Genetic Diversity and Principal Component Analysis for Yield and its component Trait in Rice

Author: Harshraj Salunkhe, Ashutosh Kumar, Bal Krishna, Nilesh Talekar and Pratiksha Pawar

Download PDF

Abstract

Breeding for aerobic rice is a vital step towards sustainable rice production, water conservation, climate change resilience, and ensuring food security for a growing global population. In this view the present investigation evaluated 30 diverse rice genotypes at the agriculture research farm of Lovely Professional University, Punjab. The study aimed to estimate genetic divergence for yield and its attributing traits during the kharif season of 2022. Randomized Block Design (RBD) with three replications was used and thirteen yield and its component traits were analysed. Among the eleven Clusters, Cluster 1 was largest comprising of thirteen genotypes followed by Cluster 2 with five genotypes. Maximum inter cluster distance was observed between cluster 2 and 11 (26.99), followed by cluster 2 and 10 (26.38) indicating that genotypes from these clusters were highly divergent and holds great promise as parents for hybridization. Maximum intra cluster distance was observed in cluster 8 (9.47) followed by cluster 2 (9.27). It reveals that genotypes present in the same cluster have low level of diversity and selection of parents with in the cluster for hybridization programme may not be considered promising. The first principal component (PC1) contributed 45.65 per cent towards variability. The characters namely, amylose content (0.23), Days to 50% flowering (0.320), plant height (0.15) and Days to maturity (0.14) explained maximum variance in this component. Among all the principal components, PC1 showed maximum variability of 45.65% with high Eigen value 1540, which decreased gradually indicating that maximum variation, was observed in PC1 as compared to the other PC’s.

Keywords

Genetic divergence, Clusters, Principal component analysis, inter cluster, intra cluster and variability

Conclusion

Based on the findings of the present investigation, it can be concluded that there is sufficient genetic variability observed in the evaluated quantitative traits. This suggests that there is potential for selecting genotypes with high grain yield by targeting different clusters and conducting appropriate crosses to improve overall yield. The study has also identified a genotype that performed exceptionally well, which could be recommended for further evaluation and potential commercialization. For future breeding programs that involve hybridization, it is advisable to select parental material from different clusters rather than within clusters. This approach would maximize the potential for combining favorable traits and enhancing overall performance. Among the eleven Clusters, Cluster 1 was largest comprising of thirteen genotypes followed by Cluster 2 with five genotypes. Maximum inter cluster distance was observed between cluster 2 and 11, followed by cluster 2 and 10 indicating that genotypes from these clusters were highly divergent and holds great promise as parents for hybridization. Maximum intra cluster distance was observed in cluster 8 followed by cluster 2. It reveals that genotypes present in the same cluster have low level of diversity and selection of parents with in the cluster for hybridization programme may not be considered promising. Additionally, it is recommended to replicate the study across multiple seasons and locations, involving a larger number of genotypes. This would provide more robust and reliable predictions of genotypic performance across diverse environments, helping to validate the current results. Furthermore, to complement the findings of the current study, it is suggested to incorporate molecular characterization techniques in future rice research. Molecular characterization can provide additional insights and confirmation of the observed outcomes, further enhancing our understanding of the genetic basis of the traits under investigation.

References

-

How to cite this article

Harshraj Salunkhe, Ashutosh Kumar, Bal Krishna, Nilesh Talekar and Pratiksha Pawar (2023). Genetic Diversity and Principal component Analysis for Yield and its component Trait in Rice. Biological Forum – An International Journal, 15(5a): 102-107.