Evaluation of maize hybrids and environmental stratification by the methods AMMI and GGE biplot

Rogério Lunezzo De Oliveira,Renzo Garcia Von Pinho,Denys Vitor Ferreira,Márcio Balestre

doi:10.1590/s1984-70332010000300010

Abstract

The purpose of this study was to evaluate yield stability, adaptability and environmental stratification by the methods AMMI (Additive Main Effects and Multiplicative Interaction Analysis) and GGE (Genotype and Genotypes by Environment Interaction) biplot and to compare the efficiency of these methods. Data from the evaluation of 20 experimental single-cross and three commercial hybrids and 11 locations, in two growing seasons, 2005/2006 and 2006/2007 were used. Analyses of variance, adaptability, stability and environmental stratification were performed. A better combination of adaptability and stability was observed in the hybrids 10 and 16, according to the graphics of AMMI and GGE biplot methods, respectively. The number of locations could be reduced by 28% based on stratification. The predictive correlation of the AMMI and GGE methods was 0.88 and 0.86, respectively. The results showed that it is possible to reduce the number of evaluation sites; AMMI tended to be more accurate than GGE analysis.

Highlights

IntroductionThere are several methodologies to assess the genotype by environment interaction (GE), of which the most commonly used are based on simple and multiple regression
The genotype by environment interaction (GE) may be reduced using specific cultivars for each environment or using cultivars with wide adaptability and good stability or by stratifying the region under study in megaenvironments with similar environmental characteristics, within which the interaction becomes insignificant (Terasawa Júnior et al 2008).There are several methodologies to assess the GE, of which the most commonly used are based on simple and multiple regression
The AMMI model has been used for environmental stratification, and stratification based on the winning

Summary

Introduction

There are several methodologies to assess the GE, of which the most commonly used are based on simple and multiple regression. Crossa (1990) argues that linear regression analysis is not informative if linearity fails, is highly dependent on the group of genotypes and environments included, and tends to simplify response models explaining the variation caused by interaction in one dimension, when it can be quite complex. Crossa (1990) suggested that the application of multivariate methods can be useful to better exploit the information contained in the data. He proposed techniques such as principal component analysis (PCA), cluster analysis and the AMMI procedure (Additive Main Effects and Multiplicative Interaction Analysis), which have gained wide application in recent years. The AMMI model has been used for environmental stratification, and stratification based on the winning genotypes has been more efficient than of other stratification methods (Pacheco et al 2003)

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Results