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Bayesian Perspective in the Selection of Bean Genotypes

Author

Listed:
  • Tâmara Rebecca A. de Oliveira
  • Moysés Nascimento
  • Paulo R. Santos
  • Kleyton Danilo S. Costa
  • Thalyson V. Lima
  • Gabriela Karoline Michelon
  • Luis Claudio de Faria
  • Antonio F. Costa
  • José Wilson da Silva
  • Geraldo A. Gravina
  • Gustavo Hugo F. de Oliveira

Abstract

Changes in the relative performance of genotypes have made it necessary for more in-depth investigations to be carried out through reliable analyses of adaptability and stability. The present study was conducted to compare the efficiency of different informative priors in the Bayesian method of Eberhart & Russel with frequentist methods. Fifteen black-bean genotypes from the municipalities of Belém do São Francisco and Petrolina (PE, Brazil) were evaluated in 2011 and 2012 in a randomized-block design with three replicates. Eberhart & Russel’s methodology was applied using the GENES software and the Bayesian procedure using the R software through the MCMCregress function of the MCMCpack package. The quality of Bayesian analysis differed according to the a priori information entered in the model. The Bayesian approach using frequentist analysis had greater accuracy in the estimate of adaptability and stability, where model 1 which uses the a priori information, was the most suitable to obtain reliable estimates according to the BayesFactor function. The inference, using information from previous studies, showed to be imprecise and equivalent to the linear-model methodology. In addition, it was realized that the input of a priori information is important because it increases the quality of the adjustment of the model.

Suggested Citation

  • Tâmara Rebecca A. de Oliveira & Moysés Nascimento & Paulo R. Santos & Kleyton Danilo S. Costa & Thalyson V. Lima & Gabriela Karoline Michelon & Luis Claudio de Faria & Antonio F. Costa & José Wilso, 2024. "Bayesian Perspective in the Selection of Bean Genotypes," Journal of Agricultural Science, Canadian Center of Science and Education, vol. 12(9), pages 173-173, April.
    • Handle: RePEc:ibn:jasjnl:v:12:y:2024:i:9:p:173
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    References listed on IDEAS

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    1. John Geweke, 1991. "Evaluating the accuracy of sampling-based approaches to the calculation of posterior moments," Staff Report 148, Federal Reserve Bank of Minneapolis.
    2. Martin, Andrew D. & Quinn, Kevin M. & Park, Jong Hee, 2011. "MCMCpack: Markov Chain Monte Carlo in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i09).
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    More about this item

    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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