Accuracy of prediction from multi-environment trials to new locations using pedigree information and environmental covariates

Abstract Plant breeding trials are usually conducted across multiple testing locations to predict genotype performances in the targeted population of environments. The predictive accuracy can be increased by the use of adequate statistical models. We compared models with and without synthetic covariates (SC) and pedigree information under the identity, the diagonal and the factor-analytic variance-covariance structures of the genotype-by-location interactions. The model comparison was made to evaluate predictive accuracy of different models in predicting genotype performances in untested locations using the mean squared error of predicted differences (MSEPD) and the Spearman rank correlation between predicted and adjusted means. A multi-environmental trial (MET) dataset evaluated for yield performance in the dry low-land sorghum (Sorghum bicolor (L.) Moench) breeding program of Ethiopia was used. For validating our models, we followed a leave-one-location-out cross-validation strategy. A total of 65 environmental covariates (ECs) obtained from the sorghum test locations were considered. From the actual ECs, SC were first extracted using multivariate partial least squared analysis. Then, the model was fitted accounting for pedigree information by linear mixed models. According to MSEPD, our results indicate that models accounting for SC improve prediction precision of genotype performances in the three of the variance-covariance structures compared to others without SC. The rank correlation was also higher for the model with the SC. When the SC was fitted, the rank correlation was 0.58 for the factor-analytic, 0.51 for the diagonal and 0.46 for the identity variance-covariance structure.