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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Miscanthus spatial location as seen by farmers: A machine learning approach to model real criteria


Miscanthus is an emerging crop with high potential for bioenergy production. Its effective sustainability depends greatly on the spatial location of this crop, although few modelling approaches have been based on real maps. To fill this gap, we propose a spatially explicit method based on real location data. We mapped all of the miscanthus fields in the supply area of a transformation plant located in east-central France. Then, we used a boosted regression tree, machine learning method, to model miscanthus presence/absence at the level of the farmer's block as mapped in the French land parcel identification system. Each of these modelling spatial units was characterised on agronomical, morphological and contextual variables selected from in-depth spatially explicit farm surveys. The model fostered a two-fold aim: to assess the farmers' decision criteria and predict miscanthus location probability. In addition, we evaluated the consequence of possible legislative constraints, which could prevent the miscanthus to be planted in protected areas or in place of grasslands. The small and complex-shaped farmer's blocks that were predicted by our model to be planted with miscanthus were also characterised by their great distance from the farm and the roads. This kind of result could provide a different perspective on the definition of "marginal land" by integrating also the farm management criteria. In conclusion, our approach elicited real farmers' criteria regarding miscanthus location to capture local specificities and explore different miscanthus location probabilities at the farm and landscape levels. (C) 2014 Elsevier Ltd. All rights reserved.

  • FR
  • Inra (FR)
Data keywords
  • machine learning
Agriculture keywords
  • agronomy
  • farm
Data topic
  • big data
  • information systems
  • modeling
Document type

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

Institutions 10 co-publis
  • Inra (FR)
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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.