Biplot analysis of multi-environment trial data: Principles and applications

By Weikai Yan, Nicholas A. Tinker. 2006

1,388 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/b96c2bd21d691fe9136aa6c3e20870add02ae17e

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In Genetics

In Plant Science

Citations in the corpus

GGE biplot and AMMI analysis of barley yield performance in Iran (2017, background): “According to described recommendation by Yan and Tinker (2006), an ideal genotype should have both high mean yield and high stability within a mega-environment.”
Integrating different stability models to investigate genotype × environment interactions and identify stable and high-yielding barley genotypes (2019, background): “(Yan et al. 2001). The principles of GGE biplots were reviewed by Yan and Tinker (2006). Both numerical and graphical models show G and GEI effects from METs that help to evaluate genotypes and identify mega-environments (Dehghani et al.”
GGE biplot analysis of genotype × environment interaction in wheat-barley disomic addition lines. (2012, background): “This line (EI1-axis) is called the axis for this environment (Yan and Tinker, 2006) and along it is the ranking of genotypes.”
Biplot Analysis of Genotype × Environment Interaction: Proceed with Caution (2009, methodology): “Recent reviews (Yan and Tinker, 2006; Gauch, 2006; Yan et al., 2007; Gauch et al., 2008) have described in detail two kinds of biplots for the analysis of a two-way GE matrix from the MET data.”
Additive Main Effects and Multiplicative Interactions (AMMI) and genotype by environment interaction (GGE) biplot analyses aid selection of high yielding and adapted finger millet varieties (2014, background): “Therefore, high yielding and relatively stable genotypes can be considered as references for genotype evaluation (Yan and Tinker, 2006).”
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).”
Diallel analysis of grain yield and resistance to seven diseases of 12 African maize (Zea mays L.) inbred lines (2010, background): “Also, H39 was close to the ideal genotype described by Yan and Tinker (2006) because it had high yield (large PC1) and was stable across environments (PC2 near zero).”
Specific adaptation of barley varieties in different locations in Ethiopia (2009, methodology): “Once the target regions are clariWed, the knowledge of GEI can reduce the cost of extensive genotype evaluation by eliminating unnecessary testing sites and by Wne-tuning breeding programs (Yan and Tinker 2006).”
A heritability-adjusted GGE biplot for test environment evaluation (2010, background): “Interpretation of the angle between two environment vectors in different GGE biplot When the GGE biplot has a perfect goodness of fit, the cosine of the angle (ajj0) between two environments is the genetic correlation between them (Gabriel 1971; Kroonenberg 1995; Yan and Tinker 2006): rgðjj0Þ 1⁄4 co”
AMMI and SREG GGE biplot analysis for matching varieties onto soybean production environments in Ethiopia (2009, methodology): “The SREG GGE model is a multiplicative model that absorbs the main effects of genotypes plus the environment interaction which are the two important factors in variety selection (Yan et al., 2000; Yan and Tinker, 2006).”
GGE Biplot vs. AMMI Analysis of Genotype-by-Environment Data (2007, methodology): “When supplemental information (e.g., data on environmental or genotypic covariates) is available, a fourth aspect, which is to understand the causes of G and GE, can be included (Yan and Hunt, 2001; Yan and Kang, 2003; Yan and Tinker , 2005b, 2006).”