Optimization of multi-environment trials for genomic selection based on crop models.

ABSTRACT:

Key message

We propose a statistical criterion to optimize multi-environment trials to predict genotype × environment interactions more efficiently, by combining crop growth models and genomic selection models. Genotype × environment interactions (GEI) are common in plant multi-environment trials (METs). In this context, models developed for genomic selection (GS) that refers to the use of genome-wide information for predicting breeding values of selection candidates need to be adapted. One promising way to increase prediction accuracy in various environments is to combine ecophysiological and genetic modelling thanks to crop growth models (CGM) incorporating genetic parameters. The efficiency of this approach relies on the quality of the parameter estimates, which depends on the e

SUBMITTER: Rincent R 

PROVIDER: S-EPMC5511605 | biostudies-literature | 2017 Aug

REPOSITORIES: biostudies-literature