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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A Bayesian approach to interpreting Staphylococcus aureus diagnostic indicators for on-farm decision making


A Bayesian network (BN) modeled Staphylococcus aureus (SA) evidence on probability of mastitis in individual cows. Herd evidence was provided by bulk tank culture (BTC), antibody test (BTMAb), somatic cell count (BTSCC), and risk factors (HRF). Culture (CC), antibody test (CowAb), Lactation-to-date somatic cell score (C-SCS), and risk factors (CRF) provided cow evidence. Herd evidence served as priors (P(SA)) to cow probabilities. Risk factors were processed as likelihoods based on strength of association to SA. Two herd scenarios were modeled. Herd-1 had moderate (17%) white Herd-2 had high risk of SA (43%). HRF had little effect on cow posterior probabilities when risk was moderate unless herd diagnostic indicators were high. Given Herd-1, P(SA) ranged from 1.7 to 91.8% depending on herd diagnostic evidence. Given Herd-2, posterior probabilities ranged from 5.8 to 97.6% with the same evidence. Two cow scenarios were modeled to evaluate effects of CRF on posteriors when combined with herd and cow diagnostic evidence. Overall likelihood was 1.33 for Cow-1 and 1.69 for Cow-2. Given Cow-2 and Herd-1, a positive BTC and high BTMAb, cow posterior probabilities exceeded 50% when C-SCS was below 3.0. Given Herd-1 and Cow-2, cow posteriors exceeded 50% when BTC was positive and C-SCS above 6.0. As strength of herd evidence increased, probability of cow infections increased, improving SA detection with less cow diagnostic evidence. Probabilities can assist dairy managers in determining relevancy of diagnostic indicators for mastitis decision-making.

  • US
  • Penn_State_University_Pk (US)
Data keywords
    Agriculture keywords
    • farm
    Data topic
    • modeling
    • decision support
    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.