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Multivariate analyses of genotype x environment interaction of popcorn

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TLDR
In this paper, the authors evaluate the genotype x environment (GxE) interaction for popcorn and to compare two multivariate analyses methods: additive main effects and multiplicative interaction (AMMI).
Abstract
The objectives of this work were to evaluate the genotype x environment (GxE) interaction for popcorn and to compare two multivariate analyses methods. Nine popcorn cultivars were sown on four dates one month apart during each of the agricultural years 1998/1999 and 1999/2000. The experiments were carried out using randomized block designs, with four replicates. The cv. Zelia contributed the least to the GxE interaction. The cv. Vicosa performed similarly to cv. Rosa-claro. Optimization of GxE was obtained for cv. CMS 42 for a favorable mega-environment, and for cv. CMS 43 for an unfavorable environment. Multivariate analysis supported the results from the method of Eberhart & Russell. The graphic analysis of the Additive Main effects and Multiplicative Interaction (AMMI) model was simple, allowing conclusions to be made about stability, genotypic performance, genetic divergence between cultivars, and the environments that optimize cultivar performance. The graphic analysis of the Genotype main effects and Genotype x Environment interaction (GGE) method added to AMMI information on environmental stratification, defining mega-environments and the cultivars that optimized performance in those mega-environments. Both methods are adequate to explain the genotype x environment interactions.

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Journal ArticleDOI

Genotype × Environment Interaction and Stability Analysis for Watermelon Fruit Yield in the United States

TL;DR: Evaluating the yield of watermelon genotypes over years and locations to identify genotypes with high stability for yield, and measuring the correlations among univariate and multivariate stability statistics found there was an advantage of hybrids over inbreds for yield components in both performance and responsiveness to favorable environments.
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Analysis of genotype x environment interaction for grain yield in Maize hybrids

TL;DR: The most stable genotype in the high yielding group in this study was CML312/TZMI 711 (X20), followed by genotypes TZMI 102/CML384 (designated as X33), and CML 312/TzMI 712 (X21), which was found to be the most ideal genotypes with both high mean yield and high stability.
Journal ArticleDOI

Dissection of genotype × environment interactions for mucilage and seed yield in Plantago species: Application of AMMI and GGE biplot analyses.

TL;DR: Based on trait variation, GGE biplot analysis identified two representative environments, one for seed yield and one for mucilage yield and content, with good discriminating ability, which should assist the breeding of new Plantago cultivars.
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Genotype by environment interaction components underlying variations in root, sugar and white sugar yield in sugar beet (Beta vulgaris L.)

TL;DR: The outcome of this study may assist breeders to save time and costs to identify representative and discriminating environments for root and sugar yield test trials and creates a corner stone for an accelerated genotype selection to be used in sweet-based programs.
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Biplot analysis of phenotypic stability in upland cotton genotypes in Mato Grosso.

TL;DR: The aim of this study was to investigate the association between the AMMI and GGE biplot methods and select cotton genotypes that simultaneously showed high productivity of seed cotton and stability in Mato Grosso environments.
References
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Journal ArticleDOI

Stability Parameters for Comparing Varieties

S. A. Eberhart, +1 more
- 01 Jan 1966 - 
TL;DR: The model, Yij = μ1 + β1Ij + δij, defines stability parameters that may be used to describe the performance of a variety over a series of environments to see whether genetic differences could be detected.
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

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

Biplot Analysis of Test Sites and Trait Relations of Soybean in Ontario

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

Statistical Analysis of Yield Trials by AMMI and GGE: Further Considerations

TL;DR: This review addresses more than 20 issues that require clarification after controversial statements and contrasting conclusions have appeared in recent reviews of two prominent statistical models for analyzing yield-trial data.
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