Principal component analysis for evaluating yield-contributing traits in rapeseed-mustard (Brassica spp.) under natural white rust infection

Abstract

The present investigation was conducted during the 2023 rabi season at Acharya Narendra Deva University of Agriculture and Technology, Ayodhya, to assess the genetic variability and identify key yield-contributing traits in 100 rapeseed mustard (Brassica spp.) genotypes under natural white rust infection using principal component analysis (PCA). Eleven quantitative traits were evaluated and PCA revealed that five principal components (PCs) had eigenvalues greater than 1, cumulatively explaining 69.51 % of the total genetic variation. The first principal component (PC1) accounted for the highest variance (17.2 %) and was primarily influenced by harvest index, seed yield per plant and days to maturity. The second principal component (PC2) contributed 16.5 % of the variance and was defined by disease intensity, days to 50 % flowering, test weight and number of siliquae per plant. Subsequent components also showed significant contributions from key agronomic traits such as biological yield, siliqua length and seeds per siliqua. The analysis helped distinguish genotypes such as PAB 9511, GIRIRAJ, JAWAHAR MUSTARD-3 and EC 399301 as superior based on their high contributions to yield traits. These findings provide valuable insights for selection and breeding strategies aimed at improving yield potential and disease resilience in rapeseed mustard.