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

Research Articles

Vol. 12 No. sp3 (2025): Advances in Plant Health Improvement for Sustainable Agriculture

Estimation of genetic variability and association studies for yield in the OsPSTOL1 gene introgressed F4 in rice (Oryza sativa L.) for phosphorus deficiency tolerance

DOI
https://doi.org/10.14719/pst.10458
Submitted
4 July 2025
Published
15-10-2025

Abstract

In India, nearly 85 % of the soils are phosphorus-deficient, significantly limiting rice production. Yield improvement based solely on phenotypic selection is challenging due to its polygenic control. This study aimed to identify superior segregants with high yield potential and phosphorus deficiency tolerance. During the Rabi season of 2020, an F4 population derived from the cross Anna (R) 4 × IR 64 Pup1 was evaluated under a non-replicated trial at the Agricultural College and Research Institute, Killikulam. Observations were recorded for eight biometrical traits. A high magnitude of variability was observed among the genotypes, as indicated by high mean values and a wide range for all studied traits. Phenotypic coefficient of variation (PCV) was higher than genotypic coefficient of variation (GCV), signifying environmental influence, while high heritability was observed for most traits. Traits such as the number of tillers per plant, number of productive tillers per plant, number of filled grains per panicle, phosphorus content in shoot, hundred-seed weight and single-plant yield exhibited high heritability coupled with high genetic advance as a percentage of the mean, indicating the predominance of additive gene action, thus making selection effective. Correlation analysis revealed that days to 50 % flowering, number of tillers per plant and panicle length had a strong positive association with grain yield per plant, suggesting their significance in yield improvement. Path analysis indicated that panicle length (0.3012) had the highest positive direct effect on grain yield per plant, followed by phosphorus content in shoot (0.2366), number of productive tillers per plant (0.1965), hundred-seed weight (0.1623) and number of filled grains per panicle (0.1423), while acid phosphatase (-0.172) and number of tillers per plant (-0.0923) had negative direct effects. Based on the overall evaluation, eleven superior families (AI-17, AI-15, AI-19, AI-16, AI-84, AI-64, AI-53, AI-71, AI-82, AI-48 and AI-11) were identified for further breeding programs aimed at improving phosphorus starvation tolerance in rice.

References

  1. 1. Samal P, Babu SC, Mondal B, Mishra SN. The global rice agriculture towards 2050: An inter-continental perspective. Outlook Agric. 2022;51(2):164–72. https://doi.org/10.1177/00307270221088338
  2. 2. Harisha T, Shruti, Anantha MS, Gireesh C, Sundaram RM, Senguttuvel P, et al. Exploring genetic variability for yield and its attributing traits in rice (Oryza sativa L.) under low soil phosphorous condition. Asian J Soil Sci Plant Nutr. 2024;10(3):297–303. https://doi.org/10.9734/ajsspn/2024/v10i3340
  3. 3. Nirubana V, Vanniarajan C, Aananthi N, Banumathy S, Thiyageshwari S, Ramalingam J. Genetic variability, cause and effect analysis for yield components and phosphorous content in rice (Oryza sativa L.) genotypes. Electron J Plant Breed. 2019;10(3):1011–8. https://doi.org/10.5958/0975-928X.2019.00130.3
  4. 4. Swamy HM, Anila M, Kale RR, Bhadana VP, Anantha MS, Brajendra P, et al. Phenotypic and molecular characterization of rice germplasm lines and identification of novel sources for low soil phosphorus tolerance in rice. Euphytica. 2019;215(7):118. https://doi.org/10.1007/s10681-019-2443-0
  5. 5. Richardson AE, Lynch JP, Ryan PR, Delhaize E, Smith FA, Smith SE, et al. Plant and microbial strategies to improve the phosphorus efficiency of agriculture. Plant Soil. 2011;349:121–56. https://doi.org/10.1007/s11104-011-0950-4
  6. 6. Manoj CA, Muralidhara B, Basavaraj PS, Gireesh C, Sundaram RM, Senguttuvel P, et al. Evaluation of rice genotypes for low phosphorus stress and identification of tolerant genotypes using stress tolerance indices. Indian J Genet Plant Breed. 2023;83(1):24–31. https://doi.org/10.31742/ISGPB.83.1.4
  7. 7. Burton GW. Quantitative inheritance in grasses. Proc VI Int Grassl Cong. 1952:277–83.
  8. 8. Lush J. Intra-sire correlations or regressions on dam as a method of estimating heritability of characteristics. Proc Am Soc Anim Prod. 1940;33:293–301. https://doi.org/10.2527/jas1940.19401293x
  9. 9. Johnson HW, Robinson H, Comstock R. Estimates of genetic and environmental variability in soybeans. Agron J. 1955;47(7):314–8. https://doi.org/10.2134/agronj1955.00021962004700070009x
  10. 10. Goulden C. Methods of statistical analysis. 2nd ed. New York: John Wiley & Sons, Inc. 1952.
  11. 11. Gowthami Sanku, Juliet Hepziba S, Sheeba A, Hemalatha G, Senthil K. Genetic variability and relatedness among yield characters in rice landraces and improved varieties. Electron J Plant Breed. 2023;13(3):932–9. https://doi.org/10.37992/2022.1303.122
  12. 12. Sundaram KM, Rajeswari S, Saraswathi R, Jeyaprakash P. Heterosis and combining ability analysis for yield-related traits in rice hybrids involving land races. Electron J Plant Breed. 2019;10(1):92–100. https://doi.org/10.5958/0975-928X.2019.00011.5
  13. 13. Tiwari DN, Tripathi SR, Tripathi MP, Khatri N, Bastola BR. Genetic variability and correlation coefficients of major traits in early maturing rice under rainfed lowland environments of Nepal. Adv Agric. 2019. https://doi.org/10.1155/2019/5975901
  14. 14. Saha SR, Hassan L, Haque MA, Islam MM, Rasel M. Genetic variability, heritability, correlation and path analyses of yield components in traditional rice (Oryza sativa L.) landraces. J Bangladesh Agric Univ. 2019;17(1):26–32. https://doi.org/10.3329/jbau.v17i1.40659
  15. 15. Dey P, Sahu S, Kar RK. Estimation of phenotypic coefficients of variation (PCV), genotypic coefficients of variation (GCV), heritability and genetic gain for yield and its components in rice landraces of Odisha. Int Agric J Environ Biotechnol. 2019;12(3):181–5. https://doi.org/10.30954/0974-1712.08.2019.1
  16. 16. Nath S, Kole PC. Genetic variability and yield analysis in rice. Electron J Plant Breed. 2021;12(1):253–8. https://doi.org/10.37992/2021.1201.039
  17. 17. Nandeshwar B, Pal S, Senapati B, De D. Genetic variability and character association among biometrical traits in F2 generation of some rice crosses. Electron J Plant Breed. 2010;1(4):758–63.
  18. 18. Savitha P, UshaKumari R. Assessment of genetic variability and correlation studies among traditional land races and improved cultivars for segregating generations of rice (Oryza sativa L.). Int J Sci Nat. 2015;6(2):135–40.
  19. 19. Behera PP, Singh SK, Singh DK. Genetic association study of rice (Oryza sativa L.) genotypes for yield and yield attributing traits over five different locations. Plant Arch. 2020;20(2):5191–6.
  20. 20. Palaniyappan S, Arunachalam P, Banumathy S, Mini ML, Muthuramu S. Genetic parameters and character association studies in rice (Oryza sativa L.). J Pharmacogn Phytochem. 2020;9(3):1652–7.
  21. 21. Thonta R, Pandey MK, Kumar R, Santhoshini. Analysis of genetic variability, heritability and genetic advance for growth and yield attributes in green gram (Vigna radiata L. Wilczek). Int J Stat Appl Math. 2023;8(3S):43–7. https://doi.org/10.22271/maths.2023.v8.i3Sa.996
  22. 22. Vennela M, Srinivas B, Reddy VR, Balram N. Studies on correlation and path coefficient analysis in hybrid rice (Oryza sativa L.) for yield and quality traits. Int J Bio-Resour Stress Manag. 2021;12(5):496–505. https://doi.org/10.23910/1.2021.2199
  23. 23. Idris A. Genetic variability and interrelationship between yield and yield components in some rice genotypes. Am J Agric. 2012;2:233–9. https://doi.org/10.9734/AJEA/2012/961
  24. 24. Abhilash R, Thirumurugan T, Sassikumar D, Chitra S. Genetic studies in F2 for biometrical traits in rice (Oryza sativa L.). Electron J Plant Breed. 2018;9(3):1067–76. https://doi.org/10.5958/0975-928X.2018.00133.3
  25. 25. Sudeepthi K, Srinivas T, Kumar BR, Jyothula D, Umar SN. Assessment of genetic variability, character association and path analysis for yield and yield component traits in rice (Oryza sativa L.). Electron J Plant Breed. 2020;11(1):144–8. https://doi.org/10.37992/2020.1101.026
  26. 26. Patel H, Patel V, Patel P, Rathod A, Pampaniya A. Genetic variability, correlation and path analysis for grain yield and component traits in F3 segregating population of rice (Oryza sativa L.). Int J Chem Stud. 2018;6(2):2327–31. https://doi.org/10.22271/chemi
  27. 27. Tripathi N, Verma O, Singh P, Rajpoot P. Studies on correlation and path coefficient analysis for yield and its components in rice (Oryza sativa L.) under salt affected soil. J Pharmacogn Phytochem. 2018;7(3):1626–9.
  28. 28. Kumar S, Chauhan M, 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):2161–4.
  29. 29. Veni BK, Lakshmi BV, Ramana J. Variability and association studies for yield components and quality parameters in rice genotypes. J Rice Res. 2013;6(2):16–23.
  30. 30. Ajmera S, Sudheer KS, RBB. Evaluation of genetic variability, heritability and genetic advance for yield and yield components in rice genotypes. Int J Pure Appl Biosci. 2017;5(4):909–15. https://doi.org/10.18782/2320-7051.5495
  31. 31. Shet RM, Rajanna M, Jagadeesha N. Genetic variability and association studies of rice (Oryza sativa L.) for iron and phosphorus use efficiency under aerobic condition. J Chem Stud. 2018;6(6):1574–7.
  32. 32. Behera B, Sahu S, Kar RK, Pandey RK. Studies on genetic variability for some metric traits in slender grain rice genotypes. J Appl Nat Sci. 2018;10(1):375–8. https://doi.org/10.31018/jans.v10i1.1633
  33. 33. Bhargava K, Shivani D, Pushpavalli SNCVL, Sundaram RM, Beulah P, Senguttuvel P. Genetic variability, correlation and path coefficient analysis in segregating population of rice. Electron J Plant Breed. 2021;12(2):549–55. https://doi.org/10.37992/2021.1202.077
  34. 34. Edukondalu B, Reddy VR, Rani TS, Kumari CA, Soundharya B. Studies on variability, heritability, correlation and path analysis for yield, yield attributes in rice (Oryza sativa L.). Int J Curr Microbiol Appl Sci. 2017;6(10):2369–76. https://doi.org/10.20546/ijcmas.2017.610.279
  35. 35. Lalitha R, Mothilal A, Arunachalam P, Senthil N, Hemalatha G. Genetic variability, correlation and path analysis of grain yield, grain quality and its associated traits in EMS derived M4 generation mutants of rice (Oryza sativa L.). Electron J Plant Breed. 2019;10(3):1140–7. https://doi.org/10.5958/0975-928X.2019.00145.5
  36. 36. Konate AK, Zongo A, Kam H, Sanni A, Audebert A. Genetic variability and correlation analysis of rice (Oryza sativa L.) inbred lines based on agro-morphological traits. Afr J Agric Res. 2016; 11(35):3340–46. https://doi.org/10.5897/ajar2016.11415
  37. 37. Md yaseen SK, Aananthi N, Pillai MA, Shoba D, Manikandan K. Genetic variability and frequency distribution studies for yield in OsPSTOL1 gene introgressed segregating populations of rice (Oryza sativa L.). J Pharmacogn Phytochem. 2020;9(3):809–12.
  38. 38. Sritama K, Biswajit P, Sabyasachi K. Study of genetic parameters and character association of different agro-morphological characters in some paddy genotypes for saline and non-saline belts of West Bengal, India. Res J Agric For Sci. 2015;3(5):6–15.
  39. 39. Bhuvaneswari S, Kumar S, Singh M, Takhellambam S, Shashidhar K, Singh R, et al. Genetic variability and association studies on grain yield components in F2 populations of black rice (Oryza sativa L.) of Manipur. IJHF. 2016. https://epubs.icar.org.in/index.php/IJHF/article/view/54802
  40. 40. Ekka RE, Sarawgi A, Kanwar RR. Genetic variability and inter-relationship analysis for various yield attributing and quality traits in traditional germplasm of rice (Oryza sativa L.). Plant Arch. 2015;15(2):637–45.
  41. 41. Shet RM, Rajanna MP, Jagadeesha N. Genetic variability and association studies of rice (Oryza sativa L.) for iron and phosphorus use efficiency under aerobic condition. J Pharmacogn Phytochem. 2018;7(6):1263–6.
  42. 42. Pavan Shankar H, Veni BK, Babu JDP, Rao VS. Assessment of genetic variability and association studies in dry direct sown rice (Oryza sativa L.). J Rice Res. 2016;9(2):11–6.
  43. 43. Sanghera GS, Kashyap SC, Parray GA. Genetic variation for grain yield and related traits in temperate red rice (Oryza sativa L.) ecotypes. Not Sci Biol. 2013;5(3):400–6. https://doi.org/10.15835/nsb539088
  44. 44. Akhtar N, Nazir M, Rabnawaz A, Mahmood T, Safdar M, Asif M, et al. Estimation of heritability, correlation and path coefficient analysis in fine grain rice (Oryza sativa L.). J Anim Plant Sci. 2011;21(4):660–4.
  45. 45. Babu VR, Shreya K, Dangi KS, Usharani G, Shankar AS. Correlation and path analysis studies in popular rice hybrids of India. Int J Sci Res Publ. 2012;2(3):1–5.
  46. 46. Maavimani M, Jebaraj S, Raveendran M. Assessment of genetic variability in recombinant inbred lines of rice derived from high temperature tolerant parent. Oryza. 2014;51(1):1–5.
  47. 47. Vanisree S, Swapna K, Raju CD, Raju CS, Sreedhar M. Genetic variability and selection criteria in rice. J Biol Sci Opin. 2013;1(4):341–6. https://doi.org/10.7897/2321-6328.01413
  48. 48. Nandan R, Singh S. Character association and path analysis in rice (Oryza sativa L.) genotypes. World J Agric Sci. 2010;6(2):201–6.
  49. 49. 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
  50. 50. Kiruba G, Aananthi N, Vellaikumar S, Thiyageshwari S, Ramalingam J. Genetic background influences phosphorous deficiency tolerance in rice (Oryza sativa L.). Int J Chem Stud. 2019;7(4):170–6.

Downloads

Download data is not yet available.