Deciphering Genetic Diversity and Scaling Exalted Genotypes of Soybean Using PCA

Abstract

Soybean (Glycine max) is a nutritionally rich legume valued for its high protein and oil content, soil-enriching nitrogen fixation, and diverse food and feed uses. Its narrow genetic base necessitates studies on genetic diversity to enhance breeding efficiency and adaptability. Techniques like principal component analysis (PCA) and cluster analysis are crucial for classifying genotypes, identifying superior lines, and improving yield potential. This study aimed to assess the genetic diversity among 44 advanced soybean genotypes using Tocher’s clustering, and Principal Component Analysis (PCA) during the Kharif season 2024 at JNKVV, Jabalpur. Tocher’s method grouped the genotypes into seven distinct clusters, with Cluster III showing the highest intra-cluster divergence and the maximum inter-cluster distance recorded between Cluster VI and Cluster III. PCA revealed that the first three principal components accounted for 76.58% of total variance, with PC1 alone contributing 51.26%, heavily influenced by key yield traits such as seed yield per plant, number of pods and seeds per plant, and biological yield. Genotypes 146, 154, 151, and 109 emerged as superior candidates for yield improvement based on high PC1 scores.