FACCE-JPI Knowledge Hub: MACSUR-Partner 25

Continued pressure on agricultural land, food insecurity and required adaptation to climate change have made integrated assessment and modelling of future agro-ecosystems development increasingly important. Various modelling tools are used to support the decision making and planning in agriculture (van Ittersum et al., 2008, Brouwer & van Ittersum, 2010; Ewert et al., 2011). Crop growth simulation models are increasingly applied, particularly in climate change-related agricultural impact assessments (Rosenzweig & Wilbanks, 2010; White et al., 2011). Model-based projections of future changes in crop productivity, for instance, are made on the basis of understanding the physical and biological processes, such as how given crops respond to reduced water supply, warmer growing seasons or changed crop and soil management (Challinor et al., 2009; Challinor, 2011; Rötter et al., 2011a).

Even though most of crop growth simulation models have been developed and evaluated at field scale, and were thus not meant for large area assessments, it has become common practice to apply them in assessing agricultural impacts and adaptation to climate variability and change from field to (supra-)national scale (van der Velde et al., 2009; van Bussel, 2011). It has been hypothesized by various authors (e.g. Palosuo et al., 2011; Rötter et al., submitted; Asseng et al., in preparation) that many of those model applications involve huge uncertainties. Recently, there have been renewed efforts in improving the understanding and reporting of the uncertainties related to crop growth and yield predictions (Rötter et al., 2011a; Ferrise et al., 2011; Borgesen & Olesen, 2011). Comparison of different modelling approaches and models can reveal the uncertainties involved. Variation of model results in model intercomparisons involves also the uncertainty related to model structure, which is probably the most important source of uncertainty and most difficult to quantify. There is both, a need for quantifying the degree of uncertainty resulting from crop models as well as to determine the relative importance of their uncertainties in climate change impact assessment (e.g. Iizumi et al., 2011). That is, how much of the uncertainty can be attributed to climate models, crop models and other basic assumptions (e.g. in emission scenarios).

Such assessment of the relative importance of uncertainties and how to reduce them, is also at the core of "The Agricultural Model Intercomparison and Improvement project (AgMIP)" (www.agmip.org). AgMIP has identified three important thematic working groups cutting across trade, crop and climate modelling: they are (i) representative agricultural development pathways, (ii) scaling methods and (iii) uncertainty analysis. In that set-up, the global AgMIP initiative shows overlaps with the objectives and tasks defined for CropM, and with FACCE-MACSUR as a whole. However, CropM and FACCE-MACSUR as a whole have the ambition to go further in terms of developing climate change risk assessment methodology than AgMIP does in other parts of the globe. Also, the high density of crop and climate data in Europe will allow the analysis of scaling and model linking methods, and uncertainty which goes well beyond the capabilities of AgMIP in other world regions.

Model intercomparisons, when combined with experimental data of the compared variables, may also be used to test the performance of different models. Such intercomparisons can help to identify those parts in models that produce systematic errors and require improvements. There is currently a number of experimental data (for wheat and barley) available across Europe which may be used for model intercomparisons. Comprehensive data sets that would allow thorough comparisons are getting increasingly scarce and call for concerted efforts to develop such high quality data sets for different locations (agro-climatic conditions) and crops in Europe.

Testing the validity of crop models for climate change impact requires consistent data sets for model input across a wide range of environmental conditions. In CropM we distinguish several work packages (WP). The aim of WP1 is to create a common protocol for model intercomparisons across a wide range of environmental conditions. Within WP2 the relevant data will be collected from partners and stored in a common database with web-based interface for easy access. Many methods of scaling up crop models from the field to the larger spatial and temporal scale have been developed, but systematic evaluations of these methods have rarely been performed. The aim of WP3 is to improve the application of crop models at different spatial (and temporal) scales in combination with models or data from other disciplines for assessing climate change impacts on food security. In order to responsibly inform decision making, research in WP4 will address issues involved to better identify, describe, quantify and communicate the sources of uncertainty in making projections of future crop response to climate change. Part of the uncertainty is related to climate projections, another part is related to projections regarding socio-economic, land use and other environmental factors that affect vulnerability of agriculture. In addition to the above research areas CropM will also ensure that the obtained scientific advances are communicated, shared and discussed with all CropM partners. WP5 will organise topical workshops on crop modelling, exchange of scientists and web-based courses. The engagement of CropM in cross-theme modelling excercises and the linking to decision makers will be ensured through WP6 which will support the formulation of policy questions for the different case studies.

FACCE MACSUR Knowledge Hub will provide significant advance in the knowledge and methodologies for risk assessment for European agriculture and food security. The project will deliver an unprecedented detailed European fully integrated analysis. The greatest knowledge
advancement will stem from the interdisciplinary and diverse framework implemented within the project. This type of broad interdisciplinary study may lead to results that are significantly different in comparison to previous endeavours in this field. FACCE MACSUR Knowledge Hub requires a European rather than a national or local approach to achieve the objectives outlined in this proposal. Specific expertise is in certain cases only available in particular EU countries and would not be possible to find in individual countries. Additionally, the exchange skills and ToK between partners will improve the level of the scientific research and will respectively enhance the status of science in the EU. Most importantly, the dissemination of data and methodologies from this project will contribute to the objectives of the European Commission which aim to coordinate the European Science Initiative by encouraging and providing the necessary tools and support for high impact, substantial science. We will be able to highlight our results internationally and will further unify the European scientific community. Reproducible and reliable results, high impact publications and the dissemination of the results at international meetings will increase the profile of European Science and will consequently improve the ability of Europe to attract foreign researchers and result in a more competitive European Research Area. Adaptation to climate change to underpin food security is by nature transboundary.