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Hutchings, N.; Weindl, I.; Topp, C.F.E.; Snow, V.O.; Rotz, A.; Raynal, H.; Özkan Gülzari, Ş.; Martin, R.; Holzworth, D.P.; Graux, A.-I.; Faverdin, P.; Del Prado, A.; Eckard, R.; Bannink, A. |
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Does collaborative farm-scale modelling address current challenges and future opportunities |
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2017 |
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FACCE MACSUR Reports |
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10 |
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L1.4-D2 |
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Resources required increasing, resources available decreasing Farm-scale modellers will need to make strategic decisions Single-owner models May continue with additional resources Risk of ‘succession’ problem Community modelling is an alternative Need to continue building a community of farm modellers |
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4978 |
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Leolini, L.; Moriondo, M.; De Cortazar-Atauri, I.; Ruiz-Ramos, M.; Nendel, C.; Roggero, P.P.; Spanna, F.; Ramos, M.C.; Costafreda-Aumedes, S.; Ferrise, R.; Bindi, M. |
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Modelling different cropping systems |
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2017 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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10 |
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C1.4-D |
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Grapevine is a worldwide valuable crop characterized by a high economic importance for the production of high quality wines. However, the impact of climate change on the narrow climate niches in which grapevine is currently cultivated constitute a great risk for future suitability of grapevine. In this context, grape simulation models are considered promising tools for their contribution to investigate plant behavior in different environments. In this study, six models developed for simulating grapevine growth and development were tested by focusing on their performances in simulating main grapevine processes under two calibration levels: minimum and full calibration. This would help to evaluate major limitations/strength points of these models, especially in the view of their application to climate change impact and adaptation assessments. Preliminary results from two models (GrapeModel and STICS) showed contrasting abilities in reproducing the observed data depending on the site, the year and the target variable considered. These results suggest that a limited dataset for model calibration would lead to poor simulation outputs. However, a more complete interpretation and detailed analysis of the results will be provided when considering the other models simulations. |
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5033 |
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Biewald, A.; Sinabell, F.; Lotze-Campen, H.; Zimmermann, A.; Lehtonen, H. |
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Global Representative Agricultural Pathways for Europe |
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2017 |
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FACCE MACSUR Reports |
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10 |
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T1.2-XC16.2 |
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Agricultural elements have been covered in the scenario process on shared socio-economic pathways (SSPs) incompletely and pathways have not been specified for the future development of the European Union. We will therefore devise a general framework on European Representative Agricultural Pathways (EU-RAPs), where we cover different aspects of agricultural development, as for example European and domestic agricultural and environmental policies, or different livestock and crop management systems, and describe future developments of the confederation of the countries of the European Union. For the agricultural elements we distinguish between elements that can be derived from the definitions in the Shared Socioeconomic Pathways, as for example irrigation efficiencies which are linked to technological development, and elements that have to be newly devised such as the development of the Common Agricultural Policy. For the future of the European Union we develop five different worlds which correspond to the SSPs. Finally both frameworks are combined. |
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TradeM |
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5034 |
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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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2270 |
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Köchy, M. |
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Strategy for the dissemination of outputs at national, EU, and global levels |
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2013 |
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FACCE MACSUR Reports |
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1 |
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D-H2.4 |
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Dissemination of FACCE MACSUR will operate in a tiered manner to the scientific community, funding agencies, decision makers in policy and agri-food businesses, and the general public. Efforts by the MACSUR hub level are targeted at the supra-national level, while individual institutions target regional and national stakeholders.The dissemination approaches will include multiple and various methods of information distribution including• scientific papers and presentations,• conferences, congresses, workshops, and courses targeted to specific stakeholders,• a fully developed and interactive website,• regular newsletters,• flyers,• alignment of methodologies and protocols with other research networks or projects. Dissemination is both passive (web site, publications) and active (flyers, presentations, workshops held jointly with other projects or organizations, Twitter). No Label |
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