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Ruminant production systems are important contributors to anthropogenic methane (CH4) emissions, but there are large uncertainties in national and global livestock CH4 inventories. Sources of uncertainty in enteric CH4 emissions include animal inventories, feed dry matter intake (DMI), ingredient and chemical composition of the diets, and CH4 emission factors. There is also significant uncertainty associated with enteric CH4 measurements. The most widely used techniques are respiration chambers, the sulfur hexafluoride (SF6) tracer technique, and the automated head-chamber system (GreenFeed; C-Lock Inc., Rapid City, SD). All 3 methods have been successfully used in a large number of experiments with dairy or beef cattle in various environmental conditions, although studies that compare techniques have reported inconsistent results. Although different types of models have been developed to predict enteric CH4 emissions, relatively simple empirical (statistical) models have been commonly used for inventory purposes because of their broad applicability and ease of use compared with more detailed empirical and process-based mechanistic models. However, extant empirical models used to predict enteric CH4 emissions suffer from narrow spatial focus, limited observations, and limitations of the statistical technique used. Therefore, prediction models must be developed from robust data sets that can only be generated through collaboration of scientists across the world. To achieve high prediction accuracy, these data sets should encompass a wide range of diets and production systems within regions and globally. Overall, enteric CH4 prediction models are based on various animal or feed characteristic inputs but are dominated by DMI in one form or another. As a result, accurate prediction of DMI is essential for accurate prediction of livestock CH4 emissions. Analysis of a large data set of individual dairy cattle data showed that simplified enteric CH4 prediction models based on DMI alone or DMI and limited feed- or animal-related inputs can predict average CH4 emission with a similar accuracy to more complex empirical models. These simplified models can be reliably used for emission inventory purposes.

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

Of all ruminant production systems, high-yielding dairy cows have the most stringent criteria onnutrition, with feed intakes up to more than three times that required for maintenance alone. Forthis reason, dairy production systems provide an interesting case study with which to explore theimplications of climate change on feed provision and utilization by the animal. Dairy productionsystems across Europe vary widely in production intensity and in nutrition strategies applied.Systems range from almost fully grazed to almost fully confined systems, and from low to highproduction intensities (per cow or per farmed hectare) of: external resource use (e.g. feedpurchased), level of farm automation and technology application, and financial investment.Irrespective of this huge variety of dairy farming systems, they have in common that home-grownroughages are an important part of the diet. Climate change will directly impact on roughageproduction and hence on: the supply and quality of roughages, the nutritional strategies adoptedand cow performance. Indirectly, through its impact on home-grown roughages climate changewill also impact on the requirements for: home-grown feed crops, purchased feed crops,supplemental by-product feeds (for example, from the food or bio-energy industries) andprocessed concentrate feeds, depending on whether production targets are to be maintained ornot. These potential consequences of climate change have been reviewed. Challenges addressedand presented here will include the need to reduce phosphorus and nitrogen surpluses and/orlosses from the system. The implications and limits to various nutritional adaptation strategies,and the alternatives available to farmers and the feed industry, will be discussed in the context ofrecent scientific insights and against the background of the models and modelling conceptscurrently in use in practice and in research.

Priorities in addressing research gaps and challenges should follow the order of im­por­tance, which in itself would be a matter of defining goals and metrics of importance, e.g. the extent, impact and likelihood of occurrence. For improving assessments of cli­mate change impacts on agriculture for achieving food security and other sustainable develop­ment goals across the European continent, the most important research gaps and challen­ges appear to be the agreement on goals with a wide range of stakeholders from policy, science, producers and society, better reflection of political and societal prefer­ences in the modelling process, and the reflection of economic decisions in farm manage­ment within models. These and other challenges could be approached in phase 3 of MACSUR.

International audience; Climate change presents major challenges for European agriculture, and the speed, nature and extent of the responses to such challenges will have far-reaching social, economic and environmental consequences. Agricultural modelling has an important role in helping decision makers better understand the costs and benefits of different adaptation strategies, as well as trade-offs and win-wins between those strategies, mitigation measures and other economic, social and environmental goals. Incorporating adaptation strategies into biophysical, bio-economic and economic model is essential to gaining a more holistic understanding of their impacts, beyond the context of specific changes and purposes. Here, the ability and potential of agricultural models to characterise different adaptation strategies was explored, using the expertise represented within the Modelling European Agriculture with Climate Change for Food Security (MACSUR) project. In two workshops, modellers identified adaptation strategies, modelling challenges and knowledge gaps. A survey was conducted to understand current modelling capacity. Challenges centred on knowledge gaps, data availability, technical issues, and stakeholder interaction (e.g. communication with, relevance for). For operational and tactical strategies (changes in practice in response to daily, monthly, or seasonal variation in conditions) most challenges were technical, relating to limitations in the processes and mechanisms represented in models. For longer term strategic climate change adaptation, uncertainty about future socio-economic context (e.g. prices and regulation) and the impact of new adaptation options (e.g. appearance of new technologies) were highlighted. Progressively novel and far-reaching strategies increasingly challenge the scope of existing models. Whilst models vary in capacity, most modellers reported a potential to better characterise adaptation. However, costs (e.g. trade-offs with processing speed) and the fact that adaptation lies beyond the initial remit of many models mean that strategic prioritisation of adaptation as a focus for modelling is key to facilitating model development to support effective stakeholder choices.

Ruminant livestock are important sources of human food and global greenhouse gas emissions. Feed degradation and methane formation by ruminants rely on metabolic interactions between rumen microbes and affect ruminant productivity. Rumen and camelid foregut microbial community composition was determined in 742 samples from 32 animal species and 35 countries, to estimate if this was influenced by diet, host species, or geography. Similar bacteria and archaea dominated in nearly all samples, while protozoal communities were more variable. The dominant bacteria are poorly characterised, but the methanogenic archaea are better known and highly conserved across the world. This universality and limited diversity could make it possible to mitigate methane emissions by developing strategies that target the few dominant methanogens. Differences in microbial community compositions were predominantly attributable to diet, with the host being less influential. There were few strong co-occurrence patterns between microbes, suggesting that major metabolic interactions are non-selective rather than specific.

European livestock agriculture is extraordinarily diverse, and so are the challenges it faces. This diversity has contributed to the development of a fragmented set of research communities. As a result, livestock research is often under-represented at policy level, despite its high relevance for the environment and food security. Understanding livestock systems and how they can sustainably adapt to global change requires inputs across research areas, including grasslands, nutrition, health, welfare and ecology. It also requires experimental researchers, modellers and stakeholders to work closely together. Networks and capacity building structures are vital to enable livestock research to meet the challenges of climate change. They need to maintain shared resources and provide non-competitive arenas to share and synthesize results for policy support. Long term strategic investment is needed to support such structures. Their leadership requires very different skills to those effective in scientific project coordination.

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.

Improving health and welfare is an important adaptation and mitigation strategy; Developing process based modelling, responsive to adaptation; Links to climate and land use change modelling are essential ; Livestock systems likely to be hit hardest by climate change; Need to develop animal health models that respond to adaptation by farmers; Bringing together direct and indirect impacts of climate change vital; Adaptation and mitigation need to be considered and modelled together; Linking models across scales is important to support policy decisions; Learning between sectors carries potential for novel solutions and methodological advances; Effective communication of outcomes to stakeholders (how?)

Animal simulation models are sets of equations used to describe biological processes. A non-steady state simulation model of cattle digestion is presented in order to represent nutrient availability as a response to feed intake pattern and the kinetics of particle size reduction. Variables representing the particle size reduction and discontinuous voluntary feed intake were included in a mechanistic model created to optimize the supplementation of sugarcane based diets. In general the predicted values were very close to observed values for fibre and nitrogen flows. The model has not shown consistent bias in relation to the behavior of the observed data of duodenal flow of neutral detergent fiber and non-ammonia nitrogen. Milk production simulations were quite close to actual values. Predictions were improved by the non steady-state model, taking into account variable intake rate in relation to the previous steady-state model. The model can be used to select strategies for supplementation of cattle fed sugarcane based diets. Modelos de simulação animal são conjuntos de equações utilizados para descrever processos biológicos. Um modelo de simulação da digestão de bovinos em condições de ingestão descontínua é apresentado com objetivo de representar a disponibilidade de nutrientes como resposta ao padrão de consumo de alimentos e à cinética da redução do tamanho de partícula. Variáveis representando a redução do tamanho de partículas e consumo de alimento descontinuo foram incluídas em um modelo mecanicista criado para aperfeiçoar a suplementação de dietas à base de cana-de-açúcar. Os valores estimados estiveram muito próximos dos valores observados para fluxos de fibra e nitrogênio. O modelo não apresentou desvios consistentes dos valores observados de fluxo duodenal de fibra em detergente neutro e nitrogênio não-amoniacal. A média geral das produções de leite foi estimada com precisão. As estimativas sob condições de disponibilidade variável de nutrientes apresentaram maior precisão quando comparadas com o modelo anterior que assumia consumo continuo de nutrientes. O modelo pode ser usado para selecionar estratégias de suplementação de dietas à base de cana-de-açúcar em vacas em lactação.