Evaluation of advanced breeding lines of Indian mustard using principal component analysis

Manoj Kumar Duppala, Beena Nair, KS Gowtham and Arti Rathod


The efficiency of a breeding program depends mainly on the direction of the correlation between yield and its component
traits, and the relative importance of each trait contributing to seed yield. The interrelationship between eighteen
quantitative and seven qualitative characters in 70 genotypes of Indian mustard were computed. Principal component
analysis was utilized to examine the variation and estimated the relative contribution of various traits towards the total
variability. In this study, component 1 had contribution from the traits such as siliqua beak length, siliqua texture, leaf
hairiness, leaf colour, dentation of leaf margin, flower petal colour and seed colour which accounted 27.6% of the total
variability. Leaf length, leaf width, plant height, main shoot length, primary branches plant-1, number of siliqua plant-1,
seed yield plot-1, seed yield plant-1 contributed 14.8% to the total variability in component 2. The remaining variability of
9.4%, 8.2%, 5.5%, 4.6%, and 4.4% was consolidated in components 3, 4, 5, 6 and 7 respectively, by various traits viz.,
spikelet fertility, single plant yield, grain length and number of productive tillers. The cumulative variance of 74.5% of
total variation among 24 characters was explained by the first seven axes. Thus, the results of principal component
analysis revealed the high level of genetic variation and the traits contributing for the variation were identified. Factor
analysis was used for understanding the data structure and traits relationship. Hence, this population can be utilized for
trait improvement in breeding programs for the traits contributing to major variation.


Divergence, factor analysis, genetic mustard, principal component analysis

Full Text:



  • There are currently no refbacks.