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| Author |
Bellocchi, G.; Ma, S.; Köchy, M.; Braunmiller, K. |
| Title |
Datasets classification and criteria for data requirements |
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2013 |
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FACCE MACSUR Reports |
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2 |
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D-L2.1.2 |
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This deliverable focuses on the collation, screening, and consolidation of data for selected grassland sites in Europe and peri-Mediterranean regions. No Label |
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MA @ admin @ |
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2245 |
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| Author |
Bellocchi, G.; Ma, S.; Köchy, M.; Braunmiller, K. |
| Title |
Identified grassland-livestock production systems and related models |
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Report |
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2013 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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2 |
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D-L2.1.1 |
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This report describes grassland-livestock production systems, as selected for model-basedstudies. A list of grassland models was identified for evaluation against such datasets(WP2) and application at reference farm (WP3) and regions (WP4) across Europe and peri-European countries. No Label |
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MA @ admin @ |
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2244 |
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Bellocchi, G.; Ma, S. |
| Title |
Results of uncalibrated grassland model runs |
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Report |
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2014 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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3 |
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D-L2.3 |
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This deliverable focuses on the some illustrative results obtained with the grassland models selected (D-L2.1.1) to simulate biomass and flux data from grassland sites in Europe and peri-Mediterranean regions (D-L2.1.1 and D-L2.1.2). This is a blind exercise, carried out without model calibration. The complete set of results will include simulations from calibrated models. The results shown are illustrative of the methodology adopted for grassland model intercomparison in MACSUR. The insights gained from this ongoing study are relevant for some crop and vegetation models, which in some cases proved comparable to grassland-specific models to simulate biomass data from managed grasslands. The results reported here cannot be considered conclusive. Additional results will be published as they become available together with calibration results, as well as the comprehensive evaluation of models with fuzzy logic-based indicators. No Label |
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MA @ admin @ |
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2234 |
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Bellocchi, G.; Rivington, M.; Acutis, M. |
| Title |
Protocol for model evaluation |
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2014 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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3 |
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D-L2.2/D |
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This deliverable focuses on the development of methods for model evaluation in order to have unambiguous indications derived from the use of several evaluation metrics. The information about model quality is aggregated into a single indicator using a fuzzy expert system that can be applied to a wide range of model estimates where suitable test data are available. This is a cross-cutting activity between CropM (C1.4) and LiveM (L2.2). No Label |
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MA @ admin @ |
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2229 |
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Bellocchi, G.; Martin, R.; Shtiliyanova, A.; Ben Touhami, H.; Carrère, P. |
| Title |
Vul’Clim – Climate change vulnerability studies in the region Auvergne (France) |
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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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Sp3-6 |
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The region Auvergne (France) is a major livestock territory in Europe (beef and dairy cattle with permanent grasslands), with a place in climate change regional studies assisting policy makers and actors in identifying adaptation and mitigation measures. Vul’Clim is a research grant (Bourse Recherche Filière) of the region Auvergne (February 2014-September 2015) to develop model-based vulnerability analysis approaches for a detailed assessment of climate change impacts at regional scale. Its main goal is the creation of a computer-aided platform for vulnerability assessment of grasslands, in interaction with stakeholders from a cluster of eco-enterprises. A modelling engine provided by the mechanistic, biogeochemical model PaSim (Pasture Simulation model) is the core of the platform. An action studies the changes of scales by varying the granularity of the data available at a given scale (e.g. climate data supplied by global scenarios) to let them being exploited at another scale (e.g. high-resolution pixels). Another action is to develop an assessment framework linking modelling tools to entry data and outputs, including a variety of components: data-entry manager at different spatial resolutions; automatic computation of indicators; gap-filling and data quality check; simulation kernel with the model(s) used; device to represent results as maps and integrated indicators. No Label |
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MA @ admin @ |
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2223 |
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