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Cereal Research Communications

, Volume 46, Issue 1, pp 146–157 | Cite as

Analysis of Multi-location Data of Hybrid Rice Trials Reveals Complex Genotype by Environment Interaction

  • R. PonnuswamyEmail author
  • A. Rathore
  • A. Vemula
  • R. R. Das
  • A. K. Singh
  • D. Balakrishnan
  • H. S. Arremsetty
  • R. B. Vemuri
  • T. Ram
Breeding

Abstract

The All India Coordinated Rice Improvement Project of ICAR-Indian Institute of Rice Research, Hyderabad organizes multi-location testing of elite lines and hybrids to test and identify new rice cultivars for the release of commercial cultivation in India. Data obtained from Initial Hybrid Rice Trials of three years were utilized to understand the genotype × environment interaction (GEI) patterns among the test locations of five different agro-ecological regions of India using GGE and AMMI biplot analysis. The combined analysis of variance and AMMI ANOVA for a yield of rice hybrids were highly significant for GEI. The GGE biplots first two PC explained 54.71%, 51.54% and 59.95% of total G + GEI variation during 2010, 2011 and 2012, respectively, whereas AMMI biplot PC1 and PC2 explained 46.62% in 2010, 36.07% in 2011 and 38.33% in 2012 of the total GEI variation. Crossover interactions, i.e. genotype rank changes across locations were observed. GGE biplot identified hybrids, viz. PAN1919, TNRH193, DRH005, VRH639, 26P29, Signet5051, KPH385, VRH667, NIPH101, SPH497, RH664 Plus and TNRH222 as stable rice hybrids. The discriminative locations identified in different test years were Coimbatore, Maruteru, VNR, Jammu, Raipur, Ludhiana, Karjat and Dabhoi. The AMMI1 biplot identified the adaptable rice hybrids viz., CNRH102, DRH005, NK6303, NK6320, DRRH78, NIPH101, Signet5050, BPH115, Bio452, NPSH2003, and DRRH83. The present study demonstrated that AMMI and GGE biplots analyses were successful in assessing genotype by environment interaction in hybrid rice trials and aided in the identification of stable and adaptable rice hybrids with higher mean and stable yields.

Keywords

G × E interaction hybrid rice AICRIP multivariate methods crop improvement 

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Notes

Acknowledgements

Authors are highly thankful to the Director, IIRR for his kind support to bring out this publication.

Supplementary material

42976_2018_4601146_MOESM1_ESM.pdf (1.5 mb)
Analysis of Multi-location Data of Hybrid Rice Trials Reveals Complex Genotype by Environment Interaction

References

  1. Agyeman, A., Parkees, E., Peprah, B.B. 2015. AMMI and GGE biplot analysis of root yield performance of cassava genotype in the forest and coastal ecology. Int. J. of Agric. Policy Res. 3:222–232.Google Scholar
  2. Babu, V.R. 2015. Rice research in India – current status and future prospects. Short course on widening the genetic base in rice through pre-breeding efforts for developing next generation varieties and hybrids. Directorate of Rice Research. Rajendranagar, Hyderabad, India, January 19–28, 2015. pp. 1–15.Google Scholar
  3. Blum, A., Pnuel, Y. 1990. Physiological attributes associated with drought resistance of wheat cultivars in a Mediterranean environment. Aust. J. of Agric. Res. 41:799–810.CrossRefGoogle Scholar
  4. Bradu, D., Gabriel, K.R. 1978. The biplot as a diagnostic tool for models of two-way tables. Technometrics 20:47–68.CrossRefGoogle Scholar
  5. Cooper, M., De Lacy, I.H. 1994. Relationships among analytic methods used to study genotypic variation and genotype-by-environment interaction in plant breeding multi-environment experiments. Theor. Appl. Genet. 88:561–572.CrossRefGoogle Scholar
  6. Cornelius, P.L., Seyedsadr, M., Crossa, J. 1992. Using the shifted multiplicative model to search for ‘separability’ in crop cultivar trials. Theor. Appl. Genet. 84:161–172.CrossRefGoogle Scholar
  7. Das, R.R., Anil Kumar, V., Rakshit, S., Maraboina, R., Panwar, S., Savadia, S., Rathore, A. 2012. Interpreting genotype by environment interaction using weather covariates. J. of Statistics and Applications 10:45–62.Google Scholar
  8. DRR Progress Report 2011 Varietal Improvement All India Coordinated Rice Improvement Project (ICAR). Directorate of Rice Research. Rajendranagar, Hyderabad – 500030, A.P., India. Vol. 1.Google Scholar
  9. DRR Progress Report 2012. Varietal Improvement All India Coordinated Rice Improvement Project (ICAR). Directorate of Rice Research. Rajendranagar, Hyderabad – 500030, A.P., India. Vol. 1.Google Scholar
  10. DRR Progress Report 2013. Varietal Improvement All India Coordinated Rice Improvement Project (ICAR). Directorate of Rice Research. Rajendranagar, Hyderabad – 500030, A.P., India. Vol. 1.Google Scholar
  11. Gauch, H.G. 1988. Model selection and validation for yield trials with interaction. Biometrics 44:705–715.CrossRefGoogle Scholar
  12. Gauch, H.G. 1992. AMMI analysis of yield trials. In: Kang, M.S., Gauch, H.G. (eds), Genotype-by-environment Interaction. CRC Press. Boca Raton, FL, USA. pp. 1–40.Google Scholar
  13. Gauch, H.G., Zobel, R.W. 1996. AMMI analysis of yield trials. In: Kang, M.S., Gauch, G. (eds), Genotype-by-environment Interaction. CRC Press. Boca Raton, FL, USA. pp. 85–122.CrossRefGoogle Scholar
  14. Gauch, H.G., Zobel, R.W. 1997. Identifying mega-environments and targeting genotypes. Crop Sci. 37:311–326.CrossRefGoogle Scholar
  15. Gauch, H.G. 2006. Statistical analysis of yield trials by AMMI and GGE. Crop Sci. 46:1488–1500.CrossRefGoogle Scholar
  16. Gollob, H.F. 1968. A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33:73–115.CrossRefGoogle Scholar
  17. IRRI 2016. World rice statistics online query facility. International Rice Research Institute (IRRI). http://www.ricestat.irri.org:8080.
  18. Jackson, P.A., Byth, D.E., Johnston, R.P., Fischer, K.S. 1993. Genotype × environment interactions in progeny from a barley cross. 1. Patterns of response among progeny genotypes for grain yield and time to anthesis. Aust. J. of Exp. Agric. 33:619–627.CrossRefGoogle Scholar
  19. Kempton, R.A. 1984. The use of biplots in interpreting variety by environment interactions. J. of Agric. Sci. 103:123–135.CrossRefGoogle Scholar
  20. Khush, G.S. 2013. Strategies for increasing the yield potential of cereals: case of rice as an example. Plant Breed. 132:433–436.Google Scholar
  21. Loffler, C.M., Salaberry, M.T., Maggio, J.C. 1986. Stability and genetic improvement of maize yield in Argentina. Euphytica 35:449–458.CrossRefGoogle Scholar
  22. Luo, J., Pan, Y.B., Que, Y., Zhang, H., Grisham, M.P., Xu, L. 2015. Biplot evaluation of test environments and identification of mega-environment for sugarcane cultivars in China. Scientific Reports 5:15505 doi: 10.1038/srep15505.CrossRefGoogle Scholar
  23. Mohamed, N.E.M. 2013. Genotype by environment interactions for grain yield in bread wheat (Triticum aestivum L.). J. of Plant Breed. and Crop Sci. 5:150–157.CrossRefGoogle Scholar
  24. Muralidharan, K., Prasad, G.S.V., Rao, C.S. 2002. Yield performance of rice genotypes in international multienvironment trials during 1976–97. Current Sci. 83:610–619.Google Scholar
  25. Ogunbayo, S.A., Sié, M., Ojo, D.K., Sanni, K.A., Akinwale, M.G., Toulou, B., Shittu, A., Idehen, E.O., Popoola, A.R., Daniel, I.O., Gregorio, G.B. 2014. Genetic variation and heritability of yield and related traits in promising rice genotypes (Oryza sativa L.). J. of Plant Breed. and Crop Sci. 6:153–159.CrossRefGoogle Scholar
  26. Reddy, P.S., Rathore, A., Reddy, B.V.S., Panwar, S. 2011. Application GGE biplot and AMMI model to evaluate sweet sorghum (Sorghum bicolor) hybrids for genotype × environment interaction and seasonal adaptation. Indian J. Agric. Sci. 81:438–444.Google Scholar
  27. Sailaja, B., Meera, S.N., Viraktamath, B.C. 2012. Manual for AICRIP Information Management System Technical Bulletin No. 61. Directorate of Rice Research (ICAR), Rajendranagar, Hyderabad – 500 030, A.P., India. 37 pp. (http://www.aicrip-intranet.in)Google Scholar
  28. Samonte, S.O.P.B., Wilson, L.T., McClung, A.M., Medley, J.C. 2005. Targeting cultivars onto rice growing environments using AMMI and SREG GGE biplot analyses. Crop Sci. 45:2414–2424.CrossRefGoogle Scholar
  29. Scapim, C.A., Oliveira, V.R., Braccini, A.L., Cruz, C.D., Andrade, C.A., Vidigal, M.C.G. 2000. Yield stability in maize (Zea mays L.) and correlations among the parameters of the Eberhart and Russel, Lin and Binns and Huehn models. Genet. and Mol. Biol. 23:387–393.CrossRefGoogle Scholar
  30. Sharma, M., Kiran Babu, T., Gaur, P.M., Ghosh, R., Rameshwar, T., Chaudhary, R.G., Upadhyay, J.P., Gupta, O., Saxena, D.R., Kaur, L., Dubey, S.C., Anandani, V.P., Harer, P.N., Rathore, A., Pande, S. 2012. Identification and multi-environment validation of resistance to Fusarium oxysporum f. sp. ciceris in chickpea. Field Crops Res. 135:82–88.CrossRefGoogle Scholar
  31. Van Eeuwijk, F.A., Denis, J.B., Kang, M.S. 1966. Incorporating additional information on genotypes and environments in models for two-way genotype by environment tables. In: Kang, M.S., Gauch, H.G. (eds), Genotype-by-Environment Interaction. CRC Press. Boca Raton, FL, USA. pp. 15–49.Google Scholar
  32. Virk, D.S., Mangat, B.K. 1991. Detection of crossover genotype by environment interactions in pearl millet. Euphytica 52:193–199.CrossRefGoogle Scholar
  33. VSN International 2015. GenStat for Windows 17th Edition. VSN International. Hemel Hempstead, UK. Web page: http://GenStat.co.ukGenStat.co.uk
  34. Yan, W., Hunt, L.A., Sheng, Q., Szlavnics, Z. 2000. Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci. 40:597–605.CrossRefGoogle Scholar
  35. Yan, W. 2001. GGE biplot a Windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agron. J. 93:1111–1118.CrossRefGoogle Scholar
  36. Yan, W., Hunt, L.A. 2002. Biplot analysis of diallel data. Crop Sci. 42:21–30.CrossRefGoogle Scholar
  37. Yan, W., Kang, M.S. 2003. GGE biplot analysis: a graphical tool for breeders, geneticists, and agronomists. CRC Press. Boca Raton, FL, USA.Google Scholar
  38. Yan, W., Tinker, N.A. 2005. An integrated system of biplot analysis for displaying, interpreting, and exploring genotype by environment interactions. Crop Sci. 45:1004–1016.CrossRefGoogle Scholar
  39. Yan, W., Tinker, N.A. 2006. Biplot analysis of multi-environment trial data: principles and applications. Can. J. of Plant Sci. 86:623–645.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest 2018

Authors and Affiliations

  • R. Ponnuswamy
    • 1
    Email author
  • A. Rathore
    • 2
  • A. Vemula
    • 2
  • R. R. Das
    • 2
  • A. K. Singh
    • 1
  • D. Balakrishnan
    • 1
  • H. S. Arremsetty
    • 1
  • R. B. Vemuri
    • 1
  • T. Ram
    • 1
  1. 1.ICAR-Indian Institute of Rice ResearchRajendranagar, HyderabadIndia
  2. 2.Statistics, Bioinformatics and Data ManagementICRISATPatancheru, HyderabadIndia

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