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: The dairy sector may expand by over 2% per annum with expiration of the milk quota system in countries with a major and intensive dairy sector. Such expansion will increase pressure to further reduce on-farm nitrogenous emission per unit of milk produced even more. A straightforward N-reducing measure is the manipulation of the cow diet resulting in a lower excretion of ammoniacal N excreted with urine in particular. However, dietary N-reducing measures also affect enteric methane emissions and P excretion. For an integral evaluation of the consequences of N-reducing dietary measures on on-farm emissions, the trade-offs between N emissions and P and methane emissions at the cow level need to be taken into account. Therefore, a simulation study was performed to simulate the consequence of various N-reducing and/or P-reducing dietary measures (altered grassland management, grass silage replaced by low-N feeds, increased concentrate allowance) on enteric methane emission and on N and P excretion. Results indicate a large scattering, but there was a trend of higher methane emissions with lower N excretion was significant. Specific measures had a synergistic effect on emissions such as the exchange of maize for grass silage. The present detailed model evaluations may aid in quantifying the extent of trade-offs between various types of emissions at the cow level, but also prove to be relevant when evaluating consequences of management options taken at the farm scale. 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