GGE bi-plot analysis for grain yield in chickpea (Cicer arietinum) under normal and heat stress conditions


403 / 307

Authors

  • UDAY CHAND JHA Scientist (Crop Improvement), Indian Institute of Pulses Research (IIPR), Kanpur, Uttar Pradesh 208 024, India
  • PARESH CHANDRA KOLE Professor (Genetics & Plant Breeding), Institute of Agriculture, Visva-Bharati University, Sriniketan, Bolpur, West Bengal 731 236, India
  • NARENDRA PRATAP SINGH Director, Indian Institute of Pulses Research, Kanpur, Uttar Pradesh 208 024, India
  • SANDIP SHIL Scientist, Central Plantation Crops Research Institute, Research Center, Mohitnagar, Jalpaiguri, West Bengal 735 102, India
  • HEMANT KUMAR Scientist, Indian Institute of Pulses Research, Kanpur, Uttar Pradesh 208 024

https://doi.org/10.56093/ijas.v89i4.88873

Keywords:

Chickpea, Genotype × environment interaction, GGE bi-plot, Heat stress

Abstract

Efficiency of genetic gain received through selection is seriously affected by genotype × environment (G×E) interaction, as G×E directly affects the stability and performance of genotype under multi environment. In the current study a total of 34 chickpea (Cicer arietinum L.) genotypes were evaluated at two locations (Kanpur and Bhopal) in two seasons (timely sown and late sown conditions) during 2016-17 to gain insights into the G × E effect and the stability of genotypes grown across the sites. Plot yield data recorded from the genotypes were analyzed by using GGE bi-plot method. The combined analysis of variance (ANOVA) revealed highly significant effects of environment on plot yield attribute among the genotypes, evaluated over the two seasons across the two locations. However, the most stable genotype across the two locations remained g 23 (JAKI 9218). Among the tested environments Bhopal timely sown (BL-T) and Bhopal late sown (BL-L) were the most representative, whereas Kanpur timely sown (KAN-T) was the least representative. Moreover, g 01 (HC 1) was the best cultivar under KAN-T and KAN-L environments whereas, g 23 (JAKI 9218) was the best cultivar under BL-T and BL-L environments.

Downloads

Download data is not yet available.

References

Badu B, Abamu F J, Menkir A, Fakorede M A B and Obeng- Antwi K. 2003. Genotype by environment interactions in the regional early maize variety trials in west and central Africa. Maydica 48: 93–104.

Dia M, Wehner T C and Arellano C. 2017. RGxE: An R program for genotype x environment interaction analysis. American Journal of Plant Sciences 8: 1672–98. DOI: https://doi.org/10.4236/ajps.2017.87116

Crossa J, Fox P N, Feiffer W H, Rajaram P S and Gauch H G. 1991. AMMI adjustment for statistical analysis of an international wheat yield trial. Theoretical and Applied Genetics 81: 27–37. DOI: https://doi.org/10.1007/BF00226108

Elbasyoni I S . 2018. Performance and stability of commercial wheat cultivars under terminal heat stress. Agronomy 8: 37. DOI: https://doi.org/10.3390/agronomy8040037

Food and Agriculture Organization of the United Nations, FAOSTAT. Rome, Italy. FAO; 2016. Available at: http://fao.org/faostat/en/#data/QC (Accessed 13 Jan 2018).

Farshadfar E, Zali H and Mohammadi R .2011. Evaluation of phenotypic stability in chickpea genotypes using GGE-Biplot. Annals of Biological Research 2(6): 282–92.

Fleury D, Jefferies S, Kuchel H, and Langridge P. 2010. Genetic and genomic tools to improve drought tolerance in wheat. Journal of Experimental Botany 61: 3211–2. DOI: https://doi.org/10.1093/jxb/erq152

Frutos E, Galindo M P and Leiva V. 2014. An interactive biplot implementation in R for modeling genotype-by-environment interaction. Stochastic Environmental Research and Risk Assessment 28: 1629–41. DOI: https://doi.org/10.1007/s00477-013-0821-z

Gauch H G. 1988. Model selection and validation for yield trials with interaction. Biometrics 44: 705–15. DOI: https://doi.org/10.2307/2531585

Gauch H G. 1992. Statistical analysis of regional yield trials: AMMI analysis of factorial designs. Elsevier, Amsterdam, The Netherlands. pp 53–110.

Graham P H and Vance C P. 2003. Legumes: importance and constraints to greater use. Plant Physiology 131: 872–7. DOI: https://doi.org/10.1104/pp.017004

Horn L, Shimelis H, Sarsu F, Mwadzingeni L and Laing M D .2018. Genotype by environment interaction for grain yield among novel cowpea (Vigna unguiculata L.) selections derived by gamma irradiation. Crop Journal 6: 306–13. DOI: https://doi.org/10.1016/j.cj.2017.10.002

Jha U C, Chaturvedi S K, Bohra A, Basu P S, Khan M S and Debmalya B. 2014. Abiotic stresses, constraints and improvement strategies in chickpea. Plant Breeding 133: 163–78. DOI: https://doi.org/10.1111/pbr.12150

Jha U C, Bohra A, Jha R, and Parida S .2017. Integrated “omics” approaches to sustain global productivity of major grain legumes under heat stress. Plant Breeding 136: 437–59. DOI: https://doi.org/10.1111/pbr.12489

Jha U C, Bohra A and Singh N P. 2014. Heat stress in crop plants: its nature, impacts and integrated breeding strategies to improve heat tolerance. Plant Breeding 133: 679–701. DOI: https://doi.org/10.1111/pbr.12217

Kaya Y, Akçura M and Taner S .2006. GGE-biplot analysis of multienvironment yield trials in bread wheat. Turkish Journal of Agricultureand Forestry 30: 325–37.

Odeseye A O, Amusa N A, Ijagbone I F, Aladele S E, and Ogunkanmi L A. 2018. Genotype by environment interactions of twenty accessions of cowpea [Vigna unguiculata (L.) Walp.] across two locations in Nigeria. Annals of Agrarian Sciences doi: 10.1016/j.aasci.2018.03.001 DOI: https://doi.org/10.1016/j.aasci.2018.03.001

Purchase J L, Hatting H and Van Deventer C S. 2000. Genotype × environment interaction of winter wheat in South Africa: II. Stability analysis of yield performance. South African Journal of Plant and Soil 17: 101–7. DOI: https://doi.org/10.1080/02571862.2000.10634878

R Core Team. 2017. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/

Rashidi M, Farshadfar E and Jowkar M M. 2013. AMMI analysis of phenotypic stability in chickpea genotypes over stress and non-stress environments. International Journal of Agriculture and Crop Sciences 5: 253–60.

Sharifi P, Aminpanah H, Erfani R, Mohaddesi A and Abbasian A. 2017. Evaluation of genotype × environment interaction in rice based on AMMI model in Iran. Rice Science 24: 173–80. DOI: https://doi.org/10.1016/j.rsci.2017.02.001

Sharma M, Ghosh R, Telangre R, Rathore A, Saifulla M, Mahalinga D M, Saxena D R and Jain Y K. 2016. Environmental influences on pigeonpea-Fusarium udum interactions and stability of genotypes to Fusarium wilt. Frontier in Plant Science 7: 253. DOI: https://doi.org/10.3389/fpls.2016.00253

Shiringani R P and Shimelis H A. 2011. Yield response and stability among cowpea genotypes at three planting dates and test environment. African Journal of Agricultural Research 6: 3259–63.

Sousa M B E, Damasceno-Silva J K, De Moura Rocha M, De Menezes Junior J A N and Lima L R L. 2018. Genotype by environment interaction in cowpea lines using GGE Biplot method. Revista Caatinga 31: 64–71. DOI: https://doi.org/10.1590/1983-21252018v31n108rc

Westcoff B. 1987. A method of analysis of the yield stability of crops. Journal of Agriculture Science 108: 267–74. DOI: https://doi.org/10.1017/S0021859600079259

Yan W, Hunt L A, Sheng Q and Szlavnics Z. 2000. Cultivar evaluation and mega environment investigation based on GGE-Biplot. Crop Science 40: 597–605. DOI: https://doi.org/10.2135/cropsci2000.403597x

Yan W and Hunt L A. 2001. Genetic and environmental causes of genotype by environment interaction for winter wheat yield in Ontario. Crop Science 41: 19–25. DOI: https://doi.org/10.2135/cropsci2001.41119x

Yan W and Hunt L A. 2002. Biplot analysis of diallel data. Crop Science 42: 21–30. DOI: https://doi.org/10.2135/cropsci2002.0021

Yan W and Tinker N A. 2006. Biplot analysis of multi-environment trial data: principles and applications. Canadian Journal of Plant Science 86: 623–45. DOI: https://doi.org/10.4141/P05-169

Zali H, Sofalian O, Hasanloo T and Asghari A. 2016. AMMI and GGE Biplot analysis of yield stability and drought tolerance in Brassica napus L. Agricultural Communications 4: 1–8. DOI: https://doi.org/10.29252/jcb.8.18.191

Downloads

Submitted

2019-04-11

Published

2019-04-11

Issue

Section

Articles

How to Cite

JHA, U. C., KOLE, P. C., SINGH, N. P., SHIL, S., & KUMAR, H. (2019). GGE bi-plot analysis for grain yield in chickpea (Cicer arietinum) under normal and heat stress conditions. The Indian Journal of Agricultural Sciences, 89(4), 721–725. https://doi.org/10.56093/ijas.v89i4.88873
Citation