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.

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Knowledge engineering approach for the analysis of viticulture


The sustainable development of agricultural regions requires a methodical approach that will make a viable land management system policy in practice. In this regard, distinctive contextual characteristics of a country (remarkable areas for raising the regional products) could be taken into account in order to promote the good character of agricultural outputs and to increase the added value of considered lands. The manuscript primarily describes in qualitative and quantitative terms a mechanism for synthesizing information about wine production characteristics and therefore provides sufficient substantive treatment of agricultural or other system interactions. Firstly, the manuscript presents a discussion about many factors that affect wine production, and secondly, the focus is more on the development of an integrated approach using numerical and symbolic reasoning. The Geoviticulture Multicriteria Climatic Classification (MCC) System is engaged with a methodology comprising three viticultural climatic indexes of different natures: hydric type (dryness index), heliothermal type (Huglin index), and Nictothermal type (Cold Night Index). The knowledge representation is symbolized with the conceptual graphs formalism and the reasoning mechanisms are based on graph operations. A visual reasoning development focuses more on the decision process that would seem more appropriate for a practice on decision_support. In addition, the decision_support System (DSS) component is illustrated with a case study of Croatia republic in very broad characterizations of its main regional grape varieties. (C) 2015 Elsevier B.V. All rights reserved.

  • FR
  • CM
  • INP_ENIT_Ecole_Natl_Ing_Tarbes (FR)
Data keywords
  • knowledge
  • reasoning
  • knowledge engineering
  • conceptual graph
  • knowledge representation
Agriculture keywords
  • agriculture
Data topic
  • semantics
Document type

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

Institutions 10 co-publis
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    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.