GENETIC ASSESSMENTS OF SOME LOCAL AND IMPORTED GENOTYPES OF RICE IN SULAIMANI GOVERNORATE - IRAQ
DOI:
https://doi.org/10.36103/y3by5h83Keywords:
Oryza sativa L., genotypes performance, locations, correlation analysis, path analysis.Abstract
This study was carried out at two locations in Sulaimani Governorate, Iraq during 2022 to investigate the genetic potential of thirty-one rice genotypes to highlights the genetic diversity among rice genotypes and emphasizes the importance of considering both genetic and environmental factors in rice breeding programs. The distinct performance of genotypes across different traits and locations provides valuable information for selecting and developing rice varieties with improved characteristics and higher yield potential. The results of the analysis of variance indicate highly significant differences among the genotypes for all studied traits at both Chalakh and Penjwen locations. This suggests a substantial genetic diversity among the studied genotypes, shows their potential as valuable genetic resources for breeding programs. Additionally, the interaction between genotypes and environments was found to be highly significant for most traits, emphasizing the importance of considering both genetic and environmental factors in rice research. G1 was superior to the other genotypes regarding the grain yield plant-1 with 60.909 and 29.004 g at Chalakh and Penjwen locations respectively. Moreover, the impact of location on the studied traits was significant, with Chalakh outperforming Penjwen in all traits. This difference in performance could be attributed to variations in climatic conditions and environmental factors between the two locations.
References
1. Abdulkhaleq, D. A. 2023. Breeding potential of rice genotypes in two agroclimatic conditions of Sulaimani - Kurdistan Region – Iraq. Iraqi J. of Agri. Sci., 54(3):806–819. https://doi.org/10.36103/ijas.v54i3.1765
2. Ahmad, F. and H. Khan, 2021. Effect of different fertilizer treatments on the performance of some local rice varieties under SRI (system of rice intensification) and conventional management practices at district Swat. Pure and Applied Biology (PAB), 5(1): 37-47. http://dx.doi.org/10.19045/bspab.2016.50006
3. Amin, E. A. 1979. Correlation and path-coefficient analysis in some short-stature rice cultivars and strains. Inter. Rice Commission Newsletter,28:19-21. https://doi.org/10.36103/ijas.v54i3.1765
4. Ashikari, M., H. Sakakibara, S. Lin, T. Yamamoto, T. Takashi, A. Nishimura, and M. Matsuoka, 2005. Cytokinin oxidase regulates rice grain production. Science, 309(5735), 741-745.
https://doi.org/10.1126/science.1113373
5. Chakraborty, A., and B. U. Choudhury 2017. Assessment of Genetic variation and divergence in bread wheat genotypes under rainfed condition of West Bengal. Int. Curr. Microbio. A. Sci., 6(8), 1498-1509.
6. Fageria, N. K., V. C. Baligar, and C. A. Jones, 2001. Growth and Mineral Nutrition of Field Crops (3rd ed.). CRC Press.
7. Gaju, O., M. P. Reynolds, D.L. Sparkes, M. J., Foulkes, and S. Mayes 2012. Relationships between large-spike phenotype, grain number, and yield potential in spring wheat. Crop Sci., 52(4), 2318-2329.
https://doi.org/10.2135/cropsci2008.05.0285
8. Hairmansis, A., B. Kustianto, Supartopo and Suwarno, 2010. Correlation analysis of agronomic characters and grain yield of rice for tidal swamp areas. Indonessian J. Agric. Sci. 11(1): 11-15.
https://doi.org/10.21082/IJAS.V11N1.2010.P11-15
9. IBM SPSS STATISTIC program 2011. Version 19 statistical software packages”. IBM Corporation, New York.
10. Iftekharuddaula, K. M., Khalida Akhtar, M. S. Hassan, K. Fatima and A. Badshah 2002. Character association and selection criteria in irrigated rice. J. Biol. Sci. 2(4):243-246.
https://doi.10.3923/jbs.2002.243.246
11. Khaliq, A., K. Ali, R. Tariq, A. Mahmood, and K. Nawaz 2016. The effect of tillage and sowing methods on growth, yield and yield components of rice. American J. P. Sci., 7(09), 1283-1295.
12. Khan, A. S., M. Imran, and M. Ashfaq 2009. Estimation of genetic variability and correlation for grain yield components in rice (Oryza sativa). Amercian-Eurasian J. Agri. Environ. Sci., 6(5): 585-590.
http://www.idosi.org/aejaes/jaes6(5)/17.pdf
13. Khush, G. S. 1995. Breaking the yield frontier of rice. GeoJournal, 35(3), 329-332.
https://www.jstor.org/stable/41146413
14. Liu, H., S. Yang, and Y. Wu 2018. The effects of panicle branch number on rice yield and the potential for yield improvement in a breeding program. Frontiers in Plant Science, 9, 869.
15. Madhavilatha, L., M. R. Sekhar, Y. Suneetha and T. Srinivas 2005. Genetic variability, correlation and path analysis for yield and quality traits in rice (Oryza sativa). Res. Crops. 6(3): 527-534.
https://doi/full/10.5555/20063026598
16. Manifesto, M. M., A. R. Schlatter, H. E. Hopp, E. Y. Suarez and J. Dubcovsky 2001. Quantitative evaluation of genetic diversity in wheat germplasm using molecular markers. Crop Breed. Gen. Cyto., 41: 682-690.
https://doi.org/10.2135/cropsci2001.413682x
17. Milla, R., C. P. Osborne, M. M. Turcotte, and C. Violle 2015. Plant domestication through an ecological lens. Trends in Eco. & Evol., 30(8), 463-469.
http://dx.doi.org/10.1016/j.tree.2015.06.006
18. Mishra, L. K., and R. K. Verma 2002. Correlation and path analysis for morphological and quality traits in rice (O. sativa L.). Plant Archives, 2(2):275-284.
https://www.cabidigitallibrary.org/doi/full/10.5555/20033021532
19. Nachimmuthu, V. V., S. Robin, D. Sudhakar, M. Raveendran, S. Rajeswari, and S. Manonmani 2014. Evaluation of rice genetic diversity and variability in a population panel by principal component analysis. Indian J. Sci. Technol. 7(10):1555–1562.
https://doi.10.17485/ijst/2014/v7i10.14
20. Panda, D. K., S.G. Sharma, and S. Rathi 2012. Correlation studies in aromatic rice genotypes under late sown conditions of Chhattisgarh. Int. J. Agri. Sci., 8(2), 310-313.
21. Peng, S., G. S. Khush, P. Virk, Q. Tang, and Y. Zou 2008. Progress in ideotype breeding to increase rice yield potential. Field Crops Res., 108(1), 32-38.
https://doi.org/10.1016/j.fcr.2008.04.001
22. Poorter, H., U. Niinemets, L. Poorter, I.J. Wright, and R. Villar 2009. Causes and consequences of variation in leaf mass per area (LMA): a meta-analysis. New Phytologist, 182(3), 565-588.
https://doi.org/10.1111/j.1469-
23. Ragarathinam, S. and V. D. G. Raja 1992. Correlation and path analysis in some rice varieties under alkaline stress. Madras Agri. Res. J. 79: 374-378.
https://www.cabidigitallibrary.org/doi/full/10.5555/19941610980
24. Rashid, K., I. Khaliq, M. O. Farooq, and M. Z. Ahsan 2014. Correlation and cluster analysis of some yield and yield related traits in rice (Oryza sativa), J. Recent. Adv. Agr., 2014, 2(6): 271-276.
25. Ravikumar, B., N. Satyanarayana, K. Chamundeswari, M. Bharathalakshmi and A. Vishnuvardhan 2015. Principal component analysis and character association for yield components in rice (Oryza sativa L.) cultivars suitable for irrigated ecosystem. Andhra Pradesh Rice Research Institute and Regional Agricultural Research Station, Maruteru, India. Current Biotica. 9(1): 25-35.
26. Sabesan, T., R. Suresh and K. Saravanan 2009. Genetic variability and correlation for yield and grain quality characters of rice grown in costal saline low land of Tamilnadu. Elect J. Plant Breeding. 1: 56-59.
27. Salah, S. S. S., M. A. E. Ahmed, and M.H. Abd El-Ghany, 2023. Agricultural and price policies analysis of the chamomile and fennel crops in Egypt. Fayoum J. Agri. Res. and Dev., 37(1): 223-231. https://doi.org/10.21608/fjard.2023.281980
28. Singh R. K., and B. D. Chaudhary, 1985. Biometrical Methods in Quantitative Genetic Analysis; Kalyani Publishers; New Delhi: pp. 215–218.
29. Singh, R., V. Yadav, D. N. Mishra and A. Yadav, 2018. Correlation and path analysis studies in Rice (Oryza sativa L.). J. Pharm. Phyto. (SP1)2084-2090.
https://www.cabidigitallibrary.org/doi/full/10.5555/19801689021
30. Sreenivasulu, N., and U. Wobus, 2013. Seed-development programs: a systems biology-based comparison between dicots and monocots. Annual Review of Plant Biology, 64, 189-217.
https://doi.org/10.1146/annurev-arplant-050312-120215
31. Surek, H., and N. Beser, 2003. Correlation and path coefficient analysis for some yield related traits in rice (Oryza sativa) under thrace conditions. Turk. J. Agric. For. 27 (2): 77-83.
32. Swamy, B. P. M., H. U. Ahmed, A. Henry, R. Mauleon, S. Dixit, P., P Vikram, ... and A. Kumar, 2013. Genetic, physiological, and gene expression analyses reveal that multiple QTL enhance yield of rice mega-variety IR64 under drought. PLOS ONE, Vol. 8,Iss:5. https://doi.org/10.1371/journal.pone.0062795
33. Tripathi, A., R. Bisen, R.P. Ahirwal, S. Paroha, R. Sahu, and A.R.G. Ranganatha 2013. Study on genetic divergence in sesame (Sesamum indicum L.) Germplasm based on morphological and quality traits. Bioscan. 2013;8(4):1387–1391.
34. Tripathi, M. K., A. Syngkon, P. Pareek, and M. N. Shrivastava 2013. Genetic management of rice variety improvement in diverse rainfed ecosystems. Vegetos- An International Journal of Plant Research, 26(2), 46-57.
35. Vivekananda, P., and S. Subramanian 2014. Genetic divergence in rainfed rice Oryza. 30: 60–62.
36. Xie, Y., L. Tian, and Y. Hu, 2019. Effects of plant density on yield and grain quality of different rice cultivars. Frontiers in Plant Science, 10, 88.
37. Xu, X., T. Li, L. Li, X. Fan, and Z. Zhang, 2017. Effects of temperature change on rice yield in the past half century in China. J. Agri. Sci. Tech., 19, 1415-1426.
38. Yong-xiang, F. 2008. Correlation and cluster analysis for yield properties of early-japonica rice in cold area. J. Anhui. Agric. Sci. 23(49): 150-186.
39. Yoshida, S., H. Ikehashi, and T. Nakamura, 1981. Grain size and grain number in rice. International Rice Research Notes, 6(1), 8-9.
40. Yuan, L. P., C. J. Dai, and C. Y. Guan, 2009. Correlation analysis between yield and grain shape traits in double-season rice. Chinese J. Rice Sci., 23(2), 205-209.
41. Zhang, Z., Y. Wang, and T. Zheng, 2018. Influence of tiller number on grain yield and quality of rice in cold area. Sci. Rep., 8(1), 1-11.
42. Zhu, X. G., S. P. Long, and D. R. Ort, 2010. Improving photosynthetic efficiency for greater yield. Annual Review of Plant Biology, 61, 235-261.


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