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Tests were done to determine high-yielding and stable durum wheat genotypes. An experiment was set up as a RCBD (Randomized Complete Block Design) with three replications on 17 advanced durum wheat genotypes under rainfed and supplementary irrigation conditions in the cropping seasons of 2011-13. Combined analysis of variance indicated that environment main effect accounted for 70.09% of total yield variation; and effects of genotype and Genotype×Environment Interaction (GEI) accounted for 2.95 and 10.71%, respectively. Results indicated remarkable difference in genotypes response across environments. G×E interaction was analyzed following Additive Main effects and Multiplicative Interaction (AMMI) model. The first two interaction Principal Component Axes (IPCA1 and IPCA2) explained 53.75 and 36.99% of total interaction effects, respectively. Based on the AMMI model, AMMI Stability Value (ASV) and Genotype Selection Index (GSI), genotypes G11, G8, and G14 were selected for all environments. According to the AMMI2 biplot, the G15, G16 and G17 exhibited specific adaptation with rainfed (E1) and irrigation (E2) environments. G3 and G4 displayed specific adaptation with rainfed (E3) environment and G10, G9, G1, and G12 indicated specific adaptability with irrigation (E4) environment. The E3 had high discrimination ability, so, this environment was considered sufficient for making genotypes recommendation. Results of this investigation illustrate that the AMMI stability parameters are suitable for characterizing stable genotypes and that the GSI parameter can detect genotypes with high grain yield and good stability for plant breeding research in durum wheat. [ABSTRACT FROM AUTHOR]

Grass pea (Lathyrus sativus L.) is a crop used for both animal and human consumption. Recently, ICARDA has developed new grass pea lines with the objectives of improving its yield potential and adaptability. The objective of this study was to investigate grain and forage yields stability of several ICARDA released grass pea lines by using additive main effects and multiplicative interactions (AMMI) and site regression genotype plus genotype-by-environment interaction (SREG GGE) model. Fourteen grass pea advanced lines were evaluated at three research stations located in semi-warm regions in Iran, during 2005-08 crop seasons. The combined ANOVA showed that forage and grain yields were significantly affected by environment (E), genotype (G) and interactions of G×E. The partitioning of the sum of squares indicated that E accounted for 85% and 91% of the total (G+E+GEI) variation for forage and grain yields, respectively; whereas, G and GEI accounted for 4 and 11% for forage yield and 2 and 7% for grain yields, respectively. Using AMMI and GGE biplots, the lines identified as stable and high grain yielding were Sel.474, Sel.669, and Sel.686, while for forage yield the lines were Sel.474, Sel.678 and Sel.669. Kermanshah and Gachsaran were selected as favorable test environments for grass pea grain and forage yield ME trials, respectively. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]