Can current crop models be used in the phenotyping era for predicting the genetic variability of yield of plants subjected to drought or high temperature?
2014
Parent, Boris | Tardieu, Francois, F. | Écophysiologie des Plantes sous Stress environnementaux (LEPSE) ; Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro) | ANR-10-BTBR-01 | ANR-10-BTBR-0001,AMAIZING,Développer de nouvelles variétés de maïs pour une agriculture durable: une approche intégrée de la génomique à la sélection(2010) | European Project: 244374,EC:FP7:KBBE,FP7-KBBE-2009-3,DROPS(2010)
International audience
Afficher plus [+] Moins [-]anglais. A crop model with genetic inputs can potentially simulate yield for a large range of genotypes, sites, and years, thereby indicating where and when a given combination of alleles confers a positive effect. We discuss to what extent current crop models, developed for predicting the effects of climate or cultivation techniques on a reference genotype, are adequate for ranking yields of a large number of genotypes in climatic scenarios with water deficit or high temperatures. We compare here the algorithms involved in 19 crop models. Marked differences exist in the representation of the combined effects of temperature and water deficit on plant development, and in the coordination of these effects with biomass production. The current literature suggests that these differences have a small impact on the yield prediction of a reference genotype because errors on the effects of different traits compensate each other. We propose that they have a larger impact if the crop model is used in a genetic context, because the model has to account for the genetic variability of studied traits. Models with explicit genetic inputs will be increasingly feasible because model parameters corresponding to each genotype can now be measured in phenotyping platforms for large plant collections. This will in turn allow prediction of parameter values from the allelic composition of genotypes. It is therefore timely to adapt crop models to this new context to simulate the allelic effects in present or future climatic scenarios with water or heat stresses.
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