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: This study evaluates the impacts of projected climate change on irrigation requirements and yields of six crops (winter wheat, winter barley, rapeseed, grain maize, potato, and sugar beet) in Europe. Furthermore, the uncertainty deriving from consideration of irrigation, CO2 effects on crop growth and transpiration, and different climate change scenarios in climate change impact assessments is quantified. Net irrigation requirement (NIR) and yields of the six crops were simulated for a baseline (1982-2006) and three SRES scenarios (B1, B2 and A1B, 2040-2064) under rainfed and irrigated conditions, using a process-based crop model, SIMPLACE <LINTUL5, DRUNIR, HEAT>. We found that projected climate change decreased NIR of the three winter crops in northern Europe (up to 81 mm), but increased NIR of all the six crops in the Mediterranean regions (up to 182 mm yr(-1)). Climate change increased yields of the three winter crops and sugar beet in middle and northern regions (up to 36%), but decreased their yields in Mediterranean countries (up to 81%). Consideration of CO2 effects can alter the direction of change in NIR for irrigated crops in the south and of yields for C3 crops in central and northern Europe. Constraining the model to rainfed conditions for spring crops led to a negative bias in simulating climate change impacts on yields (up to 44%), which was proportional to the irrigation ratio of the simulation unit. Impacts on NIR and yields were generally consistent across the three SRES scenarios for the majority of regions in Europe. We conclude that due to the magnitude of irrigation and CO2 effects, they should both be considered in the simulation of climate change impacts on crop production and water availability, particularly for crops and regions with a high proportion of irrigated crop area.

Abstract: Eleven widely used crop simulation models (APSIM, CERES, CROPSYST, COUP, DAISY, EPIC, FASSET, HERMES, MONICA, STICS and WOFOST) were tested using spring barley (Hordeum vulgare L.) data set under varying nitrogen (N) fertilizer rates from three experimental years in the boreal climate of Jokioinen, Finland. This is the largest standardized crop model inter-comparison under different levels of N supply to date. The models were calibrated using data from 2002 and 2008, of which 2008 included six N rates ranging from 0 to 150 kg N/ha. Calibration data consisted of weather, soil, phenology, leaf area index (LAI) and yield observations. The models were then tested against new data for 2009 and their performance was assessed and compared with both the two calibration years and the test year. For the calibration period, root mean square error between measurements and simulated grain dry matter yields ranged from 170 to 870 kg/ha. During the test year 2009, most models failed to accurately reproduce the observed low yield without N fertilizer as well as the steep yield response to N applications. The multi-model predictions were closer to observations than most single-model predictions, but multi-model mean could not correct systematic errors in model simulations. Variation in soil N mineralization and LAI development due to differences in weather not captured by the models most likely was the main reason for their unsatisfactory performance. This suggests the need for model improvement in soil N mineralization as a function of soil temperature and moisture. Furthermore, specific weather event impacts such as low temperatures after emergence in 2009, tending to enhance tillering, and a high precipitation event just before harvest in 2008, causing possible yield penalties, were not captured by any of the models compared in the current study.