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December 21, 2021
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April 12, 2022
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August 21, 2022
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Abstract

Total 21 high zinc rice genotypes were evaluated under five different locations for 14 different yield attributing traits, including grain yield/plant (gm) to determine the pattern of variation, the relationship among the individuals and their characteristics through Principal Component Analysis (PCA) during the Kharif-2017. PCA was done for all the locations individually as well as pooled analysis for all locations using R software. Out of the 14 PCs, the initial four PCs contributed more to the total variability. The highest cumulative variability of the first four PCs found at Bhikaripur (81.11%) followed by BHU Agriculture research farm-II (79.23%) etc. and Pooled variability was 76.61%. Pooled data analysis indicates PCA biplot or loading plot of first two principal components revealed that days to maturity, days to 1st flowering date and days to 50% flowering loaded more on the first component and number of spikelets per panicles, number of grains/panicles, grain weight per panicle, grain yield/plant accounted more variation in the second component compared to the other parameters. Thus, the pooled analysis of principal component analysis revealed the characters contributing to the variation and genetic variability that exists in these rice genotypes. This is because the genotypes BRRIdhan 72, Sambamahsuri and Swarna were identified in different principle components related to grain yield and grain quality, and were also located farthest away from biplot origin in individual PCA based biplot. So they may be employed to improve yield attributing factors like total effective tiller number. PC1, PC2 and PC3 have days to first flowering and days to 50% flowering, hence their genotypes may be valuable in producing early maturing cultivars. Thus, the results revealed that wide range of variability was shown by different traits of the genotypes which can be utilized in rice improvement programmes.

Keywords

Individual PCA PCA biplot Pooled environment Rice

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Behera, P. P., Singh, S. K., Sivasankarreddy, K., Majhi, P. K. ., Reddy, B. J., & Singh, D. K. (2022). Yield attributing traits of high zinc rice (Oryza sativa L.) genotypes with special reference to principal component analysis. Environment Conservation Journal, 23(3), 458–470. https://doi.org/10.36953/ECJ.10302233
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References

  1. Aliyu, B., Akoroda, M. O., & Padulosi, S. (2000). Variation within Vigna reticulata Hooke FII Nig. J Gene, 1-8.
  2. Aslam, M., Maqbool, M. A., Zaman, Q. U., Shahid, M., Akhtar, M. A., & Rana, A. S. (2017). Comparison of different tolerance indices and PCA biplot analysis for assessment of salinity tolerance in lentil (Lens culinaris) genotypes. International Journal of Agriculture and Biology, 19(3). DOI: https://doi.org/10.17957/IJAB/15.0308
  3. Bharadwaj, C., Satyavathi, C. T., & Subramanyam, D. (2001).Evaluation of different classifactory analysis methods in some rice (Oryza sativa) collections. Indian Journal of Agricultural Science, 71(2), 123-125.
  4. Brejda, J. J., Karlen, D. L., Smith, J. L., & Allan, D. L. (2000). Identification of regional soil quality factors and indicators II.Northern Mississippi Loess Hills and Palouse Prairie. DOI: https://doi.org/10.2136/sssaj2000.6462125x
  5. Gana, A. S. (2006). Variability studies on the response of rice varieties to biotic and abiotic stresses (Doctoral dissertation, University of Ilorin).
  6. Gour, L., Maurya, S. B., Koutu, G. K., Singh, S. K., Shukla, S. S., & Mishra, D. K. (2017). Characterization of rice (Oryza sativa L.) genotypes using principal component analysis including scree plot & rotated component matrix International Journal of Chemical Studies, 5(4), 975-83.
  7. Hossain, S., Haque, M., & Rahman, J. (2015). Genetic variability, correlation and path coefficient analysis of morphological traits in some extinct local Aman rice (Oryza sativa L). Rice Research: Open Access. DOI: https://doi.org/10.4172/2375-4338.1000158
  8. International Institute of Tropical Agriculture. (1999). DNA Marker-assisted Improvement of the Staple Crops of Sub-Saharan Africa: Proceedings of the'Workshop on DNA Markers at IITA'Held by the Crop Improvement Division, IITA, Ibadan, Nigeria, 21-22 August, 1996. IITA.
  9. International Plant Genetic Resources Institute & West Africa Rice Development Association. (2007). Descriptors for Wild and Cultivated Rice (Oryza Spp.). Bioversity International.
  10. Kambo, R., & Yerpude, A. (2014).Classification of basmati rice grain variety using image processing and principal component analysis. arXiv preprint arXiv:1405.7626. DOI: https://doi.org/10.14445/22312803/IJCTT-V11P117
  11. Kumar, V., Koutu, G.K., Singh, S.K., Mishra, D.K., Singh, P.K. & Sohgaura, N. (2014) Genetic analysis of inter sub-specific derived mapping population (RILS) for various yield and quality attributing traits in rice. International science journal. 1(3).
  12. Kumari, B. K., Kumar, B. R., Jyothula, D., & Rao, N. M. (2021).Diversity analysis in rice breeding lines for yield and its components using principal component analysis. J. Pharmacog. Phytochem, 10(1), 905-909. DOI: https://doi.org/10.22271/phyto.2021.v10.i1m.13451
  13. Maqbool, M.A., M. Aslam, H. Ali, T.M. Shah & B.M. Atta (2015b) GGE biplot analysis based selection of superior chickpea (Cicer arietinum L.) inbred lines under variable water environments. Pak. J. Bot., 47: 1901?1908
  14. Maqbool, M.A., M. Aslam, H. Ali, T.M. Shah, B. Farid & Q.U. Zaman. (2015a) Drought tolerance indices based evaluation of chickpea advanced lines under different water treatments. Res. Crops, 16: 336?344. DOI: https://doi.org/10.5958/2348-7542.2015.00049.2
  15. Morrison, D. F. (1976). Multivariate Statistical Methods: 2d Ed. McGraw-Hill.
  16. Mulyaningsih, E. S., Anggraheni, Y. G. D., Adi, E. B. M., Burhana, N., & Santun, L. (2021, March). Characterization of Performance 12 Superior Lines of Upland Rice Planted in Two Environmental Condition. In IOP Conference Series: Earth and Environmental Science (Vol. 715, No. 1, p. 012033). IOP Publishing. DOI: https://doi.org/10.1088/1755-1315/715/1/012045
  17. Nachimuthu, V. V., Robin, S., Sudhakar, D., Raveendran, M., Rajeswari, S., &Manonmani, S. (2014). Evaluation of rice genetic diversity and variability in a population panel by principal component analysis. Indian journal of Science and Technology, 7(10), 1555-1562. DOI: https://doi.org/10.17485/ijst/2014/v7i10.14
  18. Negussie, T., & Alemu, D. (2011). An Overview of the National Rice Research and Development Strategy and its Implementation. Challenges and Opportunities of Rice in Ethiopian Agricultural Development, 1-16.
  19. Olivoto, T., &Lúcio, A. D. C. (2020). metan: An R package for multi?environment trial analysis. Methods in Ecology and Evolution, 11(6), 783-789. DOI: https://doi.org/10.1111/2041-210X.13384
  20. Pachauri, A. K., Sarawgi, A. K., Bhandarkar, S., & Ojha, G. C. (2017). Agro-morphological characterization and morphological based genetic diversity analysis of Rice (Oryzasativa L.) germplasm. Journal of Pharmacognosy and Phytochemistry, 6(6), 75-80.
  21. Shahzad, Z., Rouached, H., &Rakha, A. (2014). Combating mineral malnutrition through iron and zinc biofortification of cereals. Comprehensive Reviews in Food Science and Food Safety, 13(3), 329-346. DOI: https://doi.org/10.1111/1541-4337.12063
  22. Sharma, A., Patni, B., Shankhdhar, D., & Shankhdhar, S. C. (2013). Zinc–an indispensable micronutrient. Physiology and Molecular Biology of Plants, 19(1), 11-20. DOI: https://doi.org/10.1007/s12298-012-0139-1
  23. Tiruneh, A., Gebrselassie, W., & Tesfaye, A. (2019). Genetic Diversity Study on Upland Rice (Oryzasativa L.) Genotypes Based on Morphological Traits in South western Ethiopia. Asian Journal of Crop Science, 11(1), 17-24. DOI: https://doi.org/10.3923/ajcs.2019.17.24
  24. Tuhina-Khatun, M., Hanafi, M. M., RafiiYusop, M., Wong, M. Y., Salleh, F. M., & Ferdous, J. (2015). Genetic variation, heritability, and diversity analysis of upland rice (Oryzasativa L.) genotypes based on quantitative traits. BioMed Research International, 2015. DOI: https://doi.org/10.1155/2015/290861
  25. Wogu, M. D., Omoruyi, M. I., Odeh, H. O., & Guobadia, J. N. (2011). Microbial load in ready-to-eat rice sold in Benin City. Journal of Microbiology and Antimicrobials, 3(2), 29-33.