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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Potential of genomic selection in rapeseed (Brassica napus L.) breeding


Genomic selection employs genome-wide marker data to predict genomic breeding values. In this study, a population consisting of 391 lines of elite winter oilseed rape derived from nine families was used to evaluate the prospects of genomic selection in rapeseed breeding. All lines have been phenotyped for six morphological, quality- and yield-related traits and genotyped with genome-wide SNP markers. We used ridge regression best linear unbiased prediction in combination with cross-validation and obtained medium to high prediction accuracies for the studied traits. Our results illustrate that among-family variance contributes to the prediction accuracy and can lead to an overestimation of the prospects of genomic selection within single segregating families. We also tested a scenario where estimation of effects was carried out without individuals from the family in which breeding values were predicted, which yielded lower but nevertheless attractive prediction accuracies. Taken together, our results suggest that genomic selection can be a valuable genomic approach for complex agronomic traits towards a knowledge-based breeding in rapeseed.

  • DE
  • Univ_Hohenheim (DE)
  • Leibniz_Assoc (DE)
  • Limagrain (DE)
Data keywords
  • knowledge
  • knowledge based
Agriculture keywords
    Data topic
    • big data
    Document type

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

    Institutions 10 co-publis
    • Univ_Hohenheim (DE)
    • Leibniz_Assoc (DE)
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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.