Abstract: Agricultural aspects have been covered in the scenario process on shared socio-economic pathways (SSPs), but only to a limited extent. In order to analyze the future dynamics of agricultural development they need to be complemented and specified by Representative Agricultural Pathways (RAPs), which cover different aspects of agricultural development as for example European agricultural and domestic policy, environmental policies,  different livestock management systems, cropping systems or irrigation efficiencies.In this paper we will develop a general framework for RAPs where we define for each SSP the corresponding specific agricultural development. Some aspects of the above mentioned specifics can be derived from the definitions in the SSPs, as for example irrigation efficiencies which are linked to technological development. Agricultural policies on the other hand are not included in the SSP definitions. Here we will define agricultural and environmental policies, including the available funding in each area of the common agricultural policy (CAP) (pillars 1 and 2). As RAPs can only to a small degree be developed as European guidelines and implemented unilaterally, it is important to translate the overall storylines into specific scenario parameterization at national levels. Concerned by this are 1. national policies, as well as the agri-environmental schemes of the CAP in Pillar II, 2. livestock efficiencies and the development of extensive and intensive farm management, and  3. crop management systems.Additionally we will define which respresentative concentration pathways (RCPs) will match best the future agricultural and agro-economic trajectories. The following 5 preliminary RAPs for Europe will be further developed in our analysis:EU-RAP1 (Sustainable Europe) : strong CAP, strong shift on environmental regulation, no producer support, green CAP with strong mititgation componentEU-RAP2 (Middle of the road): BAU or things will stay as they are.EU-RAP3 (Fragmented Europe): Europe breaks up, rich countries support farmers with national subsidies, poor countries do not. There is no CAP anymoreEU-RAP4 (Two Europes): Europe is divided in a poor and a rich part. In the rich part a green and environmental friendly  CAP will be implemented, in the poor part of Europe, the CAP will cease to existEU-RAP5(Fossil fueled Europe): free market world, strong institutions, weak on enviromental regulations, low domestic polices? Local green CAP without mitigation

Abstract: Highlights • Governance impacts on land use dynamics are modeled at the global scale with an agro-economic dynamic optimization model. • Improved governance performance lowers deforestation, reduces cropland expansion and increases agricultural yield. • Good governance makes a decisive difference in investment for increasing yields in developing regions. • Weak governance increases food prices, particularly in Sub-Saharan Africa and Southeast Asia. • Improving governance performance has significant impacts on poverty reduction. Abstract Deforestation, mainly caused by unsustainable agricultural expansion, results in a loss of biodiversity and an increase in greenhouse gas emissions, as well as impinges on local livelihoods. Countries’ governance performance, particularly with respect to property rights security, exerts significant impacts on land-use patterns by affecting agricultural yield-related technological investment and cropland expansion. This study aims to incorporate governance factors into a recursive agro-economic dynamic model to simulate governance impacts on land-use patterns at the global scale. Due to the difficulties of including governance indicators directly into numerical models, we use lending interest rates as discount rates to reflect risk-accounting factors associated with different governance scenarios. In addition to a reference scenario, three scenarios with high, low and mixed divergent discount rates are formed to represent weak, strong and fragmented governance. We find that weak governance leads to slower yield growth, increased cropland expansion and associated deforestation, mainly in Latin America, Sub-Saharan Africa, South Asia and Southeast Asia. This is associated with increasing food prices, particularly in Sub-Saharan Africa and Southeast Asia. By contrast, strong governance performance provides a stable political and economic situation which may bring down deforestation rates, stimulate investment in agricultural technologies, and induce fairly strong decreases in food prices.

Abstract: Ruminant production systems are important producers of food, support rural communities and culture, and help to maintain a range of ecosystem services including the sequestering of carbon in grassland soils. However, these systems also contribute significantly to climate change through greenhouse gas (GHG) emissions, while intensi- fication of production has driven biodiversity and nutrient loss, and soil degradation. Modeling can offer insights into the complexity underlying the relationships between climate change, management and policy choices, food production, and the maintenance of ecosystem services. This paper 1) provides an overview of how ruminant systems modeling supports the efforts of stakeholders and policymakers to predict, mitigate and adapt to climate change and 2) provides ideas for enhancing modeling to fulfil this role. Many grassland models can predict plant growth, yield and GHG emissions from mono-specific swards, but modeling multi-species swards, grassland quality and the impact of management changes requires further development. Current livestock models provide a good basis for predicting animal production; linking these with models of animal health and disease is a prior- ity. Farm-scale modeling provides tools for policymakers to predict the emissions of GHG and other pollutants from livestock farms, and to support the management decisions of farmers from environmental and economic standpoints. Other models focus on how policy and associated management changes affect a range of economic and environmental variables at regional, national and European scales. Models at larger scales generally utilise more empirical approaches than those applied at animal, field and farm-scales and include assumptions which may not be valid under climate change conditions. It is therefore important to continue to develop more realistic representations of processes in regional and global models, using the understanding gained from finer-scale modeling. An iterative process of model development, in which lessons learnt from mechanistic models are ap- plied to develop ‘smart’ empirical modeling, may overcome the trade-off between complexity and usability. De- veloping the modeling capacity to tackle the complex challenges related to climate change, is reliant on closer links between modelers and experimental researchers, and also requires knowledge-sharing and increasing technical compatibility across modeling disciplines. Stakeholder engagement throughout the process of model development and application is vital for the creation of relevant models, and important in reducing problems re- lated to the interpretation of modeling outcomes. Enabling modeling to meet the demands of policymakers and other stakeholders under climate change will require collaboration within adequately-resourced, long-term inter-disciplinary research networks