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

Research Articles

Vol. 13 No. 2 (2026)

Genetic divergence analysis in rice (Oryza sativa L.) genotypes for agro-morphological, yield and quality traits using Mahalanobis D2 and principal component analysis

DOI
https://doi.org/10.14719/pst.12077
Submitted
2 October 2025
Published
12-05-2026 — Updated on 19-05-2026
Versions

Abstract

Rice (Oryza sativa L.) is a major staple crop and a primary food source for a large proportion of the global population. Market acceptance of rice is governed by yield components and grain quality attributes. Assessment of genetic diversity and trait interrelationships supports effective selection in breeding programs. The present investigation was undertaken with 28 rice genotypes to estimate the nature and magnitude of genetic diversity for 22 agro-morphological, yield and quality traits. The experiment was laid out in randomized block design (RBD) with 2 replications and observations were recorded. D² statistics revealed substantial inter-cluster divergence with 4 distinct clusters identified, with the maximum inter-cluster distance was observed between cluster I and II, while the minimum inter-cluster distance was recorded between cluster I and III. Cluster IV exhibited the highest intra-cluster distance, comprising 10 genotypes followed by cluster II with 6 genotypes. Genotypes from the most divergent groups, cluster I (HUR-105 NDR-359, Sarjoo-52) and cluster II (HUR-1304, BB 11, Syamjeera and Dhaniya) were identified as potential parents for future hybridisation program. Traits including thousand seed weight, kernel length before cooking and spikelet fertility percent contributed most to total genetic divergence. Principal component analysis (PCA) showed that first 7 principal components with eigenvalues more than 1 accounted for 80.80 % of total cumulative variability, with thousand seed weight, spikelet fertility percent and kernel length before cooking emerging as major contributors. NDR-359, HUR-105, HUR-917 and Sampurna can be targeted as parents as they are diverse in nature as indicated by PCA biplot. The results demonstrate substantial phenotypic divergence among genotypes and support their strategic use in rice improvement programs.

References

  1. 1. Fukagawa NK, Ziska LH. Rice: importance for global nutrition. J Nutr Sci Vitaminol (Tokyo). 2019;65(Suppl):S2–3. https://doi.org/10.3177/jnsv.65.S2
  2. 2. Paillard S, Treyer S, Dorin B, editors. Agrimonde-scenarios and challenges for feeding the world in 2050. Dordrecht: Springer; 2014. https://doi.org/10.1007/978-94-017-8745-1
  3. 3. Jagadish SVK, Septiningsih EM, Kohli A, Thomson MJ, Ye C, Redona E, et al. Genetic advances in adapting rice to a rapidly changing climate. J Agron Crop Sci. 2012;198(5):360–73. https://doi.org/10.1111/j.1439-037X.2012.00525.x
  4. 4. Tuhina-Khatun M, Hanafi MM, Rafii MY, Wong MY, Salleh FM, Ferdous J. Genetic variation, heritability and diversity analysis of upland rice (Oryza sativa L.) genotypes based on quantitative traits. Biomed Res Int. 2015;2015:290861. https://doi.org/10.1155/2015/290861
  5. 5. Abebe T, Alamerew S, Tulu L. Genetic variability, heritability and genetic advance for yield and its related traits in rainfed lowland rice (Oryza sativa L.) genotypes at Fogera and Pawe, Ethiopia. Adv Crop Sci Technol. 2017;5(2):272. https://doi.org/10.4172/2329-8863.1000272
  6. 6. Begna T. Role and economic importance of crop genetic diversity in food security. Int J Agric Sci Food Technol. 2021;7(1):164–9. https://doi.org/10.17352/2455-815X.000104
  7. 7. Ceoloni C, Kuzmanović L, Ruggeri R, Rossini F, Forte P, Cuccurullo A, et al. Harnessing genetic diversity of wild gene pools to enhance wheat crop production and sustainability: challenges and opportunities. Diversity (Basel). 2017;9(4):55. https://doi.org/10.3390/d9040055
  8. 8. Singh HP, Raigar OP, Chahota RK. Estimation of genetic diversity and its exploitation in plant breeding. Bot Rev. 2022;88(3):413–35. https://doi.org/10.1007/s12229-021-09274-y
  9. 9. Leys C, Klein O, Dominicy Y, Ley C. Detecting multivariate outliers: use a robust variant of the Mahalanobis distance. J Exp Soc Psychol. 2018;74:150–6. https://doi.org/10.1016/j.jesp.2017.09.011
  10. 10. Mahalanobis PC. On the generalized distance in statistics. Sankhya A. 2018;80 (Suppl 1):S1–7. https://doi.org/10.1007/s13171-019-00164-5
  11. 11. Ghorbani H. Mahalanobis distance and its application for detecting multivariate outliers. Facta Univ Ser Math Inform. 2019;34(3):583–95. https://doi.org/10.22190/FUMI1903583G
  12. 12. Venujayakanth B, Dudhat AS, Swaminathan B, Anurag ML. Assessing crop genetic diversity using principal component analysis: a review. Trends Biosci. 2017;10(2):523–8.
  13. 13. Azam MG, Hossain MA, Sarker U, Alam AM, Nair RM, Roychowdhury R, et al. Genetic analyses of mungbean [Vigna radiata (L.) Wilczek] breeding traits for selecting superior genotype(s) using multivariate and multi-traits indexing approaches. Plants (Basel). 2023;12(10):1984. https://doi.org/10.3390/plants12101984
  14. 14. Rao CR. Advanced statistical methods in biometric research. New York: John Wiley & Sons; 1952.
  15. 15. Gewers FL, Ferreira GR, Arruda HF, Silva FN, Comin CH, Amancio DR, et al. Principal component analysis: a natural approach to data exploration. ACM Comput Surv. 2021;54(4):1–34. https://doi.org/10.1145/3447755
  16. 16. Rathan ND, Singh SK, Singh RK, Singh DK. Estimating genetic divergence in rice (Oryza sativa L.) using D² analysis. Electron J Plant Breed. 2020;11(3):836–40. https://doi.org/10.37992/2020.1103.137
  17. 17. Dey S, Badri J, Eswari KB, Prakasam V. Diversity analysis for yield traits and sheath blight resistance in rice genotypes. Electron J Plant Breed. 2020;11(1):60–4.
  18. 18. Singh SK, Manoj Kumar SC, Korada M, Khaire AR, Majhi PK, Singh DK, et al. Genetic variability and divergence studies for yield and its related traits in rice (Oryza sativa L.). Biol Forum Int J. 2021;13(4):687–95. https://doi.org/10.9734/ijpss/2021/v33i2430799
  19. 19. Dinesh K, Devi MS, Sreelakshmi C, Paramasiva I. Exploring the genetic diversity for yield and quality traits in indigenous landraces of rice (Oryza sativa L.). Electron J Plant Breed. 2023;14(2):502–10. https://doi.org/10.37992/2023.1402.055
  20. 20. Remme RN, Joti S, Islam MZ. Agro-morphological characterization and genetic diversity assessment of nineteen BRRI-released rice varieties. Khulna Univ Stud. 2024;21(1):978. https://doi.org/10.53808/KUS.2024.21.01.978-ls
  21. 21. Khaire AR, Singh SK, Jayasudha M, Singh A, Singh DK, Mounika K, et al. Divergence study of rice (Oryza sativa L.) genotypes for consumer-preferred quality traits. J Exp Agric Int. 2022;44(11):151–8. https://doi.org/10.9734/jeai/2022/v44i112061
  22. 22. Singh RJ, Kollipara KP, Hymowitz T. Intersubgeneric hybridization of soybeans with a wild perennial species, Glycine clandestina Wendl. Theor Appl Genet. 1987;74(3):391–6. https://doi.org/10.1007/BF00274723
  23. 23. Chandraker P, Sharma B, Parikh M, Saxena RR. Assessment of genetic diversity in aromatic short grain rice (Oryza sativa L.) genotypes using PCA and cluster analysis. Int J Plant Soil Sci. 2024;36(5):82–94. https://doi.org/10.9734/ijpss/2024/v36i54504
  24. 24. Mounika K, Singh SK, Pandey V, Singh DK, Khaire AR, Habde S, et al. Study of genetic divergence in rice (Oryza sativa L.) genotypes with high grain zinc using Mahalanobis' D² analysis. Electron J Plant Breed. 2020;11(2):367–72. https://doi.org/10.37992/2020.1102.065

Downloads

Download data is not yet available.