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
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.
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