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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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2857 |
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Hoveid, Ø. |
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What are the risks of food price changes? A time series analysis |
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2016 |
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FACCE MACSUR Reports |
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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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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 |
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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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4829 |
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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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Webber, H.; Gaiser, T.; Oomen, R.; Teixeira, E.; Zhao, G.; Wallach, D.; Zimmermann, A.; Ewert, F. |
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Title |
Uncertainty in future irrigation water demand and risk of crop failure for maize in Europe |
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2016 |
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Environmental Research Letters |
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Environ. Res. Lett. |
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crop model; impact assessment; crop water use; evapotranspiration; irrigation; drought; uncertainty |
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While crop models are widely used to assess the change in crop productivity with climate change, their skill in assessing irrigation water demand or the risk of crop failure in large area impact assessments is relatively unknown. The objective of this study is to investigate which aspects of modeling crop water use (reference crop evapotranspiration (ET0), soil water extraction, soil evaporation, soil water balance and root growth) contributes most to the variability in estimates of maize crop water use and the risk of crop failure, and demonstrate the resulting uncertainty in a climate change impact study for Europe. The SIMPLACE crop modeling framework was used to couple the LINTUL5 crop model in factorial combinations of 2-3 different approaches for simulating the 5 aspects of crop water use, resulting in 51 modeling approaches. Using experiments in France and New Zeland, analysis of total sensitivity revealed that ET0 explained the most variability in both irrigated maize water use and rainfed grain yield levels, with soil evaporation also imporatant in the French experiment. In the European impact study, net irrigation requirement differed by 36% between the Penman and Hargreaves ET0 methods in the baseline period. Average EU grain yields were similar between models, but differences approached 1-2 tonnes in parts of France and Southern Europe. EU wide esimates of crop failure in the historical period ranged between 5.4 years for Priestley-Taylor to every 7.9 years for the Penman ET0 methods. While the uncertainty in absolute values between models was significant, estimates of relative changes were similar between models, confirming the utility of crop models in assessing climate change impacts. If ET0 estimates in crop models can be improved, through the use of appropriate methods, uncertainty in irrigation water demand as well as in yield estimates under drought can be reduced. |
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2016-09-13 |
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Newsletter July |
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CropM |
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CropM; wos; ft=macsur; |
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MA @ admin @ |
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4778 |
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