Cereal Research Communications

, Volume 46, Issue 4, pp 729–738 | Cite as

Genotype by Environment Interaction for Grain Yield in Spring Barley Using Additive Main Effects and Multiplicative Interaction Model

  • K. Nowosad
  • A. Tratwal
  • J. BocianowskiEmail author
Open Access


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.


adaptability biplot grain yield spring barley stability 


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© Akadémiai Kiadó, Budapest 2018

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Authors and Affiliations

  1. 1.Department of Genetics, Plant Breeding and Seed ProductionWrocław University of Environmental and Life SciencesWrocławPoland
  2. 2.Department of Pests Methods Forecasting and Plant Protection EconomyInstitute of Plant Protection - National Research InstitutePoznańPoland
  3. 3.Department of Mathematical and Statistical MethodsPoznań University of Life SciencesPoznańPoland

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