Bayesian estimation of genotypic and phenotypic correlations from crop variety trials

Omer, Siraj Osman; Abdalla, Abdel Wahab H.; Mohammed, Mohammed H.; Singh, Murari

doi:10.1590/1984-70332016v16n1a3

Cited by 2 publications

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“…In addition, Bayesian inference enables the evaluation of experiments with unbalanced data and the study of complex statistical models (Waldmann & Ericsson, 2006;Bink et al, 2007). Consequently, its use is increasing among breeders, not only for the analysis of molecular data, but also for phenotypic data (Waldmann & Ericsson, 2006;Omer et al, 2016). In this sense, the objective was to estimate the minimum number of evaluations for assertive selection of half-sib progenies of kale by means of a repeatability study, using the Bayesian approach.…”

Section: Introductionmentioning

confidence: 99%

Determination of the optimal number of evaluations in half-sib progenies of kale by Bayesian approach

et al. 2021

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Kale has a long vegetative cycle, requiring a lot of labor, due to the need for tutoring, thinning and multiple harvests, leading to difficulties in the maintenance and evaluation of experiments. Thus, the objective was to estimate the minimum number of evaluations for the assertive selection of half-sib progenies of kale by means of a repeatability study by Bayesian approach. Twenty four half-sib progenies were evaluated in a randomized block design with four replicates and five plants per plot. The number of shoots, number of marketable leaves, fresh mass of marketable leaves and fresh mass per leaf were measured throughout 15 harvests. All traits showed high estimates of the repeatability, indicating high regularity in the expression of the traits during the harvesting period. With eight harvests it is possible to evaluate all the traits with a coefficient of determination superior to 85% in half-sib progenies of kale.

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