Statistical Analysis of Yield Trials by AMMI and GGE

By Hugh G. Jr Gauch. 2006

789 citations recorded by the source corpus.

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In Plant Science

Citations in the corpus

GGE biplot analysis of genotype × environment interaction in wheat-barley disomic addition lines. (2012, methodology): “…of determination (Pinthus, 1973), coefficient of variability (Francis and Kanneberg, 1978) and additive main effects and multiplicative interaction (AMMI) (Gauch and Zobel, 1988; Zobel et al., 1988; Gauch 1992; 2006) have been commonly used to analyze MET data to reveal patterns of GE interaction.”
GGE Biplot Analysis for Yield Stability in Multi-environment Trials of Promising Hybrid Rice (Oryza sativa L.) (2015, methodology): “Genotype × environment (GE) interaction refers to different ranking of genotypes across environments and may complement the selection process and recommendation of a genotype for a target environment (Ebdon and Gauch, 2002; Gauch, 2006).”
Achievements and limitations of contemporary common bean breeding using conventional and molecular approaches (2009, methodology): “Singular value decomposition analyses such as GGE biplots (Gabriel 1971; Yan et al. 2000), Additive Main Effects and Multiplicative Interaction (AMMI) (Gauch 1992), and Principal Component Analysis (PCA), are often used to better understand G 9 E interaction (Gauch 2006).”
PERCEPTIONS AND ADAPTATIONS OF BEEKEEPERS AND HONEY HUNTERS TO CLIMATE CHANGE: THE CASE OF THE COMMUNES OF NATITINGOU AND TANGUIETA IN NORTHWEST OF BENIN (2012, positive): “Therefore, to determine the pattern of genotype response to environment and prioritise genotypes for use in a breeding programme, quantification of genotype by environment interactions is necessary (Gauch, 2006).”
GGE Biplot Analysis of Yield Stability in Multi-environment Trials of Lentil Genotypes under Rainfed Condition (2013, background): “Plant breeders perform MET to select favorable genotypes based on both mean yield and performance stability; and to determine whether a test environment is homogeneous or should be divided into various mega-environments (Gauch, 2006; Yan and Kang, 2002).”
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): “Two frequently used statistical analyses are the additive main effects and multiplicative interaction (AMMI) model and the genotype main effects and genotype × environment interaction effects (GGE) model (Gauch, 2006).”
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).”
Analysis of Genotype × Environment Interaction (G×E) Using SAS Programming (2016, methodology): “Proponents of the AMMI and GGE biplot methods disagree on the best method for analyzing multi-environment trial data (Gauch, 2006; Yan et al., 2007), although the two methods provide similar results (Gauch, 2006).”
GGE Biplot vs. AMMI Analysis of Genotype-by-Environment Data (2007, methodology): “2 and 3) does reseparate G from GE whenever G is sizable, as pointed out by Gauch (2006); however, as discussed in the previous sections, it partitions GGE in a way that genotype evaluation and test-environment evaluation can be visually addressed in terms familiar to researchers without sacrifi…”
AMMI and SREG GGE biplot analysis for matching varieties onto soybean production environments in Ethiopia (2009, methodology): “The application of these models for explaining GEI and analyzing the performance of genotypes and test environments have been very frequent among plant breeders in recent years (Gauch, 2006; Yan et al., 2007).”
Biplot Analysis of Genotype × Environment Interaction: Proceed with Caution (2009, methodology): “As demonstrated constantly in recent reviews (e.g., Yan and Tinker, 2006; Gauch, 2006; Yan et al., 2007; Gauch et al., 2008), visual information displayed in a given biplot is indeed easier and faster to convey and grasp than tabular numerical information, thereby facilitating appropriate…”
AMMI and GGE Biplot for genotype × environment interaction: a medoid–based hierarchical cluster analysis approach for high–dimensional data (2018, methodology): “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).”
Genotype by environment interactions in barley (Hordeum vulgare L.): different responses of landraces, recombinant inbred lines and varieties to Mediterranean environment (2008, methodology): “Data were analysed by using the AMMI model ( Gauch 1988, 2006; Zobel et al. 1988):”