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AMMI (Additive Main Effects and Multiplicative Interaction ) using statgenGXE package in R software

Adaptability and yield stability are important measures for the effective cultivation of a crop species in different agro-climatic regions. The stability and adaptability of genotypes across different environments have been assessed through the application of various statistical tools such as joint regression, stability models, additive main effects, and multiplicative interaction (AMMI), and genotype main effects in addition to genotype by environment interaction (GGE) biplots. AMMI and GGE biplots are the most effective and commonly used multivariate models for the analyses of stability, adaptability, and ranking of genotypes and for selecting suitable mega environments. Both models integrate a principal component analysis (PCA) and biplot for the explanation of genotype by environment interaction (G×E). The AMMI model combines analysis of variance (ANOVA) and PCA for the stability analysis of genotypes in a multi-environment trial (MET) dataset .The AMMI stability value (ASV) is derived from the interaction principal component (IPCA1 and IPCA2) scores of the AMMI model , which is used to select the most stable genotypes across environments. In AMMI analysis, low ASV indicates high stability of genotypes across environments. However, stable genotypes may not have high mean yield performance. Genotype selection index (GSI) was developed for the selection of the best genotype, which has both high mean performance and stability. Low GSI values indicate high mean performance and stability of genotypes. The GGE biplot combines two important sources of variation in MET (Genotype and G×E). It is used for mega environment analysis (“Which- Won-Where” pattern), evaluation of genotype (ranking biplot), and environment (comparison biplot), which provides discriminating power and representation of the environments .