Cultivar Evaluation and Mega‐Environment Investigation Based on the GGE Biplot

By Weikai Yan, L. A. Hunt, Q. Sheng, Z. Szlavnics. 2000

2,098 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/ac99014a3859e8e6089880217c1c1f43637eb5a1

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Citations in the corpus

GGE Biplot vs. AMMI Analysis of Genotype-by-Environment Data (2007, methodology): “Yan et al. (2000) referred to biplots based on singular value decomposition (SVD) of environment-centered or within-environment standardized GED as " GGE biplots, " because these biplots display both G and GE, which are the two sources of variation that are relevant to cultivar evaluation (Kang,…”
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): “Visualisation of mean performance and stability for seed yield Visualisation 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).”
GGE biplot and AMMI analysis of barley yield performance in Iran (2017, methodology): “The GGE biplot methodology is composed of the biplot (Gabriel 1971) and the GGE (Yan et al. 2000) concepts.”
Introduction to a Special Issue on Genotype by Environment Interaction (2016, methodology): “This general set of methods are called “genotype main effects and G ́ E” or GGE model (Yan et al., 2000).”
Specific adaptation of barley varieties in different locations in Ethiopia (2009, background): “The underlying inXuence of the year variation was explored further by visualizing the ‘‘which won where’’ feature in the GGE biplots (Yan et al. 2000), to visualize target environments sharing the same winner genotype.”
Interpretation of hybrid × sowing date interaction for oil content and oil yield in sunflower (2012, methodology): “The results of the PLS regression model are presented with by means of biplot and interpreted by means of the “inner-product” principle (Yan et al., 2000).”
Two Types of GGE Biplots for Analyzing Multi‐Environment Trial Data (2001, background): “The first type is as first described in Yan et al. (2000).”
Identifying essential test locations for oat breeding in eastern Canada. (2010, methodology): “…quantify the relative importance of genotype main eff ect vs. genotype × location interaction, genotype + genotype × environment interaction (GGE) biplot analyses (Yan et al., 2000, 2007) were conducted to visualize the genotype × location patterns and the interrelationships between test locations.”
Biplots of Linear-Bilinear Models for Studying Crossover Genotype × Environment Interaction (2002, methodology): “Yan et al. (2000) presented standard The main purposes of this study were to investigate (i) biplots of the SREG model that helped enhance its SREG2 and SHMM2 biplots with the first multiplicative interpretation for selecting the best performing cultivars components constrained to be non-COI SREG1 ”
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.”
Methods to evaluate wheat cultivar testing environments and improve cultivar selection protocols (2006, result): “Visual examination of biplot analysis identified cultivars and environments that exhibited major sources of C E interaction as well as those that were stable, similar to results reported by Yan et al. (2000).”
A heritability-adjusted GGE biplot for test environment evaluation (2010, positive): “The SE-scaled GGE biplot has been identified as most appropriate for genotype and test environment evaluation because it accounts for heterogeneity among environments in experimental errors (Yan et al. 2000; Yan and Kang 2003; Blanche and Myers 2006).”
The use of parametric and non-parametric measures for selecting stable and adapted barley lines (2018, background): “In a MET, a set of genotypes are usually assessed over several locations and years (Yan et al. 2000).”
Biplot analysis of multi-environment trial data. (2020, methodology): “Although this type of biplot has been used previously in MET data analysis (e.g. Cooper et al., 1997), methods for the utilization of the information contained in a biplot to its fullest extent became available only recently (Yan, 1999; Yan et al., 2000).”