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

Abstract: Main text in preparation for publication in a peer-reviewed journal)

Abstract: This report provides an overview of how grasslands are represented in six different farmscale  models represented in MACSUR. A survey was conducted, followed by a workshop in  which modellers discussed the results of the survey, and identified research challenges and  knowledge gaps. The workshop was attended by grassland as well as livestock specialists.  The investigated models differed largely with respect to how grasslands were represented,  e.g. as regards weather and management factors accounted for, spatial and temporal  resolution, and output variables. All models had grassland modules that simulate DM yield  and herbage N content (or crude protein (CP) content = N content x 6.25). Many models  also simulate P content, whereas only one simulate K content. About half of the model  simulate herbage energy value and/or herbage fibre content and fibre and/or dry matter  digestibility. Critical input data required from grassland models to simulate ruminant  productivity and GHG emissions at farm scale was identified by the workshop participants.  The different types of input data required were ranked in order of importance as regards  their influence on important system outputs. For simulation of ruminant productivity and  GHG emissions, herbage DM yield was ranked as the most important input variable from  grassland models, followed by CP content together with at least one variable describing  herbage fibre characteristics. These findings suggest that work on improving the ability of  the current grassland models with respect to simulation of fibre/energy should be  prioritized in farm-scale modelling aiming at quantifying livestock production and GHG  emissions under different management regimes and climate conditions. More work is also needed on model evaluation, a task that has not been prioritized yet for some models.