Statistical analysis of yield trials by AMMI analysis of genotype × environment interaction

Abstract

SummaryThe genotype by environment interaction (GEI)) has an influence on the selection and recommendation of cultivars. The aim of this work is to study the effect of GEI and evaluate the adaptability and stability of productivity (kg/ha) of nine maize genotypes using AMMI model (Additive Main effects and Multiplicative Interaction). The AMMI model is one of the most widely used statistical tools in the analysis of multiple-environment trials. It has two purposes, namely understanding complex GEI and increasing accuracy. Nevertheless, the AMMI model is a widely used tool for the analysis of multiple-environment trials, where the data are represented by a two-way table of GEI means. In the complete tables, least squares estimation for the AMMI model is equivalent to fitting an additive two-way ANOVA model for the main effects and applying a singular value decomposition to the interaction residuals. It assumes equal weights for all GEI means implicitly. The experiments were conducted in twenty environments, and the experimental design was a randomized complete block design with four repetitions. The AMMI model identified the best combinations of genotypes and environments with respect to the response variable. This paper concerns a basic and a common application of AMMI: yield-trial analysis without consideration of special structure or additional data for either genotypes or environments.Key words: genotype environment interaction (GEI), adaptability and stability, additive main effects and multiplicative interaction model, multienvironment trials

Genotype by environment interaction on yield stability of desi type chickpea (Cicer arietinum L.) at major chickpea producing areas of Ethiopia

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Genotype by Environment Interaction Unravels Influence on Secondary Metabolite Quality in Cassava Infested by Bemisia tabaci

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Grain Yield Performance and Stability of Quality Protein Maize Single Cross Hybrids in Mid-altitude Environment in Uganda

“…Additive main effect and multiplicative interaction (AMMI) and genotype and genotype-by environment (GGE) biplot methodology have recently gained popularity and are most preferred (Yan & Kang, 2003;Badu-Apraku et al, 2013, 2015. AMMI allows exhaustive data analysis by performing regular analysis of variance (ANOVA) and estimating interaction effects through principal component analysis (PCA) which somewhat increases precision in trait estimates and enables reliable selections (Gauch et al, 2008;Hongyu et al, 2014). A complementary analytical tool to visualize GEI is the genotype plus genotype by environment (GGE) biplot (Yan & Tinker, 2002).…”