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Rivington, M. |
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Title |
Agrimod: The Agricultural Modelling Knowledge Hub Website |
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2014 |
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Agrimod is a new web-based Agricultural Modelling Knowledge Hub covering crop, livestock and trade models and the data they require, plus a wide range of supporting tools and resources. The purpose is to address the growing need, particularly in developing countries, of building national capabilities for researching agriculture and food security using models. To support research in this area, Agrimod provides a facility enabling users to access information and data needed to more successfully develop and employ agricultural modelling. Registered users can add new information about models, data, case studies, training, funding sources etc., whilst also being able to edit existing content and contribute to discussion threads on key modelling issues. It will serve as a model, data and case study inventory. The vision is to unite the existing 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 model activities are developed. Moreover, Agrimod is intended to be a user–friendly information portal to people in other areas of research or new to agricultural modelling, looking to develop skills and acquire first-hand knowledge on agricultural modelling research. Thus Agrimod serves as a central knowledge hub for information on agricultural modelling activities worldwide and can be used by MACSUR as a complimentary information dissemination tool. |
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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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5051 |
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Author |
Rivington, M. |
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Title |
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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Bellocchi, G.; Rivington, M.; Acutis, M. |
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Deliberative processes for comprehensive evaluation of agro-ecological models |
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2014 |
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Biophysical models are acknowledged for examining interactions of agro-ecological systems and fostering communication between scientists, managers and the public. As the role of models grows in importance, there is an increase in the need to assess their quality and performance (Bellocchi et al., 2010). However, the heterogeneity of factors influencing model outputs makes it difficult a full assessment of model features. Where models are used with or for stakeholders then model credibility depends not only on the outcomes of well-structured statistical evaluation but also less tangible factors may need to be addressed using complementary deliberative processes. To expand our horizons in the evaluation of crop and grassland models, approaches have been reviewed with emphasis on using combined metrics. Comprehensive evaluation of simulation models was developed to integrate expectations of stakeholders via a weighting system where lower and upper fuzzy bounds are applied to a set of evaluation metrics. A questionnaire-based survey helped understanding the multi-faceted knowledge and experience required and the substantial challenges posed by the deliberative process. MACSUR knowledge hub holds potential to advance in good modelling practice in relation with model evaluation (including access to appropriate software tools), an activity which is frequently neglected in the context of time-limited projects. |
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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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5071 |
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Rivington, M.; Wallach, D. |
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Communication strategy, including design of tools for more effective communication of uncertainty |
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2015 |
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FACCE MACSUR Reports |
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6 |
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D-C4.1.4 |
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Communication is the key link between the generation of information by MACSUR about the uncertainty of climate change impacts on future food security and how information is used by decision makers. It is therefore important to make available the common tools for reporting uncertainty, with a discussion of the advantages or difficulties of each. That is the purpose of this report. No Label |
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no |
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MA @ admin @ |
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2099 |
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Rivington, M.; Wallach, D. |
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Title |
Information to support input data quality and model improvement |
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Report |
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2015 |
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FACCE MACSUR Reports |
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6 |
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D-C4.2.4 |
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Data quality is a key factor in determining the quality of model estimates and hence a models’ overall utility. Good models run with poor quality explanatory variables and parameters will produce meaningless estimates. Many models are now well developed and have been shown to perform well where and when good quality data is available. Hence a major limitation now to further use of models in new locations and applications is likely to be the availability of good quality data. Improvements in the quality of data may be seen as the starting point of further model improvement, in that better data itself will lead to more accurate model estimates (i.e. through better calibration), and it will facilitate reduction of model residual error by enabling refinements to model equations. This report sets out why data quality is important as well as the basis for additional investment in improving data quality. No Label |
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MA @ admin @ |
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2103 |
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