GENETIC VARIABILITY AND ASSOCIATION OF CHARACTERS FOR YIELD AND YIELD COMPONENTS IN SOME FIELD PEA (Pisum sativum L.) GENOTYPES GROWN IN THE CENTRAL HIGHLANDS OF ETHIOPIA

Abstract

Thirty six field pea genotypes were evaluated at two locations in the central highlands of Ethiopia during the 2014 main cropping seasons. The objective of the study was to determine the magnitude of genetic variability and association of characters for yield and yield related traits. The study was conducted using a randomized complete block design with three replications. Data were collected on 15 quantitative traits and subjected to the analysis of variance, correlation and Mahalanobis D2 analysis using the SAS program software. The results revealed that locations effects were highly significant (p≤ 0.01) for all traits, indicating that the two locations were distinctly different. It was observed that, the high yielding ability of the test genotypes was associated with medium and late maturity. It was also observed that Phenotypic coefficient of variation (PCV) was generally higher than genotypic coefficient of variation (GCV) for all the characters considered indicating high diversity among the traits under study. Number of seeds per pod, biological and seed yield per plot exhibited high PCV and GCV as well as high heritability coupled with high genetic advance as percent of mean indicating the presence of sufficient variation that can be made use of for selection. At the phenotypic level, seed yield had positive and highly significant correlation with the characters studied except days to flowering, number of seeds per pod and harvest index. The positive and highly phenotypic correlation of biological yield, days to maturity and number of pods per plant with seed yield indicates that those traits should be used as selection criteria for maximizing seed yield in field pea. The path coefficient analysis at the phenotypic level revealed that, days to maturity, biological yield, harvest index and hundred seed weight showed higher positive direct effects on seed yield, indicating that selection of superior genotypes for seed yield on the basis of these characters would be effective. The Mahalanobis D2 statistic can be used in cluster analysis to identify groups of related clusters. The genotypes were grouped in four clusters on the basis of D2 values, suggesting that within cluster genetic diversity is narrow, but genetic diversity among clusters is greater. Therefore, exploiting the diversity among clusters would broaden the genetic base of dry pea breeding population.