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 knowledge-based decision_support system to improve sow farm productivity


A knowledge-based decision_support system (CULLSOW) was developed to early identification of sows having low prolificity performance in commercial pig farms. Prolificity is a key factor for improving productive efficiency in pig production enterprises. CULLSOW uses an optimized qualitative reasoning model that estimates the expected prolificity of each sow. The reasoning strategy is based in a two-stage process analysis: (1) reasoning based on the main factors determining prolificity behaviour; (2) refining predictions through secondary factors. CULLSOW predicts prolificity based on information available from first and second parities. CULLSOW has been implemented using Milord 11 language and has been evaluated by comparing its predictions with those obtained with a reference method (RM). The evaluation results showed that, depending in the culling strategy, CULLSOW eliminated between 48 and 64% less sows than the RM method while obtaining the same increase on herd prolificity. CULLSOW proved to be more efficient than traditional methods in identifying the sows with the lowest reproductive performance. Crown Copyright (c) 2005 Published by Elsevier Ltd. All rights reserved.

  • ES
  • CA
  • Univ_Lleida_UdL (ES)
  • AAFC_Agr_&_Agri_Food_Canada (CA)
Data keywords
  • knowledge
  • knowledge based
  • reasoning
Agriculture keywords
  • farm
Data topic
  • information systems
  • decision support
  • semantics
Document type

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

Institutions 10 co-publis
  • AAFC_Agr_&_Agri_Food_Canada (CA)
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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.