India, the largest cotton producer globally, is experiencing stagnation in productivity. Enhancing productivity necessitates selecting high-yielding hybrids based on genetic relationships among traits and principal component analysis (PCA). PCA is a statistical method that simplifies complex data by identifying key contributors to total variability, aiding hybrid selection. However, cotton yield is a complex trait influenced by multiple genes. Understanding the interaction and contribution of each trait is crucial for effective selection and yield improvement. This study examined the relationship between yield components in 45 Bt cotton hybrids for three years (2021-2023) in Abohar, Punjab. Pearson correlation coefficient and PCA were employed to analyze data. Most yield components exhibited positive correlations except for days to 50% flowering. Plant height, node ratio, number of nodes per plant, monopodial and sympodial branches per plant, bolls per plant, and boll weight showed significant positive correlations with seedcotton yield per plant. Among 10 principal components identified, three had eigenvalues exceeding one, accounting for 81.93% of total variability. The principal component with the highest variability was primarily associated with plant height, nodes per plant, seedcotton yield, number of sympodial branches, number of bolls per plant, and boll weight. Findings highlight the importance of considering multiple yield components and their interrelationships to enhance cotton productivity. Specifically, plant height, node ratio, number of nodes, monopodial and sympodial branches, bolls per plant, and boll weight were identified as key traits influencing seedcotton yield, providing valuable insights for targeted breeding efforts.
