Casadebaig, Pierre; Debaeke, Philippe; Wallach, Daniel; AGroécologie, Innovations, teRritoires  ; Institut National Polytechnique   ; Université de Toulouse -Université de Toulouse -Ecole d'Ingénieurs de Purpan  ; Institut National Polytechnique   ; Université de Toulouse -Université de Toulouse -Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement; ANR-11-BTBR-0005,SUNRISE,Ressources génétiques de tournesol pour l'amélioration de la stabilité de production d'huile sous c

A new approach to crop model calibration | A new approach to crop model calibration: Phenotyping plus post‐processing

2020

Casadebaig, Pierre | Debaeke, Philippe | Wallach, Daniel | AGroécologie, Innovations, teRritoires (AGIR) ; Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Ecole d'Ingénieurs de Purpan (INP - PURPAN) ; Institut National Polytechnique (Toulouse) (Toulouse INP) ; Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | ANR-11-BTBR-0005,SUNRISE,Ressources génétiques de tournesol pour l'amélioration de la stabilité de production d'huile sous c(2011)

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Английский. Crop models contain a number of genotype‐dependent parameters, which need to be estimated for each genotype. This is a major difficulty in crop modeling. We propose a hybrid method for adapting a crop model to new genotypes. The genotype‐dependent parameters of the model could be obtained by phenotyping (or gene‐based modeling). Then, field data (e.g., from variety trials) could be used to provide a simple empirical correction to the model, of the form a + b × an environmental variable. This approach combines the advantages of phenotyping, namely that the genotype‐specific parameters have a clear meaning and are comparable between genotypes, and the advantages of fitting the model to field data, namely that the corrected model is adapted to a specific target population. It has the advantage of being very simple to apply and furthermore gives useful information as to which environmental variables are not fully accounted for in the initial model. In this study, this empirical correction is applied to the SUNFLO crop model for sunflower (Helianthus annuus L.), using field data from a multi‐environment trial network. The empirical correction reduced mean squared error, on average, by 54% for prediction of yield and by 26% for prediction of oil content, compared with the initial model. Most of the improvement came from eliminating bias, with some further improvement from the environmental term in the regression.

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