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Plant Science Today (PST)

Research Articles

Early Access

Genetic variability and yield-determining traits in rice (Oryza sativa L.) genotypes under Bastar Plateau agro-climatic conditions

DOI
https://doi.org/10.14719/pst.14043
Submitted
8 February 2026
Published
12-11-2026

Abstract

Rice (Oryza sativa L.) is a major staple crop worldwide and improving grain yield (GY) through the identification of superior genotypes and yield-contributing traits is essential for sustainable production. This study evaluated genetic variability, character association, path coefficients and genetic diversity among 64 lowland rice genotypes under transplanted conditions using a randomised complete block design (RCBD) with two replications. Significant variability was observed among the genotypes for yield and related traits. The highest GY values were recorded in AD 21303, TKR 35, MTU 1436, RP6811-8-1-3-2-1-1-B and OR 2573-3, indicating their potential for use in breeding programmes. High heritability coupled with high genetic advance (GA) was observed for kernel length-to-breadth ratio (KLB), grain length-to-breadth ratio (GLB), kernel breadth (KB) and related traits, suggesting the predominance of additive gene action. The genotypic coefficient of variation (GCV) was slightly lower than the phenotypic coefficient of variation (PCV), indicating environmental influence on trait expression. Grain yield showed significant positive correlations with days to 50 % flowering (DFF), days to maturity (DM), number of effective tillers per plant (NET), panicle length (PL), flag leaf length (FLL), number of grains per panicle (NGPP), test weight (TW), biological yield (BY) and harvest index (HI). Path coefficient analysis revealed that BY had the highest positive direct effect on GY, followed by HI, TW, flag leaf width (FLW), FLL, NGPP, PL, kernel length (KL) and KLB ratio. Cluster analysis grouped the genotypes into seven clusters, with Cluster III exhibiting superior mean performance for GY and associated traits. The DFF, DM and plant height (PH) contributed most to genetic divergence. The results indicate substantial genetic variability and identify BY, HI, TW and grain traits as important selection criteria for improving rice yield.

References

  1. 1. Mohapatra PK, Sahu BB. Botany of rice plant. In: Mohapatra PK, Sahu BB, editors. Panicle architecture of rice and its relationship with grain filling. Cham: Springer; 2022. p. 27–48. https://doi.org/10.1007/978-3-030-67897-5
  2. 2. Bal SK, Sattar A, Nidhi, Chandran MAS, Subba Rao AVM, Manikandan N, et al. Critical weather limits for paddy rice under diverse ecosystems of India. Front Plant Sci. 2023;14:1226064. https://doi.org/10.3389/fpls.2023.1226064
  3. 3. Fahad S, Adnan M, Noor M, Arif M, Alam M, Khan IA, et al. Major constraints for global rice production. In: Hasanuzzaman M, Fujita M, Nahar K, editors. Advances in rice research for abiotic stress tolerance. London: Academic Press; 2019. p. 1–22. https://doi.org/10.1016/B978-0-12-814332-2.00001-0
  4. 4. Shukla A. Ethnic food culture of Chhattisgarh state of India. J Ethn Foods. 2021;8:28.
  5. https://doi.org/10.1186/s42779-021-00103-6
  6. 5. Jambhulkar NN, Mondal B, Paul S, Kumar GAK. Assessing the district-wise growth and instability of rice production in Chhattisgarh, India. Asian J Agric Ext Econ Sociol. 2024;42(11):87–95. https://doi.org/10.9734/ajaees/2024/v42i112593
  7. 6. Bhuarya HK, Sastri ASRAS, Chandrawanshi SK, Bobade P, Kaushik DK. Agro-climatic characterization for agro-climatic zone of Chhattisgarh. Int J Curr Microbiol Appl Sci. 2018;7(8):108–17. https://doi.org/10.20546/ijcmas.2018.708.013
  8. 7. Panse VG, Sukhatme PV. Statistical methods for agricultural workers. 4th ed. New Delhi: Indian Council of Agricultural Research; 1985.
  9. 8. Falconer DS, Mackay TFC. Introduction to quantitative genetics. 4th ed. Essex: Longman; 1996.
  10. 9. Allard RW. Principles of plant breeding. 2nd ed. New York: John Wiley & Sons; 1999.
  11. 10. Burton GW. Quantitative inheritance in grasses. Proc 6th Int Grassland Congr. 1952;1:277–83.
  12. 11. Singh RK. Biometrical methods in quantitative genetic analysis. New Delhi: Kalyani Publishers; 1981.
  13. 12. Dewey DR, Lu K. A correlation and path-coefficient analysis of components of crested wheatgrass seed production. Agron J. 1959;51(9):515–18. https://doi.org/10.2134/agronj1959.00021962005100090002x
  14. 13. Mahalanobis PC. On the generalized distance in statistics. Sankhya A. 2018;80:S1–S7.
  15. https://doi.org/10.1007/s13171-019-00164-5
  16. 14. Doshi SP, Gupta KC. Statistical package for agricultural research. New Delhi: IASRI; 1991.
  17. 15. Habib MA, Azam MG, Haque MA, Hassan L, Khatun MS, Nayak S, et al. Climate-smart rice (Oryza sativa L.) genotypes identification using stability analysis, multi-trait selection index and genotype-environment interaction at different irrigation regimes with adaptation to universal warming. Sci Rep. 2024;14(1):13836.
  18. https://doi.org/10.1038/s41598-024-64808-9
  19. 16. Chacko A, Jayalekshmy VG, Shahiba AM. Studies on PCV, GCV, heritability and genetic advance in rice genotypes for yield and yield components. Int J Plant Soil Sci. 2023;35(16):324–30. https://doi.org/10.9734/ijpss/2023/v35i163266
  20. 17. Kumar S, Chauhan MP, Tomar A, Kasana RK. Coefficient of variation (GCV & PCV), heritability and genetic advance analysis for yield contributing characters in rice (Oryza sativa L.). J Pharmacogn Phytochem. 2018;7(3):2161–64.
  21. 18. Girma BT, Kitil MA, Banje DG, Biru HM, Serbessa TB. Genetic variability study of yield and yield related traits in rice (Oryza sativa L.) genotypes. Adv Crop Sci Tech. 2018;6(4):381. https://doi.org/10.4172/2329-8863.1000381
  22. 19. Schmidt P, Hartung J, Bennewitz J, Piepho HP. Heritability in plant breeding on a genotype-difference basis. Genetics. 2019;212(4):991–1008. https://doi.org/10.1534/genetics.119.302134
  23. 20. Roy SC, Shil P. Assessment of genetic heritability in rice breeding lines based on morphological traits and caryopsis ultrastructure. Sci Rep. 2020;10(1):7830. https://doi.org/10.1038/s41598-020-63976-8
  24. 21. Satasiya PN, Patel RK, Joshi H, Patel VP, Parekh VB, Modha KG, et al. Studies on heterosis, heritability and genetic advance using generation mean analysis for yield and its attributing traits in rice under saline conditions. Pharma Innov J. 2022;11(9):2269–74.
  25. 22. Heera PK, Ram M, Murali S. Studies on correlation and path coefficient for yield and its contributing traits in rice (Oryza sativa L.). Int J Environ Clim Change. 2023;13(8):1305–20. https://doi.org/10.9734/ijecc/2023/v13i82077
  26. 23. Oladosu Y, Rafii MY, Magaji U, Abdullah N, Miah G, Chukwu SC, et al. Genotypic and phenotypic relationship among yield components in rice under tropical conditions. Biomed Res Int. 2018;2018:8936767. https://doi.org/10.1155/2018/8936767
  27. 24. Devi KR, Chandra BS, Lingaiah N, Hari Y, Venkanna V. Analysis of variability, correlation and path coefficient studies for yield and quality traits in rice (Oryza sativa L.). Agric Sci Dig. 2017;37(1):1–9. https://doi.org/10.18805/asd.v0iOF.7328
  28. 25. Mahalanobis PC. A statistical study at Chinese head measurement. J Asiatic Soc Bengal. 1928;25:301–77.
  29. 26. Murty BR, Arunachalam V. The nature of genetic divergence in relation to breeding system in some crop plants. Indian J Genet Plant Breed. 1966;26:188–98.

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