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Mansouri, M.; Dumont, B.; Destain, M.-F. |
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Title |
Predicting Grain Protein Content of Winter Wheat |
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2014 |
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22nd European Symposium on Artificial Networks, Computational Intelligence and Machine Learning. Bruges, Belgium, 2014-04-23 to 2014-04-25 |
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MA @ admin @ |
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2631 |
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Mansouri, M.; Dumont, B.; Destain, M.-F. |
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Title |
Bayesian methods for predicting and modelling winter wheat biomass |
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2014 |
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MACSUR CropM International Symposium and Workshop: Modelling climate change impacts on crop production for food security, Oslo, Norway, 2014-02-10 to 2014-02-12 |
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MA @ admin @ |
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2629 |
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Mansouri, M.; Dumont, B.; Destain, M.-F. |
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Title |
Bayesian methods for predicting LAI and soil moisture |
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2012 |
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CropM |
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11th International Conference on Precision Agriculture. Indianapolis (USA), 2012-07-15 to 2012-07-18 |
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MA @ admin @ |
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2627 |
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Author |
Mansouri, M. |
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Title |
Modeling and Prediction of Time-Varying Environmental Data Using Advanced Bayesian Methods |
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2013 |
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112-137 |
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CropM |
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IGI Global |
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Hershey PA |
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Masegosa, P.; Villacorta, C.; Cruz-Corona, S.; Garcia-Cascales, M.; Lamata, J.; Verdegay, A. |
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Exploring Innovative and Successful Applications of Soft Computing |
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MA @ admin @ |
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2625 |
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Author |
Dumont, B.; Leemans, V.; Mansouri, M.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
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Title |
Parameter identification of the STICS crop model, using an accelerated formal MCMC approach |
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Journal Article |
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Year |
2014 |
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Environmental Modelling & Software |
Abbreviated Journal |
Env. Model. Softw. |
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52 |
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121-135 |
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Keywords |
crop model; parameter estimation; bayes; stics; dream; global sensitivity-analysis; simulation-model; nitrogen balances; bayesian-approach; generic model; wheat; prediction; water; optimization; algorithm |
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This study presents a Bayesian approach for the parameters’ identification of the STICS crop model based on the recently developed Differential Evolution Adaptive Metropolis (DREAM) algorithm. The posterior distributions of nine specific crop parameters of the STICS model were sampled with the aim to improve the growth simulations of a winter wheat (Triticum aestivum L) culture. The results obtained with the DREAM algorithm were initially compared to those obtained with a Nelder-Mead Simplex algorithm embedded within the OptimiSTICS package. Then, three types of likelihood functions implemented within the DREAM algorithm were compared, namely the standard least square, the weighted least square, and a transformed likelihood function that makes explicit use of the coefficient of variation (CV). The results showed that the proposed CV likelihood function allowed taking into account both noise on measurements and heteroscedasticity which are regularly encountered in crop modelling. (C) 2013 Elsevier Ltd. All rights reserved. |
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English |
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1364-8152 |
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CropM |
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Call Number |
MA @ admin @ |
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4520 |
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