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Eberhart and Russel's Bayesian Method in the Selection of Popcorn Cultivars
Author(s) -
Couto Mauricio Farias,
Nascimento Moysés,
Amaral Antônio Teixeira,
Silva Fabyano Fonseca,
Viana Alexandre Pio,
Vivas Marcelo
Publication year - 2015
Publication title -
crop science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.76
H-Index - 147
eISSN - 1435-0653
pISSN - 0011-183X
DOI - 10.2135/cropsci2014.07.0498
Subject(s) - adaptability , a priori and a posteriori , prior probability , bayesian probability , stability (learning theory) , biology , prior information , function (biology) , selection (genetic algorithm) , statistics , mathematics , computer science , machine learning , artificial intelligence , ecology , genetics , philosophy , epistemology
The goal of this work was to estimate stability and adaptability parameters using a Bayesian approach to Eberhart and Russel's method and to assess the efficiency of using an a priori distribution. The information from assessing the popping expansion and grain yield of 16 popcorn genotypes was used in randomized block experiments implemented in five environments in the North and Northeast regions of the State of Rio de Janeiro, Brazil. The Bayesian methodology was implemented using the free software package R with the MCMCregress function of the MCMCpack package. Eberhart and Russel's method using a Bayesian technique was found to be efficient in recommending cultivars to more or less favorable environments. The incorporation of a priori information provided greater accuracy in estimating the stability and adaptability parameters. In the comparison of a priori distributions, the BayesFactor function indicated the informative a priori as the most effective for obtaining reliable estimates.