Journal ArticleDOI
Biplot Analysis of Test Sites and Trait Relations of Soybean in Ontario
Weikai Yan,Istvan Rajcan +1 more
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TLDR
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.Abstract:
Superior crop cultivars must be identified through multi-environment trials (MET) and on the basis of multiple traits. The objectives of this paper were to describe two types of biplots, the GGE biplot and the GT biplot, which graphically display genotype by environment data and genotype by trait data, respectively, and hence facilitate cultivar evaluation on the basis of MET data and multiple traits. Genotype main effect plus genotype by environment interaction effect (GGE) biplot analysis of the soybean [Glycine max (L.) Merr.] yield data for the 2800 crop heat unit area of Ontario for MET in the period 1994-1999 revealed yearly crossover genotype by site interactions. The eastern Ontario site Winchester showed a different genotype response pattern from the three southwestern Ontario sites in four of the six years. The interactions were not large enough to divide the area into different mega-environments as when analyzed over years, a single cultivar yielded the best in all four sites. The southwestern site, St. Pauls, was found to always group together with at least one of the other three sites; it did not provide unique information on genotype performance. Therefore, in future cultivar evaluations, Winchester should always be used but St. Pauls can be dismissed. Applying GT biplot to the 1994-1999 multiple trait data illustrated that GT biplots graphically displayed the interrelationships among seed yield, oil content, protein content, plant height, and days to maturity, among other traits, and facilitated visual cultivar comparisons and selection. It was found that selection for seed yield alone was not only the simplest, but also the most effective strategy in the early stages of soybean breeding.read more
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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
Singular-Value Partitioning in Biplot Analysis of Multienvironment Trial Data
TL;DR: 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.
Journal ArticleDOI
Assessment of Drought Tolerance in Segregating Populations in Durum Wheat
TL;DR: Results of calculated gain from indirect selection indicated that selection from moisture stress environment would improve yield in moisture Stress environment better than selection from non-moisture stress environment.
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.
Book
Plant Adaptation and Crop Improvement
Mark E. Cooper,Graeme Hammer +1 more
TL;DR: DeLacy et al. as discussed by the authors proposed a three-mode analytical method for the analysis of data from multi-environment trials, threemode analytical methods were used to analyse data from multienvironment trials, K.C. Cooper et al, D.E. Fox, and R.F.
Journal ArticleDOI
Two Types of GGE Biplots for Analyzing Multi-Environment Trial Data
TL;DR: This paper compares the merits of two types of GGE biplots in MET data analysis and finds that the SREG M+1 biplot is more desirable, however, in that it always explicitly indicates the average yield and stability of the genotypes and the discriminating ability and representativeness of the test environments.
Journal ArticleDOI
Interpretation of genotype × environment interaction for winter wheat yield in Ontario
Weikai Yan,L. A. Hunt +1 more
TL;DR: Analysis of the 1992 to 1998 Ontario winter wheat performance trial data indicated that plant height and maturity were the major genotypic causes of GE interaction, whereas coldTemperature in the winter and hot temperature in the summer were themajor environmental causes ofGE interaction.
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