Systematic analysis of site-specific yield distributions resulting from nitrogen management and climatic variability interactions
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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.
KeywordsNitrogen management Climatic variability LARS-WG Weather Generator STICS Soil-crop model Pearson system Probability risk assessment
The authors would like to thank the SPW (DGARNE – DGO-3) for its financial support for the project entitled ‘Suivi en temps réel de l’environnement d’une parcelle agricole par un réseau de microcapteurs en vue d’optimiser l’apport en engrais azotés’. The authors would also like to thank the OptimiSTICS team for allowing them to use the Matlab running code of the STICS model. The authors are very grateful to CRA-W, especially the Systèmes agraires, Territoire et Technologies de l’Information unit, for providing them with the Ernage station climatic database. The authors would thank Giles Collinet and Robert Oger, for their respective contribution to the field experiments and to the paper. Finally, the authors are thankful to the MACSUR knowledge hub within which the co-authors shared their experiences for this research.
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