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Wehrheim, P. |
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Agriculture and land use in the Commission proposals for the 2030 Climate and Energy Framework |
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2016 |
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FACCE MACSUR Reports |
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9 C6 - |
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Sp9-12 |
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Introduction: policy context•Impact Assessment: options, models, examples•Proposal for Effort Sharing Regulation and LULUCF Regulation•Conclusions and Outlook: more work for modellers 1. Fully in line with Paris Agreement, no backsliding on robustness and transparency2.Provides for continuity•Addresses Member States and not individual farmers or foresters•Stand-alone LULUCF pillar•No-debit rule (from KP)•Flexibility within LULUCF and from ESR to LULUCF3.Proposes limited innovations•Flexibility to the ESR up to 280 mt CO2•Aligning accounting rules (AF,CM/GM)•Defining EU-internal process to set national forest management levels•Simplifying administrationConclusions (2) |
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MA @ admin @ |
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4859 |
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Author |
Topp, K. |
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Case 4: Adaptation of European dairy farms to climate change: a case study approach |
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2016 |
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Presentation SC 2.10 Farming systems. Case 4: Adaptation of European dairy farms to climate change: a case study approach, Kairsty Topp, Scotland's Rural College, United Kingdom (2016). Presented at the international conference Adaptation Futures 2016, Rotterdam, the Netherlands. No Label |
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Rotterdam (Netherlands) |
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AdaptationFutures, 10-13 May 2016, Rotterdam |
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MA @ admin @ |
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2857 |
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Hoveid, Ø. |
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Title |
What are the risks of food price changes? A time series analysis |
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2016 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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9 C6 - |
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Sp9-2 |
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It is a widely held belief (IPCC) that climate change bringsmore risks to the worldI Since the start of MACSUR, TradeM has had risk on theagenda, but few results have so far come out. It has beenclaimed though, that there is no evidence for more risk in theglobal wheat market (Steen and Gjølberg 2014) (TradeMworkshop at Hurdalssjøen)I I have myself had the ambition of creating a dynamicstochastic model of the food system in which risk would be anintegral part, but time has been too shortI I have also pointed to methods from finance to reveal insights,and that is the road to be followed here, guided by Bølviken &Benth (2000) Buyer’s risk larger than seller’s risk — due to asymmetricdistribution of returns. Large price jumps are more likely thanequally sized price falls.I Long term positions much more risky than short term ones —as expectedI Agricultural commodities much less risky than crude oilI Price risk are related to volatility, and their changes over timewill have similar causal explanationsI Risks of producers and consumers of agricultural commoditieswill to some extent be related to the price risk, and also totheir portfolios and the co-variance between returns |
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MA @ admin @ |
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4831 |
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Sinabell, F. |
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Yield potentials and yield gaps in soybean production in Austria – a biophysical and economic assessment |
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2016 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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9 C6 - |
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Sp9-11 |
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context of analysis:• stakeholders. policy relevance: CC and protein crops• research problem:• how large is the yield gap and what can be done• data• approaches• findings• discussion and outlook yield gap analysis is a daunting task• what can be learned• economics matters: prices of crop and other crops• land expansion: more land becoming more marginal• management matters a lot but – not directly observable in data• significant knowledge gaps still there• way forward:• look at other crops• explore options to improve management |
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MA @ admin @ |
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4829 |
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Wallach, D.; Mearns, L.O.; Ruane, A.C.; Rötter, R.P.; Asseng, S. |
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Lessons from climate modeling on the design and use of ensembles for crop modeling |
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2016 |
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Climatic Change |
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Clim. Change |
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Model ensembles; Crop models; Climate models; Model weighting; Super ensembles |
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Working with ensembles of crop models is a recent but important development in crop modeling which promises to lead to better uncertainty estimates for model projections and predictions, better predictions using the ensemble mean or median, and closer collaboration within the modeling community. There are numerous open questions about the best way to create and analyze such ensembles. Much can be learned from the field of climate modeling, given its much longer experience with ensembles. We draw on that experience to identify questions and make propositions that should help make ensemble modeling with crop models more rigorous and informative. The propositions include defining criteria for acceptance of models in a crop MME, exploring criteria for evaluating the degree of relatedness of models in a MME, studying the effect of number of models in the ensemble, development of a statistical model of model sampling, creation of a repository for MME results, studies of possible differential weighting of models in an ensemble, creation of single model ensembles based on sampling from the uncertainty distribution of parameter values or inputs specifically oriented toward uncertainty estimation, the creation of super ensembles that sample more than one source of uncertainty, the analysis of super ensemble results to obtain information on total uncertainty and the separate contributions of different sources of uncertainty and finally further investigation of the use of the multi-model mean or median as a predictor. |
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English |
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0165-0009 1573-1480 |
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Review |
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CropM |
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CropM; wos; ft=macsur; wsnotyet; |
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MA @ admin @ |
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4781 |
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