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Dumont, B.; Basso, B.; Leemans, V.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
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Title  |
Yield variability linked to climate uncertainty and nitrogen fertilisation |
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Book Chapter |
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Year |
2013 |
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427-434 |
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CropM |
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Springer |
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Stafford, J.V. |
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Precision Agriculture ‘13. 9th ECPA – European Conference on Precision Agriculture, 7-11 June 2013, Lleida, Spain |
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Call Number |
MA @ admin @ |
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2410 |
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Author |
Dumont, B.; Basso, B.; Leemans, V.; Destain, J.P.; Bodson, B.; Destain, M.-F. |
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Title |
Yield variability linked to climate uncertainty and nitrogen fertilisation |
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Conference Article |
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Year |
2013 |
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CropM |
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9th ECPA - European Conference on Precision Agriculture, Lleida, Spain, 2013-07-07 to 2013-07-11 |
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no |
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MA @ admin @ |
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2411 |
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Author |
Dumont, B.; Ferrandis, V., S.; Leemans, V.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
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Title |
Vers un système de prédiction du rendement en temps réel |
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Conference Article |
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Year |
2012 |
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CropM |
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IXeme Seminaire STICS, Orléans, Sainte Montaine (France)., 2012-10-16 to 2012-10-16 |
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MA @ admin @ |
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2405 |
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Author |
Dumont, B.; Basso, B.; Leemans, V.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
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Title |
Systematic analysis of site-specific yield distributions resulting from nitrogen management and climatic variability interactions |
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Journal Article |
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Year |
2015 |
Publication |
Precision Agriculture |
Abbreviated Journal |
Precision Agric. |
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Volume |
16 |
Issue |
4 |
Pages |
361-384 |
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Keywords |
nitrogen management; climatic variability; lars-wg weather generator; stics soil-crop model; pearson system; probability risk assessment; crop model stics; fertilizer nitrogen; generic model; wheat yield; maize; simulation; skewness; field; agriculture; scenarios |
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At the plot level, crop simulation models such as STICS have the potential to evaluate risk associated with management practices. In nitrogen (N) management, however, the decision-making process is complex because the decision has to be taken without any knowledge of future weather conditions. The objective of this paper is to present a general methodology for assessing yield variability linked to climatic uncertainty and variable N rate strategies. The STICS model was coupled with the LARS-Weather Generator. The Pearson system and coefficients were used to characterise the shape of yield distribution. Alternatives to classical statistical tests were proposed for assessing the normality of distributions and conducting comparisons (namely, the Jarque-Bera and Wilcoxon tests, respectively). Finally, the focus was put on the probability risk assessment, which remains a key point within the decision process. The simulation results showed that, based on current N application practice among Belgian farmers (60-60-60 kgN ha(-1)), yield distribution was very highly significantly non-normal, with the highest degree of asymmetry characterised by a skewness value of -1.02. They showed that this strategy gave the greatest probability (60 %) of achieving yields that were superior to the mean (10.5 t ha(-1)) of the distribution. |
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English |
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1385-2256 |
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Notes |
CropM, ft_macsur |
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Call Number |
MA @ admin @ |
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4519 |
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Author |
Dumont, B.; Vancutsem, F.; Seutin, B.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
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Title |
Simulation de la croissance du blé à l’aide de modèles écophysiologiques: Synthèse bibliographique des méthodes, potentialités et limitations |
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Journal Article |
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Year |
2012 |
Publication |
Biotechnologie, Agronomie, Société et Environnement |
Abbreviated Journal |
Biotechnologie, Agronomie, Société et Environnement |
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Volume |
163 |
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Pages |
376-386 |
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Keywords |
crops; growth; soil; Triticum; wheats; calibration; optimization methods |
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Abstract |
Crop models describe the growth and development of a crop interacting with its surrounding agro-environmental conditions (soil, climate and the close conditions of the plant). However, the implementation of such models remains difficult because of the high number of explanatory variables and parameters. It often happens that important discrepancies appear between measured and simulated values. This article aims to highlight the different sources of uncertainty related to the use of crop models, as well as the actual methods that allow a compensation for or, at least, a consideration of these sources of error during analysis of the model results. This article presents a literature review, which firstly synthesises the general mathematical structure of crop models. The main criteria for evaluating crop models are then described. Finally, several methods used for improving models are given. Parameter estimation methods, including frequentist and Bayesian approaches, are presented and data assimilation methods are reviewed. |
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Language |
French |
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Notes |
CropM, ftnotmacsur |
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Call Number |
MA @ admin @ |
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4584 |
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