Row-col method associated with frequentist and Bayesian statistics in a passion fruit population

Abstract

This study was conducted to test the significance of adding row and column factors in the frequentist and Bayesian models used in the evaluation of a population of Passiflora edulis, as well as selecting promising genotypes to form the next generation. The following parameters were evaluated: number of fruits, yield, fruit weight, transverse fruit diameter, longitudinal fruit diameter, pulp percentage, skin thickness and total soluble solids. For the Bayesian model, two priors were considered, namely, inverse gamma and a priori distribution with extended parameters. The model with a priori distribution with extended parameters showed lower root mean square error and higher correlation coefficient between observed and predicted values than the inverse gamma model. Furthermore, for a selection intensity of 37%, the mixed and Bayesian models selected practically the same progenies in both experiments. The use of the 5-fold cross-validation technique indicated that both tested models were efficient.