APTIDÃO, DIVERGÊNCIA GENÉTICA E SELEÇÃO DE PROGÊNIES DE MEIOS IRMÃOS PARA PRODUÇÃO DE MILHO VERDE

Abstract

The objectives were to evaluate the potential of half sib progenies of maize for the ability to produce green maize, estimate through statistical multivariate procedures the genetic divergence and select maize progenies with the highest number of characteristics of interest. The 96 progenies were evaluated in two experiments in a randomized block design with three replications, using the variety Cativerde 02 (CATI - SP) and the hybrid AG 1051 (AGROCERES) as commercial control. 18 phenotypic characteristics were measured associated with agronomic adaptation, the potential yield and the commercial aspect of the ears of maize. The data were submitted the individual and joint analyses of variance of the experiments. The means of phenotypic variables were grouped according to Scott and Knott test. The genetic parameters were estimated using the mathematical expectation of the mean squares of the sources of variation, were considered random the effects of treatments (progenies) and experiments. The genetic divergence between treatments was obtained from the Generalized Mahalanobis Square Distance. The genotypes were grouped by genetic dissimilarity through UPGMA and Tocher Optimization methods. To check the consistency of groups Fisher discriminant analysis (1936) was applied. The phenotypic variables were submitted to principal component analysis aiming to reduce the data dimension and selection high progenies for ability to produce green maize. The results of the 18 characteristics confirmed the genetic potential of the majority half sib progenies as function the productive precocity, high yield and commercial quality of the ears of green maize when compared to commercial control. The genetic parameters estimates showed high variability among the progenies, indicating the possibility of genetic gains with artificial selection. The UPGMA cluster analysis and Tocher were effective in identifying dissimilarity genotypes groups. The UPGMA method was more sensitive than the Tocher optimization, because it enabled the formation of 11 genetically dissimilar groups. The principal component analysis (PC) reduced set of 18 variables on three principal components explaining 70 % of the total phenotypic variance. The coefficients of the eigenvectors indicated that PC1 was more related to the productive potential of green maize ears. The PC2 was more influenced by the characteristics associated with the ears commercial aspect and PC3 for adaptive characteristics of maize genotypes. The eigenvectors of the PC1 showed the characteristics RENDC, NEC, %EC, REND, PE, % EE and PEC were the most important in defining the productive potential and as well as to attend the demands of the consumer market, allowing through the scores of this the selection of 30 half sib progenies highly favorable to this ability.