Summarizing the genotype × environment interaction and mega-environments delineation using LG biplot analysis of unrepeatable multi-environment bread wheat yield trials data of southern warm and dry agro-climatic zone in Iran

Document Type : Research Paper

Authors

1 Seed and Plant Improvement Department, Fars Agriculture and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Shiraz, Iran. Department of Seed and Plant Improvement, Fars Agricultural and natural Resources Research and Education Center, AREEO, Darab, Iran

2 Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization, Karaj, IranSeed and Plant Improvement Institute, Karaj, Iran.

3 SeeSeed and Plant Improvement Department, Safiabad Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Dezful, Iran. d and plant Improvement department, Safiabad Agricultural and Natural Resource Research Center, AREEO, Dezful, Iran

4 Seed and Plant Improvement Department, Lorestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Khorramabad, Iran. Seed and plant Improvement department, Lorestan Agricultural and Natural Resource Research Center, AREEO, Khoramabad, Iran

5 Seed and Plant Improvement Department, Baloochestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Iranshahr, IranSeed and plant Improvement department, Baluoohestan Agricultural and Natural Resource Research Center, AREEO, Iranshahr, Iran

6 Seed and Plant Improvement Department, Khuzestan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Ahvaz, Iran. Seed and plant Improvement department, Khuzestan Agricultural and Natural Resource Research Center, AREEO, Ahvaz, Iran

7 Seed and Plant Improvement Department, Sistan Agricultural and Natural Resources Research and Education Center, Agricultural Research, Education and Extension Organization, Zabol, Iran. Seed and plant Improvement department, Sistan Agricultural and Natural Resource Research Center, AREEO, Zabol, Iran

8 Seed and Plant Certification and Registration Institue, Agricultural Research, Education and Extension Organization, Karaj, Iran.

10.22092/cbj.2023.361025.1077

Abstract

In this study LG (location-grouping) biplot analysis, as a new method, was used to identify repeatable and unrepeatable GEI patterns and to delineate mega-environments using grain yield data of five multi-environment bread wheat trials from six southern warm and dry agro-climatic zone of Iran including Khorramabad (KHR), Darab (DAR), Dezful (DEZ), Iranshahr (ISH), Ahvaz (AHV) and Zabol (ZAB). The trials included 18, 32, 28, 28 and 28 elite bread wheat genotypes. Each of genotype sets was evaluated in two successive cropping seasons of 2012-14, 2013-15, 2014-16, 2015-17 and 2016-18, respectively. The highest (7.99 ton ha-1) and lowest (4.33 ton ha-1) grand mean of testing locations across ten trials were observed in KHR and AHV, respectively. Results of the yearly GGE biplots based on the grain yield data from the 2012-13 to 2016-18 cropping seasons of 10 bread wheat yield trials across six locations varied from cropping cycle to cropping cycle, thus it was difficult to extract the common patterns across cropping seasons and grouping the test locations using two-year grain yield data. When these datasets were incorporated in a LG biplot analysis, six locations were divided into four MEs. The LG biplot explained 49.86% of the total variation of the two-way correlation table. KHR ZAB locations formed ME1 and ME2, respectively. AHV and Iranshahr ISH formed ME3, while DAR and DEZ grouped in ME4. Unlike ME1 and ME2, which had negative correlation with each other and with other MEs, ME3 and ME4 were weakly correlated, therefore, a genotype with the highest grain yield in ME3 may perform well in ME2, and vice versa. Result of this study can help bread wheat breeders to understand the bread wheat growing MEs in the southern warm and dry agro-climatic zone of Iran, and lead to better decision-making for the analysis of multi-environments yield trial data and to identify and release high-yielding bread wheat cultivars adapted to each ME.

Keywords

References

Akinwale, R. O., Fakorede, M. A. B., Badu-Apraku, B. and Oluwaranti, A. 2014. Assessing the usefulness of GGE biplot as a statistical tool for plant breeders and agronomists. Cereal Res. Commun. 42 (3): 534-546.
 
 
Cattivelli, L., Rizza, F., Badeck, F. W., Mazzucotelli, E. A. M., Francia, E., Marè, C., Tondelli, A. and Stanca, A. M. 2008. Drought tolerance improvement in crop plants: an integrated view from breeding to genomic. Field Crops Res. 15: 1-14.
 
 
Dardanellia, J. L., Balzarinic, M., Martíneza, M. J. and Cuniberti, M. 2006. Soybean maturity groups, environments, and their interaction define mega-environments for seed composition in Argentina. Crop Sci. 46: 1939-1947.
 
 
 Dehghani, H., Ebadi, A. and Yousefi, A. 2006. Biplot analysis of genotype by environment interaction for barley yield in Iran. Agron. J. 98: 388-393.
 
 
Esmaeilzadeh Moghaddam, M., Tahmasebi, S., Lotf Ali Ayeneh, G. A., Akbari Moghadam, H., Mahmoudi, K., Sayyahfar, M., Tabib Ghaffari, S. M. and Zali, H. 2018. Evaluation of grain yield stability of bread wheat (Triticum aestivum L.) promising lines in warm and dry regions of Iran. Iran. J. Crop Sci. 20 (1): 270-283.
 
 
Jalal Kamali, M. R. and Duveiller, E. 2006. Wheat production and research in Iran: A success story. pp. 54. In: Reynolds, M. P. and Godinez, D. (eds.) Extended abstracts of the International symposium on wheat yield potential "challenges to International wheat breeding" March 20-24th, 2006 Cd. Obregon, Mexico. CIMMYT, Mexico, DF.
 
 
Luo, J., Pan, Y. B., Que, Y., Zhang, H., Grisham, M. P. and Xu, L. 2015. Biplot evaluation of test environments and identification of mega environment for sugarcane cultivars in China. Sci. Rep. 5: e15505.
 
 
Mirosavljevic, M., Canak, P., Momcilovic, V., Jockovic, B., Zoric, M., Acin, V., Dencic, S. and Przulj, N. 2018. Identification of favorable testing location for barley breeding in south Pannonian Plain. J. Agri. Sci. 24: 303-311.
 
 
Mohammadi, R. and Amri, A. 2016. Genotype × Environment Interaction Implication: A Case Study of Durum Wheat Breeding in Iran. pp. 515-558. In: Al-Khayri, J. M., Jain, S. M. and Johnson, D. V. (eds.) Advances in Plant Breeding Strategies: Agronomic, Abiotic and Biotic Stress Traits. Springer International Publishing.
 
 
Mohammadi, R., Sadeghzadeh, B., Poursiahbidi, M. M., Ahmadi, M. M. 2020. Integrating univariate and multivariate statistical models to investigate genotype × environment interaction in durum wheat. Ann. Appl. Biol. 178(3): 450-465.
 
 
Mohammadi, R., Haghparast, R., Amri, A. and Ceccarelli, S. 2010. Yield stability of rainfed durum wheat and GGE biplot analysis of multi-environment trials. Crop Pasture Sci. 61: 92-101.
 
 
Munaro, L. B., Benin, G., Marchioro, V. S., de Assis Franco, F., Silva, R. R., da Silva, C. L. and Beche, E. 2014. Brazilian spring wheat homogeneous adaptation regions can be dissected in major mega environments. Crop Sci. 54: 1374-1383.
 
 
Patterson, H. D. and Williams, E. R. 1976. A new class of resolvable incomplete block designs. Biometrika 63 (1): 83-92.
 
 
Payne, R. W., Murray, D. A., Harding, S. A., Baird, D. B. and Soutar, D. M. 2012. Genstat® for Windows™, 15th Edition, Introduction. VSN International, Oxford.
 
 
Rakshit, S., Ganapathy, K. N., Gomashe, S. S., Rathore, A., Ghorade, R. B., Nagesh Kumar, M. V., Ganesmurthy, K., Jain, S. K., Kamtar, M. Y., Sachan, J. S., Ambekar, S. S., Ranwa, B. R., Kanawade, D. G., Balusamy, M., Kadam, D., Sarkar, A., Tonapi, V. A. and Patil, J. V. 2012. GGE biplot analysis to evaluate genotype, environment, and their interactions in sorghum multi-location data. Euphytica 185: 465-479.
 
 
 
Sabaghnia, N., Mohammadi, M. and Karimizadeh, R. 2013. Yield stability of performance in multi-environment trials of barley (Hordeum vulgare L.) genotypes. Acta Univ. Agric. Silvic. Mendel. Brun. 61 (3): 787-793.
 
 
Subira, J., Alvaro, F. Garcia Del Moral L. F. and Royo, C. 2015. Breeding effects on the cultivar × environment interaction of durum wheat yield. Eur. J. Agron. 68: 78-88. 
 
 
Swanckaert, J., Akansake, D., Adofo, K., Acheremu, K., De Boeck, B., Eyzaguirre, R., Grüneberg, W. J., Low,  J. W. and Campos, H. 2020. Variance component estimations and mega-environments for sweet potato breeding in West Africa. Crop Sci. 60: 50-61.
 
 
Tahmasebi, S., Esmaeilzadeh Moghaddam, M., Tabib Ghaffari, S. M., Sayyahfar, M., Lotfali Ayeneh, G. and Akbari Moghaddam, H. 2021. Dissection of genotype × environment interaction and assessment of adaptability and grain yield stability of spring bread wheat genotypes. Crop Breed. J. 11 (1 & 2): 11-24.
 
 
Voltas, J., Lopez-Corcoles, H. and Borras, G. 2005. Use of biplot analysis and factorial regression for the investigation of superior genotypes in multi-environment trials. Eur. J. Agron. 22: 309-324.
 
 
Xu, N. Y., Fok, M., Zhang, G. W., Li, J. and Zhou, Z. G. 2014. The application of GGE biplot analysis for evaluating test locations and mega-environment investigation of cotton regional trials. J. Integr. Agric. 13: 1921-933.
 
 
Yan, W. 2013. Biplot analysis of incomplete two-way data. Crop Sci. 53(1): 48-57.
 
 
Yan, W. 2015. Mega-environment analysis and test location evaluation based on unbalanced multiyear data. Crop Sci. 55: 113-122.
 
 
Yan, W. 2016. Analysis and handling of G×E in a practical breeding program. Crop Sci. 56: 2106-2118.
 
 
Yan, W. 2019. LG biplot: A graphical method for mega-environment investigation using existing crop variety trial data. Sci. Rep. 9 (1): e7130. https://doi.org/10.1038/s41598-019-43683-9.
 
 
Yan, W. and Kang, M. S. 2002. GGE biplot analysis: A graphical tool for breeders, geneticists, and agronomists. CRC Press. 286 pp.
 
 
Yan, W., Hunt, L. A., Sheng, Q. and Szlavnics, Z. 2000. Cultivar evaluation and mega-environment investigation based on the GGE biplot. Crop Sci. 40: 597-605.
 
 
Yan, W., Mitchell-Fetch, J., Beattie, A., Nilson, K. T., Pageau, D., De Haan, B., Hayes, M., Mountain, N., Cummiskey, A. and Mac Eachern, D. 2021. Oat mega-environments in Canada. Crop Sci. 61: 1141-1153.
 
 
Yan, W., Tinker, N. A., Bekele, W. A., Mitchell-Fetch, J. and Frégeau-Reid, J. 2019. Theoretical unification and practical integration of conventional methods and genomic selection in plant breeding. Crop Breed. Genet. Genom. 1 (2): e190003. https://doi.org/10.20900/cbgg20190003.
 
 

Files

Share

How to cite

Statistics