Biometrical Models for Predicting Future Performance in Plant Breeding.

The plant breeding process begins with the selection o f parents and crosses. Promising progeny from these crosses progress through a series o f selection stages that typically culminate in multi-environment trials. I evaluated best linear unbiased predictors (BLUP). other predictors and prediction models at the initial (cross prediction), early replicated testing and late (multi-location) stages o f a sugarcane breeding selection cycle. Model and predictor accuracy was assessed in the first two stages by using cross-validation procedures. I compared statistical models o f progeny test data in their ability to predict the cross performance o f untested sugarcane crosses. Random parental effect predictors and a random cross effect predictors were compared to mid-parent values (MPV) derived from a fixed female-male parental effect model. The cross effect model was evaluated with and without incorporating the genetic relationships among tested crosses into the BLUP derivation. Models with BLUP-based predictors showed smaller mean square prediction error and higher fidelity o f top cross identification than the MPV for all traits evaluated. The MP-BLUP was consistently the best one. Prediction o f per se (genotype) performance is needed during the selection process and requires combining information from different trials. The study investigated three mixed models involving three versions o f BLUPs estimated under different strategies, a fixed least squares genotype means model, and four check-based methods for combining information at early replicated stages. BLUP-based predictors