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Multi-Environment and Multi-Year Bayesian Analysis Approach in Coffee canephora.

André Monzoli Covre1, Flavia Alves da Silva2, Gleison Oliosi1

  • 1Centro Universitário Norte do Espírito Santo, Universidade Federal do Espírito Santo, Rodovia BR-101, Km 60, Litorâneo, São Mateus 29932-540, ES, Brazil.

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Summary

This study used a Bayesian approach to identify stable and productive Coffea canephora cv. Conilon genotypes across different regions and harvests. LB1, AD1, Peneirão, Z21, and P2 were recommended as the most productive options.

Keywords:
Markov chaincoffee productioncultivar recommendationinformative priors

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Area of Science:

  • Agricultural Science
  • Genetics
  • Biostatistics

Background:

  • Identifying stable and productive genotypes is crucial for crop improvement.
  • Coffea canephora cv. Conilon is a significant coffee species with diverse genotypes.

Purpose of the Study:

  • To discriminate 43 Coffea canephora cv. Conilon genotypes using a Bayesian approach.
  • To identify the most stable and productive genotypes across different cultivation regions and harvests.

Main Methods:

  • Bayesian methodology implemented in R language using the MCMCglmm package.
  • Randomized block design with three replications and seven plants per plot.
  • Evaluation across two producing regions (Bahia and Espírito Santo) and four harvests.

Main Results:

  • Significant genetic divergence detected among Coffea canephora genotypes.
  • Significant effects of genotype, environment, and year on performance were identified.
  • Problems with model convergence and singularity were encountered with hyper-parametrized models.

Conclusions:

  • The Bayesian approach, despite some convergence issues, effectively identified genetic divergence.
  • Genotypes LB1, AD1, Peneirão, Z21, and P2 are recommended as the most productive for the evaluated environments.
  • Environmental and yearly variations significantly impact Coffea canephora performance.