A Cross-Validation of Statistical Models for Zoned-Based Prediction in Cultivar Testing

Abstract

The principal goals of a plant breeding program are to provide breeders with cultivar information for selection purposes and to provide farmers with high-yielding and stable cultivars. For that reason, multi-environment trials need to be done to predict future cultivar yield, and a robust statistical procedure is needed to provide reliable information on the tested cultivars. In Sweden, the statistical procedure follows the tradition of modeling cultivar effects as fixed. Moreover, the analysis is performed separately by zone and level of fungicide treatment, and so the factorial information regarding cultivar x zone x fungicide combinations is not explored. Thus, the question arose whether the statistical method could be improved to increase accuracy in zone-based cultivar prediction, since the cultivar recommendation is zone based. In this paper, the performance of empirical best linear unbiased estimation (E-BLUE) and empirical best linear unbiased prediction (E-BLUP) are compared using cross-validation for winter wheat (Triticum aestivum L.) and spring barley (Hordeum vulgare L.), in single-year and multiyear series of trials. Data were obtained from three agricultural zones of Sweden. Several linear mixed models were compared, and model performance was evaluated using the mean squared error of prediction criterion. The E-BLUP method outperformed the E-BLUE method in both crops and series. The prediction accuracy for zone-based yield was improved by using E-BLUP because the random-effects assumption for cultivar x zone interaction allows information to be borrowed across zones. We conclude that E-BLUP should replace the currently used E-BLUE approach to predict zone-based cultivar yield.