Swedish MET locations can be stratified into zones, allowing borrowing information between zones when best linear unbiased prediction (BLUP) is used. Thus, in this study, we demonstrate the benefit of using environmental information (covariates) for predicting genotype performance in some new locations for Swedish winter wheat official trials. Besides, the precision can be improved when auxiliary information is available to characterize the targeted locations. Moreover, the precision of the predictions is of primary concern and should be assessed. Linear mixed modelling can provide predictions for new locations. the grower’s locations, which hardly ever coincide with the locations at which the trials were conducted. From a grower’s perspective, MET results must provide high accuracy and precision for predictions of genotype performance in new locations, i.e. Multi-environment trials (MET) are conducted to assess the performance of a set of genotypes in a target population of environments. We propose the utilisation of environmental covariates in random coefficient models to predict the genotype performances in new locations.
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