Skip to main navigation menu Skip to main content Skip to site footer

Research Articles

Vol. 12 No. 4 (2025)

Comparative multimodal statistical analysis for Yield Stability in Mutant populations of Rice (Oryza sativa L.) var. ADT (R) 47

DOI
https://doi.org/10.14719/pst.8430
Submitted
21 March 2025
Published
24-09-2025 — Updated on 16-10-2025
Versions

Abstract

Rice is an important staple food for half of the world’s population. Genetic erosion was caused by the development of high-yielding cultivars and the extensive cultivation of these kinds. Mutation breeding is promising not only for creating and utilizing the new variability but also an indispensable approach through which yield might be improved without altering the existing desirable characters. Induced mutations, have offered a single and short alternative to conventional breeding that includes isolation, screening, selection and testing generation after generation. A multi-model approach employing BLUP-based simultaneous selection, to assess the stability and performance of ADT 47 mutants genotypes with respect to the grain yield was performed. All members of the AMMI (Additive Main Effects and Multiplicative Interaction) family were less accurate than BLUP (Best linear unbiased prediction). BLUP approach involved the estimation of Harmonic mean of genotypic values (HMGV) which is used to infer both yield and stability, Relative performance of genotypic values (RPGV) involved in the estimation of mean yield and genotypic adaptability), harmonic mean of the relative performance of genotypic values (HMRPGV) tangled to evaluate stability, adaptability, and yield simultaneously. Comparing all the calculated indices, mutants viz., 160-39-1, 200-30-3, 250-36-1, 200-69-1 were highly stable for plant yield along with ADT47 control variety.

References

  1. 1. Mohidem NA, Hashim N, Shamsudin R, Che Man H. Rice for food security: Revisiting its production, diversity, rice milling process and nutrient content. Agriculture. 2022;12(6):741. https://doi.org/10.3390/agriculture12060741
  2. 2. Gomaa ME, El-Hissewy AA, Khattab AB, Abd-Allah AA. Days to heading and plant height of rice, Oryza sativa L. affected by gamma rays. Menofiya J Agric Res. 1995;20(2).
  3. 3. Li F, Shimizu A, Nishio T, Tsutsumi N, Kato H. Comparison and characterization of mutations induced by gamma-ray and carbon-ion irradiation in rice (Oryza sativa L.) using whole-genome resequencing. Genes Genome Genet. 2019;9(11):3743-51. https://doi.org/10.1534/g3.119.400555
  4. 4. Eberhart ST, Russell W. Stability parameters for comparing varieties 1. Crop Sci. 1966;6(1):36-40. https://doi.org/10.
  5. 2135/cropsci1966. 0011183X000600010011x
  6. 5. Zobel RW, Wright MJ, Gauch Jr HG. Statistical analysis of a yield trial. Agron J. 1988;80(3):388-93. https://doi.org/10.
  7. 2134/agronj1988. 00021962008000030002x
  8. 6. De Mendiburu F, Simon R. Agricolae-Ten years of an open source statistical tool for experiments in breeding, agriculture and biology. Peer J. 2015;29. https://doi.org/10.7287/peerj.preprints.1404v1
  9. 7. Ajay BC, Aravind J, Fiyaz RA. Ammistability: R package for ranking genotypes based on stability parameters derived from AMMI model. Ind J Genet Plant Breed. 2019;79(02):460-66. https://doi.org/10.31742/IJGPB.79.2.10
  10. 8. Bates D, Mächler M, Bolker B, Walker S. Fitting linear mixed-effects models using lme4. J Stat Soft. 2015;67:1-48. https://doi.org/10. 18637/jss.v067.i01
  11. 9. Shanmuganathan M, Ibrahim SM. Stability analysis for yield and its components in hybrid rice (Oryza sativa L.). Crop Res. 2005;30(1):40-45.
  12. 10. Mahalingam A, Saraswathi R, Robin S, Marimuthu T, Jayaraj T, Ramalingam J. Genetics of stability and adaptability of rice hybrids (Oryza sativa L.) for grain quality traits. Afr J Agric Res. 2013;8(22):2673-80. https://doi.org/10.5897/AJAR12.1035
  13. 11. Akhter MS, Rizwan M, Akhter M, Naeem M, Hussain W, Elahi F, et al. Genotypic and phenotypic condition coefficient analysis for yield and yield related components in basmati rice (Oryza sativa L.). Am Eurasian J Agric Environ Sci. 2014;14:1402-44. https://doi.org/10.5829 /idosi.aejaes.2014.14.12.12468
  14. 12. Vijayakumar CH, Ahmed MI, Viraktamath BC, Balakrishnan R, Ramesha MS. Genotype x environment interaction effects on yield of rice hybrids in India. Ind J Genet Plant Breed. 2001;61(02):101-06.
  15. 13. Bose LK, Jambhulkar NN, Singh ON. Additive Main effects and Multiplicative Interaction (AMMI) analysis of grain yield stability in early duration rice. J Anim Plant Sci. 2014;24(6).
  16. 14. Shinde GC, Bhingarde MT, Khairnar MN, Mehetre SS. AMMI analysis for stability of grain yield of pearl millet (Pennisetum typhoides L.) hybrids. Indian J Genet Plant Breed. 2002;62(03):215-17.
  17. 15. Ramazi M, Omidi H, Sadeghzadeh Hemayati S, Naji A. Unraveling genotypic interactions in sugar beet for enhanced yield stability and trait associations. Sci Rep. 2024;14(1):20815. https://doi.org/10.1038/s41598-024-71139-2
  18. 16. Sharifi P, Erfani A, Abbasian A, Mohaddesi A. Stability of some of rice genotypes based on WAASB and MTSI indices. Iran J Genet Plant Breed. 2020;9(2). https://doi.org/10.30479/ijgpb.2021.14432.1283
  19. 17. Santos F, Marza YF. Selection of forage oat genotypes through GGE Biplot and BLUP. bioRxiv. 2020:2020-03. https://doi.org/10.1101/ 2020.03.10.986422
  20. 18. Hassani M, Mahmoudi SB, Saremirad A, Taleghani D. Genotype by environment and genotype by yield* trait interactions in sugar beet: analyzing yield stability and determining key traits association. Sci Rep. 2023;13(1):23111. https://doi.org/10.1038/s41598-023-51061-9
  21. 19. Henderson CR. Best linear unbiased estimation and prediction under a selection model. Biometrics. 1975:423-47. https://doi.org/10. 2307/2529430
  22. 20. Pires IE, Resende MDV, Silva RL, Resende Júnior MFR. Genét flor. Viçosa, 2011 BR: UFV, 317.
  23. 21. De Resende MA, Dos Rei WG, Pereira LD, Ferreira W, Garcia MH, Santoro MM, et al. Hyperalgesia and edema responses induced by rat peripheral blood mononuclear cells incubated with carrageenin. Inflammation. 2001;25:
  24. 277-85. https://doi.org/10.1023/A:1012812124461
  25. 22. Rao AR, Prabhakaran VT. Use ofAMMI in Simultaneous selection of genotypes for yield and stability. J Ind Soc Agric Stat. 2005;59:76-82.
  26. 23. Farshadfar E. Incorporation of AMMI stability value and grain yield in a single non-parametric index (GSI) in bread wheat. Pak J Biol Sci. 2008;11(14):1791. https://doi.org/10.3923/pjbs.2008.1791.1796
  27. 24. Olivoto T, Lúcio AD, da Silva JA, Sari BG, Diel MI. Mean performance and stability in multi‐environment trials II: Selection based on multiple traits. Agron J. 2019;111(6):2961-69. https://doi.org/10.2134/agronj2019.03.0221
  28. 25. Koundinya AV, Ajeesh BR, Hegde V, Sheela MN, Mohan C, Asha KI. Genetic parameters, stability and selection of cassava genotypes between rainy and water stress conditions using AMMI, WAAS, BLUP and MTSI. Sci Hortic. 2021;281:109949. https://doi.org/10.1016/j.scienta.2021.109949
  29. 26. Zuffo AM, Steiner F, Aguilera JG, Teodoro PE, Teodoro LP, Busch A. Multi‐trait stability index: A tool for simultaneous selection of soya bean genotypes in drought and saline stress. J Agron Crop Sci. 2020;206(6):815-22. https://doi.org/10.1111/jac.12409
  30. 27. Rajabi A, Ahmadi M, Bazrafshan M, Hassani M, Saremirad A. Evaluation of resistance and determination of stability of different sugar beet (Beta vulgaris L.) genotypes in rhizomania-infected conditions. Food Sci Nutr. 2022;11(3):1403-14. https://doi.org/10.1002/fsn3.3180
  31. 28. Aruna K, Sridhara S, Sowjanya BA, KL NK, Moussa IM, Elansary HO, et al. Multi-trait stability index for identification of stable green gram (Vigna radiata (L.) Wilczek) genotypes with MYMV resistance. Heliyon. 2024;10(12). https://doi.org/10.1016/j.heliyon.2024.e32763
  32. 29. Zakir, M. (2018). Review on genotype × environment interaction in plant breeding and agronomic stability of crops. J Biol Agric Healthcare. 2018;8(12):14-21.

Downloads

Download data is not yet available.