Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Research Articles

Vol. 13 No. sp5 (2026): Recent Advances in Agriculture

Genetic diversity assessment and fusarium wilt resistance screening in chickpea genotypes using multivariate analysis

DOI
https://doi.org/10.14719/pst.13954
Submitted
2 February 2026
Published
08-07-2026

Abstract

In the present study, genetic diversity and resistance to Fusarium wilt were evaluated in 33 chickpea genotypes along with three control varieties under wilt-sick field conditions during the rabi season of 2018–19 at Mahatma Phule Krishi Vidyapeeth, Rahuri, Maharashtra, India. The experiment was laid out in a randomized block design (RBD) with 2 replications and observations were recorded for ten important agronomic and yield-related traits. Analysis of variance (ANOVA) revealed significant differences among the genotypes for all traits studied, indicating the presence of substantial genetic variability. High heritability coupled with high genetic advance (GA) was observed for the number of pods per plant, days to 50 % flowering and 100-seed weight, suggesting the predominance of additive gene action and the effectiveness of selection for these traits in breeding programmes. Genetic divergence analysis using Mahalanobis D² statistics grouped the genotypes into 7 distinct clusters, with the maximum inter-cluster distance observed between clusters III and VII, indicating wide genetic divergence among the genotypes in these clusters. Principal component analysis (PCA) further supported the diversity pattern and K-means clustering based on PCA explained 66.63 % of the total variation, providing better discrimination among genotypes. Based on combined performance for yield attributes and Fusarium wilt resistance, the genotypes Phule G 1022-10-6, Phule G 1115-13-6 and Phule G 171101 were identified as promising. Overall, this study highlights the effectiveness of multivariate statistical approaches for identifying genetically diverse and disease-resistant chickpea genotypes for use in strategic breeding programs aimed at improving yields and resistance to Fusarium wilt.

References

  1. 1. Dixit GP, Satheesh Naik S. Global research status of grain legumes. In: Fundamentals of legume breeding: a text for students and practitioners. Springer; 2025. p. 1–18. https://doi.org/10.1007/978-981-96-7838-9_1
  2. 2. Mogîldea A. Calitatea și siguranța produselor aglutenice de panificație cu utilizarea șroturilor. Universitatea Tehnică a Moldovei; 2025.
  3. 3. Mahbub R, Francis N, Blanchard C, Santhakumar A. The anti-inflammatory and antioxidant properties of chickpea hull phenolic extracts. Food Biosci. 2021;40:100850. https://doi.org/10.1016/j.fbio.2020.100850
  4. 4. Upadhyaya HD, Bajaj D, Narnoliya L, Das S, Kumar V, Gowda C, et al. Genome-wide scans for delineation of candidate genes regulating seed-protein content in chickpea. Front Plant Sci. 2016;7:302. https://doi.org/10.3389/fpls.2016.00302
  5. 5. Burton GW. Quantitative inheritance in grasses. 1952.
  6. 6. Singh RK, Chaudhary BD. Biometrical methods in quantitative genetic analysis. 1981.
  7. 7. Allard RW. Principles of plant breeding. John Wiley & Sons; 1999.
  8. 8. Team RC. R: a language and environment for statistical computing. 2020.
  9. 9. Charrad M, Ghazzali N, Boiteau V, Niknafs A. NbClust: an R package for determining the relevant number of clusters in a data set. J Stat Softw. 2014;61:1–36. https://doi.org/10.18637/jss.v061.i06
  10. 10. Hopkins B, Skellam JG. A new method for determining the type of distribution of plant individuals. Ann Bot. 1954;18(2):213–27. https://doi.org/10.1093/oxfordjournals.aob.a083391
  11. 11. Ketchen DJ, Shook CL. The application of cluster analysis in strategic management research: an analysis and critique. Strateg Manage J. 1996;17(6):441–58. https://doi.org/10.1002/(SICI)1097-0266(199606)17:6<441::AID-SMJ819>3.0.CO;2-G
  12. 12. Nunavath A, Murthy KGK, Hegde V, Reddy SM. Breeding for early flowering in chickpea (Cicer arietinum L.)–a key strategy to accelerate chickpea productivity: a review. Int J Environ Clim Change. 2020;10:271–85. https://doi.org/10.9734/ijecc/2020/v10i1230304
  13. 13. Gaur P, Pande S, Sharma H, Gowda C, Sharma K, Crouch J, et al. Genetic enhancement of stress tolerance in chickpea: present status and future prospects. 2007.
  14. 14. Yadav SS, Redden R, Chen W, Sharma B. Chickpea breeding and management. CABI; 2007. https://doi.org/10.1079/9781845932138.000
  15. 15. Poudel PP, Aryal L, Darai R, Sah R, Subedi K, Acharya B, et al. Genetic variability, heritability and genetic advance in lentil (Lens culinaris Medik.). J Agric Nat Resour. 2022;5(1):130–7. https://doi.org/10.3126/janr.v5i1.50698
  16. 16. Tarkeshwar SN, Vimal S, Chaudhary RP, Mishra G, Chaudhary AM, Singh VK. Genetic variability for seed yield and its components in Indian mustard [Brassica juncea (L.) Czern and Coss.] genotypes. Pharma Innov J. 2022;11(7):557–60.
  17. 17. Johnson HW, Robinson H, Comstock R. Estimates of genetic and environmental variability in soybeans. 1955. https://doi.org/10.2134/agronj1955.00021962004700070009x
  18. 18. Falconer DS. Introduction to quantitative genetics. Pearson Education India; 1996.
  19. 19. Deshmukh S, Basu M, Reddy P. Genetic variability, character association and path coefficients of quantitative traits in Virginia bunch varieties of groundnut. 1986.
  20. 20. Upadhyaya HD, Dwivedi SL, Baum M, Varshney RK, Udupa SM, Gowda CL, et al. Genetic structure, diversity and allelic richness in composite collection and reference set in chickpea (Cicer arietinum L.). BMC Plant Biol. 2008;8(1):106. https://doi.org/10.1186/1471-2229-8-106
  21. 21. Khan H, Paull J, Siddique K, Stoddard F. Faba bean breeding for drought-affected environments: a physiological and agronomic perspective. Field Crops Res. 2010;115(3):279–86. https://doi.org/10.1016/j.fcr.2009.09.003
  22. 22. Qulmamatova D. Chickpea (Cicer arietinum L.) genotypes evaluation for high yield through multivariate analysis. 2023. https://doi.org/10.54910/sabrao2023.55.1.10
  23. 23. Kenkel N, Derksen D, Thomas A, Watson P. Multivariate analysis in weed science research. Weed Sci. 2002;50(3):281–92. https://doi.org/10.1614/0043-1745(2002)050[0281:RMAIWS]2.0.CO;2
  24. 24. Jitendra, L.S. (2004). Inheritance of Wilt Resistance, Yield and Yield Contributing Traits in Chickpea (Cicer arietinum L.). M.Sc. (Ag.) Thesis, Department of Genetics and Plant Breeding, University of Agricultural Sciences, Dharwad, Karnataka, India.
  25. 25. Paul, M.P. (2001). Genetic Variability and Character Association Studies in Chickpea (Cicer arietinum L.). M.Sc. (Ag.) Thesis, Department of Agricultural Botany, Mahatma Phule Krishi Vidyapeeth, Rahuri, Maharashtra, India.
  26. 26. Mohanlal V, Saravanan K, Sabesan T. Application of principal component analysis (PCA) for blackgram [Vigna mungo (L.) Hepper] germplasm evaluation under normal and water stressed conditions. Legume Res. 2023;46(9).
  27. 27. Jain N, Babbar A, Rahangdale S. Principal component analysis of promising advance chickpea lines (Cicer arietinum L.). Environ Ecol. 2023;41(4D):3010–4. https://doi.org/10.60151/envec/FWZP3528
  28. 28. Rahangdale S, Singh Y, Upadhyay P, Koutu G. Principal component analysis of JNPT lines of rice for the important traits responsible for yield and quality. Indian J Genet Plant Breed. 2021;81(1):127–31. https://doi.org/10.31742/IJGPB.81.1.14
  29. 29. Syed M, Islam M, Hossain M, Alam M, Amin M. Genetic divergence in chickpea (Cicer arietinum L.). Bangladesh J Agric Res. 2012;37(1):129–36. https://doi.org/10.3329/bjar.v37i1.11184
  30. 30. Redden R, Chen W, Sharma B. Chickpea breeding and management. United Kingdom: CABI; 2005.
  31. 31. Mohammadi SA, Prasanna B. Analysis of genetic diversity in crop plants—salient statistical tools and considerations. Crop Sci. 2003;43(4):1235–48. https://doi.org/10.2135/cropsci2003.1235
  32. 32. Mondini L, Noorani A, Pagnotta MA. Assessing plant genetic diversity by molecular tools. Diversity. 2009;1(1):19–35. https://doi.org/10.3390/d1010019
  33. 33. Fu Y-B. Understanding crop genetic diversity under modern plant breeding. Theor Appl Genet. 2015;128(11):2131–42. https://doi.org/10.1007/s00122-015-2585-y

Downloads

Download data is not yet available.