Abstract
Abstract: The present study aimed to evaluate the applicability and efficiency of the FAI-BLUP index in the genetic selection of maize hybrids, using 84 maize hybrids that were evaluated for cycle, morphology, and yield traits in four environments. Models accounting for homogeneous and heterogeneous residual variances were tested, and variance components were estimated using the residual maximum likelihood. Genotypic values were predicted by best linear unbiased prediction, and factor analysis was applied to group the traits. The FAI-BLUP index was used for the selection of maize hybrids based on ideotype design. Three factors explained more than 70% of genotypic variability, with selective accuracies varying from low (0.46) to high (0.99). Predicted genetic gains were positive for traits related to yield and negative for traits related to cycle and morphology, as is desirable in maize crop.
Highlights
The growing demand for superior genotypes has led maize breeders to seek auxiliary techniques in the selection process
Multivariate analysis, which allows genetic selection based on a set of traits, is an important procedure when dealing with multi-environment trials (MET) data
The superior model that accounted for a specific residual variance structure for each trait, i.e., the model that presented the lowest Bayesian information criterion (BIC) value, was applied for the estimation of variance components and for the prediction of genotypic values
Summary
The growing demand for superior genotypes has led maize breeders to seek auxiliary techniques in the selection process. Multi-environment trials (MET) are useful for evaluating genotypes, testing their performance in a range of environments, and selecting the most superior (Alves et al 2020). Various traits have been evaluated in maize breeding, with the aim of supporting the selection and recommendation of the ideotype (i.e., genotypes with simultaneous superior performance in many traits). Multivariate analysis, which allows genetic selection based on a set of traits, is an important procedure when dealing with MET data. This multi-trait selection is relevant, as superior varieties combine optimal attributes for several traits simultaneously.
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