Main Article Content

Abstract

The intensity of trait association and genetic variability of yield attributing variables in 217 rice genotypes was investigated during kharif 2018. The existence of genetic variability among the genotypes was demonstrated by analysis of variance, which recorded significant differences for all the seven studied parameters.  The estimation of variability indicated that  The full grain number per panicle (37.2 % and 34.1 %) & single plant yield (24.7 % and 20.55 %) had the highest intensity of phenotypic coefficients of variation (PCV) and genotypic coefficients of variation ( GCV), and  High heritability along with high genetic advance as a per cent of mean (GAM) was  found in Plant height (98.9 % and 20.8 %), panicle number per plant (95.4 % and 36 %), panicle length(96.8 % and 35.9 %), full grain number per panicle(99.5 % and 61.6 %), thousand seed weight (98.1 % and 40.25 %) and single plant yield (69.2 % and 35.2 %) , depicting additive gene action in inheritance of these parameters. A simple selection procedure can help to enhance these characteristics even further. Correlation and regression coefficient findings indicated that plant height (0.193**) and the full grain number per panicle (0.177**) had a significant impact on single plant yield. The full grain number per panicle (0.265**), followed by thousand seed weight (0.194**) and plant height (0.110**), had the maximum direct positive effect on single plant yield, as per path coefficient analysis. As a result, accessions with a higher full grain number per panicle, thousand seed weight and plant height would be suitable for yield enhancement programme.

Keywords

Additive Gene Action Direct Effect Indirect Effect Inheritance and Selection

Article Details

How to Cite
Mohan, Y. C., Krishna, K., Krishna, L., Singh, T. V. J., & Jagadeeshwar, R. (2023). Genetic parameters and association analysis for grain yield and yield attributing traits in rice (Oryza sativa L.) germplasm lines . Environment Conservation Journal, 24(3), 1–7. https://doi.org/10.36953/ECJ.14032416

References

  1. Alexandratos, N., & Bruinsma, J. (2012) World Agriculture towards 2030/2050. (2013). Variability in quasi cms lines of aromatic rice (Oryza sativa L.) in BC 3 generation and their phenotypic acceptability. Eco-friendly Agriculture Journal. (6), 78-82.
  2. Awol, M., & Alise, F. (2018). Correlation and path analysis in yield and yield components in Ethiopian chick pea land races. Journal of food and agriculture 12(6), 35-37.
  3. Begum, S., Srinivas, B., Reddy, V. R., & Kumari, C. A. (2021). Multiple Regression, Correlation and Path Analysis of Gall Midge Incidence, Yield and Yield Components in Rice (Oryza sativa L.) Hybrids. Current Journal of Applied Science and Technology, 40(2), 33-45. DOI: https://doi.org/10.9734/cjast/2021/v40i231250
  4. Burton, G. W. 1952. Quantitative inheritance in grasses. Proceedings of 6th Grassland Congress J. (1), 277-281.
  5. Dhurai, S.Y., Bhati, P.K. & Saroj, S.K., (2014). Studies on genetic variability for yield and quality characters in rice (Oryza sativa L.) under integrated fertilizer management. The Bioscan, 9(2),745-748.
  6. Edukondalu, B., Reddy, V.R., Rani, T.S., Kumari, C.A. & Soundharya, B. (2017). Studies on variability, heritability, correlation and path analysis for yield, yield attributes in rice (Oryza sativa L.). International Journal of Current Microbiology and Applied Sciences. 6 (10), 2369-2376. DOI: https://doi.org/10.20546/ijcmas.2017.610.279
  7. Fathima, M.A., Geetha, S., Amudha, K. & Uma, D., (2021). Genetic variability, frequency distribution and association analysis in ADT (R) 48 x Kavuni derived F2 population of rice (Oryza sativa L.). Electronic Journal of Plant Breeding(1956). Augmented designs. Hawaiian Planter’s Rec.55:191–208.
  8. Federer, W.T. (1961). Augmented designs with one-way elimination of heterogeneity. Biometrics 20, 540–552. DOI: https://doi.org/10.2307/2527837
  9. Federer, W.T. (1991). Statistic and society. Section 7.11. 2nd ed.Marcel Dekker, New York, NY.
  10. Fitzgerald, M. A., Bergman, C. J., Resurreccion, A. P., Moller, J., Jimenez, R., Reinke, R. F., Martin, M., Blanco, P., Molina, F., Chen, M. H., Kuri, V., Romero, M. V., Habibi, F., Umemoto, T., Jongdee, S., Graterol, E., Reddy, K. R., Bassinello, P. Z., Sivakami, R., Rani, N. S., Das, S., Wang, Y. J., Indrasari, S. D., Ramli, A., Ahmad, R., Dipti, S. S., Xie, L., Lang, N. T., Singh, P., Toro, D. C., Tavasoli, F. & Mestres, C. (2009a). Addressing the dilemmas of measuring amylose in rice. Cereal Chemistry, Vol. 86 (5). 492–498 DOI: https://doi.org/10.1094/CCHEM-86-5-0492
  11. Hema, T., Saravanan, S., Kannan, R., Shoba, D. and Pillai, M. A. (2019). Studies on genetic variability, association and path coefficient analysis in F2 derivatives of CR 1009× WP 22-2 for earliness and semi-dwarfism in rice (Oryza sativa L.). Electronic Journal of Plant Breeding, 10(2): 585-591 DOI: https://doi.org/10.5958/0975-928X.2019.00074.7
  12. Johnson, H.W., Robinson, H.F. & Comstock, R.E. (1955). Estimates of genetic and environmental variability of Soybeans. Agronomy Journal. 47: 314-318. DOI: https://doi.org/10.2134/agronj1955.00021962004700070009x
  13. Khan, A.S., Ashfaq, M, & Asad, M, A. (2003).A correlation and path coefficient tanalysis for some yield components in bread wheat. Asian Journal of Plant Sciences, 2(8): 582-584. DOI: https://doi.org/10.3923/ajps.2003.582.584
  14. Lush, J.L. (1940). Correlation and regression of offspring in rams as a method of estimating heritability of characters. Proceedings of American Society of Animal Production. (33): 292-301
  15. Mishu, M.F.K., Rahman, M.W., Azad, M.A.K., Biswas, B.K., Talukder, M.A.I., Kayess, M.O., Md. Rafiqul Islam, M.R & Alam, M.R. (2016). Study on Genetic Variability and Character Association of Aromatic Rice (Oryza sativa L.) Cultivars. International Journal of Plant and Soil Science., 9(1), 1-8. DOI: https://doi.org/10.9734/IJPSS/2016/22006
  16. Nikhitha, T. C., Pushpham, R., Raveendran, M. & Manonmani, S. (2020). Genetic variability and frequency distribution studies in F2 population involving traditional variety mappillai samba. Electronic Journal of Plant Breeding, 11(3), 933-938 DOI: https://doi.org/10.37992/2020.1103.151
  17. Pragnya K., Radha Krishna, K. V., Subba Rao, L, V., & Suneetha, K. (2018). Estimation of Genetic Variability Parameters in Soft Rice (Oryza sativa L.) Genotypes. International Journal of Current Microbiology and Applied Sciences. 7(06), 2029-2042. DOI: https://doi.org/10.20546/ijcmas.2018.706.240
  18. Prasannakumari, M., Akilan, M., Kalaiselvan, S., Subramanian, A., Janaki, P. & Jeyaprakash, P. (2020). Studies on genetic parameters, correlation and path analysis for yield attributes and Iron content in a backcross population of rice [(Oryza sativa. L.)]. Electron. Journal of Plant Breeding, 11(3), 881- 886. DOI: https://doi.org/10.37992/2020.1103.144
  19. Rachana B., (2018). Variability, heritability and genetic advance for yield and its component traits in NPT core set of rice (Oryza sativa L.). Electronic Journal of Plant Breeding, 9(4), 1545-1551. DOI: https://doi.org/10.5958/0975-928X.2018.00191.6
  20. Rukmini Devi, K., Parimala, K., Venkanna, V. & Cheralu, C. (2014). Genetic variability, heritability, correlation and path analysis for yield and quality traits in rice (Oryza sativa L.). The Journal of Research PJTSAU. 42(4), 7- 14.
  21. Rukmini Devi, K., Satish Chandra, B., Lingaiah, N., Hari, Y. & Venkanna, V. (2017). Analysis of variability, correlation and path coefficient studies for yield and quality traits in rice (Oryza sativa L.). Agriculture Science Digest. 37(1), 1-9. DOI: https://doi.org/10.18805/asd.v0iOF.7328
  22. Saleh, M.M., Salem, K.F.M. & Elabd, A.B. (2020). Definition of selection criterion using correlation and path coefficient analysis in rice (Oryza sativa L.) genotypes. Bull Natl Res Cent 44, 143. DOI: https://doi.org/10.1186/s42269-020-00403-y
  23. Sandeep, S., M. Sujatha, L.V. Subbarao & Neeraja, C.N. (2018). Genetic Variability, Heritability and Genetic Advance Studies in Rice (Oryza sativa L.). International Journal of Current Microbiology and Applied Sciences. 7(12), 3719-372
  24. Shafique, M. S., Muhammad, A., Zafar, M., Muhammad, A., Awais, S., & Ahmad, M. I. (2016). Genetic variability and interrelationship of various agronomic traits using correlation and path analysis in chickpea (Cicer arietinum L.). Academia Journal of Agricultural Research, 4(2), 82-85.
  25. Shalini, S., R.A. Sheriff, R.S. Kulkami & P.Venkantarmana, (2000). Correlation and path analysis of lndian mustard germplasm. Research on Crops in India, 1(2), 226-229.
  26. Shanmugam, M., & Kalaimagal T. (2019). Genetic Variability, Correlation and Path Coefficient Analysis in Chickpea (Cicer arietinum L.) for Yield and its Component Traits. International Journal of Current Microbiology and Applied Sciences 8(5),1801-1808. DOI: https://doi.org/10.20546/ijcmas.2019.805.209
  27. Shedge, P.J., Patil, D.K., & Dawane, J.K. (2019). Correlation and Path Coefficient Analysis of Yield and Yield Components in Chickpea (Cicer arietinum L.) International Journal of Current Microbiology and Applied Sciences, 8(5), 82-85. DOI: https://doi.org/10.20546/ijcmas.2019.807.157
  28. Singh, N. & Verma, O.P., (2018). Genetic variability, heritability and genetic advance in rice (Oryza sativa L.) under salt stressed soil. Journal of Pharmacognosy and Phytochemistry, 7(3),3114-3117.
  29. Singh, R.K. & Chaudhary, B.D. (1985). Biometrical methods inquantitative genetic analysis. Kalyani Publishers, New Delhi, Ludhiana, India: 205-215.
  30. Swetha, P. B., & Lavanya, G. R. (2019). Genetic variability, heritability and character association for yield and component characters in chickpea (Cicer arietinum L.). Journal of Pharmacognosy and Phytochemistry, 8(5), 161-163.