S. Tahmasebi - 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
M. Esmaeilzadeh Moghaddam - Seed and Plant Improvement Institute, Agricultural Research, Education and Extension Organization, Karaj, IranSeed and Plant Improvement Institute, Karaj, Iran.
M. Tabib Ghaffari - 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
M. Sayyahfar - 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
Kh. Miri - 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
Gh. Lotfi Ayeneh - 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
H. Akbari Moghadam - 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
S. Osroosh - Seed and Plant Certification and Registration Institue, Agricultural Research, Education and Extension Organization, Karaj, Iran.

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 ۱۸, ۳۲, ۲۸, ۲۸ and ۲۸ elite bread wheat genotypes. Each of genotype sets was evaluated in two successive cropping seasons of ۲۰۱۲-۱۴, ۲۰۱۳-۱۵, ۲۰۱۴-۱۶, ۲۰۱۵-۱۷ and ۲۰۱۶-۱۸, respectively. The highest (۷.۹۹ ton ha-۱) and lowest (۴.۳۳ ton ha-۱) 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 ۲۰۱۲-۱۳ to ۲۰۱۶-۱۸ cropping seasons of ۱۰ 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 ۴۹.۸۶% of the total variation of the two-way correlation table. KHR ZAB locations formed ME۱ and ME۲, respectively. AHV and Iranshahr ISH formed ME۳, while DAR and DEZ grouped in ME۴. Unlike ME۱ and ME۲, which had negative correlation with each other and with other MEs, ME۳ and ME۴ were weakly correlated, therefore, a genotype with the highest grain yield in ME۳ may perform well in ME۲, 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.

Bread wheat, Cropping season, LG biplot, high yielding cultivar, total variation