STABILITY AND ADAPTABILITY OF SORGHUM HYBRIDS ELUCIDATED WITH GENOTYPE–ENVIRONMENT INTERACTION BIPLOTS

Year 2019, Volume: 24 Issue: 2, 155 - 163, 15.12.2019

Aamer Mumtaz Dilbar Hussaın Muhammad Saeed Muhammad Arshad Muhammad İrfan Yousaf

https://doi.org/10.17557/tjfc.631130

Cited By: 7

Abstract

This study was conducted to compare the performance of ten sorghum hybrids at two locations (Maize and Millets Research Institute, Yusafwala, Sahiwal, MMRI) & Sorghum Research Sub-Station, Dera Gazi Khan, D.G. Khan) for two consecutive years (2015 and 2016), i.e. in a total of four environments (MMRI-15, MMRI-16, DG Khan-15 and DG Khan-16). The experiment was conducted in a Randomized Complete Block Design with a plot size of 4 × 0.75 × 2 m. In all four environments the crop was sown in July and harvested in December. Five plants were selected randomly from each plot for data collection. The following ranges were determined in the investigated traits; grain yield (2858.34-5266.33 kg ha-1), fodder yield (28663-45667 kg ha-1), days to 50% anthesis (76-81 days) and Brix value (8.28 -18.42). Analysis of variance (ANOVA) estimates, generated by the biplot software were used for data interpretation. It was found that the influence of genotype, environment and G × E interaction was significant (P<0.05) for all traits in all environments. The data for all traits except Brix value were useful for further study. For grain yield and fodder yield, hybrid YSH-95 was the most suitable due to its higher yield and better stability. Sorghum Research Sub-Station Dera Gazi Khan (DG Khan), a non-discriminating location, were considered suitable for generally adapted hybrids and Maize and Millets Research Institute, Yusafwala, Sahiwal (MMRI), a more discriminating location, were considered best for specifically adapted hybrids. The results of which-won-where biplots showed that Lasani was the best general hybrid at both locations, whereas YSH-95 was the best hybrid for the specific environmental conditions at MMRI.

Keywords

Brix value, correlation, Maize and Millets Research Institute, stalks weight

Thanks

The authors are grateful to field staff of the Maize and Millet Research Institute, Yusafwala for their efforts. The authors thank Dr. Abid Mehmood, Director General Agri. Research (Punjab) and the Government of Punjab for their support, and K. Shashok (AuthorAID in the Eastern Mediterranean) for improving the use of English in the manuscript.

References

• Ali, S. 2011. Economic losses due to delayed conception in dairy animals of small farmers in district Gujranwala. M.Sc. (Hons) Thesis, Department of Agricultural Economics, Faculty of Agricultural Economics & Rural Sociology, University of Agriculture, Faisalabad, Pakistan. icdd.uaf.edu.pk/Publications/011.pdf (Accessed April 14, 2018).
• Ayub, M., A. Tanveer, M.A. Nadeem and S.M.A. Shah. 2004. Studies on the Fodder Yield and Quality of Sorghum Grown Alone and in Mixture with Ricebean. Pak. J. Life Soc. Sci. 2(1): 46-48. Bibi, A., H.A. Sadaqat, H.M. Akram and M.I. Mohammed. 2010. Physiological markers for screening sorghum (Sorghum bicolor) germplasm under water stress condition. Intl. J. Agri. Bio. 12(3):451-455.
• Cifuentes, R., R. Bressani and C. Rolz. 2014. The potential of sweet sorghum as a source of ethanol and protein. Energy for Sustain. Develop. 21:13–19. Crossa, J., P.N. Fox, W.H. Pfeiffer, S. Rajaram and H.G. Gauch. 1991. AMMI adjustment for statistical analysis of an international wheat yield trial. Theo. Applied Genet. 81:27–37.
• Dehghani, H., A. Ebadi and A. Yousefi. 2006. Biplot analysis of genotype by environment interaction for barley yield in Iran. Agron. J. 98:388–393.
• Dehghani, H., H. Omidi and N. Sabaghnia. 2008. Graphic analysis of trait relations of rapeseed using the biplot method. Agron. J. 100:1443–1449.
• Dhar, S., S.D. Gupta, S.N. Tripathi and S.K. Rai. 2005. Production potential of fodder sorghum (Sorghum bicolor) varieties under different nitrogen levells and sowing dates. Indian J. Agric. Sci. 75 (9): 572-575.
• Fan, X.M., M.S. Kang, H. Chen, Y. Zhang, J. Tan and C. Xu. 2007. Yield stability of maize hybrids evaluated in multi-environment trials in Yunnan, China. Agron. J. 99:220-228.
• FAOSTAT. 2017. Food and Agriculture Organization, United States. http://www.fao.org/faostat/en/#home.
• Gauch, H.G. 1988. Model selection and validation for yield trials with interaction. Biometr. 44:705–715.
• Gauch, H.G., and R.W. Zobel. 1997. Identifying mega-environment and targeting genotypes. Crop Sci. 37:381–385.
• Gauch, H.G. 1992. Statistical analysis of regional yield trials: AMMI analysis of factorial designs. Elsevier, Amsterdam, The Netherlands. pp. 53–110.
• Government of Pakistan. 2016-17. Economic Survey of Pakistan. Finance Division, Economic Advisory Wing, Islamabad. Pakistan.
• Ilker, E., H. Geren, R. Unsal, I. Sevin, F. Aykut tonk and M. Tosun. 2011. AMMI-Biplot analysis of yield performances of bread wheat cultivars grown at different locations. Turk. J. Field Crops, 16(1): 64-68.
• Kang, M.S., and D.P. Gorman. 1989. G × E Genotype × environment interaction in maize. Agron. J. 81(4):662-664.
• Kaya, Y.M., M. Akcurra and S. Taner. 2006. GGE-biplot analysis of multi-environment yield trials in bread wheat. Turkish J. Agri. For. 30:325–337.
• Kendal, E. and M.S. Sayar. 2016. The stability of some spring triticale genotypes using biplot analysis. Journal of Animal and Plant Sciences, 26(3):754-765.
• Kendal, E., M.S. Sayar, S. Tekdal, H. Aktaş, and M. Karaman. 2016. Assessment of the impact of ecological factors on yield and quality parameters in triticale using GGE biplot and AMMI analysis. Pakistan Journal of Botany, 48(5): 1903-1913.
• Khalil, I.B., H.U. Rahman, N.U. Rehman, M. Arif, I.H. Khalil, M. Iqbal, Hidayatullah, K. Afridi, M. Sajjad, M. and Ishaq. 2011. Evaluation of maize hybrids for grain yield stability in north-west of Pakistan. Sarhad J. Agri. 27(2):213-218.
• Kumar, V.C., and V. Devendra. 2010. Effect of plant age at harvest and season on the hydrocyanic acid potential of some sorghum cultivars. Indian J. Animal Nutr. 27(2):142-146.
• Mitrovic, B., D. Stanisavljevi, S. Treski, M. Stojakovic, M. Ivanovic, G. Bekavac and M. Rajkovic. 2012. Evaluation of experimental maize hybrids tested in multi-location trials using AMMI and GGE biplot analyses. Turkish J. Field Crops, 17(1):35-40
• Mohammadi, R., M. Aghaee, R. Haghparast, S.S. Pourdad, M. Rostaii, Y. Ansari, A. Abdolahi and A. Amri. 2009. Association among non-parametric measures of phenotypic stability in four annual crops. Middle & Eastern Russian J. Plant Sci. Biotech. 3(Special Issue I):20–24
• Motlhaodi, T., M. Geleta, T. Bryngelsson, M. Fatih, S. Chite and R. Ortiz. 2014. Genetic diversity in ex-situ conserved sorghum accessions of Botswana as estimated by: microsatellite markers,” Australian J. Crop Sci. 8(1):35–43.
• Mumtaz, A., D. Hussain, M. Saeed, M. Arshad, M.I. Yousaf and W. Akbar. 2017a. Association Studies of Morphological Traits in Grain Sorghum (Sorghum bicolor L.). J. Agri. Basic Sci. 2(1):37-43.
• Mumtaz, A., H.A. Sadaqat, M. Saeed, M.I. Yousaf, A. Shehzad, H.G.M.D. Ahmed. 2017b. Genetic behaviour of qualitative and seed yield-related traits in Brassica rapa. Zemdirbyste Agri. 104 (20):147–156.
• Mumtaz, A., D. Hussain, M. Saeed, M. Arshad and M.I. Yousaf. 2018. Estimation of genetic diversity in sorghum genotypes of Pakistan. J. Nat. Sci. Foundation Sri Lanka 46 (3): 271 – 280.
• Munawar, M., G. Hammad and M. Shahbaz. 2013. Evaluation of maize (Zea mays L.) hybrids under different environments by GGE Biplot analysis. American-Eurasian J. Agric. Environ. Sci. 13 (9):1252-1257.
• Pakistan Bureau of Statistics. 2015-16. Ministry of Finance. Government of Pakistan.
• Panhwar, F. 2005. Sorghum practice in Sindh, Pakistan. Publisher, Digitalverlag, GmbH, Germany PARC. 2017. Maize, Sorghum and Millets. Pakistan Agriculture Research Council, Islamabad, Pakistan.
• Putto, W., A. Patanothai, S. Jogloy and G. Hoogenboom. 2008. Determination of mega-environments for peanut breeding using the CSM-CROPGRO-Peanut model. Crop Sci. 48:973–982.
• Rakshit, S., K.N. Ganapathy, S.S. Gomashe, A. Rathore, R.B. Ghorade, M.V.N. Kumar, K. Ganesmurthy, S.K. Jain, M.Y. Kamtar, J.S. Sachan, S.S. Ambekar, B.R. Ranwa, D.G. Kanawade, M. Balusamy, D. Kadam, A. Sarkar, V.A. Tonapi and J.V. Patil. 2012. GGE biplot analysis to evaluate genotype, environment and their interactions in sorghum multi-location data. Euphytica 185:465–479.
• Rao, P.S., P.S. Reddy, A. Ratore, B.V.S. Reddy and S. Panwar. 2011. Application GGE biplot and AMMI model to evaluate sweet sorghum (Sorghum bicolor) hybrids for genotype 9 environment interaction and seasonal adaptation. Indian J. Agric. Sci. 81:438–444.
• Ritter, K.B., C.L. McIntyre, I.D. Godwin, D.R. Jordan and S.C. Chapman. 2007. An assessment of the genetic relationship between sweet and grain sorghums, within Sorghum bicolor ssp. bicolor (L.) Moench, using AFLP markers. Euphytica 157 (1-2):161–176.
• Sabaghnia N., H. Dehghani and S.H. Sabaghpour. 2008. Graphic analysis of G × E Genotype by environment interaction for lentil yield in Iran. Agron. J. 100:760–764.
• Sabaghnia, N., H. Dehghani and S.H. Sabaghpour. 2008. Graphic analysis of genotype by environment interaction for lentil yield in Iran. Agron. J. 100:760–764.
• Sayar, M.S., A.E. Anlarsal, M. Başbağ. 2013. Genotype–environment interactions and stability analysis for dry-matter yield and seed yield in Hungarian vetch (Vicia pannonica CRANTZ.). Turk. J. Field Crops, 18(2): 238-246.
• Sayar, M.S. and Y. Han. 2015. Determination of seed yield and yield components of grasspea (Lathyrus sativus L.) lines and evaluations using GGE biplot analysis method. TARIM BILIM DERG - J. Agric. Sci. 21(1): 78-92.
• Selle, P.H. 2011. The protein quality of sorghum. 22nd Annual Australian Poultry Science Symposium Sydney, New South Wales14 -16th February 2011. The Poultry Research Foundation, University of Sydney, Australia.
• USDA. 2017. United State department of agriculture, New York. https://www.usda.gov/.
• Westcoff, B. 1987. A method of analysis of the yield stability of crops. J. Agric. Sci. 108:267–274.
• Yan, W. 2001. G × E biplot- a windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agron. J. 93:1111–1118.
• Yan, W. and M.S. Kang. 2003. G × E biplot analysis: a graphical tool for breeders, geneticists, and agronomists. CRC Press.
• Yan, W., J.F. Reid, D. Pageau, R. Martin, J. Mitchell-Fetch, M. Etieenne, J. Rowsell, P. Scott, M. Price and B.D. Hann, A. Cummiskey, J. Lajeunesse, J. Durand and E. Sparry. 2010. Identifying essential test location for oat breeding in eastern Canada. Crop Sci. 50:504–515.
• Yan, W., and N.A. Tinker. 2006. Biplot analysis of multi-environment trial data: principles and applications. Canadian J. Plant Sci. 86:623–645.
• Yang, R.C., J. Crossa, P.L. Cornelius and J. Burgueno. 2009. Biplot analysis of genotype 9 environment interaction: proceed with caution. Crop Sci. 49:1564–1576.