed calibration. Generally, the results showed that the accuracy of the simulations in predicting grain N increased under detailed calibration. The models performed better in predicting the grain N of winter wheat (Triticum aestivum L.), winter barley (Hordewn vulgare L.) and spring barley (Hordeum vulgare L.) compared to spring oat (Avena saliva L.), winter rye (Secale cereale L.), pea (Piswn sativum L.) and winter oilseed rape (Brassica napus L.). These differences are linked to the intensity of parameterization with better parameterized crops showing lower prediction errors. The model performance was influenced by N fertilization and irrigation treatments, and a majority of the predictions were more accurate under low N and rainfed treatments. Moreover, the multi-model mean provided better predictions of grain N compared to any individual model. In regard to the Individual models, DAISY, FASSET, HERMES, MONICA and STICS are suitable for predicting grain N of the main crops in typical European crop rotations, which all performed well in both continuous simulation and single year simulation. Our results show that both the model initialization and the cover crop effects in crop rotations should be considered in order to achieve good performance of continuous simulation. Furthermore, the choice of either continuous simulation or single year simulation should be guided by the simulation objectives (e.g. grain yield, grain N content or N dynamics), the crop sequence (inclusion of legumes) and treatments (rate and type of N fertilizer) included in crop rotations and the model formalism. |
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2017-06-12 |
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0308-521x |
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CropM, ft_macsur |
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Call Number |
MA @ admin @ |
Serial |
4963 |
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Author |
Martre, P.; Wallach, D.; Asseng, S.; Ewert, F.; Jones, J.W.; Rötter, R.P.; Boote, K.J.; Ruane, A.C.; Thorburn, P.J.; Cammarano, D.; Hatfield, J.L.; Rosenzweig, C.; Aggarwal, P.K.; Angulo, C.; Basso, B.; Bertuzzi, P.; Biernath, C.; Brisson, N.; Challinor, A.J.; Doltra, J.; Gayler, S.; Goldberg, R.; Grant, R.F.; Heng, L.; Hooker, J.; Hunt, L.A.; Ingwersen, J.; Izaurralde, R.C.; Kersebaum, K.C.; Müller, C.; Kumar, S.N.; 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.O.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; White, J.W.; Wolf, J. |
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Title |
Multimodel ensembles of wheat growth: many models are better than one |
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Journal Article |
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Year |
2015 |
Publication |
Global Change Biology |
Abbreviated Journal |
Glob. Chang. Biol. |
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Volume |
21 |
Issue |
2 |
Pages |
911-925 |
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Keywords |
Climate; Climate Change; Environment; *Models, Biological; Seasons; Triticum/*growth & development; ecophysiological model; ensemble modeling; model intercomparison; process-based model; uncertainty; wheat (Triticum aestivum L.) |
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Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models. |
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1354-1013 |
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CropM, ftnotmacsur |
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MA @ admin @ |
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4665 |
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Ruane, A.C.; Hudson, N.I.; Asseng, S.; Camarrano, D.; Ewert, F.; Martre, P.; Boote, K.J.; Thorburn, P.J.; Aggarwal, P.K.; Angulo, C.; Basso, B.; Bertuzzi, P.; Biernath, C.; Brisson, N.; Challinor, &rew J.; Doltra, J.; Gayler, S.; Goldberg, R.; Grant, R.F.; Heng, L.; Hooker, J.; Hunt, L.A.; Ingwersen, J.; Izaurralde, R.C.; Kersebaum, K.C.; Kumar, S.N.; Müller, C.; Nendel, C.; O’Leary, G.; Olesen, J.E.; Osborne, T.M.; Palosuo, T.; Priesack, E.; Ripoche, D.; Rötter, R.P.; Semenov, M.A.; Shcherbak, I.; Steduto, P.; Stöckle, C.O.; Stratonovitch, P.; Streck, T.; Supit, I.; Tao, F.; Travasso, M.; Waha, K.; Wallach, D.; White, J.W.; Wolf, J. |
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Title |
Multi-wheat-model ensemble responses to interannual climate variability |
Type |
Journal Article |
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Year |
2016 |
Publication |
Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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Volume |
81 |
Issue |
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Pages |
86-101 |
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Keywords |
Crop modeling; Uncertainty; Multi-model ensemble; Wheat; AgMIP; Climate; impacts; Temperature; Precipitation; lnterannual variability; simulation-model; crop model; nitrogen dynamics; winter-wheat; large-area; systems simulation; farming systems; yield response; growth; water |
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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 1981-2010 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 (R-2 <= 0.24) was found between the models’ sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts. Published by Elsevier Ltd. |
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1364-8152 |
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CropM, ft_macsur |
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MA @ admin @ |
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4769 |
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