Genetics and Molecular Research · 2017 · 30 citations · 9 references
AgricultureCrop SciencesPlant SciencesCrop ProductionCropping SystemsPlant GeneticsCrop PhenotypingCrop-environment InteractionsGenotype-by-environment InteractionStability TestingYield StabilityGenotype By Environment Interaction ModelingGenotype-by-environment Yield InteractionsGenotype-environment Interaction AnalysisMixed Genetic ModelsPhenotypic Stability AnalysisUnivariate AnalysisYield Response To EnvironmentYield Stability Assessment
The wheat crop presents sensitivity to the environmental conditions culminating in the genotype x environment interaction, being crucial the use of different methodologies to guide the positioning of genotypes to certain cultivation environments. The objective of this study was to estimate the adaptability and phenotypic stability of wheat genotypes grown in the State of Rio Grande do Sul using univariate and multivariate techniques and mixed models. The yield data of 42 wheat genotypes evaluated in five environments (Cachoeira do Sul, Passo Fundo, Santo Augusto, So Gabriel, and So Luiz Gonzaga) were used in the 2012 and 2013 crop seasons. In each experiment, a randomized complete block design was used, with three replicates. In the evaluation of the genotype x environment interaction, the sum of squares relative to contribution index, the methodology based on the univariate method of Annicchiarico (1992), the multivariate method (AMMI), and the mixed models (REML and MHPRVG) were used. The favorable environments expressed by the univariate method referred to So Gabriel, Cachoeira do Sul, Passo Fundo, Santo Augusto, and So Luiz Gonzaga; for the multivariate method, only Santo Augusto was favorable to the productivity character. The genotypes CD 121 and TBIO Tibagi were adapted and stable for the univariate and multivariate methods. The genotypes TBIO Sinuelo, Quartzo, BRS 327, Mirante, Topzio, Guamirim, TBIO Seleto, Ametista, TBIO Mestre, and BRS Louro were superior through the mixed model approach. The different strategies to estimate the adaptability and phenotypic stability allowed indicating and recommending the best environments and genotypes efficiently to obtain increases in wheat grain yield.
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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
Statistical Analysis of a Yield Trial
Richard W. Zobel, Madison J. Wright, Hugh G. Gauch · Agronomy Journal · 1988 · 1.5K citations