Unraveling genetic divergence using principal component analysis for yield and fibre quality traits in cotton genotypes

Cotton (Gossypium spp.) is the most widely used natural fibre in the textile industry. Understanding genetic diversity is essential for effective genetic improvement. Principal Component Analysis (PCA) was conducted on 53 cotton genotypes and evaluated using an Augmented Block Design at the Regional Agricultural Research Station, Lam Farm, Guntur. PCA identified four principal components with eigenvalues greater than 1, which were retained for subsequent analysis The four principal components contributed 70.56% towards variability. Biplot analysis identified GP 49 (L 2387), GP 34 (L 2396) and GP 40 (L 2281) as superior for seed index, lint index and seed cotton yield per plant with lower plant height, making them ideal for yield improvement. The PCA biplot analysis also revealed that GP 49 (L2387), GP 34 (L 2396), GP 24 (L 2381), GP 25 (L 2267), GP 29 (Yaganti), GP 30 (Srinandi), GP 31 (NDLA 3116-4), GP 32 (Suvin), GP 33 (CCB 29), GP 37 (L 2275) and GP 45 (L 2385) were the most divergent genotypes as they were located far away from the origin and should be tested for their combining ability through Line x Tester mating design or diallel analysis and then could be utilized in future crop improvement programs under heterosis breeding.