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.
