Characterization of promising okra genotypes on the basis of Principal Component Analysis

Abstract: Twenty selected genetically diverse okra strains were evaluated using Principal Component and cluster analysis for the extent of variability and relationship between various economically important traits for the purpose of genetic improvement. The trial was laid out in a randomized block design (RBD). Positive significant correlation for days to 50% flowering (DF) with days to first harvest (DFH), number of pod per plant (NP) with pod yield per plant (PY) and pod yield per plot (PYP) (P<0.001) and PY with PYP (P<0.001) and negative correlation was observed for pod weight (PW) with NP (P<0.01). The analysis of extracted components, component pattern and Eigen values revealed that the first two principal components alone accounted for 53.25% of variance. First component was found heavily loaded with days to 50% flowering (DF), days to first harvest (DFH), pod length (PL), pod diameter (PD) and pod weight (PW), which comprised of fourteen genotypes in three clusters. The matrix obtained from principal component analysis revealed that the genotype Pb- 57 and HRB-9-2 found their positions in same cluster in principal space. Dominating similar prominent phenotypic characters formed separate place in principal space as coherent cluster. Cluster based inter breeding of genotypes would exhibit high hetrosis and is also likely to produce new recombinants with desired characters in okra.