Analysis of Structures of Covariance and Repeatability in Guava Segreganting Population

Abstract

-The present study was conducted with the objective of analyzing the covariance structure and repeatability estimates of the variables related to guava productivity, such as fruit weight (FW), fruit number (FN) and fruit production (FP) of three harvests, in 95 genotypes of a segregating population. The study also aims to choose the most appropriate covariance structure of the observations within the same individual by means of AIC (Akaike's Information Criterion) and SBC (Schwarz's Bayesian Criterion) criteria. A covariance structure between repeated measures could be incorporated into the statistical model, with the self-regression and compound symmetry forms being the most adequate. The values of repeatability coefficients obtained for FW (0.25), FN (0.14), and FP (0.29) were considered low, indicating that the three harvests were not sufficient to select the best individuals with greater accuracy for the study population. For the variables PF and FP, estimates of accuracy around 0.50 could be obtained from five measurements, while for the variable FN more harvests would be necessary. These values indicate that in guava-segregating populations, evaluations in the first harvests are not enough to select more stable genotypes for the variables considered in this study.Keywords: Covariance structure. Coefficient of repeatability. Psidium guajava. ANÁLISE DE ESTRUTURAS DE COVARIÂNCIAS E REPETIBILIDADE EM POPULAÇÃO SEGREGANTE DE GOIABEIRARESUMO -O presente estudo foi conduzido com o objetivo de analisar estrutura de covariância e estimativas de repetibilidade das variáveis relacionadas à produtividade da goiabeira como massa dos frutos (MF), número de frutos (NF) e produção de frutos (PROD) de três safras, em 95 genótipos de uma população segregante. E por meio dos critérios de AIC (Akaike's Information Criterion) e SBC (Schwarz's Bayesian Criterion) escolher a estrutura de covariância mais adequada das observações dentro de um mesmo indivíduo. Foi possível incorporar ao modelo estatístico, uma estrutura de covariância entre medidas repetidas, sendo as formas autorregressiva e simetria composta as mais adequadas. Os valores dos coeficientes de repetibilidade obtidos para PF (0,25), NF (0,14) e PROD (0,29) foram considerados baixos, indicando que para a população em estudo as três safras realizadas não foram suficientes para selecionar os melhores indivíduos com maior acurácia. Para as variáveis PF e PROD, a partir de cinco medições seria possível a obtenção de estimativas de acurácias em torno de 0,50. Enquanto que para a variável NF, observou-se que mais safras seriam necessárias. Estes valores indicam que em populações segregantes de goiabeira, avaliações nas primeiras safras, não são suficientes para seleção de genótipos mais estáveis para as variáveis consideradas neste estudo.Palavras-chave: Estrutura de covariância. Coeficiente de repetibilidade. Psidium guajava.

Repeatability coefficient estimates and optimum number of harvests in graft/rootstock combinations for 'tahiti' acid lime

“…In the results of this study, the estimated repeatability was classified as moderate for NFP and FYP and low for TFD and LFD. Similar findings have been reported for other fruit crops, such as banana (Lessa et al, 2014), sweet orange (Negreiros et al, 2008), cashew (Maia et al, 2016), guava (Quintal, Viana, Campos, Vivas, & Amaral Junior, 2017), mangaba (Fonseca et al, 2018), grapes (Leão, Nunes, & Souza, 2018), and cupuassu (Alcoforado, Pedrozo, Mayer, & Lima-Primo, 2019).…”

Repeatability coefficient for the determination of the optimal number of harvests for the selection of zucchini hybrids

“…As fruit yield is a trait with this profile, controlled by many genes, it is common to obtain low repeatability coefficients. This observation has already been reported for other fruit crops, such as guava (Quintal et al, 2017) and Tahiti lime (Malikouski et al, 2021).…”

Interrelations between productive, vegetative, and fruit quality traits in Tahiti acid lime through Bayesian inference

“…The variance components and genotypic values were estimated by the Markov Chain Monte Carlo (MCMC) algorithm. The repeatability model, based on plot means, was determined according to QUINTAL et al (2017), following the structure: y=Xβ+Zg+Wi+Ts+e, where y is the vector of phenotypic data, with the conditional distribution assumed as: y | β, g, i, s, , Ciência Rural, v.52, n.5, 2022. is the permanent environmental (co)variance matrix, R is the residual (co)variance matrix, and I is an identity matrix; is the vector of measurementreplication combinations (systematic effect), which comprises the effects of measurement, replicates and replication x measurement interaction, assumed as ; g is the vector of genotypic effects, assumed as g| ~ N(0, ⊗ I); i is the vector of G×M effects, assumed as i | ~ N(0, ⊗ I); s is the vector of permanent environmental effect, assumed as s| ~ N(0, ⊗ I). The terms X, Z, W, and T refer to the incidence matrix for the systematic, genotypic, G × M interaction, and permanent environmental effects, respectively.…”