Abstract: Problems related to food security and sustainable development are complex (Ericksenet al., 2009) and require consideration of biophysical, economic, political, and social factors, as well as their interactions, at the level of farms, regions, nations, and globally. While the solution to such societal problems may be largely political, there is a growing recognition of the need for science to provide sound information to decision-makers (Meinke et al., 2009). Achieving this, particularly in light of largely uncertain future climate and socio-economic changes, will necessitate integrated assessment approaches and appropriate integrated assessment modeling (IAM) tools to perform them. Recent (Ewertet al., 2009; van Ittersumet al., 2008) and ongoing (Rosenzweiget al., 2013) studies have tried to advance the integrated use of biophysical and economic models to represent better the complex interactions in agricultural systems that largely determine food supply and sustainable resource use. Nonetheless, the challenges for model integration across disciplines are substantial and range from methodological and technical details to an often still-weak conceptual basis on which to ground model integration (Ewertet al., 2009; Janssenet al., 2011). New generations of integrated assessment models based on well-understood, general relationships that are applicable to different agricultural systems across the world are still to be developed. Initial efforts are underway towards this advancement (Nelsonet al., 2014; Rosenzweiget al., 2013). Together with economic and climate models, crop models constitute an essential model group in IAM for large-area cropping systems climate change impact assessments. However, in addition to challenges associated with model integration, inadequate representation of many crops and crop management systems, as well as a lack of data for model initialization and calibration, limit the integration of crop models with climate and economic models (Ewertet al., 2014). A particular obstacle is the mismatch between the temporal and spatial scale of input/output variables required and delivered by the various models in the IAM model chain. Crop models are typically developed, tested, and calibrated for field-scale application (Booteet al., 2013; see also Part 1, Chapter 4 in this volume) and short time-series limited to one or few seasons. Although crop models are increasingly used for larger areas and longer time-periods (Bondeauet al., 2007; Deryng et al., 2011; Elliottet al., 2014) rigorous evaluation of such applications is pending. Among the different sources of uncertainty related to climate and soil data, model parameters, and structure, the uncertainty from methods used to scale-up crop models has received little attention, though recent evaluations indicate that upscaling of crop models for climate change impact assessment and the resulting errors and uncertainties deserve attention in order to advance crop modeling for climate change assessment (Ewertet al., 2014; R¨ otteret al., 2011). This reality is now reflected in the scientific agendas of new international research projects and programs such as the Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweiget al., 2013) and MACSUR (MACSUR, 2014). In this chapter, progress in evaluation of scaling methods with their related uncertainties is reviewed. Specific emphasis is on examining the results of systematic studies recently established in AgMIP and MACSUR. Main features of the respective simulation studies are presented together with preliminary results. Insights from these studies are summarized and conclusions for further work are drawn. No Label

Abstract: MACSUR — Modelling European Agriculture with Climate Change for Food Security — is a  knowledge hub that was formally created in June 2012 as a European scientific network.  The strategic aim of the knowledge hub is to create a coordinated and globally visible  network of European researchers and research groups, with intra- and interdisciplinary  interaction and shared expertise creating synergies for the development of scientific  resources (data, models, methods) to model the impacts of climate change on agriculture  and related issues. This objective encompasses a wide range of political and sociological  aspects, as well as the technical development of modelling capacity through impact  assessments at different scales and assessing uncertainties in model outcomes. We achieve  this through model intercomparisons and model improvements, harmonization and  exchange of data sets, training in the selection and use of models, assessment of benefits  of ensemble modelling, and cross-disciplinary linkages of models and tools. The project  engages with a diverse range of stakeholder groups and to support the development of  resources for capacity building of individuals and countries. Commensurate with this broad  challenge, a network of currently 300 scientists (measured by the number of individuals on  the central e-mail list) from 18 countries evolved from the original set of research groups  selected by FACCE.   In the spirit of creating and maintaining a network for intra- and interdisciplinary  knowledge exchange, network activities focused on meetings of researchers for sharing  expertise and, depending on group resources (both financial and personnel), development  of collaborative research activities. The outcome of these activities is the enhanced  knowledge of the individual researchers within the network, contributions to conference  presentations and scholarly papers, input to stakeholders and the general public, organised  courses for students, junior and senior scientists. The most visible outcome are the  scientific results of the network activities, represented in the contributions of MACSUR  members to the impressive number of more than 200 collaborative papers in peer-reviewed  publications.   Here, we present a selection of overview and cross-disciplinary papers which include  contributions from MACSUR members. It highlights the major scientific challenges  addressed, and the methodological solutions and insights obtained. Over and above these  highlights, major achievements have been reached regarding data collection, data  processing, evaluation, model testing, modelling assessments of the effects of agriculture  on ecosystem services, policy, and development of scenarios. Details on these  achievements in the context of MACSUR can be found in our online publication FACCE  MACSUR Reports at http://ojs.macsur.eu.

Abstract: Interactions of climate, soil and management practices in cropping systems can be simulated at different scales to provide information for decision making. Low resolution simulation need less effort, but important details could be lost through data aggregation effects (DAEs). This paper aims to provide a general method to assess the DAEs on weather data and the simulation of cropping systems, and further investigate how the DAEs vary with changing crop models, crops, variables and production systems. A 30-year continuous cropping system was simulated for winter wheat and silage maize and potential, water-limited and water-nitrogen-limited production situations. Climate data of 1 km resolution and aggregations to resolutions of 10 to 100 km was used as input for the simulations. The data aggregation narrowed the variation of weather data and DAEs increased with increasingly coarser spatial resolution, causing the loss of hot spots in simulated results. Spatial patterns were similar across different resolutions. Consistent with DAEs on weather data, the DAEs on simulated yield (0 to 1.2 t ha-1 for winter wheat and 0 to 1.7 t ha-1 for silage maize), evapotranspiration (3 to 45 mm yr-1 for winter wheat and 4 to 40 mm yr-1 for silage maize), and water use efficiency (0.02 to 0.25 kg m-3­ for winter wheat and 0.04 to 0.4 kg m-3­ for silage maize), increased with coarser spatial resolution. Thus, if spatial information is needed for local management decisions, higher resolution is needed to adequately capture the spatial heterogeneity or hot spots in the region.