Rivington, M. (2014). Agrimod: The Agricultural Modelling Knowledge Hub Website. FACCE MACSUR Mid-term Scientific Conference, 3(S) Sassari, Italy.
Abstract: 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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Rivington, M., & Wallach, D. (2015). Quantified Evidence of Error Propagation (Vol. 6).
Abstract: Error propagation within models is an issue that requires a structured approach involving the testing of individual equations and evaluation of the consequences of error creation from imperfect equation and model structure on estimates of interest made by a model. This report briefly covers some of the key issues in error propagation and sets out several concepts, across a range of complexity, that may be used to organise an investigation into error propagation. No Label
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Rivington, M., & Wallach, D. (2015). Information to support input data quality and model improvement (Vol. 6).
Abstract: 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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Rivington, M., & Wallach, D. (2015). Communication strategy, including design of tools for more effective communication of uncertainty (Vol. 6).
Abstract: 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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Wallach, D., & Rivington, M. (2015). Standardised methods and protocols based on current best practices to conduct sensitivity analysis (Vol. 6).
Abstract: The purpose of this report is to propose a general procedure for sensitivity analysis when used to evaluate system sensitivity to climate change, including uncertainty information. While sensitivity analysis has been largely used to evaluate how uncertainties in inputs or parameters propagate through the model and manifest themselves in uncertainties in model outputs, there is much less experience with sensitivity analysis as a tool for studying how sensitive a system is to changes in inputs. This report should help make clear the differences between these two uses of sensitivity analysis, and provide guidance as to the procedure for using sensitivity analysis for evaluating system sensitivity to climate change. No Label
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