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Bellocchi, G., B.; Brilli, L.; Ferrise, R.; Dibari, C.; Bindi, M. |
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Title |
Model comparison and improvement: Links established with other consortia |
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Report |
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2017 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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10 |
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XC1.3-D |
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XC1 has established links to other research activities and consortia on model comparison and improvement. They include the global initiatives AgMIP (http://www.agmip.org ) and GRA (http://www.globalresearchalliance.org), and the EU-FP7 project MODEXTREME (http://modextreme.org ). These links have allowed sharing and communication of recent results and methods, and have created opportunities for future research calls. |
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MA @ admin @ |
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4941 |
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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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Title |
Modelling European ruminant production systems: Facing the challenges of climate change |
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Report |
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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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MA @ admin @ |
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4947 |
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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. |
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Challenges and research gaps in the area of integrated climate change risk assessment for European agriculture and food security |
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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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Hamidov, A.; Helming, K.; Bellocchi, G.; Bojar, W.; Dalgaard, T.; Ghaley, B.B.; Hoffmann, C.; Holman, I.; Holzkämper, A.; Krzeminska, D.; Kværnø, S.H.; Lehtonen, H.; Niedrist, G.; Øygarden, L.; Reidsma, P.; Roggero, P.P.; Rusu, T.; Santos, C.; Seddaiu, G.; Skarbøvik, E.; Ventrella, D.; Żarski, J.; Schönhart, M. |
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Title |
Impacts of climate change adaptation options on soil functions: A review of European case-studies |
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2018 |
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Land Degradation & Development |
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Land Degradation & Development |
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29 |
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8 |
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2378-2389 |
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Keywords |
agricultural adaptation; DPSIR; regional case-studies; soil degradation; Sustainable Development Goals; Agricultural Practices; Ecosystem Services; Land Management; Netherlands; Farm; Environment; Challenges; Catchments; Framework; Nitrogen |
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Soils are vital for supporting food security and other ecosystem services. Climate change can affect soil functions both directly and indirectly. Direct effects include temperature, precipitation, and moisture regime changes. Indirect effects include those that are induced by adaptations such as irrigation, crop rotation changes, and tillage practices. Although extensive knowledge is available on the direct effects, an understanding of the indirect effects of agricultural adaptation options is less complete. A review of 20 agricultural adaptation case-studies across Europe was conducted to assess implications to soil threats and soil functions and the link to the Sustainable Development Goals (SDGs). The major findings are as follows: (a) adaptation options reflect local conditions; (b) reduced soil erosion threats and increased soil organic carbon are expected, although compaction may increase in some areas; (c) most adaptation options are anticipated to improve the soil functions of food and biomass production, soil organic carbon storage, and storing, filtering, transforming, and recycling capacities, whereas possible implications for soil biodiversity are largely unknown; and (d) the linkage between soil functions and the SDGs implies improvements to SDG 2 (achieving food security and promoting sustainable agriculture) and SDG 13 (taking action on climate change), whereas the relationship to SDG 15 (using terrestrial ecosystems sustainably) is largely unknown. The conclusion is drawn that agricultural adaptation options, even when focused on increasing yields, have the potential to outweigh the negative direct effects of climate change on soil degradation in many European regions. |
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2018-10-16 |
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1085-3278 |
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XC, TradeM, ft_macsur |
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MA @ admin @ |
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5210 |
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Ben Touhami, H.; Bellocchi, G. |
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Bayesian calibration of the Pasture Simulation model (PaSim) to simulate European grasslands under water stress |
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Journal Article |
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2015 |
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Ecological Informatics |
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Ecological Informatics |
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30 |
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356-364 |
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Bayesian calibration framework; Grasslands; Pasture Simulation model; (PaSim); integrated assessment models; chain monte-carlo; climate-change; computation; impacts; vulnerability; likelihoods; france |
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As modeling becomes a more widespread practice in the agro-environmental sciences, scientists need reliable tools to calibrate models against ever more complex and detailed data. We present a generic Bayesian computation framework for grassland simulation, which enables parameter estimation in the Bayesian formalism by using Monte Carlo approaches. We outline the underlying rationale, discuss the computational issues, and provide results from an application of the Pasture Simulation model (PaSim) to three European grasslands. The framework was suited to investigate the challenging problem of calibrating complex biophysical models to data from altered scenarios generated by precipitation reduction (water stress conditions). It was used to infer the parameters of manipulated grassland systems and to assess the gain in uncertainty reduction by updating parameter distributions using measurements of the output variables. |
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1574-9541 |
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CropM, LiveM, ft_macsur |
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MA @ admin @ |
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4697 |
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