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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 |
Abbreviated Journal |
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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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Wallach, D.; Thorburn, P.; Asseng, S.; Challinor, A.J.; Ewert, F.; Jones, J.W.; Rötter, R.; Ruane, A. |
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Overview paper on comprehensive framework for assessment of error and uncertainty in crop model predictions |
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Report |
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2016 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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8 |
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C4.1-D |
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MACSUR_ACK; CropM |
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Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. Several ways of quantifying prediction uncertainty have been explored in the literature, but there have been no studies of how the different approaches are related to one another, and how they are related to some overall measure of prediction uncertainty. Here we show that all the different approaches can be related to two different viewpoints about the model; either the model is treated as a fixed predictor with some average error, or the model can be treated as a random variable with uncertainty in one or more of model structure, model inputs and model parameters. We discuss the differences, and show how mean squared error of prediction can be estimated in both cases. The results can be used to put uncertainty estimates into a more general framework and to relate different uncertainty estimates to one another and to overall prediction uncertainty. This should lead to a better understanding of crop model prediction uncertainty and the underlying causes of that uncertainty. This study was published as (Wallach et al. 2016) |
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MA @ office @ |
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2954 |
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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 |
Abbreviated Journal |
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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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Heinschink, K.; Lembacher, F.; Sinabell, F.; Trible, C. |
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Title |
Crop production costs in Austria: Comparison of simulated results and farm observations |
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2016 |
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Jahrbuch der ÖGA |
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26 |
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33-34 |
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26. Jahrestagung der Österreichischen Gesellschaft für Agrarökonomie, 2016-09-15 to 2016-09-16, Vienna, Austria |
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MA @ admin @ |
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5025 |
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Pulina, A.; Bellocchi, G.; Seddaiu, G.; Roggero, P.P. |
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Scenario analysis of alternative management options on the forage production and greenhouse gas emissions in Mediterranean grasslands |
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2016 |
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Abbreviated Journal |
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116 |
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263-266 |
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LiveM; MACSUR_ACK |
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TI @ martin.koechy @ |
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2940 |
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