SUMMARY
Article Details: Received: 2020-11-30 | Accepted: 2020-12-09 | Available online: 2021-06-30 https://doi.org/10.15414/afz.2021.24.02.117-123 Multi-environment trials were conducted in two locations (Algiers and Setif) during two crop seasons in order to assess the responses of 17 genotype of barley (Hordeum vulgare L.) by evaluation of genotype-by-environment interactions (GEI) on grain yield and determine the stable genotypes. Results showed significant (p <0.001) effects of environment and genotypes and their interaction on grain yield. The genotypes had different behavior conducting to yield variation in the tested locations. So, selection could consider a specific adaptation of the genotypes and their yield stability. The Additive main effects and multiplicative interaction analysis is a useful tool allowing to explore important information on the obtained results; it revealed that ‘Plaisant/ charan01’ is the most stable genotype followed by ‘Barberousse’ and ‘Barberousse/Chorokhod’, while ‘Begonia’ and ‘Plaisant’ were unstable with specific adaptation to Setif location during 2018/19. the cultivar ‘Express’ presented a high productivity.Keywords: AMMI analysis, barley, genotype by environment interaction, grain yield, stability ReferencesAbdipur, M. & Vaezi, B. (2014). Analysis of the genotype-by-environment interaction of winter barley tested in the rain-fed regions of Iran by AMMi adjustment. Bulgarian Journal of Agricultural Science, 20(2), 421–427. https://www.agrojournal.org/20/02-27.htmlChalak, L. et al. (2015). Performance of 50 Lebanese barley landraces (Hordeum vulgare L. subsp. vulgare) in two locations under rainfed conditions. Annals of Agricultural Sciences, 60(2), 325–334. http://dx.doi.org/10.1016/j.aoas.2015.11.005Alfian, F. H. & Halimatus, S. (2016). On The Development of Statistical Modeling in Plant Breeding: An Approach of Row-Column Interaction Models (RCIM) For Generalized AMMI Models with Deviance Analysis. Agriculture and Agricultural Science Procedia, 9(1), 134–145. https://doi.org/10.1016/j.aaspro.2016.02.108Bouzerzour, H. & Dekhili, M. (1995). Heritabilities, gains from selection and genetic correlations for grain yield of barley grown in two contrasting environments. Field Crops Research, 41(3), 173–178. http://dx.doi.org/10.1016/0378-4290(95)00005-BDe Mendiburu, F. (2017). Agricolae: Statistical procedures for agricultural research. R package version, 1.2-8. Retrieved November 14, 2020 from https://tarwi.lamolina.edu.pe/~fmendiburu/Dogan, Y. et al. (2016). Identifying of relationship between traits and grain yield in spring barley by GGE biplot analysis. Agriculture and Forestry, 62(4), 239–252. http://dx.doi.org/10.17707/AgricultForest.62.4.25Farshadfar, E. et al. (2011). AMMI stability value and simultaneous estimation of yield and yield stability in bread wheat (Triticum aestivum L.). Australian Journal of Crop Science, 5(13), 1837–1844. http://www.cropj.com/farshadfar_5_13_2011_1837_1844.pdfFarshadfar, E. et al. (2012). GGE biplot analysis of genotype × environment interaction in wheat-barley disomic addition lines. Australian Journal of Crop Science, 6(6), 1074–1079. http://www.cropj.com/farshadfar_6_6_2012_1074_1079.pdfGauch, H.G. (1988). Model selection and validation for yield trials with interaction. Biometrics, 44(3), 705–715. http://dx.doi.org/10.2307/2531585Gauch, H.G. et al. (2008). Statistical analysis of yield trials by AMMI and GGE: Further considerations. Crop Science, 48(3), 866–889. https://doi.org/10.2135/cropsci2007.09.0513Halimatus, S. & Alfian, F. H. (2016). AMMI Model for Yield Estimation in Multi-Environment Trials: A Comparison to BLUP. Agriculture and Agricultural Science Procedia, 9(1), 163–169. https://doi.org/10.1016/j.aaspro.2016.02.113Vishnu, K. et al. (2016). AMMI, GGE biplots and regression analysis to comprehend the G × E interaction in multi-environment barley trials. Indian Journal of Genetics and Plant Breeding, 76(2), 202–204. https://dx.doi.org/10.5958/0975-6906.2016.00033.XMirosavljevic, M. et al. (2014). Analysis of new experimental barley genotype performance for grain yield using AMMI biplot. Selekcija I semenarstvo, 20(1), 27–36. In Bosnian. http://dx.doi.org/10.5937/SelSem1401027MPeyman, S. et al. (2017). Evaluation of Genotype × Environment Interaction in Rice Based on AMMI Model in Iran. Rice Science, 24(3), 173–180. https://doi.org/10.1016/j.rsci.2017.02.001Purchase, J.L. et al. (2000). Genotype × environment interaction of winter wheat (Triticum aestivum L.) in South Africa: II. Stability analysis of yield performance. South African Journal of Plant and Soil, 17(3), 101–107. http://dx.doi.org/10.1080/02571862.2000.10634878Rodrigues, P.C. et al. (2016). A robust AMMI model for the analysis of genotype-by-environment data. Bioinformatics, 32(1), 58–66. http://dx.doi.org/10.1093/bioinformatics/btv533Romagosa, I. & Fox, P.N. (1993). Genotype X environment interaction and adaption. In Hayward, M.D. et al. (eds.) Plant breeding principles and prospects. Plant Breeding Series. Dordrecht: Springer (pp. 373–390). https://doi.org/10.1007/978-94-011-1524-7_23Temesgen, B. et al. (2015). Genotype X Environment Interaction and Yield Stability of Bread Wheat (Triticum aestivum L.) Genotype in Ethiopia using the Ammi Analysis. Journal of Biology, Agriculture and Healthcare, 5(11), 129–139. https:// www.iiste.org/Journals/index.php/JBAH/article/view/23245Yan, W. et al. (2007). GGE biplot vs. AMMI analysis of genotype by environment data. Crop science, 47(2), 643–653. http://dx.doi.org/10.2135/cropsci2006.06.0374Zadoks, J.C. et al. (1974). A decimal code for the growth stages of cereals. Weed Research, 14(6), 415–421. http://dx.doi.org/10.1111/j.1365-3180.1974.tb01084.xZobel, R.W. et al. (1988). Statistical analysis of a yield trial. Agronomy Journal, 80(3), 388–393. http://dx.doi.org/10.2134/agronj1988.00021962008000030002x