More than one-third of the world's population relies on rice as a main food source, and it is a model cereal species used as a genetic study platform for gene functions. 25 genotypes were examined for the ten different traits in the present study. To evaluate the relative contribution of several qualities to overall variability, Principal Component Analysis was used. It was discovered that three components have Eigen values greater than 1. A total of 49.20, 24.50, and 10.30 percent of the variability was supplied by PCs 1, 2, and 3. They indicated the traits causing the variation and collectively accounted for 84% of the variability of the genotypes employed in the study.

Eigen value, genotypes, PCA, rice, variability

The coefficients of proper vectors in Principal Component Analysis (PCA) indicate the significance of independent figures in contributing to each primary component. On the other hand, the phenotypic value of each variable assesses its importance and contribution to the total variance. By considering variables with higher retention rates and significant contributions to the interpretation of variability, one can prioritize their usage in breeding programs for yield improvement. The analysis revealed five prominent components (PC1, PC2 and PC3) that collectively accounted for 84% of the total variation. These components play a crucial role in understanding the variability and can be crucial in designing successful breeding programs.

Deepak Meena, Manoj Kumar, Rukoo Chawla, Hitesh Kumar Koli and Naven Kumar Meena (2023). Principal component analysis for yield and related attributes in rice genotypes. Biological Forum – An International Journal, 15(8): 191-195.