Assessment of genotype × environment interaction in oilseed sunflower genotypes under normal and limited irrigation conditions using GGE Biplot method

Akbari, Nasrin; Darvishzadeh, Reza

doi:10.30473/cb.2023.68979.1921

Assessment of genotype × environment interaction in oilseed sunflower genotypes under normal and limited irrigation conditions using GGE Biplot method

Document Type : Research Paper

Authors

Department of Plant Production and Genetics, Faculty of Agriculture, Urmia University, Urmia, Iran.

10.30473/cb.2023.68979.1921

Abstract

With expensing the amount of data, speed and accuracy in breeding evaluations are very important. The multivariate statistical methods, such as GGE Biplot that reduce the data volume and computational complexity, help in this direction. The use of GGE is useful for introducing genotypes with high stability and performance. Therefore, in order to introduce a stable genotype with high adaptation to drought stress, 100 oilseed sunflower genotypes were evaluated in a 10×10 simple lattice design under normal and limited irrigation conditions during two successive years (2013-2014). The results of composite variance analysis revealed a significant difference among genotypes in terms of the evaluated agromorphological traits. Based on graphical evaluation of genotype × environment interaction using GGE Biplot in metan program under R, genotypes 57 (SDR19), 41 (F1250/03), 8 (254-ENSAT), 24 (8ASB2) and 26 (H049+FSB) were introduced as the best genotypes in terms of stability and performance. The genotype 8 (254-ENSAT) had the highest performance among all genotypes in all environments. Meanwhile, genotypes 26 (H049+FSB) had the highest performance in Y1D (First year-limited irrigation) and Y1N (First year-normal irrigation) environments, and genotypes 57 (SDR19), 41 (F1250/03) and 24 (8ASB2) had the highest performance in Y2D (Second year -limited irrigation) and Y2N (Second year -normal irrigation) environments. Based on the results, genotype with code number of 8 with high and stable performance can be used in all environments as a parent in the development of high-yielding and stress-tolerant hybrids. The results show that GGE Biplot is a useful statistical method to achieve practical and accurate results.

Keywords

Main Subjects

Molecular Plant Breeding

References

Ansarifard, I., Mostafavi, K., Khosroshahli, M., Bihamta, M.R. & Ramshini, H. (2020). A study on genotype–environment interaction based on GGE biplot graphical method in sunflower genotypes (Helianthus annuus L.). Food Science & Nutrition, 8(7), 3327-3334. Cherinet, A., Abebe, W., Molla, M., Tazebachew, A., Desalew, F., Esmelealem, M., & Jemal, E. (2016). GGE stability analysis of seed yield in sunflower genotypes (Helianthus annuus L.) in Western Amhara region, Ethiopia. International Journal of Plant Breeding and Genetics, 10(2), 104-109. Crossa, J., Franco, J. & Edmeades, G.O. (1996). Experimental designs and the analysis of multilocation trials of maize grown under drought stress. CIMMYT, 524-536. Cornelius, P.L., Crossa, J., and Seyedsadr, M.S., 1996. Statistical tests and estimates of multiplicative models for GE interaction. In: kang, M.S. & H.G. Jr. Gauch (Eds.). Genotype-by- Environment Interaction. (pp.199-234). CRC Press, Boca Raton, Florida. Eberhart, S.T. & Russell, W. (1966). Stability parameters for comparing varieties. Crop Science, 6(1), 36–40. Enyew, M., Feyissa, T., Geleta M., Tesfaye, K., Hammenhag, C. & Carlsson, A.S. (2021). Genotype by environment interaction, correlation, AMMI, GGE biplot and cluster analysis for grain yield and other agronomic traits in sorghum (Sorghum bicolor L. Moench). PLoS ONE, 16(10): e0258211. https://doi. org/10.1371/journal.pone.0258211. Frutos, E., Galindo, M.P. & Leiva, V. (2014). Interactive biplot implementation in R for modeling genotype-environment interaction. Stochastic Environmental Research and Risk Assessment, 28(7), 1629-1641. Gauch, H. Jr. (1992). Statistical analysis of regional yield trials: AMMI analysis of factorial designs: Elsevier Science Publishers. Gauch, H.G. & Zobel, R.W. (1996). AMMI analysis of yield trials, 85-122 pp. In: Kang, M.S., Gauch, H.G. (eds.) Genotype by environment interaction, 1-14 pp., CRC press. Boca Raton. Gauch, H.G., Piepho, H.P. &Annicchiarico, P. (2008). Statistical analysis of yield trials by AMMI and GGE: Further considerations. Crop Science, 48, 866–89. https:// doi. org/ 10. 2135/ crops ci2007. 09. 0513. Ghaffari, M., Gholizadeh, A., Andarkhor, S.A., Siahbidi, A.Z., Kalantar Ahmadi, S.A., Shariati, F. & Rezaeizad, A. (2022). Graphic analysis of genotype, environment and genotype × environment interaction on the seed yield of sunflower. Iranian Journal of Field Crop Science, 53(3), 65-75. (in Persian). Hussain Shah, M., Rauf, S., Nazir, S., Ortiz, R., Naveed, A. & Fatima, S. (2023). Stability analyses of sunflower (Helianthus annuus L.) hybrids for oleicacid and yield traits under multi location trials in Pakistan. Italian Journal of Agronomy, 18, 2079. Jamshidmoghaddam, M. & Pourdad, S.S. (2013). Genotype× environment interactions for seed yield in rainfedwinter safflower (Carthamus tinctorius L.) multi-environment trials in Iran. Euphytica, 190(3), 357–69. Mądry, W., Talbot, M., Ukalski, K., Drzazga, T. & Iwańska, M. (2006). Podstawy teoretyczne znaczenia efektów genotypowych i interakcyjnych w hodowli roślin na przykładzie pszenicy ozimej. Biul. IHAR, 240(241), 13–32. (in Polish). Morsali Aghajari, F., Darvishzadeh, R., Hatami Maleki, H., Gholinezhad, E. & Kalantar, A. (2019). Selection of salinity tolerant lines of sunflower using some physiological characteristics. Journal of Crop Breeding, 11(31), 185-195. (in Persian). Musa-Khalifani, K., Darvishzadeh, R., Abrinbana, M. & Hadi, Alipour, H. (2021). Unraveling genotype-isolate interaction in sunflower (Helianthus annuus L.)- Sclerotinia pathosystem using GGE biplot method. Journal of Plant Physiology and Breeding, 11(1), 109-121. Khalifani, S., Darvishzadeh, R., Azad, N. & Seyed Rahmani, R. (2022). Prediction of sunflower grain yield under normal and salinity stress by RBF, MLP and, CNN models. Industrial Crops & Products, 189, 115762. Olanrewaju, O.S., Oyatomi, O., Babalola, O.O. & Abberton, M. (2021). GGE Biplot Analysis of Genotype-Environment Interaction and Yield Stability in Bambara Groundnut. Agronomy, 11, 1839. https:// doi.org/10.3390/agronomy11091839. Oral, E., Kendal, E. & Dogan, Y. (2018). Oral E, Kendal E, Dogan Y (2018). Selection the best barley genotypes to multi and special environments by AMMI and GGE biplot models. Fresenius Environmental Bulletin, 27(7), 5179-5187. Plavsin, I., Gunjaca, J., Simek, R. & Novoselovic, D. (2021). Capturing GEI patterns for quality traits in biparental wheat populations. Agronomy, 11(6), 1022. Rakshit, S., Ganapathy, K.N., Gomashe, S.S., Swapna, M., More, A., Gadakh, S.R., Ghorade, R.B., Kajjidoni, S.T., Solanki, B.G., Biradar, B.D. & Prabhakar, A. (2014). GGE biplot analysis of genotype × environment interaction in rabi grain sorghum [Sorghum bicolor (Sorghum bicolor L.) Moench]. Indian Journal of Genetics and Plant Breeding, 74(4s), 558-563. Rauf, S. )2019(. Breeding strategies for sunflower (Helianthus annuus L.) genetic improvement. In: Al-Khayri J, Jain S, Johnson D (eds). Advances in Plant Breeding Strategies: Industrial and Food Crops Cham, Springer, 637-73. Saremi Rad, A., Mostafavi, K. & Mohammadi, A. (2020). Genotype- environment interaction study base GGE biplot method for kernel yield in sunflower (Helianthus annuus L.) cultivars. Journal of Crop Breeding, 12(34), 43-53. (in Persian). Saeidnia, F., Taherian, M. & Nazeri, S.M. (2023). Graphical analysis of multi‑environmental trials for wheat grain yield based on GGE‑biplot analysis under diverse sowing dates. BMC Plant Biology, 23: 198. Sharma, S.P., Leskovar, D.I., Crosby, K.M. & Ibrahim, A. (2020). GGE biplot analysis of genotype-by-environment interactions for melon fruit yield and quality traits. American Society for Horticultural Science, 1, 1–10. Singh, C., Gupta, A., Gupta, V., Kumar, P., Sendhil, R., Tyagi, B.S., Singh, G., Chatrath, R. & Singh, G.P. (2019). Genotype x environment interaction analysis of multi-environment wheat trials in India using AMMI and GGE biplot models. Crop Breeding and Applied Biotechnology, 19(3), 309 -318. Silva, R.R. & Benin, G. (2012). Biplot analysis: concepts, interpretations and uses. Ciência Rural, 42(8), 1404-1412. Yan, W., Hunt, L., Sheng, Q. & Szlavnics, Z. (2000). Cultivar evaluation and mega-environment investigation basedon the GGE biplot. Crop Science, 40(3), 597–605. Yan, W. (2001). GGE biplot - a windows application for graphical analysis of multi-environment trial data and other types of two-way data. Agronomy Journal, 93, 1111-1118. Yan, W., & Kang, M.S. (2002). GGE Biplot Analysis: A Graphical Tool for Breeders, Geneticists, and Agronomists (1st ed.). CRC Press. https://doi.org/10.1201/9781420040371. Yan, W. & Tinker, N.A. (2006). Biplot analysis of multi-environment trial data: Principles and applications. Canadian Journal of Plant Science, 86(3), 623-645. Yasar, M., Çil, A.N. & Çil, A. (2023). Investigation of Genotype × environment interaction in some sunflower (Helianthus annuus L.) genotypes in different environmental conditions. MAS Journal of Applied Sciences, 8(1), 41-55. https://doi.org/10.5281/zenodo.7642289 Xu, Y., Zhang, X., Li, H., Zheng, H., Zhang, J., Olsen, M.S., Varshney, R.K., Prasanna, B.M. & Qian Qian, Q. (2022). Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. Molecular Plant, 15(11), 1664-1695.