Journal of Crop Science and Biotechnology

, Volume 22, Issue 5, pp 425–449 | Cite as

Genotype by Environment (G×E) Interaction Study on Yield Traits in Different Maturity Groups of Rice

  • Swapna Jadhav
  • Divya BalakrishnanEmail author
  • Gouri Shankar V
  • Kavitha Beerelli
  • Gowthami Chandu
  • Sarla Neelamraju
Research Article


Rice production is affected by emerging problems of climate change and over-utilization of resources. To obtain consistent yield across diverse environments, a variety should have adaptability and stability to fit into various growing conditions. G×E interaction and stability performance of 59 rice lines of different maturity durations were investigated for grain yield-related traits in three environments. This study was carried out to identify stable lines for varietal development as well as to identify parental lines with stable contributing traits for further breeding programs. AMMI and GGE analysis showed significant genotype, environment, and G×E interaction indicating the presence of variability among the genotypes and environments. The G×E interaction effect showed that the genotypes responded differently to the variation in environmental conditions or seasonal fluctuations and explained that most of the traits were contributed mainly by genotype, followed by environment and their interaction. As per AMMI biplot analysis, environment1 was identified as the best suited for potential expression of grain yield and related traits. Results of stability analysis revealed that early and mid-early genotypes NH776, NH4371, 27K, NH686, 258S, NH219, and Tellahamsa were identified as the best stable genotypes across all the three seasons for single plant grain yield and hence suitable for wider environments. These selected genotypes can be suggested for hybridization in further breeding programs to develop early genotypes with high yield. The stable early and mid-early lines with high yield potential will be tested in multi-location trials for commercial cultivation.

Key words

Adaptability biplot G×E mutants wild introgression lines 


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  1. Badu-Apraku B, Oyekunle M, Obeng-Antwi K, Osuman AS, Ado SG, Coulibay N, Yallou CG, Abdulai M, Boakyewaa GA, Didjeira A. 2012. Performance of extra-early maize cultivars based on GGE biplot and AMMI analysis. J. Agric. Sci. Cambridge 150: 475–486CrossRefGoogle Scholar
  2. Balakrishnan D, Subrahmanyam D, Badri J, Raju AK, Rao VY, Kavitha B, Sukumar M, Malathi S, Revathi P, Padmavathi G, Babu VR, Sarla, N. 2016. Genotype × environment interactions of yield traits in backcross introgression lines derived from Oryza sativa cv. Swarna/Oryza nivara. Front. Plant Sci., Google Scholar
  3. Bose KL, Jambhulkar NN, Pande K. 2014. Genotype by Environment interaction and stability analysis for rice genotypes under Boro condition. Genetika, 46: 521–528CrossRefGoogle Scholar
  4. Bueno CS, Lafarge T. 2017. Maturity groups and growing seasons as key sources of variation to consider within breeding programs for high yielding rice in the tropics. Euphytica 213: 74. Scholar
  5. Cornelius PL, Crossa J, Seyedsadr MS. 1996. Statistical tests and estimators of multiplicative models for genotype-byenvironment interaction. p. 199–234. In: MS Kang, HG Gauch, eds., Genotype-by-environment interaction. CRC Press, Boca Raton, FL, USAGoogle Scholar
  6. Crossa J, Cornelius PL, Yan W. 2002. Biplots of linear-bilinear models for studying crossover genotype - environment interaction. Crop Sci. 42: 619–633CrossRefGoogle Scholar
  7. Crossa J, Gauch HGJ, Zobel RW. 1990. Additive main effects and multiplicative interaction analysis of two international maize cultivar trials. Crop Sci. 30: 493–500CrossRefGoogle Scholar
  8. Dewi KA, Chozin AM, Triwidodo H, Aswidinnoor H. 2014. Genotype × environment interaction, and stability analysis in lowland rice promising genotypes. Int. J. Agron. Agric. Res. 5(5), 74–84Google Scholar
  9. Ebdon JS, Gauch HG. 2002. Additive main effect and multiplicative interaction analysis of national turf grass performance trials: II. Cultivar recommendations. Crop Sci. 42, 497–506CrossRefGoogle Scholar
  10. Filho CJM, Resende MDV, Morais OP, Castro AP, GuimarAes EP, Pereira JA, Utumi MM, Breseghello F. 2013. Upland rice breeding in Brazil: a simultaneous genotypic evaluation of stability, adaptability and grain yield. Euphytica 192, 117–129CrossRefGoogle Scholar
  11. Food and Agriculture Organization (FAO), FAO Statistical Data Base, Scholar
  12. Gauch HG. 1993. Matmodel Version 2.0. AMMI and related analysis for two-way data matrices. Micro Computer Power, Ithaca, New York, USAGoogle Scholar
  13. Gauch HG. 2006. Statistical analysis of yield trials by AMMI and GGE. Crop Science. 46, 1488–1500CrossRefGoogle Scholar
  14. Indiastat 2015-16. India rice acreage, production and productivity. Available online from: Scholar
  15. IRRI. 2013. Standard Evaluation System (SES) for Rice, 5th ed. IRRI, PhilippinesGoogle Scholar
  16. Kang MS. 1993. Simultaneous selection for yield and stability in crop performance trials: consequences for growers. Agron. J. 85, 754–757CrossRefGoogle Scholar
  17. Lee S, Jia MH, Jia Y, Liu G. 2014. Tagging quantitative trait loci for heading date and plant-height in important breeding parents of rice (Oryza sativa). Euphytica 197: 191–200, CrossRefGoogle Scholar
  18. McLaren CG, Chaudhary C. 1994. Use of additive main effects and multiplicative interaction models to analyse multilocation rice variety trials. Paper presented at the FCSSP Conference, Puerto Princesa, Palawan, PhilippinesGoogle Scholar
  19. Protection of Plant Varieties and Farmer's Rights Authority (PPV and FRA). 2007. ‘Specific DUS test guidelines for twelve notified crops–rice (Oryza sativa L.)’, Plant Var. J. India, 1, pp 151–169Google Scholar
  20. Rakshit S, Ganapathy KN, Gomashe SS, Dhandapani A, Swapna M, Mehtre SP. 2016. Analysis of Indian post-rainy sorghum multi-location trial data reveals complexity of genotype × environment interaction J. Agric. Sci. 1, 1–16, doi: 10.1017/ S0021859616000137Google Scholar
  21. Rasul G, Glover KD, Krishnan GP, Padmanaban G, Jixiang W, Berzonsky WA, Fofana B. 2017. Genetic analyses using GGE model and a mixed linear model approach, and stability analyses using AMMI bi-plot for late-maturity alpha-amylase activity in bread wheat genotypes. Genetica 145(3), 259–268CrossRefGoogle Scholar
  22. Sharma, R.C., Smith, E.L and McNew, R.W. 1987. Stability of harvest index and grain yield in winter wheat. Crop Sci. 27, 104–108CrossRefGoogle Scholar
  23. Statista. 2015-16. Global rice acreage and production. Available online from: Google Scholar
  24. Tariku S, Lakew T, Bitew M, Asfaw M. 2013. Genotype by environment interaction and grain yield stability analysis of rice (Oryza sativa L.) genotypes evaluated in north western Ethiopia. Net J. Agric. Sci. 1, 10–16Google Scholar
  25. Thillainathan M, Fernandez GC. 2001. SAS application for tai's stability analysis and AMMI model in genotype x environmental interaction (GEI) effects. J. Hered. 92(4), 367–371CrossRefGoogle Scholar
  26. Worku M, Makumbi D, Beyene Y, Das B, Mugo S, Pixley K, Bziger M, Owino F, Olsen M, Asea G, Prasanna BM. 2016. Grain yield performance and flowering synchrony of CIMMYT's tropical maize (Zea mays L.) parental inbred lines and single crosses. Euphytica 211, 395, doi:10.1007/s10681-016-1758-3CrossRefGoogle Scholar
  27. Yan W, Hunt LA, Sheng Q, Szlavnics Z. 2000. Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci. 40(3), 597–605CrossRefGoogle Scholar
  28. Yan W, Kang MS. 2003. GGE biplot analysis: A graphical tool for breeders, geneticists and agronomists. 1st Edn. CRC Press LLC, Boca Raton, Florida, pp. 271Google Scholar
  29. Zobel RW, Wright MJ, Gauch HG. 1988. Statistical analysis of yield trial. Agron. J. 80, 388–393CrossRefGoogle Scholar

Copyright information

© Korean Society of Crop Science and Springer 2019

Authors and Affiliations

  • Swapna Jadhav
    • 1
    • 2
  • Divya Balakrishnan
    • 1
    Email author
  • Gouri Shankar V
    • 2
  • Kavitha Beerelli
    • 1
  • Gowthami Chandu
    • 1
  • Sarla Neelamraju
    • 1
  1. 1.CAR-Indian Institute of Rice ResearchHyderabadIndia
  2. 2.College of Agriculture, PJTSAU, RajendranagarHyderabadIndia

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