Abstract: Global climate impact studies with crop models suggest that including nitrogen and water limitation causes greater negative climate change impacts on actual yields compared to water-limitation only. We simulated water limited and nitrogen water limited yields across the EU-27 to 2050 for six key crops with the SIMPLACE<LINTUL5, DRUNIR, HEAT> model to assess how important consideration of nitrogen limitation is in climate impact studies for European cropping systems. We further investigated how crop nitrogen use may change under future climate change scenarios. Our results suggest that inclusion of nitrogen limitation hardly changed crop yield response to climate for the spring-sown crops considered (grain maize, potato, and sugar beet). However, for winter-sown crops (winter barley, winter rapeseed and winter wheat), simulated impacts to 2050 were more negative when nitrogen limitation was considered, especially with high levels of water stress. Future nitrogen use rates are likely to decrease due to climate change for spring-sown crops, largely in parallel with their yields. These results imply that climate change impact studies for winter-sown crops should consider N-fertilization. Specification of future N fertilization rates is a methodological challenge that is likely to need integrated assessment models to address.

Abstract: Climate and soil data at coarse resolution are often used as input for crop models in order to simulate crop yields at larger scales, e.g. at regional or national level, potentially leading to biased yield estimates. While the response to data resolution differs between crop models, it is unknown how the spatial aggregation of different types of input data interacts and contributes to this so-called aggregation effect. An ensemble of crop models was run with soil and climate input data at different spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. For this purpose, climate time series were averaged spatially and soil data was aggregated by selecting the dominant soil type with a representative soil profile based on a soil map at the scale of 1:50,000. Yields of winter wheat and silage maize were simulated under potential, water-limited and water-nitrogen-limited production conditions. Crop yields from soil and climate aggregation were evaluated separately.Mean of crop yields of the region and over the simulation period were reasonably reproduced by most models regardless of input data resolution, either using aggregated soil or climate as input. However, larger aggregation effects were observed at higher temporal resolution (e.g. annual yields). Models revealed similar spatial patterns in yield. Being distinct for soil and climate aggregation, these patterns indicate a larger impact of soil aggregation on the spatial distribution of simulated crop yield for this region. Additionally, models differed considerably in their susceptibility to input data aggregation. The results reveal the importance of model ensemble assessments and the relevance of data aggregation when short simulation periods are considered. No Label

Abstract: Field-scale crop models are often applied at spatial resolutions coarser than that of the arable field. However, little is known about the response of the models to spatially aggregated climate input data and why these responses can differ across models. Depending on the model, regional yield estimates from large-scale simulations may be biased, compared to simulations with high-resolution input data. We evaluated this so-called aggregation effect for 13 crop models for the region of North Rhine-Westphalia in Germany. The models were supplied with climate data of 1 km resolution and spatial aggregates of up to 100 km resolution raster. The models were used with 2 crops (winter wheat and silage maize) and 3 production situations (potential, water-limited and nitrogen-water-limited growth) to improve the understanding of errors in model simulations related to data aggregation and possible interactions with the model structure. The most important climate variables identified in determining the model-specific input data aggregation on simulated yields were mainly related to changes in radiation (wheat) and temperature (maize). Additionally, aggregation effects were systematic, regardless of the extent of the effect. Climate input data aggregation changed the mean simulated regional yield by up to 0.2 t ha(-1), whereas simulated yields from single years and models differed considerably, depending on the data aggregation. This implies that large-scale crop yield simulations are robust against climate data aggregation. However, large-scale simulations can be systematically biased when being evaluated at higher temporal or spatial resolution depending on the model and its parameterization.