@article{1075,
author = "Gebeyaw Achenef",
abstract = "The genotypes showed different performances in a different environment known as genotype-environment interaction. This genotype by environment interaction (GEI) is a critical concern to agricultural researchers to improve crop varieties. The existence of G×E interaction makes it difficult to select best performing genotypes during testing of a large number of genotypes and environments over the season and multiple testing sites. The phenotypic response occurs in unpredictable ways across diverse and changing environmental conditions due to the complexity of mechanisms and processes. GEI can be better understood by breeders with the help of analytical tools. In order to make more informed breeding decisions, it is important to use appropriate strategies to analyze genotype by environment interactions. Predicting genotype performance as accurately as possible requires the use of good analytical methods. By modeling the GE interaction, phenotypic stability can be measured in several ways. Methods such as univariate (parametric and non-parametric) and multivariate analysis can be used in these studies. Additive Main Effects and Multiplicative Interactions (AMMI) models combine analysis of variance with principal component analysis for genotype and environment effects. Comparatively to other types of biplots, the GGE biplot displays the Genotype plus Genotype by Environment (GGE) part of a MET data set. Cultivar evaluation requires the use of GGE biplots to display information relevant to the evaluation of cultivars. AMMI and GGE Biplot provide information about genotypes with a broad range of adaptability, as well as the additive main effect and multiplicative interaction (AMMI) methodology. By displaying mega-environments and adaptive responses graphically, AMMI provides greater opportunities than other similar tools. In this review, we evaluated different stability models as well as conventional and new analytical methodologies used to measure genotype by environment interaction.",
issn = "23483997",
journal = "IJRAS",
keywords = "AMMI, GGE Biplot, Multi-Environment, Statistical Model, Stability",
month = "July",
number = "4",
pages = "103-120",
title = "{A}dvancement of {A}nalytical {M}odels quantifying {G} × {E} {I}nteractions and {S}tability analysis in {M}ulti-{E}nvironment {T}ria",
volume = "9",
year = "2022",
}