Genetics and Molecular Research · 2016 · 42 citations · 13 references
AgricultureAgronomyCrop SciencesCrop ImprovementCrop ProductionPlant GeneticsAgronomic Performance EvaluationAgronomic Trait EvaluationCrop-environment InteractionsGenotype-by-environment InteractionMulti-environment TrialsYield StabilityBiplot AnalysisGenotype By Environment Interaction ModelingGenotype-by-environment Yield InteractionsGenotype-environment Interaction AnalysisGge Biplot AnalysisMain EffectsPhenotypic Stability AnalysisYield-related Trait Assessment
Seed cotton yield is a trait governed by multiple genes that cause changes in the performance of genotypes depending on the cultivation environment. Breeding programs examine the genotype x environment interaction (GE) using precise statistical methods, such as AMMI (additive main effects and multiplicative interaction) and GGE biplot (genotype main effects + genotype x environment interaction). The AMMI method combines the analysis of variance and principal components, to adjust the main effects (genotypes and environments) and the effects of GE interaction, respectively. The GGE biplot groups the genotype additive effect together with the multiplicative effect of the GE interaction, and submits both of these to the principal components analysis. 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. Trials were conducted with cotton cultivars in eight environments across Mato Grosso State in the 2008/2009 crop season. The experiment used a randomized block design with 16 genotypes and four replicates per genotype x environment combination. Data for seeds cotton productivity were analyzed by AMMI and GGE biplot methods. Both methods were concordant in the discrimination of environments and genotypes for phenotypic stability. The genotypes BRS ARAÇÁ and LD 05 CV had high seed cotton productivity and phenotypic stability, and could be grown in all environments across Mato Grosso State.
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R: A Language and Environment for Statistical Computing
R Core Team · 2000 · 353K citations · Full text
Natural Sciences, Computer Science, Statistical Computing +10
Cultivar Evaluation and Mega‐Environment Investigation Based on the GGE Biplot
Weikai Yan, L. A. Hunt, Qinglai Sheng et al. · Crop Science · 2000 · 1.9K citations
Biplot Analysis of Test Sites and Trait Relations of Soybean in Ontario
Weikai Yan, Istvan Rajcan · Crop Science · 2002 · 904 citations