Abstract
The genotype x environment interaction is frequently observed in many crops and studies on environmental stratification and genotype adaptability have been proposed to understand it. The aim of this study was to carry out factor analysis in data from multi-environment experiments by the mixed model approach (REML/BLUP). Instead of adjusted phenotypic means, a matrix containing the genotypic effects added to the effects of the genotype x environment interaction (G+GE) was used, predicted via REML/BLUP in joint analysis (designated as R-FGGE). In the study, data from 36 common bean lines evaluated in 15 environments were used. By this proposal, 46.7% of the environments were gathered in two groups, one with four and the other with three environments. The R-FGGE has the same characteristics as the previous proposals, that is, ease of identification of mega-environments and genotypes with broad adaptability, along with the advantages associated with the mixed model methodology.
Highlights
Plant breeders consistently come up against the phenomenon of the genotype x environment interaction (GxE) in the activities of breeding programs, especially in the phases of evaluating genotypes for recommendation to producers
The genotype x environment interaction is frequently observed in many crops and studies on environmental stratification and genotype adaptability have been proposed to understand it
The aim of this study was to carry out factor analysis in data from multi-environment experiments by the mixed model approach (REML/Best Linear Unbiased Predictions (BLUPs))
Summary
Plant breeders consistently come up against the phenomenon of the genotype x environment interaction (GxE) in the activities of breeding programs, especially in the phases of evaluating genotypes for recommendation to producers. Silva et al (2011) mentioned that methods that use a multivariate approach, such as the Additive Main Effects and Multiplicative Interaction (AMMI) model (Zobel et al 1988) and Genotype plus Genotype x Environment (GGE) biplot (Yan et al 2000), more properly explain the main effects (genotypes and environments) and their interaction. Another multivariate method, proposed by Murakami and Cruz (2004), is factor analysis of the matrix of the adjusted phenotypic mean values of the genotypes in the environments. The authors mentioned that this modification resulted in more precise estimates
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