GGE Biplot vs. AMMI Analysis of Genotype-by-Environment Data

By Weikai Yan, Manjit S. Kang, Baoluo Ma, S. Woods, P. Cornelius. 2007

1,482 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/cccc26476f619838cff07b9a266f0f8af4cd42fa

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Citations in the corpus

Genotype × Environment interaction and stability analyses of yield and yield components of established and mutant rice genotypes tested in multiple locations in Malaysia* (2017, methodology): “The ‘which-wonwhere’ pattern proposed by Yan et al. (2000) is an effective tool for visualization of the patterns between genotypes and environments interaction (Yan et al. 2007).”
EVALUATION OF EXPERIMENTAL MAIZE HYBRIDS TESTED IN MULTI- LOCATION TRIALS USING AMMI AND GGE BIPLOT ANALYSES (2012, methodology): “According to Yan et al. (2007), AMMI biplot can not be accurately presented, especially when performances of individual genotypes in certain environments are observed, and are necessary to use AMMI2 charts for assessment of a so called “which won where“ model.”
Genotype by environment interactions in forest tree breeding: review of methodology and perspectives on research and application (2017, methodology): “Biplot analysis combines analysis of variance (ANOVA) and principal component analysis (PCA) and allows visualisation of results (Gauch 1992; Mandel 1971; Yan et al. 2007).”
Specific adaptation of barley varieties in different locations in Ethiopia (2009, background): “The nature of GEI that cause the crossovers among winning genotypes can be positively exploited by selecting diVerent cultivars for the two mega-environments (Ceccarelli et al. 1998; Annicchiarico et al. 2005; Yan et al. 2007).”
Analysis of Genotype × Environment Interaction (G×E) Using SAS Programming (2016, methodology): “…genotype scores, enhancing the biplot for comparing genotypes, and a value of 2 indicates Agronomy Journa l • Volume 108, Issue 5 • 2016 1851 that singular values are partitioned into environment scores, enhancing the biplot for visualizing the relationships among environments (Yan et al., 2007).”
Statistical analysis of yield trials by AMMI analysis of genotype × environment interaction (2014, methodology): “Applications of the AMMI model to yield trials have appeared frequently during the last two decades, and there have been several recent review articles (Dias and Krzanowski, 2006; Gauch, 2006; Yan et al., 2007; Gauch et al., 2008; Yan et al., 2009; Gauch, 2013; Rodrigues et al., 2014).”
AMMI and SREG GGE biplot analysis for matching varieties onto soybean production environments in Ethiopia (2009, methodology): “GGE biplots use the primary and secondary effects from SREG analysis and are useful in maga-environment analysis, test environments, and genotypes evaluation (Yan et al. 2007).”
Biplot Analysis of Genotype × Environment Interaction: Proceed with Caution (2009, methodology): “The discussion from recent works suggests, however, that either genotypic or environmental eff ects (or thus interaction eff ects) should be random (Baker, 1996; Piepho, 1998; Smith et al., 2005; Yang 2007).”
GGE Biplot Analysis of Yield Stability in Multi-environment Trials of Lentil Genotypes under Rainfed Condition (2013, methodology): “Detailed accounts of different models have been presented by Yan and Kang (2002) and Yan et al. (2007).”
AMMI and GGE Biplot for genotype × environment interaction: a medoid–based hierarchical cluster analysis approach for high–dimensional data (2018, methodology): “Also it has to be considered that, as mentioned by Yan et al. (2007), G and GE are first of all mathematical definitions.”
GGE Biplot and AMMI Analysis of Yield Performance of Barley Genotypes across Different Environments in Iran (2014, methodology): “Because of the inner-product property of the biplot, the projections of the genotype markers on the “average environment axis” are proportional to the rank-two approximation of the genotype means representing the main effects of the genotypes (Yan et al., 2007).”