Comparação do GGE biplot-ponderado e AMMI- ponderado com outros modelos de interação genótipo x ambiente

Abstract

Comparison of weighted-GGE biplot and weighted-AMMI with other models of interaction genotype × environment Genotype × environment interaction (GEI) is an extremely important issue in plant breeding and production. The selection and recommendation of superior genotypes are hampered due to the occurrence of GEI and represents a major challenge for researchers. In this context, biplot analyzes have been increasingly used in analyzing agronomic data, in which data are represented by a table of two entries of means of GEI. However, the particularities in the biplot graphic hamper its interpretation, and could lead the researcher to errors. There are several models in the literature for DGE analysis (GEI data), among them, the most used are the AMMI model (Additive Main effects and Multiplicative Interaction Models) and GGE biplot (Genotype main effects + Genotype environment interaction). The AMMI model is a statistical method to understand the structure of interactions between genotypes and environments, combining the analysis of variance and principal component analysis, to adjust, respectively, the main effects (G and E) and the effects of GEI. The GGE Biplot groups genotype of additive effect with multiplicative effect of GEI, and submit these to the principal component analysis. There are two problems in using these models: i) can only be used to analyze MET data (multi-environments), which has a unique feature and ii) whose environments are heterogeneous. This paper aims to propose new W-GGE biplot models (Weighted Genotype main effects + Genotype environment interaction) and AMMI-weighted multi-environments for data analysis, and make a comparison between the existing models as AMMI and GGE biplot; mega-environment analysis; genotype evaluation, test environment within each mega-environment and understand the causes of GEI.

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