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Heinschink et al. |
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Index-based costs of livestock production (INCAP.l) in Austria – the suckler cow and beef calf production activity |
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2016 |
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FACCE MACSUR Reports |
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SP8-1 |
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LiveM2016: International livestock modelling conference – Modelling grassland-livestock systems under climate change |
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no |
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MA @ admin @ |
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4846 |
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Leclère, D.; Havlík, P. |
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Modelling heat stress on livestock: how can we reach long-term and global coverage |
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2016 |
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FACCE MACSUR Reports |
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8 |
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SP8-12 |
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LiveM2016: International livestock modelling conference – Modelling grassland-livestock systems under climate change |
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MA @ admin @ |
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4839 |
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Schönhart, M. |
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Uncertainties from Climate Change on Farms and Ecosystem Services of a Grassland Dominated Austrian Landscape |
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2016 |
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FACCE MACSUR Reports |
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9 C6 - |
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Sp9-9 |
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MACSUR 1: development of a method to analysefarm and landscape scale impacts of CC, mitigationand adaptation effects– cropland dominated landscape, crop choice and soilmanagement– climate model uncertainty• Now: test and improve the robustness of the method– grassland landscape, cropland expansion and livestock– uncertainty analysis– variability of weather conditions High spatial resolution creates interfaces to disciplinarymodels and indicators• Challenging data & modelling demand• Increasing productivity can increase intensification pressures• Threatened permanent (extensive) grasslands and landscape elements, but• subject to resource constraints, costs and prices• Future RDP and environmental policy design (e.g. WFD) may need to takechanging productivity into account• Future research: analyze uncertainties & environmentalimpacts• Ensembles of crop and grassland models• Sensitivity analysis on economic input parameters• Qualitative surveys with agricultural experts and farmers |
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4832 |
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Kipling, R.; Scollan, N.; Bannink, A.; van Middelkoop, J. |
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From diversity to strategy: Livestock research for effective policy in a climate change world |
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2016 |
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FACCE MACSUR Reports |
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8 |
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H0.3-D1 |
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policy brief, networking |
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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. ï‚· Long term strategic investment is needed to support such structures. Their leadership requires very different skills to those effective in scientific project coordination. |
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MA @ admin @ |
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2269 |
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Semenov, M.A.; Stratonovitch, P. |
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Local-scale CMIP5-based climate scenarios for MACSUR2 |
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2016 |
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FACCE MACSUR Reports |
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8 |
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C2.2-D |
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CropM |
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Climate sensitivity of GCMs was used to select 5 GCMs from the CMIP5 ensemble for impact studies in MACSUR2. Selected GCMs for MACSUR2 are EC-EARTH (7), GFDL-CM3 (8) HadGEM2-ES (10), MIROC5 (13), and MPI-ESM-MR (15). These GCMs are evenly distributed among CMIP5 (Fig 1) and should capture, in principal, climate uncertainty of the CMIP5 ensemble. Using 5 GCMs will enable us to assess uncertainties in impacts related to uncertainty in climate projections. The selection of GCMs in MACSUR2 has a good overlap with selections of GCMs used in CORDEX and AgMIP projects. We used the LARS-WG generator to construct local-scale CMIP5-based climate scenarios for Europe (Semenov & Stratonovitch, 2015). Fifteen sites were selected in Europe for MACSUR2. For each site and each selected GCM, 100 yrs climate daily data were generated by LARS-WG for RCP4.5 and RCP8.5 emission scenarios and for baseline and 3 future periods: near-term (2021-2040), mid-term (2041-2060) and long-term (2081-2100). |
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MA @ admin @ |
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2270 |
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