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• Management choices can influenced significantly model output variables at regional scale. • Management effect is usually stronger than scaling effect, particularly on regional yield. • Scaling and management effects varied greatly between crop models and output variables. • Management and scaling effects are greater when analyzed for individual year. Due to the more frequent use of crop models at regional and national scale, the effects of spatial data input resolution have gained increased attention. However, little is known about the influence of variability in crop management on model outputs. A constant and uniform crop management is often considered over the simulated area and period. This study determines the influence of crop management adapted to climatic conditions and input data resolution on regional-scale outputs of crop models. For this purpose, winter wheat and maize were simulated over 30 years with spatially and temporally uniform management or adaptive management for North Rhine-Westphalia (˜34 083 km²), Germany. Adaptive management to local climatic conditions was used for 1) sowing date, 2) N fertilization dates, 3) N amounts, and 4) crop cycle length. Therefore, the models were applied with four different management sets for each crop. Input data for climate, soil and management were selected at five resolutions, from 1 × 1 km to 100 × 100 km grid size. Overall, 11 crop models were used to predict regional mean crop yield, actual evapotranspiration, and drainage. Adaptive management had little effect (<10% difference) on the 30-year mean of the three output variables for most models and did not depend on soil, climate, and management resolution. Nevertheless, the effect was substantial for certain models, up to 31% on yield, 27% on evapotranspiration, and 12% on drainage compared to the uniform management reference. In general, effects were stronger on yield than on evapotranspiration and drainage, which had little sensitivity to changes in management. Scaling effects were generally lower than management effects on yield and evapotranspiration as opposed to drainage. Despite this trend, sensitivity to management and scaling varied greatly among the models. At the annual scale, effects were stronger in certain years, particularly the management effect on yield. These results imply that depending on the model, the representation of management should be carefully chosen, particularly when simulating yields and for predictions on annual scale. [ABSTRACT FROM AUTHOR]

Abstract: Cropping system models are powerful tools for regional impact assessment, but their input data requirements for large heterogeneous areas are difficult to fulfil. Hence, the objectives of this paper are to present low-data approaches for specifying detailed management data required by cropping system models, and for calibrating default crop parameters applied to 12 regions in the European Union (EU). Various downscaling and upscaling procedures for different data types are applied to address both objectives. The Agricultural Production and Externalities Simulator (APES) model is used for illustrative purposes. Combining easy-to-collect regional crop management information and expert knowledge enables to develop generic, expert-based rules for specifying crop management. Effects of these expert-based management rules on simulated yields and nitrogen leaching are illustrated using APES. Simulated yields of grain maize, soft wheat and durum wheat using default crop parameters for phenology are compared with crop yields observed in 12 EU regions. The accuracy of the simulated yields was variable, but generally poor. A regional calibration factor Kpheno is developed based on the temperature sum of the average sowing and harvest dates of the three crops in each region. Applying this calibration factor improved the simulated yields in all cases. Results suggest that it is possible to develop expert-based management rules and to capture yield variation across the EU by using the presented low-data approaches. [Copyright &y& Elsevier]