Analysis of Multi-environmental Trial Data Using AMMI and GGE Biplot on Barley Genotypes Evaluated in Tigray

Barley (Hordeum vulgare L.) was one of the first plants people grew for food, and now it's grown all over the world and it has a special role in Ethiopian agriculture. However, production is affected by environment interaction and lack of stable genotypes across locations. The presence of genotype-environment interaction (GEI) influences production making the selection of cultivars in a complex process. Since, this experiment were conducted for forty barley genotypes by Alpha lattice design using two replications at three locations in Tigray during 2017 and 2018 cropping seasons considering each year-location combination as a different environments. The study carried out with objectives to estimate magnitude of genotype by environment interaction, comparing AMMI and GGE to evaluate stability of genotypes and identifying superior genotypes. We observed significance effects in all sources of combined ANOVA, since the grain yields of all 40 barley genotypes were significantly affected by environment, which accounted for 40.6 % of the total variation, whereas genotype and genotypeenvironment interaction accounted for 21.12 % and 23.07 %, respectively. The two most used methods to analyze GEI and evaluate genotypes are AMMI and GGE Biplot, being used for the analysis of multi environment trials data (MET).Both models were equivalent for the data’s evaluation, but GGE permitting increased reliability in the selection of superior cultivars (Which-won-where pattern) and test environments (discrimitiveness vs. representativeness).Wricke’s ecovalence, Finley-Wilkinson, Shukla’s stability, Lin&Binns cultivar superiority measure, AMMI Stability Value (ASV), and YSI stability analysis measures also used to identify stable genotypes, and G19, G36, G5 are the most stable genotypes in almost the stability analysis measures, since they are superior genotypes with all test environments. While the genotypes G33, G32, G12 and G18 also the instable genotypes in the test environments. GGE Biplot view of relation among test environments of this study showed that; Among the testing environments Hagereselam 2018 is an ideal testing location to identify stable and high yielding genotypes followed by Ayba 2018, since Hagereselam 2018 and Ayba 2018 are most applicable test locations for identifying stable and high yielding barley genotypes for the region. Mean performance and stability of GGE biplot indicated that G24 had the ideal genotype with highest mean yield as well as stability with desirable genotypes G20, G19, G5, G36, while G33 and G30 had the lowest mean yield and less stability genotypes in all the six test environments.