Ewert, F., Rötter, R., & Brüser, K. (2015). CropM: Understanding and Modelling Impacts of Climate Change on Crop Production. In FACCE MACSUR Reports (Vol. 6, pp. SP6–2). Brussels.
Abstract: Key ambition:To developa shared comprehensive information system on the impacts of climate change on European crop production and food securityfirst shared pan-continental assessments and tools(Full) range of important crops and important crop rotationsImproved management and analysis of dataModel improvement (stresses and factors not yet accounted for)Advanced scaling methodsAdvanced link to farm and sector modelsComprehensive uncertainty assessment and reportingTo train integrative crop modelerData. for better understanding and modelling climate change impactEvaluation of data quality (platinum, gold, silver)Quantify data gaps for modellingEmpirical analysis of crop responses to past climate variability and changeObserved adaptation options and their efficacyEffect of extreme events (past analysis and projections)Climate change scenariosConcept for data management, data journalUncertaintyMethodology & protocols for uncertainty analysisMethodology for standardized model evaluationLocal-scale climate scenarios & uncertainties in climate projectionsBasic methodology for probabilistic assessment of CC impacts using impact response surfacesMethodology for probabilistic evaluation of alternative adaptation options Main aims in MACSUR2:Improve crop model to better capture extremesComplement knowledge from crop models with empirical crop-weather analysisConsider management variables in simulationsFull range of methods for analysing uncertainty in climate impact assessmentsEvaluate potential adaptation optionsContributing to cross-cutting issues and case studies.Further the links with other modelling activitiesLink local to European and global responses No Label
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Wallach, D., & Rivington, M. (2014). A framework for assessing the uncertainty in crop model predictions (Vol. 3).
Abstract: It is of major importance in modeling to understand and quantify the uncertainty in model predictions, both in order to know how much confidence to have in those predictions, and as a first step toward model improvement. Here we show that there are basically three different approaches to evaluating uncertainty, and we explain the advantages and drawbacks of each. This is a necessary first step toward developing protocols for evaluation of uncertainty and so obtaining a clearer picture of the reliability of crop models. No Label
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Hoveid, Ø. (2016). What are the risks of food price changes? A time series analysis (Vol. 9 C6 -).
Abstract: 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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Wehrheim, P. (2016). Agriculture and land use in the Commission proposals for the 2030 Climate and Energy Framework (Vol. 9 C6 -).
Abstract: 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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Saetnan, E. R. (2015). Capacity building strategy (Vol. 7).
Abstract: Introduction Raising the capacity of established researchers Capacity for cross-theme collaboration Short “Master Classes” Raising the capacity of early career researchers PhD/ECR training courses Training integrative and international modellers through a Marie Curie ITN Raising the capacity of our stakeholders MACSUR input to the Advanced Training Partnership (ATP)
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