Abstract In many cases, traditional analysis of breeding trials based on analysis of variance (ANOVA) do not allow a suitable genetic evaluation. Alternatively, mixed model-based approaches create the possibility of dealing with unbalanced data and modeling spatial trends. The aims of this study were to compare the goodness-of-fit of the model and the genotype ranking through different residual modeling approaches and to select the best performing tropical wheat genotypes based on the best-fitting model. A panel of tropical wheat genotypes was evaluated in three field trials conducted between 2020 and 2021 for grain yield. Linear mixed model analyses were used on the data to estimate the genetic parameters and to predict the genotypic values in analyses of single- and multi-environment trials. Accounting for spatial trends in the analyses of single- and multi-environment trials provides better outcomes than the compound symmetry model does. cases ANOVA (ANOVA evaluation Alternatively modelbased goodnessoffit goodness fit bestfitting fitting 202 yield single multienvironment multi environment does 20 2

ABSTRACT A large set of variables is assessed for progeny selection in a plant-breeding program and other agronomic fields. The meta-analysis of the coefficient of variation (CVe) produces information for researchers and breeders on the experimental quality of trials. This analysis can also be applied in the decision-making process of the experimental plan regarding the experimental design, the number of repetitions, and the treatments and plants/progenies to be measured. In this study, we evaluated the dataset distribution and the descriptive statistics of CVe through the Frequentist and Bayesian approaches, aiming to establish the credibility and confidence intervals. We submitted CVe data of ten wheat (Triticum aestivum L.) traits reported in 1,068 articles published to the Bayesian and Frequentist analyses. Sample data were analyzed via Gamma and normal models. We selected the model with the lowest Akaike Information Criterion (AIC) value, and then we tested three link functions. In the Bayesian analysis, uniform distributions were used as non-informative priors for the Gamma distribution parameters with three ranges of q~U (a,b,). Thus, the prior probability density function was given by: p θ = 1 β - α , θ ∈ α , β . The Bayesian and Frequentist approaches with the Gamma model presented similar results for CVe; however, the range Bayesian credible intervals was narrower than the Frequentist confidence intervals. Gamma distribution fitted the CVe data better than the normal distribution. The credible and confidence intervals of CVe were successfully applied to wheat traits and could be used as experimental accuracy measurements in other experiments.

RESUMO: As análises de correlações canônicas baseadas nas correlações genotípicas, permitem determinar associações entre grupos de caracteres e realizar a seleção direta ou indireta de genótipos superiores. Objetivou-se com este trabalho investigar a existência de relações lineares e multivariadas entre caracteres de alta e baixa herdabilidade via análise de correlações canônicas com base nas correlações genotípicas. O experimento foi conduzido no Campo Experimental Professor Diogo Alves de Melo da Universidade Federal de Viçosa, em Viçosa, Minas Gerais. 90 cultivares de trigo foram avaliadas sob o delineamento alpha-lattice 9 × 10, com três repetições e parcelas constituídas por quatro linhas de três metros espaçadas a 0.20 metros. Os grupos canônicos foram estabelecidos entre altura de espiga e planta, dias para o espigamento, número de espiguetas por espiga e número de grãos por espiga (Grupo I) e, peso de espiga, massa de grãos da espiga, massa de 100 grãos, peso hectolitro e produtividade de grãos (Grupo II). Houve dependência entre os grupos estabelecidos, o que permitiu a investigação das relações entre os caracteres com base em seus valores genotípicos. Os caracteres ciclo e altura de plantas podem ser utilizados para a seleção indireta de genótipos superiores em peso hectolitro e produtividade, fatores estes importantes para indústrias e produtores.

ABSTRACT: Canonical correlation analysis based on genotypic correlations allows determining the associations between groups of traits and carrying out the direct or indirect selection of superior genotypes. This study investigated the existence of linear and multivariate relationships between high and low heritability traits via canonical correlation analysis based on genotypic correlations. The experiment was conducted at the Professor Diogo Alves de Melo Experimental Field at the Universidade Federal de Viçosa, in Viçosa, MG. 90 wheat cultivars were evaluated under a 9 × 10 alpha-lattice design, with three replications and plots consisting of four rows of three meters spaced at 0.20 meters. Canonical groups were established between spike height and plant height, days for heading, number of spikelets per spike, and number of grains per spike (Group I) and, spike weight, spike grain mass, 100-grain mass, hectoliter weight, and grain yield (Group II). There was dependence between the established groups, which allowed the investigation of the relationships between traits based on their genotypic values. The traits cycle and plant height can be used for indirect selection of genotypes superior in hectoliter weight and grain yield, which are important factors for industries and farmers.