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
Representative soil profiles for the Harmonized World Soil Database at different spatial resolutions for agricultural modelling applications
Agricultural modellers often need detailed soil profile data with which to run their models. We combine an extensive soil profile database with the Harmonized World Soil Database, a 30 arcsecond raster database of soil information worldwide, and describe a statistical process to identify representative soil profiles for each of its 188 distinct soil types at different spatial resolutions. We then outline a method to cluster the soils in the Harmonized World Soil Database to produce soil maps at coarser resolution, and we describe derived global soil maps at spatial resolutions of 5 and 10 arcmin, which may be more practical for some large-scale modelling studies. The derived data files allow a user to select any point or area on land and then to access the set of soil profiles pertaining to the mapping unit selected, which are available in a format suitable for use in modelling applications. In situations where the user has little or no other information about the soils in the region of study, the methods described can be used to produce plausible soil profile information based on the most up-to-date global soil map currently available. (C) 2015 Elsevier Ltd. All rights reserved.
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