U.S. Department of Agriculture: Agricultural Research Service, Lincoln, Nebraska
Date of this Version
3-2017
Citation
ANNUAL REPORT OF THE BEAN IMPROVEMENT COOPERATIVE, No. 60, March 2017. Published by USDA.
Abstract
INTRODUCTION The genetic diversity present in local and improved bean cultivars allows exploring an existing variability already adapted to the specific climatic conditions, contributing a lot to breeding programs. Morphoagronomic characteristics are considered of great importance, since they allow the direct evaluation of agronomic interest characters. The analyze of the relative importance of the characters makes it possible to discard characteristics that contribute less to the discrimination of the evaluated materials, reducing costs and labor in the next experiments, being that the minor importance characteristics may be those that show less variability or that are represented by other one (Cruz, Regazzi e Carneiro, 2012). The present work had the objective of accessing the genetic variability of common bean cultivars and identify the characteristics of greater relative importance.
MATERIAL AND METHODS Thirty-nine bean cultivars were used for the characterization, 20 belonging to the carioca commercial group and 19 to the black commercial group (tests were performed independently for each commercial group). All cultivars come from breeding programs of public or private institutions from Brazil. The trials were established in four environments in the state of Paraná- BR, two in the 2014/2015 rainy season, in Ponta Grossa and Guarapuava, and two in the dry season of 2015, in Ponta Grossa and Santa Tereza do Oeste. The experimental design was a randomized complete block with three replicates and plots consisting of four rows of 4 meters spaced 0.5 m, with a population of 12 plants per linear meter, considering the two central lines as a useful plot. The quantitative descriptors evaluated are on Table 1. The evaluations for the descriptors were carried out in a sample of ten plants of each experimental plot (except PROD), and for the statistical analysis the average of each plot was used. The relative importance of the quantitative variables studied was analyzed by the Singh method (Singh, 1981) using the Genes computational program (Cruz, 2013).
Comments
U.S. government work.