Abstract
The aim of this study was to compare the multivariate methods GGE (Genotype main effects and Genotype x Environment interaction) and AMMI (Additive Main effects and Multiplicative Interaction) with the method of Eberhart and Russell for interpreting genotype x environment interaction. The AMMI and GGE analysis explained around 50% of the sum of squares of the genotype x environment interaction, whereas the method of Eberhart and Russell explained only 9.1 and 15.8% each year. The cultivars classified as minor contribution to the genotype x environment interaction by methods of AMMI and GGE were also the same classification method of Eberhart and Russell. The AMMI and the GGE biplot analyses are more efficient than the Eberhart and Russell. The GGE biplot explains a higher proportion of the sum of squares of the GxE interaction and is more informative with regards to environments and cultivar performance than the AMMI analysis.
Highlights
The genotype x environment interaction is important for plant breeding because it affects the genetic gain and recommendation and selection of cultivars with wide adaptability (Deitos et al 2006, Souza et al 2009)
The AMMI and the GGE biplot analyses are more efficient than the Eberhart and Russell
The results revealed the desirability of replacing an organic-soil location with a sand-soil location in the final testing stage of this sugarcane breeding and selection program
Summary
The genotype x environment interaction is important for plant breeding because it affects the genetic gain and recommendation and selection of cultivars with wide adaptability (Deitos et al 2006, Souza et al 2009). Eberhart and Russell (1966) developed a methodology for identifying cultivars with greater adaptability and stability that has been widely used in the identification of genotypes for this purpose (Miranda et al 1998, Grunvald et al 2008). Yan et al (2007) concluded that both GGE biplot analysis and AMMI analysis combine rather than separate G and GE in mega-environment analysis and genotype evaluation. The authors maintain that the GGE biplot is superior to the AMMI1 graph in mega-environment analysis and genotype evaluation because it better explains G+GE and has the innerproduct property of the biplot. Model diagnosis for each dataset is useful, but the accuracy gained from model diagnosis should not be overstated
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