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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Machine Learning as an aid to management decisions on high somatic cell counts in dairy farms


High somatic cell counts (SCC) is associated with mastitis infection, in dairy herds, worldwide. This work describes Machine Learning (ML) techniques designed to improve the information offered to farmers on animals producing high SCCs according to particular herd profiles. The analysed population included 71 dairy farms in Asturias (Northern Spain) and a total of 2,407 lactating cows. Four sources of information were available: a) a questionnaire survey describing facilities, milking routines and management practices of the farms studied; b) dairy recording information; c) classification of the cows suspected of being healthy or subclinical mastitic according to farmers' expertise; and d) positive or negative scores with respect to the California Mastitis Test (CMT). The decimal logarithm of the SCC (linear score), lactation number, herd size, lactating cows per milker, milk urea concentration, number of clusters per milker and actual SCC are shown to be the most informative attributes for mimicking both farmers' expertise or CMT performance in order to identify animals producing persistently high SCCs in dairy herds. However, to improve the identification of cows suspected of being non-healthy, the system uses other information related to management and milking routines. Decision rules to predict CMT performance can provide useful, additional information to farmers to improve the management of dairy herds included in milk recording programs.

  • ES
  • SERIDA_Reg_Serv_Agro_Food_Res_&_Dev (ES)
  • CAPSA_Food (ES)
Data keywords
  • machine learning
Agriculture keywords
  • farm
Data topic
  • big data
  • modeling
  • sensors
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.