GENETIC VARIABILITY AND DIVERSITY ANALYSIS IN INDIAN MUSTARD (Brassica juncea (L.) Czern and Coss.)

K. SAI KRISHNA¹*, N. HARSHA VARDHAN², A. PRADEEP GOUD³, B.G. SURESH⁴, V.M. PRASAD^5
1Department of Genetics and Plant Breeding, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, 211007, Uttar Pradesh, India
²Department of Genetics and Plant Breeding, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, 211007, Uttar Pradesh, India
³Department of Genetics and Plant Breeding, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, 211007, Uttar Pradesh, India
⁴Department of Genetics and Plant Breeding, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, 211007, Uttar Pradesh, India
⁵Department of Genetics and Plant Breeding, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, 211007, Uttar Pradesh, India
* Corresponding Author : saikrishnak003@gmail.com

Received : 01-04-2021     Accepted : 26-04-2021     Published : 30-04-2021
Volume : 13     Issue : 4       Pages : 824 - 827
Genetics 13.4 (2021):824-827

Keywords : Analysis of Variance, Cluster mean, Diversity Analysis, Euclidean distance, Genetic advance as percent of mean, Heritability and Indian mustard
Conflict of Interest : None declared
Acknowledgements/Funding : Authors are thankful to Department of Genetics and Plant Breeding, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj, 211007, Uttar Pradesh, India
Author Contribution : All authors equally contributed

Background: Indian mustard (Brassica juncea (L.) Czern & Coss.) is an important oil seed crop grown in rabi season, extensively grown in Rajasthan, Haryana, Madhya Pradesh, Uttar Pradesh and Gujarat. The present research is carried out on 40 Indian mustard genotypes to identify the genetically diverse genotypes and using them in further breeding programme that aimed to improve yield and yield attributing characters. Methods: These genotypes are evaluated under randomized block design with three replications and observations were recorded from five randomly selected plants for 14 quantitative traits from each replication that are effective to identify yield potential from the forty genotypes and the experiment is conducted at field experimentation center, Department of Genetics and Plant Breeding, Sam Higginbottom University of Agriculture, Technology and Sciences, Prayagraj during rabi 2019-20. A detailed analysis of the results on Indian mustard varieties are studied using different statistical tools in this project. Results: Analysis of variance revealed that all the traits have significant variability and thus have adequate scope for the selection of superior genotypes that can be used in further research. Higher estimates of GCV and PCV were recorded for number of secondary branches and number of siliquae per plant; indicating that these characters could be selection tools for yield improvement. High heritability coupled with high genetic advance as percent mean is observed in number of secondary branches and test weight (g) indicating the scope for their improvement through selection. The 40 genotypes are grouped into V clusters using Torcher’s method of clustering. Among the 5 clusters, cluster III has maximum genotypes (13), followed by cluster II (11), cluster I has 7 genotypes and cluster IV has 5 genotypes followed by cluster V (3) with least number of genotypes. The maximum intra-cluster distance is observed for cluster V (4.43) followed by cluster I (3.87) and cluster IV (3.77). From the cluster mean values, using of genotypes from cluster I and cluster IV in crossing programme will give desirable recombinants for early maturity along with seed yield per plant. Diversity analysis also revealed that cluster I and cluster V has the highest inter-cluster distance (7.92) followed by cluster IV and V (7.66) indicating that the genotypes of these clusters have maximum diversity and crossing programme between these clusters will yield better segregants. Euclidean distance matrix also suggests that the crosses of Jagannath × RGN 236, BR 40 × RB 50 and Jagannath × Pusa Bold are expected to give better yield segregants.

1. Panse V.G. and Sukhatme P.V. (1967) ICAR Publications, New Delhi.
2. Burton G. W. (1952) Proc. 6th Int. Grassland Cong., 1, 211-283.
3. Burton G. W. and Devane E. H. (1953) Agron. J., 45,478-481.
4. Lush J. L. (1945) Proc. Am. Soc. Animal Prod., 33, 292-301.
5. Johnson H. W., Robinson H. F. and Comstock R. E. (1955) Agron J., 47,1314-1318.
6. Mahalanobis P.C. (1936) Proceedings of National Academy of Sciences, India, 2, 49-55.
7. Sohan R. and Verma N. (2010) Journal of Oilseeds Research, 27 (2), 170-171.
8. Verma U., Thakral N.K. and Neeru (2016) International Journal of Applied Mathematics & Statistical Sciences, 5(1), 25-34.
9. Tantuway G., Srivastava K., Trikey A. (2018) Plant Archives, 18 (2), 2245-2251.
10. Singh K.H., Shakya R. and Mahawar R.K. (2014) Sabrao Journals of Breeding and Genetics, 46(2), 329-339.
11. Adhikari B.N., Joshi B.P., Shrestha J. and Bhatta N.R. (2018) Journal of Agriculture and Natural Resources, 1(1), 149-160.
12. Kumari A. and Kumari V. (2018) The Pharma Innovation Journal, 7(7), 290-296.
13. Lodhi B., Thakral N.K., Singh D., Avtar R. and Bahadur R. (2013) Journal of Oilseed Brassica, 4(2), 57-60.
14. Lyngdoh Y., Kanaujia S.P. and Shah P. (2017) International Journal of Recent Scientific Research, 8(8), 1938-1939.
15. Bansal M.P., Panwar D.V.S., and Naidu M.R. (1990) Research and Development Reporter, 7, 90-96.
16. Pandey R., Kumar B. and Kumar M. (2013) American-Eurasian Journal Agriculture and Environment Science, 13(3), 348-351.
17. Singh K.H., Shakya R. and Mahawar R.K. (2014) Sabrao Journals of Breeding and Genetics, 46 (2), 329-339.
18. Shekhawat N., Jadeja G.C., Singh J. and Ramesh (2014) The Bioscan, 9(2), 713-717.
19. Iqbal M.S., Haque M.S., Nath U.K. and Hamim I. (2014) International Journal of Agricultural Science Research, 3(8), 141-156.
20. Bin D., Singh S.K. and Dwivedi V.K. (2015) Indian Journal of Agriculture Research, 49(6), 554-557.
21. Devi T.R., Devi N.D., Vivekananda Y. and Sharma P.R. (2017) Electronic Journal of Plant Breeding, 8(3), 749-753.
22. Akabari and Niranjana M. (2015) International Journal of Agricultural Sciences, 11 (1), 35-39.
23. Ali N., Fartullah, Khan N.U., Rabbani M.A., Hussain L., Ali S., Khan S.A. and Kakkar M.Q. (2017) Pakistan Journal of Botany, 49(5), 1889-1901.
24. Dilip T.S., Singh S.K. and Kumar R. (2016) Technofame- A Journal of Multidisciplinary Advance Research, 5 (2), 07-10.
25. Kumar B. and Pandey A. (2012) Madras Agric. J., 100 (1-3), 62-66.
26. Singh S., Dwivedhi A.K., Kumar O. and Kumar K. (2018) International Journal of Current Microbiology and Applied Sciences, 7(3), 350-352.