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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Fuzzy knowledge-based model for soil condition assessment in Argentinean cropping systems


A knowledge-based system (KBS) for assessing soil condition in agroecosystems is presented. The KBS Was built through expert opinion elicitation and available scientific data using fuzzy logic. The system is structured into three main elements: (1) input variables that represent the physical domain of soil condition assessment and are related to environmental and crop management conditions; (2) primary modules that describe the fuzzy nature of the soil indicators and; (3) secondary modules that represent the elicited knowledge on soil condition assessment from an expert panel. The application of the KBS on data on crop fields from Inland Pampa (Argentina) indicated that soil nitrogen depletion poses a hazard for soil health as no crop was able to accomplish more than 50% of the sustainability criteria elicited for soil nitrogen extraction from the system. Conversely, soil carbon and physical conditions exhibited values closer to the desirable scenarios elicited by the fuzzy if-then rules, with Values of 0.84, 0.71 and 0.74 for maize, soybean and wheat, respectively, where higher indicator values reflect better soil condition assessment. No significant differences were observed in the overall soil degradation module between crops, with values of 0.64 for maize and wheat and 0.67 for soybean. The KBS developed in this work provided an alternative modeling tool for assessing agroecosystem condition when knowledge regarding long-term assessment is imprecise and uncertain. (c) 2008 Elsevier Ltd. All rights reserved.

  • AR
    Data keywords
    • knowledge
    • knowledge based
    • research data
    Agriculture keywords
    • crop system
    Data topic
    • information systems
    • modeling
    • 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.