To date, projections of European crop yields under climate change have been based almost entirely on the outputs of crop-growth models. While this strategy can provide good estimates of the effects of climatic factors, soil conditions and management on crop yield, these models usually do not capture all of the important aspects related to crop management, or the relevant environmental factors. Moreover, crop-simulation studies often have severe limitations with respect to the number of crops covered or the spatial extent. The present study based on agroclimatic índices, pro vides a general picture of agroclimatic conditions in western and central Europe (study área lays between 8.5°W-27°E and 37-63.5°N), which allows for a more general assessment of climate-change impacts. The results obtained from the analysis of data from 86 different sites were clustered according to an environmental stratification of Europe. The analysis was carried for the baseline and future climate conditions (time horizons of 2030, 2050 and with a global temperature increase of 5 °C) based on outputs of three global circulation models. For many environmental zones, there were clear signs of deteriorating agroclimatic condition in terms of increased drought stress and shortening of the active growing season, which in some regions become increasingly squeezed between a cold winter and a hot summer. For most zones the projections show a marked need for adaptive measures to either increase soil water availability or drought resistance of crops. This study concludes that rainfed agriculture is likely to face more climate-related risks, although the analyzed agroclimatic indicators will probably remain at a level that should permit rainfed production. However, results suggests that there is a risk of increasing number of extremely unfavorable years in many climate zones, which might result in higher interannual yield variability and constitute a challenge for proper crop management.

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The impacts of climate change on ecosystem services are complex in the sense that effective prediction requires consideration of a wide range of factors. Useful analysis of climate-change impacts on crops and native plant systems will often require consideration of the wide array of other biota that interact with plants, including plant diseases, animal herbivores, and weeds. We present a framework for analysis of complexity in climate-change effects mediated by plant disease. This framework can support evaluation of the level of model complexity likely to be required for analysing climate-change impacts mediated by disease. Our analysis incorporates consideration of the following set of questions for a particular host, pathogen, host-pathogen combination, or geographic region. 1. Are multiple biological interactions important? 2. Are there environmental thresholds for population responses? 3. Are there indirect effects of global change factors on disease development? 4. Are spatial components of epidemic processes affected by climate? 5. Are there feedback loops for management? 6. Are networks for intervention technologies slower than epidemic networks? 7. Are there effects of plant disease on multiple ecosystem services? 8. Are there feedback loops from plant disease to climate change? Evaluation of these questions will help in gauging system complexity, as illustrated for fusarium head blight and potato late blight. In practice, it may be necessary to expand models to include more components, identify those components that are the most important, and synthesize such models to include the optimal level of complexity for planning and research prioritization.

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We show the error in water-limited yields simulated by crop models which is associated with spatially aggregated soil and climate input data. Crop simulations at large scales (regional, national, continental) frequently use input data of low resolution. Therefore, climate and soil data are often generated via averaging and sampling by area majority. This may bias simulated yields at large scales, varying largely across models. Thus, we evaluated the error associated with spatially aggregated soil and climate data for 14 crop models. Yields of winter wheat and silage maize were simulated under water-limited production conditions. We calculated this error from crop yields simulated at spatial resolutions from 1 to 100 km for the state of North Rhine-Westphalia, Germany. Most models showed yields biased by <15% when aggregating only soil data. The relative mean absolute error (rMAE) of most models using aggregated soil data was in the range or larger than the inter-annual or inter-model variability in yields. This error increased further when both climate and soil data were aggregated. Distinct error patterns indicate that the rMAE may be estimated from few soil variables. Illustrating the range of these aggregation effects across models, this study is a first step towards an ex-ante assessment of aggregation errors in large-scale simulations.