In general, estimates of broad-sense heritability, selective accuracy, and predicted selection gains were slightly higher when obtained using MTM and RRM. Genetic variability between maize hybrids were assessed for GY. Moreover, for RRM the genetic effects were modeled by Legendre polynomials of order two. The selected MTM, RRM, and CSM models fit heterogeneous residuals. Genetic gains were predicted considering four selection intensities (5, 10, 15, and 20 hybrids). The best fit MTM, RRM, and CSM were identified by the Akaike information criterion (AIC), and the significance of the genetic effects were tested using the likelihood ratio test (LRT). Variance components were estimated by restricted maximum likelihood (REML), and genetic values were predicted by best linear unbiased prediction (BLUP). For this, a data set with 84 maize hybrids evaluated across four environments for the trait grain yield (GY) was used. Thus, the objective of this study was to compare the effectiveness of multiple-trait models (MTM), random regression models (RRM), and compound symmetry models (CSM) in the analysis of multi-environment trials (MET) in maize breeding.
An efficient and informative statistical method to analyze genotype-by-environment interaction (G圎) is needed in maize breeding programs.