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Öğe AMMI Model to Assess Durum Wheat Genotypes in Multi-Environment Trials(JOURNAL OF AGRICULTURAL SCIENCE AND TECHNOLOGY, 2018) Tekdal, S; Kendal, E.The goal of this research was to assess the stability and yield performance of 150 durum wheat genotypes in multi-environment trials in two locations (Diyarbakir and Kiziltepe), in 2011-2012, and 2012-2013 growing seasons. The trials were designed by Lattice Experimental Design with two replications (incomplete block design). The AMMI (Additive Main Effects and Multiplicative Interaction) and GEI (GenotypexEnvironment Interaction) analysis were used in the study to estimate GEI effects on grain yield, because of plant breeders' great interest in these models for breeding programs. AMMI evaluation indicated that genotypes made the most important contributions to treatments Sum of Squares (59.8%), environments (3.5%), and GEI (36.7%), respectively, suggesting that grain yield had been affected by environment. IPCA 1 and IPCA 2 axes (Principal Component) were significant as P< 0.01 and explained 63.8 and 36.2%, respectively. Results showed that Kiziltepe 2013 was more stable and high yielding, meanwhile Diyarbakir 2012 and Diyarbakir 2013 environments were unstable and low yielding. According to stability variance, usually the province lines were more productive and stable than some old cultivars and many landraces/genotypes. Moreover, genotype G24 was more effective in all environments. The GEI model according to AMMI analysis suggested that this genotype can be considered as a candidate, due to extensive adaptability and high performances in all environments.Öğe INVESTIGATION OF GENOTYPES BY ENVIRONMENT INTERACTION USING GGE BIPLOT ANALYSIS IN BARLEY(SCIBULCOM LTD, 2016) Kendal, E.; Aktas, H.The aim of this study was to examine the effects of genotype x environment interaction (GET) on grain yield, components, and quality characteristics using genotype. main effect (G) and genotype x environment interaction by GGE biplot analysis. We observed significant differences among genotypes in grain yield, yield components, and quality traits; the relationships between yield components were used to identify three groups. The GGE biplot indicated that E4 (Diyarbakir 2009/10), E5 (Hani 2010/11) and E6 (Diyarbakir 2010/11) were ideal environments for all traits, and E4 was a highly efficient model for grain yield. The biplot analysis showed that genotype 1 (G1) was the best genotype in terms of yield and other components, and G11 was efficient for quality parameters only; thus, these two genotypes can be recommended to release in barley breeding program. Consequently, the study showed that biplot analyses is a good analysis method and will be used to make specific selection for multi-factorial studies and specific conditions.