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Bayesian GGE biplot models applied to maize multi-environments trials.

L A de Oliveira1, C P da Silva2, J J Nuvunga2

  • 1Faculdade de Ciências Exatas e Tecnologia, Universidade Federal da Grande Dourados, Dourados, MS, Brasil.

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Summary
This summary is machine-generated.

The Bayesian approach enhances genotype x environment (GGE) biplot analysis by incorporating uncertainty, improving genotype selection for stability and adaptability in plant breeding.

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Area of Science:

  • Agricultural science
  • Biometrics
  • Plant breeding

Background:

  • Additive Main Effects and Multiplicative Interaction (AMMI) and Genotype Main Effects and Genotype x Environment Interaction (GGE) models are key for genotype x environment studies.
  • Traditional GGE biplots have limitations, including inability to handle variance heterogeneity and missing data, and lack of uncertainty measures.

Purpose of the Study:

  • To apply a Bayesian approach to GGE biplot models.
  • To assess the implications for selecting stable and adapted genotypes.

Main Methods:

  • Bayesian approach applied to GGE biplot models.
  • Utilized non-informative priors.
  • Incorporated credible regions into biplots.

Main Results:

  • Bayesian GGE biplots were consistent with traditional analyses.
  • Credible regions allowed probabilistic distinction of genotype performance and genotype-environment relationships.
  • Identified groups of genotypes and environments with similar adaptability and stability effects.

Conclusions:

  • The Bayesian approach provides a probabilistic framework for genotype and environment analysis.
  • Incorporating uncertainty in biplots is crucial for breeders' decisions on stability, adaptability, and mega-environment definition.