| Records |
| Author |
Schmidhuber, J. |
| Title |
The Food Equation”: Taking a long/term View on World Agriculture, Climate Change and Food Security |
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2015 |
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FACCE MACSUR Reports |
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4 |
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SP4-15 |
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TradeM International Workshop 2014 »Economics of integrated assessment approaches for agriculture and the food sector«, 25–27 November 2014, Hurdalsjø, Norway |
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MA @ admin @ |
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2205 |
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Lotze-Campen, H. |
| Title |
EU-level assessments and scenarios |
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Conference Article |
| Year |
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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no |
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MA @ admin @ |
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2087 |
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Schönhart, M.; Schauppenlehner, T.; Kuttner, M.; Schmid, E. |
| Title |
Integrated Assessment of Climate Change Mitigation and Adaptation Impacts at Landscape level: Mostviertel, Austria |
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Conference Article |
| Year |
2015 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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6 |
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SP6-6 |
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ConclusionsIncreasing productivity can increase intensification pressuresThreatened permanent (extensive) grasslands and landscape elements, butsubject to resource constraints, costs and prices andfuture production potential to increase global food supplyFuture RDP and environmental policy design (e.g. WFD) should take changing productivity into accountHeterogeneity matters at farm and regional levelChanging relative competitiveness of farmsFuture research: analyze uncertainties 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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Approved |
no |
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MA @ admin @ |
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2085 |
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Scollan, N.; Bannink, A.; Kipling, R.; Saetnan, E.; Van Middelkoop, J. |
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Livestock and feed production, especially dairy and beef |
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Conference Article |
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2015 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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6 |
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Sp6-3 |
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Improving health and welfare is an important adaptation and mitigation strategyDeveloping process based modelling, responsive to adaptationLinks to climate and land use change modelling are essential Livestock systems likely to be hit hardest by climate changeNeed to develop animal health models that respond to adaptation by farmersBringing together direct and indirect impacts of climate change vitalAdaptation and mitigation need to be considered and modelled togetherLinking models across scales is important to support policy decisionsLearning between sectors carries potential for novel solutions and methodological advancesEffective communication of outcomes to stakeholders (how?) 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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Approved |
no |
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MA @ admin @ |
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2084 |
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Wallach, D.; Thorburn, P.; Asseng, S.; Challinor, A.J.; Ewert, F.; Jones, J.W.; Rötter, R.; Ruane, A. |
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Overview paper on comprehensive framework for assessment of error and uncertainty in crop model predictions |
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Report |
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2016 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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8 |
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C4.1-D |
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MACSUR_ACK; CropM |
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Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. Several ways of quantifying prediction uncertainty have been explored in the literature, but there have been no studies of how the different approaches are related to one another, and how they are related to some overall measure of prediction uncertainty. Here we show that all the different approaches can be related to two different viewpoints about the model; either the model is treated as a fixed predictor with some average error, or the model can be treated as a random variable with uncertainty in one or more of model structure, model inputs and model parameters. We discuss the differences, and show how mean squared error of prediction can be estimated in both cases. The results can be used to put uncertainty estimates into a more general framework and to relate different uncertainty estimates to one another and to overall prediction uncertainty. This should lead to a better understanding of crop model prediction uncertainty and the underlying causes of that uncertainty. This study was published as (Wallach et al. 2016) |
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MA @ office @ |
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2954 |
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