GENETIC MODELS FOR PREDICTING MAIZE SINGLE-CROSS PERFORMANCE IN UNBALANCED YIELD TRIAL DATA

Methods for predicting single-cross performance would facilitate maize (Zea mays L.) hybrid development. The objective of this study was to evaluate different genetic models for best linear unbiased prediction (BLUP) of single-cross performance in unbalanced yield data. Sixty-sev en single crosses between nine Iowa Stiff Stalk Synthetic (SSS) and 16 non-SSS inbreds were evaluated separately in 31 different sets of mul tilocation yield trials from 1989 to 1993. Sets of p single crosses we re chosen randomly as predictor hybrids. Yields of the remaining m = ( 67 - p) missing single crosses were predicted as y(m) = C-MP C-PP(-1) y(P), where y(M) = m x 1 vector of predicted yields; C-MP = m x p matr ix of genetic covariances between the missing and predictor hybrids; C -PP = p x p phenotypic covariance matrix among the predictor hybrids; and y(P) = p x 1 vector of average yields of the predictor hybrids, co rrected for yield trial effects. Correlations between predicted and ob served single-cross yields in the unbalanced data set ranged from 0.58 3 to 0.749. The correlations ranged from 0.388 to 0.493 when the missi ng and predictor hybrids had no parental inbreds in common, suggesting that BLUP may be useful for preliminary screening of single crosses b etween inbreds that have not been tested in any hybrid combination. Pr ediction of specific combining ability (SCA) was not effective because SCA variance was small. Genetic models that included additive x addit ive epistasis did not lead to better predictions of single-cross perfo rmance compared with the model that included only testcross additive a nd SCA effects.