Regression and AMMI Analyses of Genotype × Environment Interactions: An Empirical Comparison

Abstract

Joint regression analysis (JRA) is a popular method for analyzing genotype × environment (G × E) interactions, but multivariate techniques such as AMMI (additive main effects and multiplicative interaction) analysis have been recently advocated. The objective of this study was to investigate and compare empirically the effectiveness of the two techniques under differing environmental diversity and numbers of environments, and when log-transformed data were used for JRA. I analyzed grain yield data from three seasons of a regional bread wheat (Triticum aestivum L.) yield trial, grown at 30 to 40 sites. Sites were split into irrigated-high rainfall and rainfed-low rainfall groups. Three equal-size samples differing in environmental diversity and three similar diversity samples differing in numbers of sites were also formed. Both raw and log10-transformed data were used for JRA. The fitting mode of the MATMODEL program (version 2.0) was used on raw data for AMMI analysis. Percentages of interaction sum of squares (SS) accounted for by heterogeneity of regression in JRA were generally low (mean = 11%) and unaffected by diversity of the samples, but inversely related to number of sites in the similar-diversity samples. In contrast, percentages of interaction SS accounted for by first principal components in AMMI analyses were generally high (mean = 37%) and unaffected by diversity or number of sites in the samples. These percentages were always higher for AMMI than for JRA, regardless of whether log-transformed data were used for JRA. The use of AMMI is recommended for detailed studies of G × E effects, especially for large regional or international trials.