Correlation and Principal Component Analysis of Yield-related Traits in Castor (Ricinus communis L.) Genotypes

Abstract

Castor (Ricinus communis L.) is a vital crop had wide range of industrial applications. It is widely used in the production of biodiesel, soaps, inks, varnishes, linoleum, and plasticizers. Understanding genetic diversity is essential for effective breeding strategies. In this study, 40 castor genotypes were evaluated using correlation, principal component analysis (PCA). Correlation analysis revealed that characters with plant height upto primary spike, effective primary spike length, number of capsules on primary spike and 100 seed weight displayed significant and positive correlation of total seed yield. PCA identified five components (with Eigenvalues >1), explaining 85.85% of the total variation. PC-I showed strong positive loadings for number of capsules to primary spike, effective primary spike length, 100 seed weight, total seed weight and number of effective spikes per plant. Considering the mean values of canonical vectors, genotypes ACI-01, ACI-26, and ACI-28 emerged as promising genotypes for use in breeding programmes.