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Author Mansouri, M.; Dumont, B.; Destain, M.-F.
Title Predicting Grain Protein Content of Winter Wheat Type Conference Article
Year 2014 Publication Abbreviated Journal
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Area Expedition Conference 22nd European Symposium on Artificial Networks, Computational Intelligence and Machine Learning. Bruges, Belgium, 2014-04-23 to 2014-04-25
Notes Approved no
Call Number MA @ admin @ Serial 2631
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Author Mansouri, M.; Dumont, B.; Destain, M.-F.
Title Bayesian methods for predicting and modelling winter wheat biomass Type Conference Article
Year 2014 Publication Abbreviated Journal
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Area Expedition Conference 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
Notes Approved no
Call Number MA @ admin @ Serial 2629
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Author Mansouri, M.; Dumont, B.; Destain, M.-F.
Title Bayesian methods for predicting LAI and soil moisture Type Conference Article
Year 2012 Publication Abbreviated Journal
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Keywords CropM
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Area Expedition Conference 11th International Conference on Precision Agriculture. Indianapolis (USA), 2012-07-15 to 2012-07-18
Notes Approved no
Call Number MA @ admin @ Serial 2627
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Author Mansouri, M.
Title Modeling and Prediction of Time-Varying Environmental Data Using Advanced Bayesian Methods Type Book Chapter
Year 2013 Publication Abbreviated Journal
Volume Issue (up) Pages 112-137
Keywords CropM
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Publisher IGI Global Place of Publication Hershey PA Editor Masegosa, P.; Villacorta, C.; Cruz-Corona, S.; Garcia-Cascales, M.; Lamata, J.; Verdegay, A.
Language Summary Language Original Title
Series Editor Series Title Exploring Innovative and Successful Applications of Soft Computing Abbreviated Series Title
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Area Expedition Conference
Notes Approved no
Call Number MA @ admin @ Serial 2625
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Author Dumont, B.; Leemans, V.; Mansouri, M.; Bodson, B.; Destain, J.-P.; Destain, M.-F.
Title Parameter identification of the STICS crop model, using an accelerated formal MCMC approach Type Journal Article
Year 2014 Publication Environmental Modelling & Software Abbreviated Journal Env. Model. Softw.
Volume 52 Issue (up) Pages 121-135
Keywords crop model; parameter estimation; bayes; stics; dream; global sensitivity-analysis; simulation-model; nitrogen balances; bayesian-approach; generic model; wheat; prediction; water; optimization; algorithm
Abstract 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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ISSN 1364-8152 ISBN Medium Article
Area Expedition Conference
Notes CropM Approved no
Call Number MA @ admin @ Serial 4520
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