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.

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

Abstract: 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