Genetic Divergence and Principal Component Analysis for Seed Yield and Yield- contributing Traits in Field Pea (Pisum sativum L.)

Background: Field pea (Pisum sativum L.) is a nutritionally important legume, but its productivity is constrained by a narrow genetic base, limiting scope for yield improvement. Therefore, assessing genetic divergence and variability using multivariate tools like D² analysis and PCA is essential to identify diverse genotypes and key yield-contributing traits for effective breeding programs. Aim: Field pea (Pisum sativum L.) is a nutritionally rich and economically important cool-season legume that plays a key role in sustainable cropping systems. However, the narrow genetic base of cultivated germplasm limits the scope for yield enhancement and genetic improvement. Hence, the present investigation was undertaken to evaluate genetic divergence and analyze trait variability among field pea genotypes using multivariate approaches such as principal component analysis, in order to identify diverse and promising parental lines for future breeding programs. Study Design: The study used three replications in randomized complete block design (RCBD). Place and Duration: The experiment was conducted during the rabi growing season 2019-2020, at the Breeder Seed Production Unit, Department of Genetics and Plant Breeding, College of Agriculture, JNKVV, Jabalpur, Madhya Pradesh. Methodology: The present study, 40 field pea genotypes were analyzed for genetic diversity using Mahalanobis's D2 statistics and principal component analysis (PCA). 19 key quantitative traits were recorded and genotypes were grouped into 10 clusters based on genetic distance using tocher’s procedure. Results: Genetics divergence and principal component analysis (PCA) were applied to assess seed yield and yield-contributing traits in field pea germplasm. The genotypes under the experiment were grouped into 10 clusters based on genetic divergence analysis. The highest number of genotypes found in cluster I. the highest intra cluster distance was found in cluster III followed by cluster I. the highest inter cluster divergence was observed between genotypes of cluster IV and X followed by cluster V and X. Cluster IX have early flowering and maturity genotypes. Pod cluster per plant has highest value in cluster I followed by plant height and pod bearing length has highest value in cluster III. According to Principal component analysis (PCA) indicated that the out of 19, only 6 principal components exhibited more than 1.0 eigen value and showed 81.66% of the total variance. The first principal component accounted for 27.51% of the variability and was mainly associated with yield-related traits. The genotypes FP-14-34, Lep-260 and FP-14-36 has highest positive PC values for yield-related traits. Yield-related traits exhibited the highest loading values on the principal components, with number of nodes per plant identified as the most important trait contributing to separation and should therefore be prioritized for future breeding programmes. Conclusion: The present investigation revealed substantial genetic divergence among field pea (Pisum sativum L.) genotypes, confirming the utility of D² analysis and principal component analysis in characterizing genetic variability. The identification of diverse clusters and key yield-contributing traits offers valuable insights for the selection of superior and genetically divergent parents. The use of such genotypes in hybridization programmes is expected to enhance recombination efficiency and facilitate the development of high-yielding field pea varieties.