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  • 1 Agricultural Institute Osijek Južno predgraðe 17 31000 Osijek Croatia
  • | 2 University of Zagreb Department of Plant Breeding, Genetics and Biometrics, Faculty of Agriculture Svetošimunska cesta 25 10000 Zagreb Croatia
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Ten winter wheat ( Triticum aestivum L.) cultivars were tested in randomized complete block design (RCBD) trials at one location (Osijek) for several agronomic and quality traits through six growing seasons (1996/97–2001/02). Data were employed to develop modeling strategy for exploring genotype by environment interaction (GEI) by using models based on information on genotypic and environmental variables. The relative size, hence importance of the GEI compared to main effects of genotypes and environments was estimated for all effects from simple additive model (genotypes, environments and residuals, last including both GEI and experimental error) while the AMMI2 model was used as a basis for comparison of the GEI patterns. The final step in modeling strategy was fitting factorial regression models to all analyzed traits using available genotypic and environmental covariates, until the best fit solution was found for each analyzed trait.Comparing the relative sizes of genotypic and GEI effects, the last one was sizeable smaller, for all traits except grain yield (GY), thousand-kernel weight (TKW), and Hagberg falling number (HFN). Fitting of genotypic and environmental covariates resulted in various solutions for different traits, most frequently employing single genotypic covariate — Glu-A1.Regardless of their relatively small size, the GEI effects in wheat quality traits can offer a better insight into fluctuations of varietal quality over a range of environmental conditions, as they can be successfully modeled using various genotypic and environmental covariates. The advantage of described approach is attainable in virtually any breeding program, because during the implementation of the program breeders routinely score for a number of genotypic and environmental variables.

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