Genetic divergence in the common bean (Phaseolus vulgaris L.) in the Cerrado-Pantanal ecotone.

Evaluating genetic diversity among genotypes is important for providing parameters for the identification of superior genotypes, because the choice of parents that form segregating populations is crucial. Our objectives were to i) evaluate agronomic performance; ii) compare clustering methods; iii) ascertain the relative contributions of the variables evaluated; and iv) identify the most promising hybrids to produce superior segregating populations. The trial was conducted in 2015 at the State University of Mato Grosso do Sul, Brazil. We used a randomized block design with three replications, and recorded the days to emergence, days to flowering, days to maturity, plant height, number of branches, number of pods, number of seeds per pod, weight of 100 grains, and productivity. The genetic diversity of the genotypes was determined by cluster analysis using two dissimilarity measures: the Euclidean distance and the standardized mean Mahalanobis distance using the Ward hierarchical method. The genotypes 'CNFC 10762', 'IAC Dawn', and 'BRS Style' had the highest grain yields, and clusters that were based on the Euclidean distance differed from those based on the Mahalanobis distance, the second being more precise. The yield grain character has greater relevance to the dispute. Hybrids with a high heterotic effect can be obtained by crossing 'IAC Alvorada' with 'CNFC 10762', 'IAC Alvorada' with 'CNFC 10764', and 'BRS Style' with 'IAC Alvorada'.

Bayesian approach increases accuracy when selecting cowpea genotypes with high adaptability and phenotypic stability.

This study aimed to verify that a Bayesian approach could be used for the selection of upright cowpea genotypes with high adaptability and phenotypic stability, and the study also evaluated the efficiency of using informative and minimally informative a priori distributions. Six trials were conducted in randomized blocks, and the grain yield of 17 upright cowpea genotypes was assessed. To represent the minimally informative a priori distributions, a probability distribution with high variance was used, and a meta-analysis concept was adopted to represent the informative a priori distributions. Bayes factors were used to conduct comparisons between the a priori distributions. The Bayesian approach was effective for selection of upright cowpea genotypes with high adaptability and phenotypic stability using the Eberhart and Russell method. Bayes factors indicated that the use of informative a priori distributions provided more accurate results than minimally informative a priori distributions.