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  • Author or Editor: M. Studnicki x
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Eleven spring wheat cultivars were compared in terms of the stability of their grain yield and grain quality. The cultivars’ stability was evaluated separately at two different crop management levels – moderate-input management and high-input management. Three stability models were used for the two crop management levels based on a linear mixed model framework with restricted maximum likelihood. The Shukla model was the most appropriate for the evaluation of stability of tested spring wheat cultivars. The thousand-grain weight, starch content, Zeleny sedimentation value and test weight were characterized, and the stability ranking cultivars at moderate-input management level was mostly consistent with the rank of cultivars 24 for high-input management level. For grain yield, grain protein content and wet gluten content, the stability rankings were not consistent. Cultivars ‘Monsun’ and ‘Parabola’ are the most stable cultivars for grain yield in moderate-input management and high-input management, respectively. Cultivar ‘Hewilla’ was the stable cultivar for all quality traits at moderate-input management. Cultivar ‘Arabella’ was the most stable cultivar at high-input management level.

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The grouping of locations from local-scale multi-environmental trials (METs) into megaenvironments has been criticized. Some European countries, e.g. the Czech Republic, Poland and Germany, have been characterized as possessing homogeneous environmental conditions. For aligned environmental conditions, it has been assumed that cultivar rankings will be similar and consequently cannot be used to designate mega-environments. An example of METs at the local scale is the Polish Post Registration Variety Testing System. The objective of this study was to determine groups of test sites within 16 Polish regions which are characterized by similar yield ranking of 50 winter wheat cultivars over three growing seasons (2011–2013). The compatibility of these cultivar yield rankings across regions was evaluated using Pearson correlation coefficients. Thereby, the 16 regions were divided into six groups (mega-environments) of locations. Regions within each group have similar cultivar rankings, whereas between groups, we observed different cultivar rankings, indicating crossover interactions. Besides similar cultivar yield responses the regions within megaenvironments were characterized also by similar environmental (soil and/or climate) conditions.

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