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Author Destain, M.-F.
Title Filtering methods for predicting and modelling wheat yield in the context of climate change Type Conference Article
Year 2014 Publication Abbreviated Journal
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Abstract In this paper, an Improved Particle Filtering (IPF) based on minimizing Kullback-Leibler divergence will be proposed for biomass prediction of a wheat crop model in the context of climate change including heat and drought stresses.In a first stage, the performances of the proposed technique will be compared with those of the extended Kalman filter (EKF), unscented Kalman filter (UKF), Particle filter (PF). In a second stage, the state estimation techniques EKF, UKF, PF and IPF will be used for updating prediction of the model in order to predict winter wheat biomass, in specific field conditions, during several contrasted weather conditions. In a third stage, the effects of practical challenges on the performances of the state estimation algorithms will be assessed. Such practical challenges include the effect of measurement noise on the estimation performances and the measurement frequency of state variables.The first results show that the UKF provides a higher accuracy than the EKF due to the limited ability of EKF to accurately estimate the mean and covariance matrix of the estimated states through lineralization of the nonlinear process model. The results also show that the IPF provides a significant improvement over PF because, unlike the PF which depends on the choice of sampling distribution used to estimate the posterior distribution, the IPF yields an optimum choice of the sampling distribution, which also accounts for the observed data. For all techniques, the practical challenges affect the estimation accuracy as well as the convergence of the estimated states and parameters. However, the IPF can still provide both convergence as well as accuracy over other estimation methods. These advantages are precious in presence of high climate stresses.
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Series Editor Series Title Abbreviated Series Title FACCE MACSUR Mid-term Scientific Conference
Series Volume (down) 3(S) Sassari, Italy Series Issue Edition
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Area Expedition Conference FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy
Notes Approved no
Call Number MA @ admin @ Serial 5083
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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 Dumont, B.; Basso, B.; Bodson, B.; Destain, J.-P.; Destain, M.-F.
Title Assessing and modeling economic and environmental impact of wheat nitrogen management in Belgium Type Journal Article
Year 2016 Publication Environmental Modelling & Software Abbreviated Journal Env. Model. Softw.
Volume 79 Issue Pages 184-196
Keywords Tactical nitrogen management; Climatic variability; Probability risk; assessment; LARS-WG; Crop model; STICS; stics crop model; generic model; simulation; yield; water; soil; fertilizer; behavior; climate; maize
Abstract Future progress in wheat yield will rely on identifying genotypes & management practices better adapted to the fluctuating environment Nitrogen (N) fertilization is probably the most important practice impacting crop growth. However, the adverse environmental impacts of inappropriate N management (e.g., lixiviation) must be considered in the decision-making process. A formal decisional algorithm was developed to tactically optimize the economic & environmental N fertilization in wheat. Climatic uncertainty analysis was performed using stochastic weather time-series (LARS-WG). Crop growth was simulated using STICS model. Experiments were conducted to support the algorithm recommendations: winter wheat was sown between 2008 & 2014 in a classic loamy soil of the Hesbaye Region, Belgium (temperate climate). Results indicated that, most of the time, the third N fertilization applied at flag-leaf stage by farmers could be reduced. Environmental decision criterion is most of the time the limiting factor in comparison to the revenues expected by farmers. (C) 2016 Elsevier Ltd. All rights reserved.
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Language English Summary Language Original Title
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ISSN 1364-8152 ISBN Medium Article
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Notes CropM, ft_macsur Approved no
Call Number MA @ admin @ Serial 4749
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