Accuracy of Multi-Environmental Trials in Predicting New Environments Using Different Approaches Based on Environmental Covariates: A Case in Barley (Hordeum vulgare L.) Breeding

Abstract

One of the current innovations in predicting genotype performances in a target population of environments is integrating environmental covariates (ECs) into multi-environment trial (MET) data analysis. In this study, a MET data set of a barley (Hordeum vulgare L.) breeding program in the years 2016 and 2017 was used. We evaluated and compared different approaches of using ECs in predicting genotype performances into new environments. The comparison was done using the mean squared error of predicted differences (MSEPD) under different linear mixed models. The MSEPD was computed for the new environments using a cross-validation mechanism that drops out one environment at a time. Our results show that models with ECs resulted in smaller MSEPD compared with the model without ECs. Among the different approaches, the reduced rank regression approach with one component resulted in the smallest MSEPD followed by fitting both the first and the second synthetic covariates of the extended Finlay–Wilkinson regression. Overall, there is a potential gain in predictive accuracy in MET with integrating ECs in plant breeding programs.