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Braunmiller, K.; Köchy, M. |
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Grassland datasets |
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2013 |
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FACCE MACSUR Reports |
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1 |
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D-L1.3 |
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In the MACSUR project, there are several grassland models in use that were designed for and adjusted with data from different climatic regions. To be able to run these modelsfor a wide geographical range, there is a need to validate and calibrate them on the same basis.Therefore, a high-quality dataset is needed, which includes a wide range of climatic conditions, management systems and other variables.Through this search 23 grassland related institutes from eleven countries were found and contacted, where 12 of them responded to the request. Nine institutes from cooler (e.g. Finland) and warmer regions (e.g. Israel) are now willing to provide their experimental data. One contributor is even planning to join the project bringing its own grassland model.These new grassland datasets cover in addition to already available ones (Fig. 1) a wide range of climatic regions for a substantiated calibration and validation of the models. Data supplied by the institutes have been checked for internal consistency and cast into a common format. The data have been passed on to WP L2 (Model intercomparison on climate change in relation to livestock and grassland). No Label |
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MA @ admin @ |
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2258 |
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Bellocchi, G.; Ma, S.; Köchy, M.; Braunmiller, K. |
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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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Bellocchi, G.; Ma, S.; Köchy, M.; Braunmiller, K. |
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Title |
Identified grassland-livestock production systems and related models |
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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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Braunmiller, K.; Köchy, M. |
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Title |
Background information on Shared Socioeconomic Pathways for use in MACSUR case studies |
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2013 |
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FACCE MACSUR Reports |
Abbreviated Journal |
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2 |
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R-H2.1 |
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This document is intended to aid in the development of regional Representative Agricultural Pathways in Europe for use in MACSUR case studies, especially the regional pilot studies. We present overviews of existing characterisations of RCPs, SSPs, SPAs, RAPs and more detailed descriptions of the scenarios and assumptions relevant for MACSUR. No Label |
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MA @ admin @ |
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2238 |
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Author |
Rötter, R.P.; Semenov, M.A. |
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Title |
Development of methods for the probabilistic assessment of climate change impacts on crop production |
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2014 |
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FACCE MACSUR Reports |
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3 |
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D-C4.4.1 |
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Various attempts have been made to determine the relative importance of uncertainties in climate change impact assessments stemming from climate projections and crop models, respectively, and to analyse yield outputs probabilistically. For example, in the ENSEMBLES project, probabilistic climate projections (Harris et al. 2010) have been applied in conjunction with impact response surfaces (IRS), constructed by using impact models, to estimate the future likelihood (risk) of exceeding critical thresholds of crop yield impact (see, Fronzek et al., 2011, for an explanation of the method). In this task, we aimed to further develop and operationalize these methods and testing them in different case study regions in Europe. The method combines results of a sensitivity analysis of (one or more) impact model(s) with probabilistic projections of future temperature and precipitation (Fronzek et al., 2011). Such an overlay is one way of portraying probabilistic estimates of future impacts. By further accounting for the uncertainties in crop and biophysical parameters (using perturbed parameter approaches), the outcome represents an ensemble of impact risk estimates, encapsulating both climate and crop model uncertainties. No Label |
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MA @ admin @ |
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2233 |
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