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

Research Articles

Vol. 12 No. 1 (2025)

Unveiling genetic variability and cause-effect relationships of morphological traits of rice (Oryza sativa L.) genotypes in the Terai agro-climatic zone of West Bengal

DOI
https://doi.org/10.14719/pst.6729
Submitted
16 December 2024
Published
04-03-2025 — Updated on 18-03-2025
Versions

Abstract

Rice (Oryza sativa L.) is an essential staple food crop for billions of people worldwide. Understanding genetic variability is essential for breeding programs targeting yield and resilience. This study assessed 52 rice genotypes across two kharif seasons (2021–23) in West Bengal's Terai zone. Key traits were evaluated using a randomized block design (RBD) with three replications. Key characteristics such as plant height (PH), days to 50 % flowering (DFF), number of effective tillers per plant (NETP), panicle length (PL), number of grains per panicle (NGPP),1000–grain weight (TGW), grain yield per plant (GY_P), and harvest index (HI) were assessed. Statistical analysis revealed significant variability among genotypes. Among the traits, GY_P exhibited the highest genotypic (GCV) and phenotypic coefficients of variation (PCV). Additionally, PH, NETP, NGPP, TGW, HI and GY_P exhibited high heritability and genetic advancement, highlighting their potential for selection. Path coefficient analysis identified HI and NGPP as the most influential traits that directly and positively affected GY_P. Principal Component Analysis (PCA) attributed 78.61 % of the total variation to four principal components, highlighting HI, GY_P, NGPP and PL as major contributors to genetic diversity. Genotypes such as Piolee, Motia Saru, and Dhan Sali exhibited superior performance for specific traits, making them potential candidates for future breeding programs. These findings emphasize the potential of exploiting genetic variability in rice to develop high–yielding, regionally adapted varieties, contributing to sustainable agricultural practices in the Terai region.

References

  1. Hashim N, Ali MM, Mahadi MR, Abdullah AF, Wayayok A, Kassim MSM, Jamaluddin A. Smart farming for sustainable rice production: An insight into application, challenge and future prospect. Rice Sci. 2024;31(1):47–61. https://doi.org/10.1016/j.rsci.2023.08.004
  2. Costello C, Cao L, Gelcich S, Cisneros–Mata MÁ, Free CM, Froeh–lich HE, et al. The future of food from the sea. Nature. 2020;588:95–100. https://doi.org/10.1038/s41586-020-2616-y
  3. Martos V, Ahmad A, Cartujo P, Ordoñez J. Ensuring agricultural sustainability through remote sensing in the era of agriculture 5.0. Appl Sci. 2021;11(5911):1–26. https://doi.org/10.3390/app11135911
  4. Sadhu S, Kole PC. Revolutionizing rice grain quality: A holistic review integrating conventional and molecular approaches. Int J Bio–Res Stress Manage. 2024;15(8):01–12. https://doi.org/10.23910/1.2024.5489
  5. Bhargavi B, Suneetha Y, Kumar J A, Srinivas T. Genetic divergence studies for yield and quality traits in high protein landraces of rice (Oryza sativa L.). Plant Sci Today. 2023;10(2):195–204. https://doi.org/10.14719/pst.2091
  6. Bordoloi D, Sarma D, Barua SN, Das R, Das BK. Morpho–molecular and nutritional profiling for yield improvement and value addition of indigenous aromatic Joha rice of Assam. Sci Rep. 2024;14(3509):1–23. https://doi.org/10.1038/s41598–023–42874–9
  7. Debsharma SK, Syed MA, Ali MH, Maniruzzaman S, Roy PR, Brestic M, et al. Harnessing on genetic variability and diversity of rice (Oryza sativa L.) genotypes based on quantitative and qualitative traits for desirable crossing materials. Genes. 2023;14(10):1–21. https://doi.org/10.3390/genes14010010
  8. Khan MA, Mahmud A, Islam MN, Ghosh UK, Hossain MS. Genetic variability and agronomic performances of rice genotypes in different growing seasons in Bangladesh. J Agric Food Res. 2023;14(100750):1–12. https://doi.org/10.1016/j.jafr.2023.100750
  9. Emi FR, Khatun H, Yasmine F, Hasan AK, Hossain MA. Morphological variability and genetic diversity of Aman rice germplasm of Bangladesh cultivated in Mymensingh region. Plant Sci Today. 2021;8(4):972–85. https://doi.org/10.14719/pst.1304
  10. Acevedo–Siaca LG, Coe R, Quick WP, Long SP. Evaluating natural variation, heritability and genetic advance of photosynthetic traits in rice (Oryza sativa). Plant Breed. 2021;140(5):745–57. https://doi.org/10.1111/pbr.12965
  11. Sur B, Rout S, Singla S, Mandal R, Nath S, Maying B, et al. Evaluation of mustard genotypes [Brassica juncea (L.) Czern and Coss] for quantitative traits and character association of seed yield and yield components at sub Himalayan region of West Bengal (India). Plant Sci Today. 2023;10(1):166–78. https://doi.org/10.14719/pst.1948
  12. Teklu DH, Shimelis H, Tesfaye A, Mashilo J. Genetic diversity and association of yield-related traits in sesame. Plant Breed. 2021;140(2):331–41. https://doi.org/10.1111/pbr.12911
  13. Kumari M, Babu GS, Patil NB. Genetic diversity analysis of rice (Oryza sativa L.) germplasm under aerobic condition. Bangladesh J Bot. 2016;45(3):727–32.
  14. Ali Z, Naeem M, Rafiq I, Ikram RM, Akram MI, Taha EI, et al. Study of variability, combining ability, genetics and association of different yield-related traits in rice (Oryza sativa L.) genotypes. Genet Resour Crop Evol. 2024;23:1–4. https://doi.org/10.1007/s10722-024-02131-y
  15. Sruthi SR, Laleeth KN, Kishore D, Ivin JS, Anbuselvam Y. Principal component analysis in rice (Oryza sativa L.) varieties for three seasons in Annamalai Nagar, an east coast region of Tamil Nadu. Plant Sci Today. 2024;11(4):634–39. https://doi.org/10.14719/pst.4105
  16. Roy A, Rout S, Hijam L, Sadhu S, Pavithra S, Ghosh A, et al. Multivariate genetic analyses unveil the complexity of grain yield and attributing traits diversity in Oryza sativa L. landraces from North– Eastern India. Plant Sci Today. 2024;11(2): 29–37. https://doi.org/10.14719/pst.2500
  17. Gayathridevi G, Shanthi P, Suresh R, Manonmani S, Geetha S, Sathyabama K, Geetha P. Genetic variability, association and multivariate analysis for yield and yield parameters in rice (Oryza sativa L.) landraces. Electron J Plant Breed. 2023;14(3):991–99. https://doi.org/10.37992/2023.1403.136
  18. James M, Magudeeswari P, Lap B, Lokeshkumar K, Tyagi W, Rai M. Estimation of genetic components and assessing heterosis association with molecular markers in half diallel derived rice hybrids developed with landraces of NE India. Electron J Plant Breed. 2024;15(3):566–73. https://doi.org/10.37992/2024.1503.085
  19. Mendiburu FD. Agricolae: statistical procedures for agricultural research [internet]. R package version 1.3–5; 2021 [cited 18 Sept 2024] Available from: https://CRAN.R–project.org/package=agricolae
  20. Sivasubramaniam S, Menon MP. Genotypic and phenotypic variability in rice. Madras Agric J. 1973;60:1093–96.
  21. Johnson HW, Robinson HF, Comstock RE. Estimates of genetic and environmental variability in soybean. Agron J. 1955;47(7):314–18. https://doi.org/10.2134/agronj1955.56
  22. Allard RW. Principles of plant breeding. New York: John Willy and Sons, Inc.; 1960 https://doi.org/10.1002/bimj.19630050408
  23. Popat R, Patel R, Parmar D. Genetic variability analysis for plant breeding research [internet]. R package version 0.1.0; 2020 [cited 18 Sept 2024] Available from: https://CRAN.R–project.org/package=variability
  24. Olivoto T, Lucio AD metan: an R package for multi–environment trial analysis. Methods Ecol Evol. 2020;11:783–89. https://doi.org/10.1111/2041–210X.13384
  25. Wei T, Simko V. R package 'corrplot': Visualization of a correlation matrix (Version 0.92) [internet]; 2021 [cited 18 Sept 2024] Available from: https://github.com/taiyun/corrplot
  26. Mohanty SP, Khan A, Patra S, Behera S, Nayak AK, Upadhyaya S, et al. Unraveling the genetic diversity in selected rice cultivars released in the last 60 years using gene-based yield–related markers. Genet Resour Crop Evol. 2024;1:1–5. https://doi.org/10.1007/s10722–024–02175–0
  27. Sudeepthi K, Srinivas TV, Kumar BR, Jyothula DP, 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):144–48. https://doi.org/10.37992/2020.1101.026
  28. Sadhana P, Raju CD, Rao LV, Kuna A. Studies on variability, correlation and path coefficient analysis for yield and quality traits in rice (Oryza sativa L.) genotypes. Electron J Plant Breed. 2022;13(2):670–78. https://doi.org/10.37992/2022.1302.084
  29. Shankari MM, Suresh R, Manonmani S, Raveendran M, Prasad VB, Muthuramu S. Performance of qDTY QTL introgressed lines of rice (Oryza sativa L.) under target production environment. Electron J Plant Breed. 2023;14(3):1016–25. https://doi.org/10.37992/2023.1403.135
  30. Jasmin SA, Prasanth PH, Ramchander S, Kumar PD, Devasena N, Naveenkumar R, et al. Assessment of variability parameters and diversity of panicle architectural traits associated with yield in rice (Oryza sativa L.). Plant Sci Today. 2024;11(1):109–18. https://doi.org/10.14719/pst.2658
  31. Selvarani N, Jeyaprakash P, Shanmuganathan M, Janaki D. Hotspot screening of early maturing rice genotypes and genetic variability studies under sodicity. Electron J Plant Breed. 2022;13(3):918–26. https://doi.org/10.37992/2022.1303.120
  32. Mondal S, Pradhan P, Das B, Kumar D, Paramanik B, Yonzone R, et al. Genetic characterization and diversity analysis of indigenous aromatic rice. Heliyon. 2024;10(10):1?14. https://doi.org/10.1016/j.heliyon.2024.e31232
  33. Prajapati MR, Bala M, Patel VP, Patel RK, Sushmitha US, Kyada AD, et al. Analysis of genetic variability and correlation for yield and its attributing traits in F2 population of rice (Oryza sativa L.). Electron J Plant Breed. 2022;13(3):983–90. https://doi.org/10.37992/2022.1303.127
  34. Chaturvedi S, Lal P, Pandey MP, Verma S, Singh AP. Component analysis for grain yield in hybrid rice under tarai condition. Oryza. 2008;45(1):1–6.
  35. Ullah MZ, Bashar MK, Bhuiyan MS, Khalequzzaman M, Hasan MJ. Interrelationship and cause-effect analysis among morpho-physiological traits in biroin rice of Bangladesh. Int J Plant Breed Genet. 2011;5(3):246–54. https://doi.org/10.3923/ijpbg.2011.246.254
  36. Brejda JJ, Moorman TB, Karlen DL, Dao TH. Identification of regional soil quality factors and indicators I. Central and Southern high plains. Soil Sci Soc Am J. 2000;64(6):2115–24. https://doi.org/10.2136/sssaj2000.6462115x
  37. Sheela KS, Robin S, Manonmani S. Principal component analysis for grain quality characters in rice germplasm. Electron J Plant Breed. 2020;11(01):127–31. https://doi.org/10.37992/2020.1101.023
  38. Gour L, Maurya SB, Koutu GK, Singh SK, Shukla SS, Mishra DK. Characterization of rice (Oryza sativa L.) genotypes using principal component analysis including scree plot and rotated component matrix. Int J Chem Stud. 2017;5(4):975–83.
  39. Christina GR, Thirumurugan T, Jeyaprakash P, Rajanbabu V. Principal component analysis of yield and yield related traits in rice (Oryza sativa L.) landraces. Electron J Plant Breed. 2021;12(3):907–11. https://doi.org/10.37992/2021.1203.125
  40. Lakshmi M, Shanmuganathan M, Jeyaprakash P, Ramesh T. Genetic variability and diversity analysis in selected rice (Oryza sativa L.) varieties. Electron J Plant Breed. 2022;13(3):959–66. https://doi.org/10.37992/2022.1303.124

Downloads

Download data is not yet available.