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Bellocchi, G. |
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Fuzzy-logic based multi-site crop model evaluation |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-5 |
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The most common way to evaluate simulation models is to quantify the agreement between observations and simulations via statistical metrics such as the root mean squared error and the linear regression coefficient of determination. It is agreed that the aggregation of metrics of different nature intro integrated indicators offers a valuable way to assess models. Expanded notions of model evaluation that have recently emerged, based on the trade-off between properties of the model and agreement between predictions and actual data under contrasting conditions, integrate sensitivity analysis measures and information criteria for model selection, as well as concepts of model robustness, and point to expert judgments to explore the importance of different metrics. As a FACCE MACSUR CropM-LiveM action, a composite indicator (MQIm: Model Quality Indicator for multi-site assessment) was elaborated, by a group of specialists, on metrics commonly used to evaluate crop models (with extension to grassland models) while also integrating aspects of model complexity and stability of performances. The indicator, based on fuzzy bounds applied to a set of weighed metrics, was first revised by a broader group of modellers and then assessed via questionnaire survey of scientists and end-users. We document a crop model evaluation in Europe and assess to what extent the MQIm reflects the main components of model quality and supports inferences about model performances. 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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2120 |
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Biewald, A.; Sinabell, F.; Lotze-Campen, H.; Zimmermann, A.; Lehtonen, H. |
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Global Representative Agricultural Pathways for Europe |
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2017 |
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FACCE MACSUR Reports |
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10 |
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T1.2-XC16.2 |
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Agricultural elements have been covered in the scenario process on shared socio-economic pathways (SSPs) incompletely and pathways have not been specified for the future development of the European Union. We will therefore devise a general framework on European Representative Agricultural Pathways (EU-RAPs), where we cover different aspects of agricultural development, as for example European and domestic agricultural and environmental policies, or different livestock and crop management systems, and describe future developments of the confederation of the countries of the European Union. For the agricultural elements we distinguish between elements that can be derived from the definitions in the Shared Socioeconomic Pathways, as for example irrigation efficiencies which are linked to technological development, and elements that have to be newly devised such as the development of the Common Agricultural Policy. For the future of the European Union we develop five different worlds which correspond to the SSPs. Finally both frameworks are combined. |
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TradeM |
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MA @ admin @ |
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5034 |
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Sándor et al. |
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Global Research Alliance on Greenhouse Gases – benchmark and ensemble crop and grassland model estimates |
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2016 |
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FACCE MACSUR Reports |
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8 |
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SP8-14 |
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Conference presentation PDF |
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LiveM2016: International livestock modelling conference – Modelling grassland-livestock systems under climate change |
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MA @ admin @ |
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4848 |
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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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Trnka, M. |
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Guidelines on extending on-going experiments with additional measurements to support crop modelling – Field experimental protocol |
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2013 |
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FACCE MACSUR Reports |
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2 |
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D-C2.3 |
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The input data necessary for crop model simulations and data for their calibration/validation (and thus requirements for observations and measurements in suitable experiments) are listed. A list of possible seasonal observations/measurements that could be carried out in existing experiments to increase their potential for crop modelling studies is also provided. The general methodology suitable to be used is outlined, but in all cases the selected method depends strongly on the experimental set-up and facilities/instruments at the disposal of the experimentalists. Such methodologies needs to be documented and preferably benchmarked against standard methods. No Label |
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MA @ admin @ |
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2240 |
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