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Author |
Dumont, B.; Ferrandis, V., S.; Leemans, V.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
Title |
Vers un système de prédiction du rendement en temps réel |
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Conference Article |
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2012 |
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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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Dumont, B.; Basso, B.; Leemans, V.; Destain, J.P.; Bodson, B.; Destain, M.-F. |
Title |
Yield variability linked to climate uncertainty and nitrogen fertilisation |
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Conference Article |
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2013 |
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9th ECPA - European Conference on Precision Agriculture, Lleida, Spain, 2013-07-07 to 2013-07-11 |
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MA @ admin @ |
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2411 |
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Dumont, B.; Basso, B.; Leemans, V.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
Title |
A Site-Specific Grain Yield Response Surface : Computing the Identity Card of a Crop Under Different Nitrogen Management Scenarios |
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Conference Article |
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2013 |
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CropM |
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Proceedings of the EFITA-WCCA-CIGR 2013 - Sustainable Agriculture through ICT innovation, 2013-11-03 to 2013-11-06, Torino |
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MA @ admin @ |
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2408 |
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Dumont, B.; Basso, B.; Leemans, V.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
Title |
Yield variability linked to climate uncertainty and nitrogen fertilisation |
Type |
Book Chapter |
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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MA @ admin @ |
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2410 |
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Dumont, B.; Basso, B.; Leemans, V.; Bodson, B.; Destain, J.-P.; Destain, M.-F. |
Title |
A comparison of within-season yield prediction algorithms based on crop model behaviour analysis |
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Journal Article |
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2015 |
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Agricultural and Forest Meteorology |
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Agricultural and Forest Meteorology |
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204 |
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10-21 |
Keywords |
stics crop model; climate variability; lars-wg; yield prediction; log-normal distribution; convergence in law theorem; central limit theorem; weather generator; nitrogen balances; generic model; wheat; simulation; climate; stics; variability; skewness; efficiency |
Abstract |
The development of methodologies for predicting crop yield, in real-time and in response to different agro-climatic conditions, could help to improve the farm management decision process by providing an analysis of expected yields in relation to the costs of investment in particular practices. Based on the use of crop models, this paper compares the ability of two methodologies to predict wheat yield (Triticum aestivum L.), one based on stochastically generated climatic data and the other on mean climate data. It was shown that the numerical experimental yield distribution could be considered as a log-normal distribution. This function is representative of the overall model behaviour. The lack of statistical differences between the numerical realisations and the logistic curve showed in turn that the Generalised Central Limit Theorem (GCLT) was applicable to our case study. In addition, the predictions obtained using both climatic inputs were found to be similar at the inter and intra-annual time-steps, with the root mean square and normalised deviation values below an acceptable level of 10% in 90% of the climatic situations. The predictive observed lead-times were also similar for both approaches. Given (i) the mathematical formulation of crop models, (ii) the applicability of the CLT and GLTC to the climatic inputs and model outputs, respectively, and (iii) the equivalence of the predictive abilities, it could be concluded that the two methodologies were equally valid in terms of yield prediction. These observations indicated that the Convergence in Law Theorem was applicable in this case study. For purely predictive purposes, the findings favoured an algorithm based on a mean climate approach, which needed far less time (by 300-fold) to run and converge on same predictive lead time than the stochastic approach. (C) 2015 Elsevier B.V. All rights reserved. |
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English |
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0168-1923 |
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CropM |
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MA @ admin @ |
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4647 |
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