Genetic Analysis of Finger Millet (Eleusine coracana (L.) Garetn) Germplasm through Principle component Analysis and D2 Cluster Analysis in Himachal Pradesh

Author: Shailja, Ritika Singh, Alok Kumar, Anupam Kumar and Shivani Kaundal

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Abstract

A field experiment was conducted on 37 genotypes including 2 local checks were grown in a Randomized Block Design (RBD) with three replications during kharif, 2022 at Research Farm of School of Agriculture, Abhilashi University Chail chowk Mandi, Himachal Pradesh to study the nature and magnitude of divergence by using Mahalanobis D2 statistics. The observations for 19 morphological characters was recorded. Two techniques, principal component analysis and cluster analysis were applied. Principal component analysis indicates that four principal components PC-1, PC-2, PC-3 and PC-4 explains 30.76%, 21.65%, 16.14% and 11.23% respectively of the total variation. Principal component analysis showed that first principle component had maximum of 30.76% of total variation, while the first four principle component axes together explained 82.78% of variations. On the basis of Euclidean distance, 37 genotypes were grouped into 7 different clusters using cluster analysis. Cluster 1 had highest number of genotypes followed by cluster 3 with 4 genotypes, cluster 2, 4, 5, 6 and 7 with one genotype. Therefore, there was a significant diversity among these clusters and genotypes from these clusters could be used as parents for hybridization. Grain yield contributed maximum toward the genetic divergence in 37 genotypes of finger millet. For majority of the desirable traits, including biological yield, 100 seed weight, flag leaf blade width, peduncle length and number of fingers per ear, cluster 5 exhibited the highest cluster mean. Clustering through D2 analysis revealed maximum inter cluster distance between cluster 6 and 7 (5053.66) followed by cluster 6 and 4 (4962.99), cluster 2 and 6 (4524.50), 1 and 6 (4208.39), 5 and 7 (4073.57) cluster 2 and 7 (3715.87). The result of the present study could be exploited in planning and execution of future breeding strategy in finger millet.

Keywords

Principal component analysis, Finger millet, Cluster analysis, RBD, Euclidean distance, Genotype, Hybridization

Conclusion

In conclusion, Principal component analysis for 19 quantitative traits revealed six principal components out of which maximum variability was found in first four components which contributed 82.78% to variance. Cluster analysis for yield and yield contributing traits classified all 37 genotypes of finger millet into seven clusters by using Tocher’s method. Cluster 1 included maximum number of genotypes followed by cluster 3 with four genotypes, cluster 2,4,5,6 and 7 with one genotype each indicating wide diversity from whole set as well as from each other. Grain yield contributed maximum toward the total genetic divergence. Clustering through D2 analysis revealed maximum inter cluster distance between cluster 6 and 7 (5053.66) followed by cluster 6 and 4 (4962.99), cluster 2 and 6 (4524.50), 1 and 6 (4208.39), 5 and 7 (4073.57) cluster 2 and 7 (3715.87). Therefore, there was a significant diversity among these clusters and genotypes from these clusters could be used as parents for hybridization.

References

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How to cite this article

Shailja, Ritika Singh, Alok Kumar, Anupam Kumar and Shivani Kaundal (2023). Genetic Analysis of Finger Millet (Eleusine coracana (L.) Garetn) Germplasm through Principle component Analysis and D2 Cluster Analysis in Himachal Pradesh. Biological Forum – An International Journal, 15(10): 1037-1043.