Abstract: This paper will begin by defining some of the challenges that we face on the MACSUR project in terms of evaluating model uncertainty and carrying out model integration. I will briefly review what cloud technologies are available, followed with some suggestions about how those cloud technologies can be used in order to contribute to meeting the challenges set out in the first part of the paper.’Month 12’ deliverable for WP1 is a review of the opportunities for using cloud computing to develop the potential for model inter-comparison and interlinking in MACSUR. A challenging aspect of compiling this review is that before an ‘opportunity’ for any kind of model linking/comparison can be identified, a lot of information about the specifics of extant models and workflows must be gathered from each of the three themes (TradeM, CropM, and LiveM).This deliverable must, however, be more than just saying ‘these are the computing tools that we can use to.’. There are a number of different challenges at different levels; a hierarchy of challenges, if you like. For example, in order to get models ‘talking’ to one another, adequate protocols for the transference of data and scaleability will need to be established, and then things like uncertainty analysis for these integrated models will need to be addressed. Further issues exist relating to human behaviour and logistics (e.g. MACSUR is a large project with many members from all over Europe, with substantial distances between many of it’s members).The term “Cloud” is very ambiguous, and Cloud Computing covers a huge range of services, and a number of innovative tools exist which can make international collaborative research more effective. Two examples (already implemented on the MACSUR website) are: a discussion forum (where project members can create topics, make or reply to posts, and upload documents) and a complete surveying platform (to provide an un-restricted and fully featured survey platform for MACSUR members’ information gathering needs.) No Label

Abstract: This paper addresses protein crop production in Europe. European food and feed industries highly depend on imported protein crops and derived products and climate change is likely to affect domestic protein crop production and thus the import dependency. The recent reform of EU agricultural policy reform aims at promoting climate friendly agricultural practices and stimulating the production of protein crops. We choose three contrasting climate change scenarios as well as specifications of the recent CAP reform in order to investigate how farmers might adapt to changing land use restrictions and climate conditions. Output response, land allocation and nitrogen use are the main variables of interest. Exemplified for Austrian cropland, we apply an integrated modeling framework consisting of a statistical climate change model, a crop rotation model, the bio-physical process model EPIC, and the economic bottom-up land use optimization model BiomAT. This model maximizes total gross margins by optimizing for land use and crop management practices for different scenarios of climate change and market conditions. Results obtained at a 1 km grid are aggregated to the national level. The model results indicate that changes in policy conditions, cropland use, and flexibility in crop management practices may have stronger effects on total protein crop production than climate change in the next decades. An expansion of current protein crop production leads to an increase in marginal opportunity costs, reduces mineral fertilizer input demand, and mainly replaces maize in the crop rotations. No Label