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Kipling, R.; Topp, K.; Don, A. |
| Title |
Appropriate meta-data for modellers |
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2014 |
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FACCE MACSUR Reports |
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3 |
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D-L1.4.1 |
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Report D-L1.4.1 provided an overview of the data and related resources available online and through EU funded projects, relating to soil organic carbon (SOC), and carbon sequestration in grasslands in particular. Building on D-L1.4.1, the report presented here discusses how meta-data describing these types of data (and experimental data more generally) can best be presented in an online resource useful to grassland modellers requiring data to use in their modelling work. Identifying the useful categories of meta-data is a necessary precursor to providing such a resource, which could facilitate better communication between modelling and experimental research groups, allowing researchers to more efficiently locate relevant data and to link up with other scientists working on similar topics. A survey among grassland modelling teams and an assessment of online meta-data resources was used to produce recommendations about the meta-data categories that should be included in an online resource. The categories are generic, so that the recommendations can be followed in the design of meta-data resources for the more general agricultural modelling community. No Label |
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MA @ admin @ |
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2235 |
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Kipling, R.; Topp, K.; Don, A. |
| Title |
The availability of carbon sequestration data in Europe |
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Report |
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2015 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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4 |
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D-L1.4.2 |
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With growing interest in the carbon sequestration potential of soils, experimental research and mapping projects have produced a wealth of datasets in this subject area. However, the coverage, quality and scope of available data vary widely across Europe, and the extent to which these data are accessible to experimental researchers and modellers is also highly variable. This report describes the availability of soil carbon data at the global and European levels, and reviews the on-line resources for accessing these data and meta-data. The extent to which researchers in the field share findings, based on institutional links in projects and on-line resources, is investigated. Future priorities for research and data accessibility relating to carbon sequestration are discussed. Many soil data resources are available online. Global and European soil data portals draw together much information from across Europe, and include the outcomes of major soil carbon mapping exercises. However, much project and national research is not accessible through these portals, and information on datasets derived from many research initiatives is difficult or impossible to locate online. Data on carbon sequestration (carbon fluxes in soils) specifically is more limited, although some such datasets are available through the general soil data resources described. Improved clarity in the presentation of research, and work to link more national and sub-national data to European and global online resources is required, with initiatives such as GSIF (Global Soil Information Facility) active in encouraging direct reporting of soil-related data at the global level. Priorities for research on SOC stocks include measuring carbon storage below the topsoil (>30cm), improving records of SOC in peatlands, improving the number and distribution of samples available for Europe-wide soil carbon mapping, and developing recognised methodological standards to allow easier comparisons of datasets. In the field of carbon sequestration research specifically, priorities include linking long-term SOC data to historical land use, developing understanding of the movement of SOC between top-soil and sub-soil and increasing dialogue between modellers and empirical researchers to improve dynamic modelling of SOC. No Label |
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MA @ admin @ |
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2214 |
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Kipling, R.P.; Bannink, A.; Bellocchi, G.; Dalgaard, T.; Fox, N.J.; Hutchings, N.J.; Kjeldsen, C.; Lacetera, N.; Sinabell, F.; Topp, C.F.E.; van Oijen, M.; Virkajärvi, P.; Scollan, N.D. |
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Modelling European ruminant production systems: Facing the challenges of climate change |
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2017 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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10 |
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L1.1-D1 |
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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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LiveM |
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no |
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MA @ admin @ |
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4947 |
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Kipling, R. |
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LiveM2016: International livestock modelling conference – Modelling grassland-livestock systems under climate change |
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2016 |
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FACCE MACSUR Reports |
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8 |
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L0.1-D1 |
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MA @ admin @ |
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4841 |
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Köchy, M.; Bishop, J.; Lehtonen, H.; Scollan, N.; Webber, H.; Zimmermann, A.; Bellocchi, G.; Bannink, A.; Biewald, A.; Ferrise, R.; Helming, K.; Kipling, R.P.; Milford, A.; Özkan Gülzari, Ş.; Ruiz-Ramos, M.; Curth-van Middelkoop, J. |
| Title |
Challenges and research gaps in the area of integrated climate change risk assessment for European agriculture and food security |
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Report |
| Year |
2017 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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10 |
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H0.1-D |
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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. |
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MA @ admin @ |
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4950 |
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