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Nendel, C.; Ewert, F.; Rötter, R.P.; Rosenzweig, C.; Jones, J.W.; Hatfield, J.L.; Asseng, S.; Ruane, A.C.; Banse, M.; Tiffin, R.; Brouwer, F.; Sinabell, F.; Scollan, N.; Meijs, J.; Angulo, C.; Antle, J.M.; Baigorria, G.; Basso, B.; Bindi, M.; Boote, K.J.; Gaiser, T.; Janssen, S.; Kersebaum, K.C.; Nelson, G.; Olesen, J.E.; Palosuo, T.; Porter, C.H.; Porter, J.R.; Rivington, M.; Semenov, M.; Stewart, D.; Thorburn, P.; Trnka, M.; van Ittersum, M.K.; Verhagen, J.; Wallach, D.; Winter, J.M. |
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Addressing challenges and uncertainties for, the use of agro-ecosystem models to, assess climate change impact and food security across scales |
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2013 |
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CropM |
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Climate Change and Regional Responses Conference, Dresden, 2013-05-27 to 2013-05-27 |
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MA @ admin @ |
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2679 |
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Wallach, D.; Rivington, M. |
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Development of a common set of methods and protocols for assessing and communicating uncertainties |
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2013 |
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FACCE MACSUR Reports |
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2 |
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D-C4.1.1 |
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This reports sets out an outline approach to create definitions of uncertainty and how it might be classified. This is not a prescriptive approach rather it should be seen as a starting point from which further development can be made by consensus with CropM partners and across MACSUR Themes. We propose both a numerical quantification of uncertainty and text based classification scheme. The rational is to be able to both establish the terms and definitions in quantifying the impact of uncertainty on model estimates and have a scheme to enable identification of connectivity between types and sources of uncertainty. The aim is to establish a common set of terms and structure within which they operate that can be used to guide work within CropM. No Label |
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MA @ admin @ |
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2241 |
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Wallach, D.; Rivington, M. |
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A framework for assessing the uncertainty in crop model predictions |
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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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Bellocchi, G.; Rivington, M.; Acutis, M. |
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Protocol for model evaluation |
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2014 |
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FACCE MACSUR Reports |
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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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Rivington, M. |
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AgriMod – The Agricultural Modelling Knowledge Hub |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-49 |
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Agrimod serves as a central knowledge hub for information on agricultural modelling activities worldwide. The vision is to unite the agricultural modelling community by providing a platform whereby models can be showcased, their applications discussed and new collaborations built, streamlining the process by which new modelling activities are developed. Agrimod covers spatial scales from cells to globe, temporal scales from minutes to centuries. There is a limitless coverage of research issues, bounded only by their relevance to agriculture, as the platform is open-ended: details about models, data or case studies can be up-dated; issues or concepts can be raised and discussed. The scope is limited only by the willingness of users to participate. 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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2164 |
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