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Hutchings, N., Sanders, D., Özkan, S., & De, H., Michel. (2014). Farm model comparison..
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Van den Pol-van Dasselaar, A., Bellocchi, G., Hutchings, N., Olesen, J., & Saetnan, E. (2014). AnimalChange. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: The EU-FP7 project AnimalChange (AN Integration of Mitigation and Adaptation options for sustainable Livestock production under climate CHANGE, http://www.animalchange.eu, 2011-2015) addresses mitigation and adaptation options and provides scientific guidance for their integration in sustainable development pathways for livestock production under climate change in Europe, Northern and Sub-Saharan Africa, and Latin America. The project provides insights, innovations, tools and models for livestock production incorporating socio-economic and environmental (particularly GHG emission) variables. Scenario studies are carried out at scales ranging from animal and pasture, to farm and to region, for given management options. A wide range of livestock production systems is included in the project. The core analytical spine of the project is a series of coupled biophysical and socio-economic models combined with experimentation. This allows exploring future scenarios for the livestock sector under baseline and atmospheric CO2 stabilization scenarios. These scenarios are first constructed and then elaborated and enriched by breakthrough mitigation and adaptation options at field and animal scales, integrated and evaluated at farm scale and finally used to assess policy options and their socio-economic consequences. The modelling results are useful for governments, agricultural and food industry and the agricultural sector (farmers). There are many synergies between the European activities of AnimalChange and those of the LiveM theme of MACSUR, in particular with respect to access to livestock production datasets, dialogue with stakeholders and comparison and integration of grassland and livestock models with crop and socio-economic models in pilot studies at a variety of scales.
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Hutchings, N. (2014). Farm-scale modelling. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: —
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Dalgaard, T., Hutchings, N., & Noe, E. (2014). Methods for regional scale farming systems modelling and uncertainty assessment – sustainability assessment case studies of production, nutrient losses and greenhouse gas emissions from grassland based systems. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: In the EU Joint-Programming-Initiative: Modelling European Agriculturewith Climate Change for Food Security (MACSUR, LiveM: http://www.macsur.eu/index.php/livestock-modelling) we develop a research frameworkfor the modelling and sustainability assessment of livestock and grasslandbased farming systems at farm and regional scales.Based on results from related research and model development in Denmark,methodologies used for regional scaling, the description of data requirementsand sources, and methods to predict the effect and effectiveness of climate-and environment related policy measures are developed. In this study we present results from farm modelling in a study areaaround Viborg, Western Denmark using the http://www.Farm-N.dk/ model (Env.Pol. 159 3183-3192), including thedistribution of N-surpluses into different types of losses, and a comparisonwith empirical studies of farm nitrogen balances in the Danish study and fiveadditional European landscapes (Biogeosciences 9, 5303–5321). Based on this,methods and development needs for the mapping and uncertainty assessment ofnutrient losses and greenhouse gas emissions are discussed, referring to the presentdevelopment of the Farm-AC model and ongoing scenario studies in e.g. the www.dNmark.org project. In these scenarios, regional-scale policy measures areimplemented via the responses of a range of stakeholders, such as farmers,public interest groups, regulators and politicians. When modelling the outcomeof the policy measures implementation, it is often assumed that stakeholdersrespond as economically rational entities. However, social and cultural factorsare also known to play a role and modelling methods that permit these factorsto be taken into account will also be discussed.
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Kipling, R. P., Bannink, A., Bellocchi, G., Dalgaard, T., Fox, N. J., Hutchings, N. J., et al. (2017). Modelling European ruminant production systems: Facing the challenges of climate change (Vol. 10).
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
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