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

Abstract: The objective of the Tanzanian case study region is to provide MACSUR II a platform to concentrate climate research to a focal region in Morogoro and Dodoma in Tanzania. Hence, synergies among a number of other research projects will be used, which all focus on food security related to climate change.Within the case study region the projects aim at improving the food situation for the most-vulnerable rural poor population in Tanzania. They are designed to identify successful food securing upgrading strategies and innovations along local and regional food value chains, test and adjust them to site-specific, sustainable settings and tailor these concepts to be disseminated for national outreach. After the project lifetime, the results can be implemented at different levels of policy, extension and research.The basic concept applies the following steps in an iterative and partly recurrent procedure: (1) A stakeholder involvement process will be set up from the beginning as an integral part of most analytical steps; (2) case study sites within the focal regions Morogoro and Dodoma will be selected, set up and typologies of food value chains developed; (3) success stories of secure food production and/or good practice along the food value chains will be screened and inventoried; (4) an integrated in-depth analyses of food value chain components, their costs, benefits and impacts will be carried out; (5) a few of the most promising good practices with regard to positive impacts and implementation will be participatively discussed and identified for subsequent in-depth testing; (6) an in-depth participative field testing and/or analysis of selected, most promising technologies will be conducted for all food value chain components and requirements for implementation identified; (7) transferability and implementation capability will be assessed for different scenarios and for future condition simulations (model analysis); (8) a meta-model analysis including risk analysis and final proofing will identify hot spots of most sensitive, fragile regions and the potentials for alleviating food insecurity. Hence, the research concept`s main focuses on local and regional food security related to climate change, but the research design implies a national outreach for Tanzania as a whole.The research activities will be embedded into local and regional strategies to assess potential impacts and trade-offs and to be able to up-scale lessons learnt in a generic manner to regions with specific bio-physical, socio-cultural and economic conditions.Spatial Design: Conducting a literature research on relevant characteristic a considering the spatial design:two focal regions in Tanzania (Morogoro, Dodoma),each region with two case study sites (CSS) consisting of at least one local marketplace and surrounding 2-3 villages,the two CSS among the target regions are selected to differ in factors such as market and capital access for investments, remoteness, population density, land availability, soil types, infrastructure, facilities, and others,create sufficiently diverse environmental and socio-economic conditions for investigating food securing technologies along FVC and allowing for testing the transfer of results to other Tanzanian regions.Main selection criteria for regions: two climates types:semi-arid Dodoma (350-500mm),semi-humid Morogoro region (600-800mm)clear distinction between the regions.other criteria within the regions:rather similar climate (must) +-80mm,weak and good market access (must) (=market and capital access for investments),rainfed crop–livestock systems oriented, not too strongly paddy rice oriented (< 20% rice) (must),village size: approx. 800-1500 households (must)Stunting cases, logistics and infrastructure, different wards, land availability, facilities, capital, soil types, and population density etc. No Label

Abstract: While intercomparison of process-based crop models for projections under climate change is being intensively studied at European as well as at the global scale, little effort has been made for comparing statistical models. In this study, several regression techniques (ordinary least squares, stepwise, shrinkage methods, principle components and partial least squares) were combined with different types of climate input variables (with different temporal resolution) in order to define a large range of statistical models. Each model was fitted to winter oilseed rape data collected in 689, 325 and 173 field experiments carried out in Denmark, Germany, and Czech Republic, respectively. The fitted models were then used to predict yield of winter oilseed rape in the field experiments during more than 20 years, up to 2013.  Interpretability of the estimated climate variable effects and accuracy of yield predictions were both analysed. Results suggest that recent statistical methods (e.g., shrinkage methods) may have considerable capabilities to complement traditional statistical methods in yield prediction. The selection of the most influential variables was strongly influenced by the statistical method used to analyse the data. Among the most recent statistical methods, the uncertainties in projecting yield of winter oilseed rape under climate change were mainly due to residual errors and uncertainty in estimated parameter values, and not to model choice. No Label