Singular-Value Partitioning in Biplot Analysis of Multienvironment Trial Data
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TLDR
In this paper, the authors compared four singular-value scaling methods: genotype-focused scaling, environment-focused, symmetric scaling, and equal-space scaling for which-won-where pattern of the MET data.Abstract:
Multienvironment trials (MET) are conducted every year for all major crops throughout the world, and best use of the information contained in MET data for cultivar evaluation and recommendation has been an important issue in plant breeding and agricultural research. A genotype main effect plus genotype × environment interaction (GGE) biplot based on MET data allows visualizing (i) the which-won-where pattern of the MET, (ii) the interrelationship among test environments, and (iii) the ranking of genotypes based on both mean performance and stability. Correct visualization of these aspects, however, requires appropriate singular-value (SV) partitioning between the genotype and environment eigenvectors. This paper compares four SV scaling methods. Genotype-focused scaling partitions the entire SV to the genotype eigenvectors; environment-focused scaling partitions the entire SV to the environment eigenvectors; symmetrical scaling splits the SV symmetrically between the genotype and the environment eigenvectors; and equal-space scaling splits the SV such that genotype markers and environment markers take equal biplot space. It is recommended that the genotype-focused scaling be used in visualizing the interrelationship and comparison among genotypes and the environment-focused scaling be used in visualizing the interrelationship and comparison among environments. All scaling methods are equally valid in visualizing the which-won-where pattern of the MET data, but the symmetric scaling is preferred because it has all properties intermediate between the genotype- and the environment-focused scaling methods.read more
Citations
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Journal ArticleDOI
Biplot analysis of multi-environment trial data: Principles and applications
Weikai Yan,Nicholas A. Tinker +1 more
TL;DR: The basic principles of biplot analysis are reviewed and recent developments in its application in analyzing multi-environment trail (MET) data are reviewed, with the aim of providing a working guide for breeders, agronomists, and other agricultural scientists on bi plot analysis and interpretation.
Journal ArticleDOI
GGE Biplot vs. AMMI Analysis of Genotype-by-Environment Data
TL;DR: The main conclusions are: both GGE biplot analysis and AMMI analysis combine rather than separate G and GE in mega-environment analysis and genotype evaluation, and the G GE biplot is superior to the AMMI1 graph in Mega-Environment analysis and Genotype evaluation.
Journal ArticleDOI
Statistical Analysis of Yield Trials by AMMI and GGE
TL;DR: A systematic comparison of the Additive Main effects and Multiplicative Interaction model, GGE, and other SVD-based model families is presented, using both statistical theory and empirical investigations, while considering both current practices and best practices.
Journal ArticleDOI
An Integrated Biplot Analysis System for Displaying, Interpreting, and Exploring Genotype × Environment Interaction
Weikai Yan,Nicholas A. Tinker +1 more
TL;DR: It is suggested that the GGE biplot, the genotype × trait bi plot, and the covariate-effect biplot be used jointly to better understand and more fully explore MET data.
Journal Article
GGE-Biplot analysis of multi-environment yield trials in bread wheat
TL;DR: In this paper, the yield data of 25 bread wheat genotypes tested across 9 rain-fed environments during the 2002-2003 growing season were analyzed using the GGE (i.e., G, genotype + GEI, genotypes-by-environment interaction) biplot method, and the first two principal components (PC1 and PC2) were used to create a 2-dimensional GGE-biplot and explained 46.2% and 15.8% of GGE sum of squares (SS), respectively.
References
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Journal ArticleDOI
The biplot graphic display of matrices with application to principal component analysis
TL;DR: In this article, a matrix of rank two can be represented as a biplot, which consists of a vector for each row and a column, chosen so that any element of the matrix is exactly the inner product of the vectors corresponding to its row and to its column.
Journal ArticleDOI
Cultivar Evaluation and Mega-Environment Investigation Based on the GGE Biplot
TL;DR: This paper presents a GGE (i.e., G + GE) biplot, which is constructed by the first two symmetrically scaled principal components (PC1 and PC2) derived from singular value decomposition of environment-centered MET data.
Journal ArticleDOI
GGEbiplot—A Windows Application for Graphical Analysis of Multienvironment Trial Data and Other Types of Two-Way Data
TL;DR: GGEbiplot as mentioned in this paper is a Windows application that performs biplot analysis of two-way MET data analysis, and it also produces an WHAT IS A GGE BIPLOT? interactive show of the data.
Journal ArticleDOI
Biplot Analysis of Test Sites and Trait Relations of Soybean in Ontario
Weikai Yan,Istvan Rajcan +1 more
TL;DR: Two types of biplots are described, the GGE biplot and the GT biplot, which graphically display genotype by environment data and genotypes by trait data, respectively, and hence facilitate cultivar evaluation on the basis of MET data and multiple traits.
Journal ArticleDOI
Model selection and validation for yield trials with interaction
TL;DR: AMMI analysis of yield trial data is a useful extension of the more familiar ANOVA, PCA, and linear regression procedures, particularly given a large genotype-by-environment interaction.
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