Challenges for agro-ecosystem modelling in climate change risk assessment for major European crops and farming systems

Modelling European Agriculture with Climate Change for Food Security (MACSUR) is a knowledge hub exploiting and improving data, methods and modelling tools for a detailed climate change risk assessment. The hub comprises 73 interacting agricultural (crop, livestock, trade) scientific and modelling research groups from 16 European countries and Israel. The crop modelling (CropM) component of MACSUR concentrates on overcoming weaknesses in crop modelling approaches and tools with specific attention to exploiting data on important European field crops, crop rotations and farming systems, and the modelling of diverse (mitigative) adaptation options. CropM outputs are scaled up to farm, regional and (supra-) national level as required for concerted integrated studies on the European agri-food sector and its contribution to global food security under climate change. The specific objectives of CropM are: (i) to conduct crop model intercomparisons to detect deficiencies, (ii) compile data in support of model improvements, (iii) advance scaling methods and model linkages, (iv) improve climate scenario data and impact uncertainty analysis, (v) build research capacity in these areas, and (vi) combine new knowledge and tools with those from livestock and trade modellers to allow interdisciplinary studies and interaction with a diverse range of stakeholders for climate change impact assessments. We identify requirements for improving model simulations, e.g. concerning impacts of heat and drought stress as well as intense rainfall and warm winters on crop yield. We show possibilities for enhancing methods of linking models and data of different resolutions. Finally, we give examples of how to improve quantification and reporting of crop impact uncertainties including the contribution from various sources (i.e. emission scenarios, climate modelling, downscaling of climate model data and crop impact modelling itself).