Two Types of GGE Biplots for Analyzing Multi‐Environment Trial Data

By Weikai Yan, P. Cornelius, Jose Crossa, L. A. Hunt. 2001

410 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/09b88cdc4535161d7d7900350e682bdcb1ca903f

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In Genetics

In Plant Science

Citations in the corpus

Biplot Analysis of Genotype by Environment Interaction for Barley Yield in Iran (2006, background): “The first is to display the “which-won-where” pattern of data that may lead to the identification of high-yielding and stable cultivars and the second is to identify discriminating and representative test environments ( Yan et al., 2001 ).”
GGE-Biplot Analysis of Multi-Environment Yield Trials in Bread Wheat (2006, methodology): “Visualization of the which-won-where pattern of MEYTs data is important for studying the possible existence of different mega-environments (ME) in a region (Gauch and Zobel, 1997; Yan et al., 2000, 2001).”
Performance of extra-early maize cultivars based on GGE biplot and AMMI analysis (2011, methodology): “The GGE biplot explained 0·74 of the G+GE, while the AMMI analysis captured 0·13 of the total G + GE, indicating that GGE biplot accounted for more of G+GE variation in grain yield of extra-early cultivars than AMMI graph.”
Integrating different stability models to investigate genotype × environment interactions and identify stable and high-yielding barley genotypes (2019, background): “This technique offers a powerful solution for determining the pattern of genotypic responses across environments in METs S. Ghasemi Safiabad Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization (AREEO), Dezful, Iran H. Moradkha”
Biplot Analysis of Genotype × Environment Interaction: Proceed with Caution (2009, background): “Furthermore, as Yan et al. (2001) pointed out, the which-won-where patterns are identifi able only if the target mega-environment is adequately sampled and if the correlation between the genotypic PC1 scores and the genotype main eff ects is positive and almost perfect (>0.95).”
Biplot analysis of multi-environment trial data. (2020, methodology): “The recently developed GGE-biplot method (Yan et al., 2000, 2001) provides a more elegant and useful display of MET data.”
Singular‐Value Partitioning in Biplot Analysis of Multienvironment Trial Data (2002, methodology): “…scaling partitions the among genotypes, which facilitate comparison among entire SV to the genotype eigenvectors; environment-focused scaling genotypes and genotype ranking on both mean yield and partitions the entire SV to the environment eigenvectors; symmetrical stability (Yan et al., 2001).”
Soybean Maturity Groups, Environments, and Their Interaction Define Mega-environments for Seed Composition in Argentina (2006, methodology): “For each biplot, the most responsive MG in explaining MG and MG 3 E interactions were identified by drawing a convex polygon on vertex MG, which was sectored as suggested by Yan et al. (2001) to identify ME. All analyses were performed independently for each crop year data, and the consistencies a”
Statistical Analysis of Yield Trials by AMMI and GGE (2006, background): “3. Predictable GE9 associated with locations increases the number and value of megaenvironments, whereas unpredictable GE9 associated with years decreases this number (Gauch, 1992, p. 217– 230; Annicchiarico, 1997a, 1997b; Annicchiarico et al., 2000; Atlin et al., 2000).”
Biplot Analysis of Test Sites and Trait Relations of Soybean in Ontario. (2002, background): “As a rule, the vertex genotype in each sector is the bestRESULTS AND DISCUSSION genotype at sites whose markers fall into the respective Yearly Genotype by Site Relationships sector (Yan, 1999; Yan et al., 2000, 2001).”
Genotype by environment interactions in barley (Hordeum vulgare L.): different responses of landraces, recombinant inbred lines and varieties to Mediterranean environment (2008, background): “Another model that has gained importance in investigating the role of genotype, environment and GEI effects in yield-trial experiments is the genotype main effects and genotype 9 environment interaction (GGE) biplot ( Yan et al. 2000, 2001, 2007; Yan and Kang 2002; Yan and Tinker 2005; Samonte et a”
Application GGE biplot and AMMI model to evaluate sweet sorghum (Sorghum bicolor) hybrids for genotype ◊ environment interaction and seasonal adaptation (2011, background): “Usually a large number of genotypes are tested across a number of sites, seasons and years, and it is often difficult to determine the pattern of genotypic response across locations or seasons without the help of graphical display of the data (Yan et al. 2001).”