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

Research Articles

Vol. 13 No. sp5 (2026): Recent Advances in Agriculture

Identification of elite product lines through genetic and multivariate analyses for dry direct-seeded rice systems

DOI
https://doi.org/10.14719/pst.15614
Submitted
19 May 2026
Published
27-07-2026

Abstract

Dry direct-seeded rice (DDSR) is a promising substitute for puddled transplanted rice, particularly in regions facing climate-induced water scarcity, high greenhouse gas emissions and labour shortages. In view of these emerging challenges, the current investigation was undertaken to quantify genetic variability, understand trait interrelationships, perform cluster grouping and identify superior breeding lines suited to dry direct-seeded rice ecosystems. During the kharif season of 2022, 80 genotypes comprising 71 advanced breeding lines developed at International Rice Research Institute (IRRI), South Asia Hub, Hyderabad, along with nine adapted checks, were evaluated under dry direct-seeded conditions at Agricultural Research Station, Kunaram, Professor Jayashankar Telangana Agricultural University. Restricted maximum likelihood analysis revealed significant genetic variation for all seven agro-morphological traits studied. Genetic parameters with violin plots displayed early maturity, moderate plant height, stable test grain weight and clear yield differences among genotypes, providing a strong basis for selecting lines suited to dry direct-seeded ecosystems. High heritability (84.4 %) coupled with high genetic advance (31.2 %) for grain yield confirmed that direct selection would be effective. Phenotypic correlation analysis, histograms and scatter plots showed that grain yield did not exhibit a significant positive association with any single component trait, suggesting that yield is governed by the coordinated performance of multiple traits rather than the dominance of a single character. The first two principal components accounted for 44.46 % of the total variation, revealing meaningful trait associations and multivariate differences among genotypes. Cluster analysis grouped the genotypes into 10 distinct clusters with contrasting trait combinations, providing practical guidance for parent selection and crossing strategies. Five elite breeding lines were identified and nominated to the All India Coordinated Rice Improvement Programme for multi-location national testing, of which IR144564-3-47-1-1-B and IR127364-46-2-1 subsequently advanced to AVT-I and AVT-II, respectively, demonstrating the effectiveness of integrated genetic and multivariate analyses in identifying promising materials for DDSR varietal development.

References

  1. 1. Khush GS. What it will take to feed 5.0 billion rice consumers in 2030. Plant Mol Biol. 2005;59:1–6. https://doi.org/10.1007/s11103-005-2159-5
  2. 2. Alam M, Lou G, Abbas W, Osti R, Ahmad A, Bista S, et al. Improving rice grain quality through ecotype breeding for enhancing food and nutritional security in Asia–Pacific region. Rice. 2024;17:47. https://doi.org/10.1186/s12284-024-00725-9
  3. 3. Jehangir IA, Raja W, Hussain A, Al-Shuraym LA, Sayed SM, Lone AH, et al. Herbicide effectiveness and crop yield responses in direct-seeded rice: Insights into sustainable weed management. Adv Weed Sci. 2024;42:e020240004. https://doi.org/10.51694/AdvWeedSci/2024;42:00012
  4. 4. Xu L, Li X, Wang X, Xiong D, Wang F. Comparing the grain yields of direct-seeded and transplanted rice: A meta-analysis. Agronomy. 2019;9(11):767. https://doi.org/10.3390/agronomy9110767
  5. 5. Department of Agriculture and Farmers Welfare. Rice area in lakh ha, production in lakh tonnes & yield in kg/ha for the year 2024–25. Unified Portal for Agricultural Statistics.
  6. 6. Mondal M, Biswas B, Garai S, Jana S, Dey S, Mandal TK, et al. Seedbed management, transplanting methods and irrigation regimes influence the growth, productivity and economics of summer rice (Oryza sativa L.). Disc Sustainability. 2024;5(1). https://doi.org/10.1007/s43621-024-00300-8
  7. 7. Yadav DB, Yadav A, Vats AK, Gill G, Malik RK. Direct-seeded rice in sequence with zero-tillage wheat in north-western India: Addressing system-based sustainability issues. SN Appl Sci. 2021;3:844. https://doi.org/10.1007/s42452-021-04827-7
  8. 8. Sandhu N, Yadav S, Catolos M, Cruz MTS, Kumar A. Developing climate-resilient, direct-seeded, adapted multiple-stress-tolerant rice applying genomics-assisted breeding. Front Plant Sci. 2021;12:637488. https://doi.org/10.3389/fpls.2021.637488
  9. 9. Gupta DK, Bhatia A, Kumar A, Das TK, Jain N, Tomer R, et al. Mitigation of greenhouse gas emission from rice–wheat system of the Indo-Gangetic plains: Through tillage, irrigation and fertilizer management. Agric Ecosyst Environ. 2016;230:1–9. https://doi.org/10.1016/j.agee.2016.05.023
  10. 10. Negi P, Rane J, Wagh RS, Bhor TJ, Godse DD, Jadhav P, et al. Direct-seeded rice: Genetic improvement of game-changing traits for better adaption. Rice Sci. 2024;31(4):417–33. https://doi.org/10.1016/j.rsci.2024.04.006
  11. 11. Mahajan G, Singh K, Singh N, Kaur R, Chauhan BS. Screening of water-efficient rice genotypes for dry direct seeding in South Asia. Arch Agron Soil Sci. 2018;64(1):103–15. https://doi.org/10.1080/03650340.2017.1337891
  12. 12. Kumar S, Kumar S, Krishnan GS, Mohapatra T. Molecular basis of genetic plasticity to varying environmental conditions on growing rice by dry/direct-sowing and exposure to drought stress: Insights for DSR varietal development. Front Plant Sci. 2022;13:1013207. https://doi.org/10.3389/fpls.2022.1013207
  13. 13. Sandhu N, Subedi SR, Singh VK, Sinha P, Kumar S, Singh SP, et al. Deciphering the genetic basis of root morphology, nutrient uptake, yield, and yield-related traits in rice under dry direct-seeded cultivation systems. Sci Rep. 2019;9(1):9334. https://doi.org/10.1038/s41598-019-45770-3
  14. 14. Hapsari RT, Purwoko BS, Dewi IS, Ardie SW, Aswidinnoor H. Utilization of genetic resources, genetic diversity and genetic variability for selecting new restorer lines of rice (Oryza sativa L.). Genes. 2022;13(12):2227. https://doi.org/10.3390/genes13122227
  15. 15. Zayed B, Bassiouni S, Okasha A, Abdelhamed M, Soltan S, Negm M. Path coefficient, eigenvalues and genetic parameters in Egyptian rice (Oryza sativa L.) under aerobic conditions. SABRAO J Breed Genet. 2023;55(1):131–45. https://doi.org/10.54910/sabrao2023.55.1.13
  16. 16. Dewey DR, Lu KH. A correlation and path-coefficient analysis of components of crested wheatgrass seed production. Agron J. 1959;51(9):515–18. https://doi.org/10.2134/agronj1959.00021962005100090002x
  17. 17. Sruthi SR, Laleeth Kumar N, Kishore K, Johnny SI, 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). https://doi.org/10.14719/pst.4105
  18. 18. Kaiser HF. The application of electronic computers to factor analysis. Educ Psychol Meas. 1960;20(1):141–151. https://doi.org/10.1177/001316446002000116
  19. 19. Roy SC, Shil P. Assessment of genetic heritability in rice breeding lines based on morphological traits and caryopsis ultrastructure. Sci Rep. 2020;10:7830. https://doi.org/10.1038/s41598-020-63976-8
  20. 20. Islam MZ, Chakrabarty T, Akter N, Khalequzzaman M, Prince MFRK, Pittendrigh BR, et al. Genetic variability, correlation and path coefficient analysis of phenotypic traits and genetic diversity of Aman rice landraces (Oryza sativa L.). Sci Rep. 2025;15(1):18606. https://doi.org/10.1038/s41598-025-03547-x
  21. 21. Liang Z, Ruiz-Menjivar J, Zhang L, Zhang J, Shen X. Examining the effects of adopting early maturing crop varieties on agricultural productivity, climate change adaptation, and mitigation. Int J Low-Carbon Technol. 2024;19:1256–74. https://doi.org/10.1093/ijlct/ctad150
  22. 22. Chacko A, Jayalekshmy VG, Shahiba AM. Studies on PCV, GCV, heritability, and genetic advance in rice genotypes for yield and yield components. Int J Plant Soil Sci. 2023;35(16):324. https://doi.org/10.9734/ijpss/2023/v35i163266
  23. 23. Kumar S, Jayasudha S, Rajani, Singh SK. Genetic variability, heritability and genetic advance for yield and quality traits in rice (Oryza sativa L.). Indian J Agric Res. 2025;1–8. https://doi.org/10.18805/IJARe.A-6402
  24. 24. Gai D, Wang Y, Li H, Shi B, Liu Y, Zhang Q, et al. Appropriate seeding rate facilitates the simultaneous enhancement of population yield and lodging resistance in direct-seeded rice. Front Plant Sci. 2025;16:1622993. https://doi.org/10.3389/fpls.2025.1622993
  25. 25. Chamara BS, Marambe B, Kumar V, Ismail AM, Septiningsih EM, Chauhan BS. Optimizing sowing and flooding depth for anaerobic germination-tolerant genotypes to enhance crop establishment, early growth, and weed management in dry-seeded rice (Oryza sativa L.). Front Plant Sci. 2018;9:1654. https://doi.org/10.3389/fpls.2018.01654
  26. 26. Bhandari A, Sandhu N, Bartholomé J, Cao-Hamadoun TV, Ahmadi N, Kumari N, et al. Genome-wide association study for yield and yield related traits under reproductive stage drought in a diverse indica-aus rice panel. Rice. 2020;13(1). https://doi.org/10.1186/s12284-020-00406-3
  27. 27. Sar P, Gupta S, Behera M, Chakraborty K, Ngangkham U, Verma BC, et al. Exploring genetic diversity within aus rice germplasm: Insights into the variations in agro-morphological traits. Rice. 2024;17(1). https://doi.org/10.1186/s12284-024-00700-4
  28. 28. Ahmad MS, Wu B, Wang H, Kang D. Field screening of rice germplasm (Oryza sativa L. ssp. japonica) based on days to flowering for drought escape. Plants. 2020;9(5):609. https://doi.org/10.3390/plants9050609
  29. 29. Bassuony NN, Zsembeli J, Nofal RS, Essa WM, Fazaa M. Genetic variability of some physiological traits, yield components and grain quality characters in rice entries. Agriculture. 2022;68(4):143–54. https://doi.org/10.2478/agri-2022-0013
  30. 30. Rathan ND, Singh SK, Singh RK, Singh DK. Estimating genetic divergence in rice (Oryza sativa L.) using D² analysis. Electron J Plant Breed. 2020;11(3):836–40. https://doi.org/10.37992/2020.1103.137
  31. 31. Pallavi M, Prasad BD, Shanthi P, Reddy V, Kumar AN. Multi trait genotype-ideotype distance index (MGIDI) for early seedling vigour and yield related traits to identify elite lines in rice (Oryza sativa L.). Electron J Plant Breed. 2024;15(1). https://doi.org/10.37992/2024.1501.020
  32. 32. Goyal A, Sharma PC, Munjal R, Krishnamurthy SL, Jat HS, Lokeshkumar BM, et al. Identifying rice genotypes suitable for aerobic direct-seeded conditions. Environ Sci Proc. 2022;16:68. https://doi.org/10.3390/environsciproc2022016068

Downloads

Download data is not yet available.