e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

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:

Discover all records
Home page


The use of Slingram EM38 data for topsoil and subsoil geoelectrical characterization with a Bayesian inversion


We use the Bayesian method to invert a simple two-layer pedological horizon (1-D with a topsoil and a subsoil) of a surveyed site to be assessed. We show how the Bayesian method is well suited to the determination of topsoil/subsoil features, and can be used in particular as a tool for the analysis of parameters to be retrieved in terms of information content. Our approach is devoted mainly to the assessment of topsoil thickness, and of topsoil and subsoil conductivities, which are provided in terms of probability density functions. We first summarize the methodology implemented with the Geonics EM38-MK2 conductivity meter, and discuss the adaptation of field procedures and post-processing methods to mitigate the effects of drift and bias. We briefly review some non-Bayesian approaches, and then develop the Bayesian approach for the context of our geophysical survey, highlighting its merits. Positivity constraints (on thickness and conductivity) are included in the form of log parameters. A priori knowledge, based on an objective choice made by the geophysicist, is naturally included in the Bayesian scheme. We discuss the equivalence problem associated with the application of the Slingram method to soil structure analysis. The survey of a luvisol at the Kwazulu-Natal (South Africa) site of Potshini is used to illustrate an ecological application of the Slingram and Bayesian methods, used to define the geo-electrical structure of the near-surface soil. These algorithms have demonstrated their usefulness in mapping the clay content of the Bt horizon associated with the control of encroaching trees. (C) 2013 Elsevier B.V. All rights reserved.

  • VN
  • FR
  • ZA
  • Univ_KwaZulu_Natal_UKZN (ZA)
  • IRD (FR)
  • Univ_Paris_06_Pierre_et_Marie_Curie (FR)
Data keywords
  • knowledge
Agriculture keywords
    Data topic
    • information systems
    Document type

    Inappropriate format for Document type, expected simple value but got array, please use list format

    Institutions 10 co-publis
      Powered by Lodex 8.20.3
      logo commission europeenne
      e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
      Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.