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
SPATIALLY EXPLICIT LOAD ENRICHMENT CALCULATION TOOL TO IDENTIFY POTENTIAL E. COLI SOURCES IN WATERSHEDS
In 2006, bacterial pathogens were the leading cause of water quality, concerns in the U.S. With more than 300 water bodies in the state of Texas failing to meet water quality standards because of bacteria, managing bacteria pollution commanded the attention of regulatory agencies, researchers, and stakeholders across Texas. In order to assess, monitor, and manage water quality, it was necessary to characterize the sources of pathogens within the watershed. The objective of this study was to develop a spatially explicit method to estimate potential E. coli loads in Plum Creek watershed in east central Texas. Locations of contributing non-point and point sources in the watershed were defined using Geographic Information Systems (GIS). By distributing livestock, wildlife, wastewater treatment plants, septic systems, and pet sources, the bacterial load in the watershed was spatially characterized. Contributions front each source were quantified by applying source specific bacterial production rates, and ranking of each contributing source was assessed for the entire watershed. Cluster and discriminant analyses were used to identify similar regions within the watershed for,selecting appropriate best management practices. Based on the statistical analysis and the spatially explicit method, four clusters of subwatersheds were found and characterized. The analysis provided a basis for development of spatially explicit identification of best management practices (BMPs) to be applied within the Watershed Protection Plan (WPP).
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