Statistical Analysis of Yield Trials by AMMI and GGE: Further Considerations

By Hugh G. Jr Gauch, Hans-Peter Piepho, P. Annicchiarico. 2008

628 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fcab47ce0e561c5f5108af0f38cb5b0aacf8a47e

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Citations in the corpus

AMMI and GGE Biplot for genotype × environment interaction: a medoid–based hierarchical cluster analysis approach for high–dimensional data (2018, background): “This difference is the main topic of discussion among authors concerned with the effectiveness of the two models in depicting adaptive responses of genotypes over environments (Yan and Tinker, 2005; Gauch, 2006; Yan and Tinker, 2006; Yan et. al., 2007; Gauch et al., 2008).”
EVALUATION OF EXPERIMENTAL MAIZE HYBRIDS TESTED IN MULTI- LOCATION TRIALS USING AMMI AND GGE BIPLOT ANALYSES (2012, methodology): “…does not offer any advantage to the breeder for genotypic and location evaluation when analyzing METs data (Setimela et al., 2007) In contrast, Gauch et al. (2008) argue that a shortcoming of GGE biplot method for evaluation of the genotypes is in its impossibility to separate G from GE,…”
Biplot Analysis of Genotype × Environment Interaction: Proceed with Caution (2009, methodology): “Yan and Tinker (2006), Yan et al. (2007), and Gauch et al. (2008) have all used the same data set as given in Table 1 to provide detailed descriptions of mega-environment delineation, genotype evaluation and test-environment evaluation based on GGE or AMMI biplots.”
A heritability-adjusted GGE biplot for test environment evaluation (2010, methodology): “Similarly, for genotype evaluation, Gauch et al. (2008) presented a scatter plot of mean versus stability and argued against the GGE biplot approach.”
GGE Biplot Analysis of Yield Stability in Multi-environment Trials of Lentil Genotypes under Rainfed Condition (2013, background): “It may also reduce the selection efficiency in different breeding programs because in a GE interaction, measured traits are less predictable and cannot be interpreted using main effects (genotype or environment) and need more analysis (Gauch et al., 2008).”
Statistical analysis of yield trials by AMMI analysis of genotype × environment interaction (2014, methodology): “The AMMI analysis can be used efficiently in the identification of superior environmental conditions for agricultural exploitation (selection of growing locations) and superior mean performance genotypes (Gauch et al., 2008; Yan, 2010).”
Performance of extra-early maize cultivars based on GGE biplot and AMMI analysis (2011, background): “Several reviews have compared and contrasted AMMI and the GGE biplot with respect to their suitability for G×E analysis (Gauch 2006; Yan & Tinker 2006; Yan et al. 2007; Gauch et al. 2008; Yang et al. 2009).”
Genotype × environment interaction by AMMI and GGE biplot analysis in three consecutive generations of wheat (Triticum aestivum) under normal and drought stress conditions. (2013, methodology): “For the research purpose of gaining accuracy, AMMI and GGE (as well as the shifted multiplicative model, etc.) are all equally capable (Gauch et al, 2008).”
Genotype by environment interaction components underlying variations in root, sugar and white sugar yield in sugar beet (Beta vulgaris L.) (2018, background): “In addition, by adding mega-environment boundaries, breeders can determine whether a test location is predictive for a given environment or else frequently crosses mega-environment boundaries from year to year (Gauch et al. 2008).”
GGE Biplot Analysis for Yield Stability in Multi-environment Trials of Promising Hybrid Rice (Oryza sativa L.) (2015, background): “…E-mail: anowaraa@yahoo.com 2 Akter et al It may also reduce the selection efficiency in different breeding programs because in a GE interaction, measured traits are less predictable and cannot be interpreted using main effects (genotype or environment) and need more analysis (Gauch et al., 2008).”
Genotype × environment interaction and genetic improvement for yield and yield stability of rainfed durum wheat in Iran (2012, neutral): “In recent literature, utility of AMMI analysis and GGE biplot analysis to visualize and interpret GE interaction data is being widely debated (Gauch 2006; Yan et al. 2007; Gauch et al. 2008; Yang et al. 2009).”
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): “Although Gauch et al. (2008) criticized the use of GGE biplot method for decomposing G + GE, however, the advantage of G + GE observation in a biplot graph revealed that this method is a better alternative to the AMMI analysis, in which direct visualization of G effect is not possible in the AMMI2…”