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Plant Science Today (PST)

Research Articles

Vol. 11 No. 2 (2024)

Multivariate Genetic Analyses Unveil the Complexity of Grain Yield and Attributing Traits Diversity in Oryza sativa L. Landraces from North-Eastern India

DOI
https://doi.org/10.14719/pst.2500
Submitted
12 March 2023
Published
19-02-2024 — Updated on 01-04-2024
Versions

Abstract

In the North-Eastern region of India, rice stands as the predominant staple, with diverse cultivars evolving over the past six decades. This study systematically evaluated 20 rice landraces, analyzing eleven variables related to yield and its attributing traits. The aim was to identify promising genotypes for potential breeding programs and to ascertain the minimum number of components essential for explaining the total diversity. Among the eleven principal components (PCs) examined, four PCs exhibited eigenvalues surpassing 1.0, collectively contributing to 80.45% of the total variability in the traits. PC1, which explained 31.19% of the overall variance, was associated with plant height, days to 50% flowering, panicle length, grain breadth, and grain length-to-breadth ratio. Utilizing cluster analysis, the 20 rice landraces were categorized into seven distinct clusters. Maximum inter-cluster divergence was observed between clusters VI and I, as well as clusters VI and V, indicating greater genetic distinctiveness among genotypes in these clusters compared to others. Notably, rice landraces such as Borosolpana, Phougak, Satyaranjan, Kakcheng Phou, Moniram, Kanaklata, and Bahadur were identified as genetically divergent. These genotypes hold promise for generating segregating populations, serving as valuable source materials for targeted yield improvement through meticulous selection, as indicated by inter-cluster distances.

References

  1. Azeez MA, Adubi AO, Durodola FA. Landraces and crop genetic improvement. In Rediscovery of Landraces as a Resource for the Future. IntechOpen. 2018 Sep 12. http://dx.doi.org/10.5772/Intechopen.75944
  2. United Nations, Department of economic and social welfares. World Population Prospectus; 2022.
  3. Akinwale MG, Gregorio G, Nwilene F, Akinyele BO, Ogunbayo SA, Odiyi AC. Heritability and correlation coefficient analysis for yield and its components in rice (Oryza sativa L.). Afr J Plant sci. 2011 Mar 31;5(3):207-12. https://doi.org/10.3923/ijpbg.2011.224.234
  4. Chang TT, Bardenas EA. The morphology and varietal characteristics of the rice plant. Int Rice Res Inst. 1965.
  5. Francis N, Packiaraj D. Genetic uniformity of varieties and an assessment on the diversity among the elite varieties of rice (Oryza sativa L.). Electron J Plant Breed. 2020;11(3):896-900. https://doi.org/10.37992/2020.1103.146
  6. Hoque A, Begum SN, Robin AH, Hassan L. Partitioning of rice (Oryza sativa L.) genotypes based on morphometric diversity. Am J Exp Agric. 2015;7:242-50. https://doi.org/10.9734/AJEA/2015/15687
  7. Fowler C, Mooney PR. Shattering: Food, politics and the loss of genetic diversity. University of Arizona Press; 1990.
  8. Mahajan RK, Mehan DK. Principal component analysis in rice. Crop Improvement. 1980;7(2):83-87.
  9. Sheela KS, Robin S, Manonmani S. Principal component analysis for grain quality characters in rice germplasm. Elect J Plant Breed. 2020 Apr 6;11(1):127-31. https://doi.org/10.37992/2020.1101.023
  10. Jolliffe IT. Principal component analysis for special types of data. Springer New York. 2002;p. 338-73. https://doi.org/10.1007/0-387-22440-8_13
  11. Panse VG, Sukhatme PV. Statistical methods for Agricultural worker. Indian Council of Agricultural Research. New Delhi; 1969.
  12. Rao CR. Advance statistical methods in biometrical research edition I. John Willey and Sons, New York. Rapeseed cultivars. J Appl Biol Sci. 1952;2(3):35-39.
  13. Ward. Hierarchical grouping to optimize an objective function. J Am Stat Asso. 1963;48:236-44. https:// dx.doi.org/10.1080/01621459.1963.105008
  14. Galili T. Dendextend: An R package for visualizing, adjusting and comparing trees of hierarchical clustering. Bioinformatics; 2015. https://doi.org/10.1093/bioinformatics/btv428
  15. Le S, Josse J, Husson F. FactoMine R: An R package for multivariate analysis. Journal of Statistical Software. 2008;25(1):1-18. https://doi.org/10.18637/jss.v025.i01
  16. Kassambara A, Mundt F. Factoextra: Extract and visualize the results of multivariate data analyses. R Package Version 1.0.7.; 2020. https://CRAN.R-project.org/package=factoextra
  17. Zaman MR, Paul DNR, Kabir MS, Mahbub MAA, Bhuiya MAA. Assessment of character contribution to the divergence for some rice varieties. Asian J of Plant Sci. 2005;4:388-91. https://doi.org/10.3923/ajps.2005.388.391
  18. Saxesena RR, Lal GM, Yadav PS, Vishwakarma MK. Diversity analysis and identification of promising lines for hybridization in field pea (Pisum sativum L.). The Bioscan. 2013 Nov 21;8(4):1437-40.
  19. Tripathi A, Kumar S, Singh MK, Kumar A, Karnwal MK. Phenotypic assessment of rice (Oryza sativa L.) genotypes for genetic variability and varietal diversity under direct seeded condition. J Appl Nat Sci. 2017;9(1):6-8. https://doi.org/10.31018/jans.v9i1.1138
  20. Beevi AH, Venkatesan M. Genetic divergence studies in rice genotypes under saline condition. Int J Current Adv Res. 2015;4:6-8.
  21. Lakshmi M, Shanmuganathan M, Jeyaprakash P, Ramesh T. Genetic variability and diversity analysis in selected rice (Oryza sativa L.) varieties. Electron J of Plant Breed. 2022;13(3):959-66. https://doi.org/10.37992/2022.1303.124
  22. Lavanya1 K, Suman K, Fiyaz AR, Chiranjeevi M, Surender R, Krishna Satya A, Sudhakar P, Subba Rao LV. Phenotypic assessment of rice landraces for genetic variability and diversity studies under heat stress. Oryza. 2022;59(1):31-38. DOI https://doi.org/10.35709/ory.2022.59.1.4
  23. Mishra LK, Sarawgi AK, Mishra RK. Genetic diversity for morphological and quality traits in rice (Oryza sativa L.). Adv Plant Sci. 2003;16(1):287-93.
  24. Chaturvedi HP, Maurya DM. Genetic divergence analysis in rice (Oryza sativa L.). Adv Plant Sci. 2005;18(1):349-53.
  25. Zewdu Z, Abebe T, Mitiku T, Worede F, Dessie A, Berie A, Atnaf M. Performance evaluation and yield stability of upland rice (Oryza sativa L.) varieties in Ethiopia. Cogent Food & Agriculture. 2020;6(1):1842679. https://doi.org/10.1080/23311932.2020.1842679
  26. Nayak AR, Chaudhury D, Reddy JN. Genetic divergence in scented rice. Oryza. 2004;41(384):79-82.
  27. Ovung CY, Lal GM, Rai PK. Studies on genetic diversity in Rice (Oryza sativa L.) Journal of Agricultural Technology. 2012;8(3):1059-65.
  28. Anderson TW. An introduction to multivariate analysis. Wiley Eastem Pvt. Ltd. New Delhi. 1972; p. 512.
  29. Morrison DE. Multivariate statistical methods. 2nd ed. 4th Print, McGraw Hill Kogakusta Ltd; 1978.
  30. Sathya Sheela KRV, Robin S, Manonmani S. Principal component analysis for grain quality characters in rice germplasm. Electron J Plant Breed. 2019;11(1):127-31. https://doi.org/10.37992/2020.1101.023
  31. Ashfaq M, Khan AS, Khan SHU, Ahmad R. Association of various morphological traits with yield and genetic divergence in rice (Oryza Sativa L.). Int J Agri and Biol. 2012;14:55-62.
  32. Maji AT, Shaibu AA. Application of principal component analysis for rice germplasm characterization and evaluation. J Plant Breed Crop Sci. 2012;4(6):87-93. https://doi.org/10.5897/JPBCS11.093
  33. Ahmed MSU, Khalequzzaman MMD, Bashar K, Shamsuddin AKM. Agro-morphological, physico-chemical and molecular characterization of rice germplasm with similar names of Bangladesh. Rice Sci. 2016;23(4):211-18. https://doi.org/10.1016/j.rsci.2016.06.004
  34. Dhakal A, Pokhrel A, Sharma S, Poudel A. Multivariate analysis of phenotypic diversity of rice (Oryza sativa L.) landraces from Lamjung and Tanahun districts of Nepal. Int J of Agron. 2020;1-8. https://doi.org/10.1155/2020/8867961
  35. Singh OV, Gowthami R, Singh K, Shekhawat N. Genetic divergence studies in pearl millet germplasm based on principal component analysis. Int J Curr Microbiol App Sci. 2018;7(06):522-27. doi: https://doi.org/10.20546/ijcmas.2018.706.059
  36. Shanmugam A, Suresh R, Ramanathan A, Anandhi P, Sassikumar S. Unravelling genetic diversity of South Indian rice landraces based on yield and its components. Elect J Plant Breed. 2023;14(1):160-69. https://doi.org/10.37992/2023.1401.007
  37. Chakravorty A, Ghosh PD, Sahu PK. Multivariate analysis of phenotypic diversity of landraces of rice of West Bengal. Ame J Exp Agri. 2013;3(1):110. https://doi.org/10.9734/AJEA/2013/2303
  38. Shoba D, Vijayan R, Robin S, Manivannan N, Iyanar K, Arunachalam P, Nadarajan N,Pillai MA, Geetha S. Assessment of genetic diversity in aromatic rice (Oryza sativa L.) germplasm using PCA and cluster analysis. Electron J Plant Breed. 2019 Oct 1;10(3):1095-104. https://doi.org/10.5958/0975-928X.2019.00140.6
  39. Mulsanti IW, Risliawati A, Yunan N. Agro-morphological characterization based genetic diversity of Indonesian local rice germplasm. Earth Env Sci. 2021;948. doi: 10.1088/1755-1315/948/1/012004

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