Monoculture and use of disease resistant varieties on large scale usually leads to selection of new pathogen races able to overcome the resistance. The use of variety mixtures can significantly improve the control of the disease and provides stable yield among different environments. The objective of this study was to assess genotype by environment interaction for grain yield in spring barley genotypes grown in two places different in terms of soil and meteorological conditions by the additive main effects and multiplicative interaction model. The study comprised 25 spring barley genotypes (five cultivars: Basza, Blask, Skarb, Rubinek and Antek, and 20, two- and three-component mixtures), analyzed in eight environments (compilations of two locations and four years) through field trials arranged in a randomized complete block design, with three replicates. Grain yield of the tested genotypes varied from 32.88 to 74.31 dt/ha throughout the eight environments, with an average of 54.69 dt/ha. In the variance analysis, 68.80% of the total grain yield variation was explained by environment, 6.20% by differences between genotypes, and 7.76% by genotype by environment interaction. Grain yield is highly influenced by environmental factors.
Abakemal, D., Shimelis, H., Derera, J. 2016. Genotype-by-environment interaction and yield stability of quality protein maize hybrids developed from tropical-highland adapted inbred lines. Euphytica 209:757–769.
Anandan, A., Sabesan, T., Eswaran, R., Rajiv, G., Muthalagan, N., Suresh, R. 2009. Appraisal of environmental interaction on quality traits of rice by additive main effects and multiplicative interaction analysis. Cereal Res. Commun. 37:131–140.
Andrade, M.I., Naico, A., Ricardo, J., Eyzaguirre, R., Makunde, G.S., Ortiz, R., Grüneberg, W.J. 2016. Genotype × environment interaction and selection for drought adaptation in sweetpotato (Ipomoea batatas [L.] Lam.) in Mozambique. Euphytica 209:261–280.
Barot, S., Allard, V., Cantarel, A., Enjalbert, J., Gauffreteau, A., Goldringer, I., Lata, J., Le Roux, X., Niboyet, A., Porcher, E. 2017. Designing mixtures of varieties for multifunctional agriculture with the help of ecology. A review. Agron. Sustain. Dev. 37:13.
Elakhdar, A., Kumamaru, T., Smith, K.P., Brueggeman, R.S., Capo-Chichi, L.J.A., Solanki, S. 2017. Genotype by environment interactions (GEIs) for barley grain yield under salt stress condition. J. Crop Sci. Biotech. 20:193–204.
Finckh, M.R., Gacek, E.S., Czembor, H.J., Wolfe, M.S. 1998. Host frequency and density effects on disease and field in mixtures of barley. Plant Pathol. 48:807–816.
Fox, P.N., Crossa, J., Ramagosa, I. 1997. Multienvironment testing and genotype environment interaction. In: Kempton, R.A., Fox, P.N. (eds.), Statistical methods for plant variety evaluation. Chapman & Hall. London, UK. pp. 117–138.
Gauch, H.G., Zobel, R.W. 1990. Imputing missing yield trial data. Theor. Appl. Genet. 79:753–761.
Gabriel, K.R. 1978. Least squares approximation of matrices by additive and multiplicative models. J. Roy. Stat. Soc. B Met. 40:186–196.
Gollob, H.F. 1968. A statistical model which combines features of factor analytic and analysis of variance techniques. Psychometrika 33:73–115.
Hristov, N., Mladenov, N., Djuric, V., Kondic-Spika, A., Marjanovic-Jeromela, A., Simic, D. 2010. Genotype by environment interactions in wheat quality breeding programs in southeast Europe. Euphytica 174:315–324.
Kieloch, R., Weber, R. 2015. Influence of different herbicides on the performance of spring barley (Hordeum vulgare) cultivars in Lower Silesia region, Poland. Int. J. Agric. Biol. 17:181–186.
Mijić, A., Krizmanić, M., Liović, I., Zdunić, Z., Marić, S. 2007. Response of sunflower hybrids to growing in different environments. Cereal Res. Commun. 35:781–784.
Nowosad, K., Liersch, A., Popławska, W., Bocianowski, J. 2016. Genotype by environment interaction for seed yield in rapeseed (Brassica napus L.) using additive main effects and multiplicative interaction model. Euphytica 208:187–194.
Nowosad, K., Liersch, A., Poplawska, W., Bocianowski, J. 2017. Genotype by environment interaction for oil content in winter oilseed rape (Brassica napus L.) using additive main effects and multiplicative interaction model. Indian J. Genet. Pl. Br. 77:293–297.
Paroda, R.S., Hayes, J.D. 1971. An investigation of genotype-environment interactions for rate of ear emergence in spring barley. Heredity 26:157–175.
Philips, S.L., Wolfe, M.S. 2005. Evolutionary plant breeding for low input systems. J. Agr. Sci. 143:245–254.
Purchase, J.L. 1997. Parametric analysis to describe G × E interaction and yield stability in winter wheat. PhD Thesis, University of the Orange Free State, Bloemfontein, South Africa.
Shafii, B., Mahler, K.A., Price, W.J., Auld, D.L. 1992. Genotype × environment interaction effects on winter rapeseed yield and oil content. Crop Sci. 32:922–927.
Solonechnyi, P., Vasko, N., Naumov, A., Solonechnaya, O., Vazhenina, O., Bondareva, O., Logvinenko, Y. 2015. GGE biplot analysis of genotype by environment interaction of spring barley varieties. Zemdirbyste 102:431–436.
Vargas, W., Crossa, J., van Eeuwijk, F.A., Ramirez, E., Sayre, K. 1999. Using partial least squars regression, factorial regression and AMMI models for interpreting genotype-by-environment interaction. Crop Sci. 39:955–967.
Zhang, H., Berger, J.D., Milroy, S.P. 2013. Genotype × environment interaction studies highlight the role of phenology in specific adaptation of canola (Brassica napus) to contrasting Mediterranean climates. Field Crop. Res. 144:77–88.
Zobel, R.W., Wright, M.J., Gauch, H.G. 1988. Statistical analysis of yield trial. Agron. J. 80:388–393.
Communicated by E. Khlestkina
About this article
Cite this article
Nowosad, K., Tratwal, A. & Bocianowski, J. Genotype by Environment Interaction for Grain Yield in Spring Barley Using Additive Main Effects and Multiplicative Interaction Model. CEREAL RESEARCH COMMUNICATIONS 46, 729–738 (2018). https://doi.org/10.1556/0806.46.2018.046
- grain yield
- spring barley