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Dumont, B.; Basso, B.; Destain, J.-P.; Bodson, B.; Destain, M.-F. |
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A Comparison of Optimal Nitrogen Fertilisation Strategies Using Current and Future Stochastically Generated Climatic Conditions |
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2014 |
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MACSUR CropM International Symposium and Workshop: Modelling climate change impacts on crop production for food security, Oslo, Norway, 2014-02-10 to 2014-02-12 |
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MA @ admin @ |
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2412 |
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Pasqui, M.; Quaresima, S.; Tomozeiu, R.; Dono, G.; Doro, L.; Cortignani, R.; Ledda, L.; Roggero, P.P. |
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A comprehensive climate characterization of the Oristano (Sardinia) regional pilot case study |
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2014 |
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In order to assess probability distributions of critical response variables in a full crop modelling system, a complete climate characterization has been implemented to identify principal variability components in the Oristano (Sardinia) regional pilot study area with a particular emphasis on current vs near future climate. |
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FACCE MACSUR Mid-term Scientific Conference |
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3(S) Sassari, Italy |
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FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
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MA @ admin @ |
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5046 |
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Bourne, M.G.; Philippidis, G. |
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A computable general equilibrium assessment of Spain’s greenhouse gas emissions policies and abatement options |
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2014 |
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Employing a recursive dynamic computable general equilibrium (CGE) model of the Spanish economy, this study aims to characterise the potential impact of Kyoto and European Union environmental policy targets on the Spanish economy up to 2020, with a focus on the agricultural sector. The model code is modified to characterise the emissions trading scheme (ETS), emissions quotas and carbon taxes, whilst emissions reductions are applied to all six registered greenhouse gases (GHGs). As extensions to this work, the study attempts to integrate the use of ‘Marginal Abatement Cost’ (MAC) curves for emissions reductions within the agricultural sector, and econometric estimates of the effects of global warming on land productivity in Spain. |
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FACCE MACSUR Mid-term Scientific Conference |
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3(S) Sassari, Italy |
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FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
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no |
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MA @ admin @ |
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5047 |
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Mueller, C. |
| Title |
A crop modeling response to economists’ wishlists |
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2014 |
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Assessments of climate change impacts on agricultural markets and land-use patterns rely on quantification of climate change impacts on the spatial patterns of land productivity. We supply a set of climate impact scenarios on agricultural land productivity derived from two climate models and two biophysical crop growth models to account for some of the uncertainty inherent in climate and impact models. Aggregation in space and time leads to information losses that can determine climate change impacts on agricultural markets and land-use patterns because often aggregation is across steep gradients from low to high impacts or from increases to decreases. The four climate change impact scenarios supplied here were designed to represent the most significant impacts (high emission scenario only, assumed ineffectiveness of carbon dioxide fertilization on agricultural yields, no adjustments in management) but are consistent with the assumption that changes in agricultural practices are covered in the economic models. Globally, production of individual crops decrease by 10 to 38% under these climate change scenarios, with large uncertainties in spatial patterns that are determined by both the uncertainty in climate projections and the choice of impact model. This uncertainty in climate impact on crop productivity needs to be considered by economic assessments of climate change. |
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FACCE MACSUR Mid-term Scientific Conference |
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3(S) Sassari, Italy |
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FACCE MACSUR Mid-term Scientific Conference, 2014-04-01 to 2014-04-04, Sassari, Italy |
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no |
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MA @ admin @ |
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5048 |
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| Author |
Wallach, D.; Rivington, M. |
| Title |
A framework for assessing the uncertainty in crop model predictions |
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Report |
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2014 |
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FACCE MACSUR Reports |
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3 |
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D-C4.1.2 |
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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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MA @ admin @ |
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2231 |
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