Study of repeatability and phenotypical stabilization in kale using frequentist, Bayesian and bootstrap resampling approaches

Abstract

The aim of this study was to obtain information for the genetic improvement of kale through repeatability and phenotypic stabilization studies and to compare methodologies that represent the reliability of the estimated parameters. Thirty-three half-sib progenies were evaluated in a randomized block design with three replicates and six plants per plot. Eight harvests were evaluated in terms of the yield of fresh leaves, number of shoots, number of leaves and average mass of leaves. Then, a phenotypic repeatability and stabilization study was performed, estimating the genetic parameters σ2a, σ²g, σ²e, and the coefficient of environmental variation and repeatability using the frequentist and Bayesian methodologies. To evaluate the reliability of these estimates, intervals were obtained using the frequentist, Bayesian and bootstrap methods. It was verified that the reliable selection of progenies of half-sib of kale can be achieved in four harvests that were realized between 95 and 170 days after planting. It was observed that the frequentist and Bayesian methodologies are better suited to obtain reliable estimates of the genetic parameters evaluated, as the last one provided smaller amplitudes for the obtained intervals. The bootstrap methodologies are not recommended for phenotypic repeatability and stabilization studies in kale.

Bayesian and classical approaches for the estimation of genetic parameters and coefficients of repeatability of acerola quality traits

“…The estimates of genetic parameters found in this study indicate similar results for different approaches (Bayesian × classical methods), which agrees with studies on kale (Brito et al, 2019) and physic nut (Jatropha curcas L.) (Evangelista et al, 2022). Some advantages of Bayesian inference in relation to frequentist determination of variance components (and consequently coefficients of repeatability) include the feasibility of using previous data information (priori), the lower sensitivity to outliers and lower data assumptions, such as experimental balance and high sample size (Singh, L-Yassin, & Omer, 2015), resulting in more accurate estimates of variance, as reported in guava (Silva et al, 2020) and soybean (Volpato et al, 2019).…”

Alternatives to the Classical Frequentist Confidence Interval for Describing Zero-Inflated Leaf Disease Severity

“…Confidence intervals are used to assess the reliability of estimates obtained from a random sample (SEVERIANO et al, 2011;BRITO et al, 2019). Confidence intervals can be determined by frequentist or computationally intensive methods.…”