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
Development of a model of data-flows for precision agriculture based on a collaborative research project
The understanding of actual and potential data-flows in the practice of precision agriculture (PA) is an essential prerequisite for the optimisation and automation of information management in this field. As a contribution to this process, this paper presents an analysis of the data-flows within the work of a collaborative research project concerned with the testing of the methods developed within the project on two demonstration fields. This work provides a good case study for the modelling of a range of data-flows covering a broad spectrum of PA techniques. Using the notation and software tools for the Unified Modeling Language (UML), a complete model of all identified data-flows was created. Individual data-streams relating to particular source or product datasets were then extracted from this model. These data-streams present a practical application of the model in identifying the benefit that may be obtained from a particular gathered dataset (e.g. yield data) or in identifying the data that must be gathered to generate a particular product dataset (e.g. sustainability indicators). Whilst the current model is focussed on one particular research project, it has potential to be extended to cover more generally the common practice of precision agriculture. Such a model may then be used by farmers as a roadmap for the adoption for precision agriculture by allowing them to determine what datasets are available to them or may be easily collected and what products they may generate from these, or vice versa to identify what datasets they must obtain in order to generate a particular dataset of interest. (C) 2008 Elsevier B.V. All rights reserved.
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