Unravelling the Genetic Variability of Seed Yield and Its Components in Sesame (Sesamum Indicum L.) Through Multivariate Analysis

In this present study, Principal Component Analysis and cluster analysis were employed to assess the genetic variability among ninety-six advanced breeding lines of sesame with four checks viz., DS-5, DSS-9, JTS-8 and TKG-22 based on morphological traits.Principal Component Analysis revealed that the first five principal components accounted for 64.10% of the total variation among genotypes.These components were associated with traits such as number of primary branches per plant, number of productive branches per plant, number of productive capsules per plant, seed yield (kg/ha), days to maturity and plant height.A biplot analysis showed distinct grouping patterns of genotypes based on their morphological traits.Cluster analysis further classified the genotypes into eight clusters, indicating a high degree of heterogeneity.By leveraging the diverse genotypes and identifying key traits through multivariate analyses, breeders can make informed selections and develop improved sesame varieties with enhanced productivity and quality.