Variability, Correlation Patterns and Principal Component Analysis (PCA) for Seed Yield and Contributing Traits in Castor (Ricinus communis L.)

Castor (Ricinus communis L.) is a vital crop for industrial applications in more than 250 products including lubricants, paints, cosmetics, pharmaceuticals etc. This study is an attempt to understand the genetic diversity in 15 male (monoecious) and 15 female (pistillate) advanced breeding lines of castor. 11 quantitative traits were subjected to analysis of variance, correlation analysis, principal component analysis (PCA) and K-means clustering. Significant genetic variability and trait correlations were noticed, revealing opportunities for targeted improvement in castor. Clustering identified six distinct genetic groups, facilitating the identification of diverse parental lines. Principal component analysis elucidated key contributors of variation, enabling informed breeding decisions. This comprehensive study provides a foundation for further improvement in seed yield, oil content and environmental resilience in castor.