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
Regional overview of nutrient load in Europe - challenges when using a large-scale model approach, E-HYPE
The homogenous set-up of the HYPE model for Europe (E-HYPE) gives an overview of riverine nutrient transport from land to sea and surface water concentrations across the continent. Results indicate that loads and concentrations of total nitrogen are highest in the western part of Europe, draining to the North Atlantic Ocean. High phosphorous concentrations were more dispersed and coincided principally with major urban centres. Spatially-consistent moderate total phosphorous loads were also seen across the agricultural regions of Western Europe and north of the Black Sea. By analysing where modelled data and observations agree or disagree it may be possible to identify major knowledge gaps in the model. Spatial variation in results can help contribute to understanding of hydrological and nutrient processes in the wide variety of climates, physiological and anthropogenic conditions represented across the European continent. The predictability is limited by the quality of the continental-scale input data and the optimisation of model parameters to multiple sites.
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