Gopi Krishnan A1*, Pandiyan M2, Thilagam P1, Veeramani P1 and Nanthakumar S1
1Agricultural Research Station, Virinjipuram, Vellore District, Tamil Nadu, India
2Regional Research Station, Virudhachalam, Cuddallore District, TamilNadu, India
*Corresponding Author: Gopi Krishnan A, Agricultural Research Station, Virinjipuram, Vellore District, Tamil Nadu, India.
Received: December 06, 2021; Published: January 13, 2022
A collection of 32 redgram genotypes were evaluated for nine morphological and phenological characters by principal component analysis for determining pattern of genetic diversity and relationship among individuals. The largest variation was observed for seed yield per plant with coefficient of variation of 74.01% followed by number of pods per plant (69.63), plant height (53.47) and number of branches (42.16). The least variation was observed in pod length with coefficient of variation of 10.60%. Principal component analysis was used to assess the variation and relative contribution of various traits towards total variability. In this study, principal component 1 had the contribution from the traits such as days to maturity, days to 50% flowering, plant height and 100 seed weight, number of branches, number of pods per plant and seed yield per plant which accounted to 34.54% of the total variability. The principal component 2 explained 26.82% of total variability from days to 50% flowering and days to maturity. Number of branches and plant height had contributed 16.03% of total variability in principal component 3. The principal component 4 explained 11.40% of total variability from the number of branches, number of seeds per pod, pod length, 100 seed weight and plant height. The cumulative variance of 88.80% of total variation among ten characters was explained by first four axes. Thus, the results of principal component analysis used in the study had revealed the high level of genetic variation and the traits controlling for the variation were identified. Hence, these entries can be utilized for trait improvement in breeding programs for the traits contributing for major variation. Correlation analysis revealed that number of pods per plant and plant height had significant and positive association with seed yield per plant and also exhibited significant positive inter correlation among them. Cluster analysis depicted two clusters and identified the groups of cultivars those were more closely related.
Keywords: Pigeonpea; Principal Component Analysis; Diversity
Citation: Gopi Krishnan A., et al. Evaluation of Pigeonpea (Cajanus cajan (L.) Millsp.) Genetic Diversity Using Principal Component Analysis". Acta Scientific Agriculture 6.2 (2022): 22-30.
Copyright: © 2022 Gopi Krishnan A., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.