Genetic analysis of groundnut (Arachis hypogaea L.) genotypes for yield and oil quality parameters - Umadevi, M; Vanitha, K; Mythili, S R; Shanthi , P; Kavithamani , D; Arulselvi, S (Plant Science Today, 2025)
Genetic variability is a foundation for advancing crop improvement programs. The effectiveness of selection is influenced by the characteristics, scope, and degree of genetic variability found in the material, as well as the extent to which this variability is heritable. This study assessed fifteen traits, including yield and oil quality parameters, in 55 groundnut accessions from diverse origins. The analysis of genetic parameters, including phenotypic coefficient of variation (PCV), genotypic coefficient of variation (GCV), heritability, genetic advance as a percentage of the mean (GAM), skewness, and kurtosis revealed significant genetic variation for several key traits. Notably, the traits viz., the number of branches(NB)/plant, hundred pod weight, shelling percentage(SP), oil yield/plant, and oleic acid(OA) content exhibited high PCV, GCV, heritability, and GAM. The analysis showed significant genetic variability and a predominance of additive gene effects, suggesting phenotypic selection as an effective approach for groundnut improvement. Association analysis revealed positive genotypic and phenotypic correlations of single plant yield(SPY) with traits like days to first flowering(DFiF), NB per plant, number of pods(NP) per plant, hundred pod weight, oil yield per plant (OYPP), and OA content. Principal component analysis (PCA) identified five principal components with eigenvalues greater than 1, explaining 75.13% of the total variation. A biplot constructed using the first two PCs visually represented the importance of NP/plant, NB/plant, oil yield/plant, and OA content for yield improvement strategies. Cluster analysis efficiently grouped the 55 genotypes into five distinct clusters. The high OA lines "Girnar 4" and "Girnar 5" were clustered together. This information suggests that selecting accessions from clusters with greater genetic distance can be a valuable strategy to maximize genetic variability within breeding programs.