Abstract: Impacts of climate change on European agricultural production, land use and the environment depend on its impact on crop yields. However, many impact studies assume that crop management remains unchanged in future scenarios, while farmers may adapt their sowing dates and cultivar thermal time requirements to minimize yield losses or realize yield gains. The main objective of this study was to investigate the sensitivity of climate change impacts on European crop yields, land use, production and environmental variables to adaptations in crops sowing dates and varieties’ thermal time requirements. A crop, economic and environmental model were coupled in an integrated assessment modelling approach for six important crops, for 27 countries of the European Union (EU27) to assess results of three SRES climate change scenarios to 2050. Crop yields under climate change were simulated considering three different management cases; (i) no change in crop management from baseline conditions (NoAd), (ii) adaptation of sowing date and thermal time requirements to give highest yields to 2050 (Opt) and (iii) a more conservative adaptation of sowing date and thermal time requirements (Act). Averaged across EU27, relative changes in water-limited crop yields due to climate change and increased CO2 varied between -6 and + 21% considering NoAd management, whereas impacts with Opt management varied between + 12 and + 53%, and those under Act management between 2 and + 27%. However, relative yield increases under climate change increased to + 17 and + 51% when technology progress was also considered. Importantly, the sensitivity to crop management assumptions of land use, production and environmental impacts were less pronounced than for crop yields due to the influence of corresponding market, farm resource and land allocation adjustments along the model chain acting via economic optimization of yields. We conclude that assumptions about crop sowing dates and thermal time requirements affect impact variables but to a different extent and generally decreasing for variables affected by economic drivers.

Abstract: Assessment of climate change impacts on crops in regions of complex orography such as the Iberian Peninsula (IP) requires climate model output which is able to describe accurately the observed climate. The high resolution of output provided by Regional Climate Models (RCMs) is expected to be a suitable tool to describe regional and local climatic features, although their simulation results may still present biases. For these reasons, we compared several post-processing methods to correct or reduce the biases of RCM simulations from the ENSEMBLES project for the IP. The bias-corrected datasets were also evaluated in terms of their applicability and consequences in improving the results of a crop model to simulate maize growth and development at two IP locations, using this crop as a reference for summer cropping systems in the region. The use of bias-corrected climate runs improved crop phenology and yield simulation overall and reduced the inter-model variability and thus the uncertainty. The number of observational stations underlying each reference observational dataset used to correct the bias affected the correction performance. Although no single technique showed to be the best one, some methods proved to be more adequate for small initial biases, while others were useful when initial biases were so large as to prevent data application for impact studies. An initial evaluation of the climate data, the bias correction/reduction method and the consequences for impact assessment would be needed to design the most robust, reduced uncertainty ensemble for a specific combination of location, crop, and crop management.

Abstract: The complexity of risks posed by climate change and possible adaptations for crop production has called for integrated assessment and modelling (IAM) approaches linking biophysical and economic models. This paper attempts to provide an overview of the present state of crop modelling to assess climate change risks to food production and to which extent crop models comply with IAM demands. Considerable progress has been made in modelling effects of climate variables, where crop models best satisfy IAM demands. Demands are partly satisfied for simulating commonly required assessment variables. However, progress on the number of simulated crops, uncertainty propagation related to model parameters and structure, adaptations and scaling are less advanced and lagging behind IAM demands. The limitations are considered substantial and apply to a different extent to all crop models. Overcoming these limitations will require joint efforts, and consideration of novel modelling approaches.