Abstract:RESUMENLos objetivos del presente trabajo fueron evaluar la precisión del valor estimado de la heredabilidad determinada por medio de la desviación estándar, considerando un enfoque Bayesiano, y comparar tal estimativa con el procedimiento clásico. Se utilizaron datos de un ensayo de progenie con 39 familias de Eucalyptus cladocalyx. La variable dependiente usada fue el diámetro basal del fuste medido a los seis años de edad. El método Bayesiano fue implementado por medio del algoritmo de Cadenas Independiente… Show more
“…Mora et al (2009) used the Bayesian approach to predict the heritability values of forest species, and concluded that this method made it possible to obtain low standard deviation values associated with heritability, which makes it an important tool for genetic breeding. Silva et al (2013) used the Bayesian approach to select sugarcane families (Saccharum officinarum L.), and found that the informative a priori not only influenced the genetic effects, but it also influenced variance and heritability.…”
ABSTRACT. This study aimed to verify that a Bayesian approach could be used for the selection of upright cowpea genotypes with high adaptability and phenotypic stability, and the study also evaluated the efficiency of using informative and minimally informative a priori distributions. Six trials were conducted in randomized blocks, and the grain yield of 17 upright cowpea genotypes was assessed. To represent the minimally informative a priori distributions, a probability distribution with high variance was used, and a meta-analysis concept was adopted to represent the informative a priori distributions. Bayes factors were used to conduct comparisons between the a priori distributions. The Bayesian approach was effective for selection of upright cowpea genotypes with high adaptability and phenotypic stability using the Eberhart and Russell method. Bayes factors indicated that the use of informative a priori distributions provided more accurate results than minimally informative a priori distributions.
“…Mora et al (2009) used the Bayesian approach to predict the heritability values of forest species, and concluded that this method made it possible to obtain low standard deviation values associated with heritability, which makes it an important tool for genetic breeding. Silva et al (2013) used the Bayesian approach to select sugarcane families (Saccharum officinarum L.), and found that the informative a priori not only influenced the genetic effects, but it also influenced variance and heritability.…”
ABSTRACT. This study aimed to verify that a Bayesian approach could be used for the selection of upright cowpea genotypes with high adaptability and phenotypic stability, and the study also evaluated the efficiency of using informative and minimally informative a priori distributions. Six trials were conducted in randomized blocks, and the grain yield of 17 upright cowpea genotypes was assessed. To represent the minimally informative a priori distributions, a probability distribution with high variance was used, and a meta-analysis concept was adopted to represent the informative a priori distributions. Bayes factors were used to conduct comparisons between the a priori distributions. The Bayesian approach was effective for selection of upright cowpea genotypes with high adaptability and phenotypic stability using the Eberhart and Russell method. Bayes factors indicated that the use of informative a priori distributions provided more accurate results than minimally informative a priori distributions.
“…Silva et al (2009) showed that calculating genotypic probabilities increased the precision of additive effect estimates and decreased the error estimate associated with the Bayesian model. Mora et al (2009) used Bayesian analysis to predict the inheritability values for forest species and concluded that the Bayesian method makes it possible to obtain low standard deviation values associated with inheritability, which makes it an important tool for the genetic assessment and inference of perennial species. Balestre et al (2012) conducted a Bayesian survey of multiple corn ( Zea mays L.) characteristics, in which they highlighted the importance of pleiotropic effects in the study of the inheritance of quantitative characteristics.…”
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.
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