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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The feasibility of co-existence between conventional and genetically modified crops: Using machine learning to analyse the output of simulation models


Simulation models are a commonly used tool for the study of the co-existence of conventional and genetically modified (GM) crops. Among other things, they allow us to investigate the effects of using different crop varieties, cropping systems and farming practices on the levels of adventitious presence of GM material in conventional crops. We propose to use machine learning methods to analyse the output of simulation models to learn co-existence rules that directly link the above mentioned causes and effects. The outputs of the GENESYS model, designed to study the co-existence of conventional and GM oilseed rape crops, were analysed by using the machine learning methods of regression tree induction and relational decision tree induction. Co-existence and adventitious presence of GM material were studied in several contexts, including gene flow between pairs of fields, the interactions of this process with farming practices (cropping systems), and gene flow in the context of an entire field plan. Accurate models were learned, which also make use of the relational aspects of a field plan, using information on the neighboring fields of a field, and the farming practices applied in it. The use of relational decision tree induction to analyse the results of simulation models is a novel approach and holds the promise of learning more general co-existence rules by allowing us to vary the target field within a chosen field plan, as well as to consider completely different field plans at the same time. (C) 2008 Elsevier B.V. All rights reserved.

  • SI
  • BE
  • FR
  • Katholieke_Univ_Leuven (BE)
  • Inra (FR)
Data keywords
  • machine learning
Agriculture keywords
  • crop system
  • farming
Data topic
  • big data
  • 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.