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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Categorising farmers' practices to reformulate a problem in partnership: A method for building situation-specific typologies


In the present socio-economic context in which the social functions of agriculture are being questioned, the transformation of production practices has become a subject of discussion between farmers, development agents and researchers. The diversity of agricultural practices may prove an asset for the development of a multifunctional agriculture, as it may show the complementary nature of farms as well is the innovations they carry out. It is therefore important to be able to represent this diversity in order to enrich such discussions and help researchers and development agents reformulate issues in partnership. However there is no methodology for classifying practices which thus remain confined to monographic studies. Grounded in an engineering research setting, this paper suggests a method for formalising typologies of practices, according to two theoretical choices. Following Rosch's cognitive theory on prototypical categorisation, our method aims at formalising types as poles seen as average patterns for categories instead of squares with clear-cut boundaries. In a constructivist and situated perspective, the method is supported by collectively setting and such knowledge engineering tools as repertory-grids: it aims at collectively defining types of practice combinations linked to the expertise of participants to the project and to the problem they intend to tackle. The method consists of four stages, mainly iterative, which are illustrated along the way by examples from our engineering setting: stage A): construction of the setting, which involves choosing the zone, building a database identifying people involved in agricultural activity choosing a population sample for the interview stage (B), and clarifying each participant's expectations and role; stage B): semi-structured interviews with farmers, followed by the writing up of a summary sheet to form the basis for collective work in stage C stage C): the first step in the formalisation of the typology is to characterize the criteria, and then to build prototypes using a multivariate analysis and numerous group discussions on preliminary formalisations. In accordance with the prototypical theory, types are firstly defined by more typical practices, and structural data are examined afterwards to deepen types' description. The resulting typologies then reveal the strategic choices of farmers and their "management styles"; stage D): even though the problem is redefined in a separate step, this aspect frequently emerges during the preceding stages which allow researchers and development agents to review their future actions. Apart from the raw result of this categorisation process, the method has proven its usefulness in that it enlarges the participants representations of farm diversity. As an iterative learning loop, it has also provoked discussions on extension practices. This method may seem cumbersome as it requires the acquisition of a lot of data on practices and is therefore limited to research-development partnerships. Nevertheless, it has proven a useful tool for "exploratory partnerships", by enhancing mutual understanding and stimulating collective exploration of the problem. In that sense, a typology is not a goal in itself, but a means of collectively redefining the problem.

  • FR
  • Inra (FR)
Data keywords
  • knowledge
  • knowledge engineering
Agriculture keywords
  • farm
  • agriculture
Data topic
  • decision support
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.