The e-ROSA project seeks to build a shared vision of a future sustainable e-infrastructure for research and education in agriculture in order to promote Open Science in this field and as such contribute to addressing related societal challenges. In order to achieve this goal, e-ROSA’s first objective is to bring together the relevant scientific communities and stakeholders and engage them in the process of coelaboration of an ambitious, practical roadmap that provides the basis for the design and implementation of such an e-infrastructure in the years to come.
This website highlights the results of a bibliometric analysis conducted at a global scale in order to identify key scientists and associated research performing organisations (e.g. public research institutes, universities, Research & Development departments of private companies) that work in the field of agricultural data sources and services. If you have any comment or feedback on the bibliometric study, please use the online form.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Lessons from a comprehensive validation of an agent based-model: The experience of the Pampas Model of Argentinean agricultural systems
There are few published examples of comprehensively validated large-scale land-use agent-based models (ABMs). We present guidelines for doing so, and provide an example in the context of the Pampas Model (PM), an ABM aimed to explore the dynamics of structural and land use changes in the agricultural systems of the Argentine Pampas. Many complementary strategies are proposed for validation of ABM's. We adopted a validation framework that relies on two main streams: (a) validation of model processes and components during model development, which involved a literature survey, design based on similar models, involvement of stakeholders, and focused test scenarios and (b) empirical validation, which involved comparisons of model outputs from multiple realistic simulations against real world data. The design process ensured a realistic model ontology and representative behavioral rules. As result, we obtained reasonable outcomes from a set of initial and simplified scenarios: the PM successfully reproduced the direction of the primary observed structural and land tenure patterns, even before calibration. The empirical validation process lead to tuning and further development of the PM. After this, the PM was able to reproduce not only the direction but also the magnitude of the observed changes. The main lesson from our validation process is the need for multiple validation strategies, including empirical validation. Approaches intended to validate model processes and components may lead to structurally realistic models. However, some kind of subsequent empirical validation is needed to assess the model's ability to reproduce observed results. (C) 2013 Elsevier B.V. All rights reserved.
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