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The need for more comparisons among models is widely recognized. This study aimed to compare three different modelling approaches for their capability to simulate and predict trends and patterns of winter wheat yield in Western Germany. The three modelling approaches included an empirical model, a process-based model (LINTUL2), and a metamodel derived from the process-based model. The models outcomes were aggregated to general climate zones level of Western Germany to allow for a comparison with agricultural census data for validation purposes. The spatial patterns and temporal trends of winter wheat yield seemed to be better represented by the empirical model (R²= 70%, RMSE= 0.48 t ha^-1 yr^-1, and CV-RMSE= 8%) than by the LINTUL2 model (R²= 65%, RMSE= 0.67 t ha^-1 yr^-1, and CV-RMSE=11%) and the metamodel (R²= 57%, RMSE= 0.77 t ha^-1 yr^-1, and CV-RMSE=13%). All models demonstrated a similar order of magnitude of yield prediction and associated uncertainties. The suitability of the three models is context dependent. Empirical modelling is most suitable to analyze and project past and current crop-yield patterns, while crop growth simulation models are more suited for future projections with climate scenarios. The derived metamodels are fast reliable alternatives for areas with well calibrated crop growth simulation models. A model comparison helps to reveal shortcomings and strengths of the models. In our case, a performance comparison between the three modelling approaches indicated that, for simulating winter wheat growth in Western Germany, higher sensitivity to soil depth and lower sensitivity to drought in the LINTUL2 model would probably lead to better predictions.

Keywords: Crop growth simulation model, Climate Change, Metamodel, Regression analysis, LINTUL2

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