Multivariate analysis of soybean genotypes: Uncovering agro-morphological insights

Soybean [Glycine max (L.) Merrill] is a vital legume crop known for its high protein and oil content, playing a crucial role in global food security and industrial applications. Its adaptability to diverse environments and nutritional importance underscores the need for improving genetic diversity to enhance yield potential and stress resilience. This study analyzed genetic diversity among fifty soybean genotypes using cluster analysis and Principal Component Analysis (PCA) based on eleven morphological and quantitative traits. The genotypes were evaluated at the Soybean Breeding Farm, Jawaharlal Nehru Krishi Vishwa Vidyalaya (JNKVV), Jabalpur, in a Randomized Complete Block Design with three replications over three seasons (Kharif 2019, 2020, and summer 2021) under rainfed and irrigated conditions. Cluster analysis using the D² method revealed distinct genetic groupings, with genotypes forming 10 clusters in environment E1, where Cluster I comprised 24 genotypes, and Cluster V included 9 genotypes; similar patterns were observed in environments E2, E3, and pooled data across years. PCA highlighted the number of pods per plant, number of seeds per plant, days to flower initiation, and days to maturity as key traits driving genetic variability. Traits such as the number of primary branches per plant, number of pods per plant, and number of seeds per plant significantly influenced genetic variability. Promising genotypes, including JS 22-78, JS 22-66, JS 22-88, AGS 31, JS 22-77, JS 22-90, JS 22-106, JS 22-69, JS 22-10, JS 22-84, JS 22-91, JS 22-92, and AGS 48, exhibited high genetic diversity, demonstrating the utility of cluster analysis and PCA in facilitating the selection of superior genotypes from diverse germplasm pools.