Ahmadi, J., Mohammadi, A., Najafi Mirak, T. (2012). Targeting Promising Bread Wheat (Triticum aestivum L.) Lines for Cold Climate Growing Environments Using AMMI and SREG GGE Biplot Analyses. , 14(3), 645-657.
J. Ahmadi; A. Mohammadi; T. Najafi Mirak. "Targeting Promising Bread Wheat (Triticum aestivum L.) Lines for Cold Climate Growing Environments Using AMMI and SREG GGE Biplot Analyses". , 14, 3, 2012, 645-657.
Ahmadi, J., Mohammadi, A., Najafi Mirak, T. (2012). 'Targeting Promising Bread Wheat (Triticum aestivum L.) Lines for Cold Climate Growing Environments Using AMMI and SREG GGE Biplot Analyses', , 14(3), pp. 645-657.
Ahmadi, J., Mohammadi, A., Najafi Mirak, T. Targeting Promising Bread Wheat (Triticum aestivum L.) Lines for Cold Climate Growing Environments Using AMMI and SREG GGE Biplot Analyses. , 2012; 14(3): 645-657.

Targeting Promising Bread Wheat (Triticum aestivum L.) Lines for Cold Climate Growing Environments Using AMMI and SREG GGE Biplot Analyses

Journal of Agricultural Science and Technology
Article 18, Volume 14, Issue 3, 2012, Pages 645-657    PDF (312.85 K)
Authors
J. Ahmadi* 1; A. Mohammadi2; T. Najafi Mirak3
1Department of Genetic and Plant Breeding, International University of Imam Khomeini, Qazvin, Islamic Republic of Iran.
2Islami Azad University of Karaj
3Seed and Plant Improvement Institute, Karaj, Islamic Republic of Iran.
Abstract
Genotype×environment interactions (GEIs) can affect breeding programs because they often complicate the evaluation and selection of superior genotypes. This drawback can be reduced by gaining insights into GEI processes and genotype adaptation. The objectives of this research were to evaluate: (1) the yield stability of promising wheat lines across locations and (2) the relationship among the test environments for selecting superior lines within the cold climate mega-environments of Iran. A total of 35 wheat promising lines were grown at 7 locations during the 2008-2009 cropping season. Combined analysis of variance showed that the environment (E) accounted for 75.7% of the model sum of squares. The magnitude of the GEI sum of squares was about three times larger than that for genotypes. To determine the effects of GEI on yields, the data were subjected to the additive main effects and multiplicative interaction (AMMI) and genotype+(genotype×environment) interaction (GGE) biplot analysis. The AMMI1 model was found to explain up to 88% of the main and interaction effects. According to the AMMI1 and GGE biplots, the lines G5 and G4 were found to produce high and stable yields across environments. There were three mega-environments (Euromieh and Ardebil as mega-environment I, Mashhad, Arak, Hamedan and Jolgerokh as mega-environment II, and Karaj as mega-environment III) according to the site regression genotype (SREG) GGE model. Application of AMMI and GGE biplots facilitated visual comparison and identification of superior genotypes for each target set of environments.
Keywords
AMMI; GEI; GGE biplot; Wheat; Yield stability
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