Navegando por Autor "Oliveira, Divani de Carvalho"

Agora exibindo 1 - 1 de 1

Diversidade genética de Schizolobium parahyba var. Amazonicum via biometria de sementes
(2019-12-05) Oliveira, Divani de Carvalho; Gallo, Ricardo; http://lattes.cnpq.br/5160912065817980; http://lattes.cnpq.br/4412602011492691
Morphometric analyzes in forest seeds can generate relevant information that help in breeding programs, indicating genetic variability among individuals of the same species. Knowing the genetic characteristics of parica seeds (Schizolobium parahyba var. Amazonicum (Huber ex Ducke) Barneby) helps in choosing materials with desirable characteristics to be used in breeding programs, seeking to obtain greater productive potential and may contribute to the advancement of breeding genetic of the species. The objective of this study was to characterize genetic diversity by biometric seed evaluation of S. parahyba var. amazonicum. The seeds were collected in the municipality of Paranaita, Mato Grosso, in forest fragments. Subsequently, 424 seeds from the 6 mother trees were analyzed. The characteristics evaluated were length, width, thickness and weight. The analysis of variance was performed on the collected data and the averages were compared with each other by the Scott-Knott clustering test at the 5% probability level. Genetic dissimilarity was verified by the generalized Mahalanobis distance using the Unweighted Pair Group Mean Average (UPGMA) method, Tocher optimization, canonical variables (VC) and character importance. The results showed great genotypic diversity for the evaluated seeds (especially seed thickness and width), and it was possible to group the mother trees. The result of cluster analysis based on the generalized distance of Mahalanobis (D2) by the Tocher optimization method, showed the formation of two distinct groups, such result reveals a great genetic diversity among the studied genotypes. According to the selective accuracy, it was possible to verify that the methodology used was adequate and of very high selective accuracy. Thus, it was verified that the parica matrices have great potential for use in breeding programs and to highlight seed collection areas.