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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Real-time visual/near-infrared analysis of milk-clotting parameters for industrial applications


The economical profitability of the dairy industry is based on the quality of the bulk milk collected in the farms, therefore it was based on the herd level rather than on the individual animals at real time. Udder infection and stage of lactation are directly related to the quality of milk produced on the herd level. However, improvement of milk quality requires testing each animal's milk separately and continuously. Recently, it was postulated that online equipment can estimate milk quality according to its clotting parameters, and thus result in better economical return for cheese making. This study further investigated the potential application of the AfiLab (TM) equipment to provide real-time analysis of milk-clotting parameters for cheese manufacture and cheese yield on quarter (1018) and individual cow (277) levels. Days in milk, lactose, log SCC and udder infection were found to have a significant effect on curd firmness and cheese properties and yield. The results clearly indicate that: (a) the parameter Afi-CF determined with the AfiLab (TM) is suitable for assessing milk quality for its clotting parameters, a value which is not provided by merely measuring fat and protein content on the gland and the cow levels; (b) bacterial type is the single major cause of reduced milk quality, with variations depending on the bacterial species; and (c) early and late lactation also had negative effects on milk-clotting parameters. Cheese made from the various milk samples that were determined by the Afilab (TM) to be of higher quality for cheese making resulted in higher yield and better texture, which were related mainly to the bacterial species and stage of lactation.

  • IL
    Data keywords
    • real time analysis
    Agriculture keywords
    • farm
    Data topic
    • big data
    • information systems
    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.