Journal of Advances in Biology & Biotechnology

Soybean (Glycine max [L.] Merrill) is a globally significant leguminous crop, valued for its high protein and oil content, symbiotic nitrogen fixation ability, and adaptability to diverse agro-climatic conditions. Despite increased cultivation and advancements in agronomic practices, its productivity remains constrained due to a narrow genetic base and the complex polygenic nature of yield-related traits. The present investigation aimed to assess genetic divergence and identify key traits contributing to phenotypic variability in soybean employing Mahalanobis D² statistics and Principal Component Analysis (PCA). The experiment was conducted during the Kharif 2023 at the Research Farm, Department of Genetics and Plant Breeding, RVSKVV, Gwalior, Madhya Pradesh, India. Ninety-two soybean genotypes were evaluated using a Randomized Block Design with two replications for 10 quantitative traits viz., days to 50% flowering, days to maturity, plant height (cm), numbers of primary branches per plant, numbers of pods per plant, numbers of seeds per pod, 100- seed weight (g), biological yield (g), harvest index (%) and yield per plant (g). Mahalanobis D² analysis grouped the genotypes into eight clusters, revealing presence of substantial genetic divergence. Biological yield, plant height, and yield per plant were the major contributors to total divergence. Significant inter-cluster distances were investigated, particularly between Clusters IV and VIII, suggested the existence of highly divergent genotypes suitable for use in hybridization programme. Cluster VII and VIII were identified as potential sources for improving yield and biomass traits. PCA revealed that four principal components with eigenvalues greater than one accounted for 72.18% of the total variation. PC1 contributed the most (28.82%). The Scree plot confirmed the significance of the first four PCs, enabling dimensional reduction and efficient trait prioritization. This integrated approach demonstrated the effectiveness of multivariate analysis in exploring genetic variability and supports the strategic selection of parents for soybean improvement. The findings hold promise for enhancing productivity, adaptability, and sustainability in future breeding programmes targeting for diverse agro-ecological environments.