Prediction of genotypic values of maize hybrids in unbalanced experiments
Roberto Fritsche-Neto, Manoel Carlos Gonçalves, Roland Vencovsky, and Cláudio Lopes de Souza Junior
The objective of this study was to evaluate whether the REML/BLUP can be useful for predicting the genotypic values of maize hybrids in a group of unbalanced experiments. A set of 256 single-crosses were evaluated in 13 environments for grain yield, plant height and plant lodging. Sets of hybrids within environments and sets of environments were withdrawn from the experiments to simulate unbalanced data, and the hybrid predictions of the unbalanced data were computed by the REML/BLUP, simulated using the bootstrap resampling procedure. The coefficients of determination and percentage of selection coincidence were computed for the predicted genotypic values of unbalanced data and their means from the balanced data. The REML/BLUP method accurately predicted the genotypic values of missing hybrids under losses of up to 20% of hybrids within environments or a reduction of 23% of the environments, even in the presence of significant and complex hybrid x environment interaction.