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Author Brouwer, F.; Sinabell, F.
Title (up) Three years of collaboration in TradeM – Agricultural markets and prices Type Conference Article
Year 2015 Publication FACCE MACSUR Reports Abbreviated Journal
Volume 6 Issue Pages SP6-4
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Abstract Some farmers may claim that climate change adaptation is easy compared to the difficulties caused by policiesAction based on weather observations only, is insufficient for farmers to respond to climate change. Researchers need support from farmers in understanding the responses in practice.Policies might be too slow to respond to needs for change in agriculture. Winners and losers seem to be observed everywhere.The impacts of climate change is heterogeneous among farm types and regionsEffects beyond 2050 remain largely unclear, mainly because the effects of extreme events are not consideredVariability of yields is important to farm incomes, but most studies only consider average changesFarmers are ready to design their site-specific adaptation response providing that new knowledge and learning spaces are available. A learning process based on integrated models, assessment of short- and long-term effects, is needed for farmers to adapt to climate change, price fluctuations and policy change. No Label
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Area Expedition Conference Climate-change impacts on farming systems in the next decades: Why worry when you have CAP? A FACCE MACSUR workshop for policymakers
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Call Number MA @ admin @ Serial 2343
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Author Fetzel et al.
Title (up) Towards sustainable livestock production systems: Analyzing ecological constraints to grazing intensity Type Report
Year 2016 Publication FACCE MACSUR Reports Abbreviated Journal
Volume 8 Issue Pages SP8-8
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Call Number MA @ admin @ Serial 4833
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Author Bannink, A.
Title (up) Trade-offs of dietary N-reducing dietary measures on enteric methane emission and P excretion in lactating cows Type
Year 2015 Publication FACCE MACSUR Reports Abbreviated Journal
Volume 5 Issue Pages Sp5-2
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Abstract The dairy sector may expand by over 2% per annum with expiration of the milk quota system in countries with a major and intensive dairy sector. Such expansion will increase pressure to further reduce on-farm nitrogenous emission per unit of milk produced even more. A straightforward N-reducing measure is the manipulation of the cow diet resulting in a lower excretion of ammoniacal N excreted with urine in particular. However, dietary N-reducing measures also affect enteric methane emissions and P excretion. For an integral evaluation of the consequences of N-reducing dietary measures on on-farm emissions, the trade-offs between N emissions and P and methane emissions at the cow level need to be taken into account. Therefore, a simulation study was performed to simulate the consequence of various N-reducing and/or P-reducing dietary measures (altered grassland management, grass silage replaced by low-N feeds, increased concentrate allowance) on enteric methane emission and on N and P excretion. Results indicate a large scattering, but there was a trend of higher methane emissions with lower N excretion was significant. Specific measures had a synergistic effect on emissions such as the exchange of maize for grass silage. The present detailed model evaluations may aid in quantifying the extent of trade-offs between various types of emissions at the cow level, but also prove to be relevant when evaluating consequences of management options taken at the farm scale. No Label
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Area Expedition Conference MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK
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Call Number MA @ admin @ Serial 2117
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Author Schönhart, M.
Title (up) Uncertainties from Climate Change on Farms and Ecosystem Services of a Grassland Dominated Austrian Landscape Type Report
Year 2016 Publication FACCE MACSUR Reports Abbreviated Journal
Volume 9 C6 - Issue Pages Sp9-9
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Abstract 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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Call Number MA @ admin @ Serial 4832
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Author Rolinski, S.; Sætnan, E.
Title (up) Uncertainties in climate change prediction and modelling Type Report
Year 2013 Publication FACCE MACSUR Reports Abbreviated Journal
Volume 1 Issue Pages D-L1.5
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Abstract As models become increasingly complex and integrated, uncertainty among model  parameters, variables and processes become critical for evaluating model outcomes and  predictions. A framework for understanding uncertainty in climate modelling has been  developed by the IPCC and EEA which provides a framework for discussion of uncertainty  in models in general. Here we report on a review of this framework along with the results  of a survey of sources of uncertainty in livestock and grassland models. Along with the  identification of key sources of uncertainty in livestock and grassland modelling, the  survey highlighted the need for a development of a common typology for uncertainty.  When collaborating across traditionally separate research fields, or when communicating  with stakeholders, differences in understanding, interpretation or emphasis can cause  confusion. Further work in MACSUR should focus on improving model intercomparison  methods to better understand model uncertainties, and improve availability of high  quality datasets which can reduce model uncertainties. No Label
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Call Number MA @ admin @ Serial 2259
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