EMPIRICAL BEST LINEAR UNBIASED PREDICTION IN CULTIVAR TRIALS USING FACTOR-ANALYTIC VARIANCE-COVARIANCE STRUCTURES

Results of multi-environment trials to evaluate new plant cultivars ma y be displayed in a two-way table of genotypes by environments. Differ ent estimators are available to fill the cells of such tables. It has been shown previously that the predictive accuracy of the simple genot ype by environment mean is often lower than that of other estimators, e.g. least-squares estimators based on multiplicative models, such as the additive main effects multiplicative interaction (AMMI) model, or empirical best-linear unbiased predictors (BLUPs) based on a two-way a nalysis-of-variance (ANOVA) model. This paper proposes a method to obt ain BLUPs based on models with multiplicative terms. It is shown by cr oss-validation using five real data sets (oilseed rape, Brassica napus L.) that the predictive accuracy of BLUPs based on models with multip licative terms may be better than that of least-squares estimators bas ed on the same models and also better than BLUPs based on ANOVA models .