GGEbiplot—A Windows Application for Graphical Analysis of Multienvironment Trial Data and Other Types of Two-Way Data

By Weikai Yan. 2001

1,317 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/a00658a570a627dc1d50bc460c7e9f153b727fb0

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In Genetics

In Plant Science

Citations in the corpus

Identifying Discriminating Locations for Cultivar Selection in Louisiana (2006, methodology): “In addition to exhibiting a high level of discrimination, an ideal test location should also be representative of the target growing region, or megaenvironment (Lubbers,2003). Yan(2001) andYanetal.(2001)discussed the use of GGE Biplot Pattern Explorer to categorize locations into megaenvironments.”
GGE biplot and AMMI analysis of barley yield performance in Iran (2017, background): “Yan (2001) reported that the ‘which-won-where’ pattern is another important graphical pattern for studying the MET data for identification of different megaenvironments.”
Identifying essential test locations for oat breeding in eastern Canada. (2010, methodology): “Analysis of variance and GGE biplot analysis were conducted using the GGEbiplot software (Yan, 2001).”
GGE biplot analysis of genotype × environment interaction in wheat-barley disomic addition lines. (2012, methodology): “The data were graphically analyzed for interpreting GE interaction using the GGEbiplot software (Yan, 2001).”
EVALUATION OF EXPERIMENTAL MAIZE HYBRIDS TESTED IN MULTI- LOCATION TRIALS USING AMMI AND GGE BIPLOT ANALYSES (2012, methodology): “The graph “which won where” enables the allocation (identification) of potential mega-environments (Yan et al., 2000 and Yan and Hunt, 2001).”
Breeding Line Selection Based on Multiple Traits (2008, methodology): “These steps were performed in one strike using the “Multi-Trait Selection” module of the GGEbiplot software ( Yan, 2001; http://ggebiplot.com/biplot-breeder’s_kit.htm), as displayed in Fig. 1. Genotype by trait biplots, also generated using the GGEbiplot software, were used to graphically explore t”
Statistical Analysis of Yield Trials by AMMI and GGE (2006, methodology): “Yan (2001) claimed that the average tester (genotype) coordinate (ATC) has an abscissa that approximates G and an ordinate that approximates GE. “The ATC x-axis passes through the biplot origin and the marker of the average environment, which is defined by the average PC1 and PC2 scores over all en”
Graphic Analysis of Genotype, Environment, Nitrogen Fertilizer, and Their Interactions on Spring Wheat Yield (2004, methodology): “A GGE biplot could be visualized from increased from 0.5 kg of wheat grain per kg of N (P various aspects (Yan, 2001, 2002; Yan et al. 2000; Yan0.”
Biplot analysis of multi-environment trial data: Principles and applications (2006, methodology): “GGE biplot analysis has evolved into a comprehensive analysis system whereby most questions that may be asked of a genotype by environment table can be graphically addressed (Yan et al. 2000; Yan 2001; Yan and Kang 2003; Yan and Tinker 2005a).”
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): “…interaction) biplot model, provides breeders a more complete and visual evaluation of all aspects of the data by creating a biplot that simultaneously represents mean performance and stability, as well as identifying mega-environments (Ding et al., 2007; Kang, 1993; Yan, 2001; Yan and Kang, 2003).”
Achievements and limitations of contemporary common bean breeding using conventional and molecular approaches (2009, methodology): “The GGE biplot provides a useful tool for data analysis and allows the visual appraisal of the structure of large data matrices (Laffont et al. 2007; Yan 2001).”