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Menardo et al. |
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INNO Mil-CH4 GHG Emissions from Milk Production |
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2016 |
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FACCE MACSUR Reports |
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8 |
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SP8-4 |
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LiveM2016: International livestock modelling conference – Modelling grassland-livestock systems under climate change |
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no |
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MA @ admin @ |
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4845 |
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Matthews, A. |
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Is agriculture off the hook in the EU’s 2030 Climate Policy |
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2016 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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9 C6 - |
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Sp9-6 |
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EU climate policy and AFOLU•Overall 2030 level of ambition agreed by European Council October 2014•Commission ESR proposal July 2016 – sharing of effort in NETS across MS plus trading mechanisms•Commission LULUCF proposal – integration of LULUCF into climate policy•AFOLU mitigation pursued through CAP as well as flanking environmental policies•No specific EU targets for agricultural mitigation in NETS•Ultimately, how AFOLU mitigation is pursued will depend on MS decisions2Implications of EU bubble•Commission has put in place trading mechanisms in NETS sectors to ensure least-cost fulfilment of overall EU targets•Challenge of MS ESR targets also depends on use MS make of trading mechanisms•MS have not to date made use of these mechanisms and prefer to meet targets domestically•A number of MS have domestic targets in addition to EU targets•ESR IA looked at adding central information site, central market place for AEA transfers or mandatory auctioning•Links with annual monitoring and 5-year legal compliance checks (2027 and 2032) |
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MA @ admin @ |
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4843 |
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Martre, P. |
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Reducing uncertainty in prediction of wheat performance under climate change |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-38 |
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Projections of climate change impacts on crop performances are inherently uncertain. However, multimodel uncertainty analysis of crop responses is rare because systematic and objective comparisons among process-based crop simulation models are difficult. Here we report on the Agricultural Model Intercomparison and Improvement Project ensemble of 30 wheat models tested using both crop and climate observed data in diverse environments, including infra-red heating field experiments, for their accuracy in simulating multiple crop growth, N economy and yield variables. The relative error averaged over models in reproducing observations was 24-38% for the different end-of-season variables. Clusters of wheat models organized by their correlations with temperature, precipitation, and solar radiation revealed common characteristics of climatic responses; however, models are rarely in the same cluster when comparing across sites. We also found that the amount of information used for calibration has only a minor effect on model ensemble climatic responses, but can be large for any single model. When simulating impacts assuming a mid-century A2 emissions scenario for climate projections from 16 downscaled general circulation models and 26 wheat models, a greater proportion of the uncertainty in climate change impact projections was due to variations among wheat models rather than to variations among climate models. Uncertainties in simulated impacts increased with atmospheric [CO2] and associated warming. Extrapolating the model ensemble temperature response (at current atmospheric [CO2]) indicated that warming is already reducing yields at a majority of wheat-growing locations. Finally, only a very weak relationship was found between the models’ sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs. In conclusion, uncertainties in prediction of climate change impacts on crop performance can be reduced by improving temperature and CO2 relationships in models and are better quantified through use of impact ensembles. No Label |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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MA @ admin @ |
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2153 |
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Author |
Marton, T. |
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Assessing the impact of agro-climatic factors and farm characteristics on the yield variation of the Norwegian fruit sector |
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2016 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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9 C6 - |
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Sp9-5 |
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Main drivers of ag. yields:–Technology–R&D (new hybrids etc.)–Weather–Etc.•Common sense and anecdotal observations (remember the Tromsø presentation) revealed extreme events tended to impact wide geographic areas•This was called the «systemic» nature of agriculture No semi-aggregation farm-level•Not the boring corn, maize, wheat fruits•No OLS-like Pearson correlation or functional form approach for conditioning spatial correlations on weather SDM•Finally, if we are smart enough to set the explanatory proxies in a meaningful way presumably we can make the distinction between the effects of, say draught and extreme heat.•And much more in policy relevance |
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no |
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MA @ admin @ |
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4857 |
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Lotze-Campen, H. |
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EU-level assessments and scenarios |
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2015 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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6 |
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SP6-8 |
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Shared socio-economic pathways are used to look at particular possible futures of major trends in global socio-economic trends (e.g. global population, GDP, urbanization, strength of political institutions, international trade). These scenarios make no inference to their likelihood of becoming true. These scenarios are used in MACSUR to assess different questions, e.g.•What is the future of agricultural prices?•How will agricultural production and food consumption evolve?•How will climate change impacts and mitigation affect…–Prices–Land use–Trade–Undernourishment No Label |
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Brussels |
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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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MA @ admin @ |
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2087 |
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