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.
You can access and play with the graphs:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Networks of practice for co-construction of agricultural decision_support systems: Case studies of precision dairy farms in Australia
The on-farm use of commercial decision_support systems (DSSs) presents learning and adaptation challenges for farmers and their social learning networks. A study of six Australian dairy farms installing new precision dairy farming technology was undertaken to develop an in-depth picture of the issues occurring at the interface where precision farming data and decision-making meet. A qualitative exploratory case study method was used, with farmers each interviewed up to five times from pre-installation until 2 years of use. A three-phase learning trajectory was observed amongst farmers involving early learning, consolidation, and advanced use. Farmers exhibited experiential learning but also learned via interaction with a network of on- and off-farm contacts forming a network of practice around the new users. This precision dairy farming network of practice formed a vital method of exchanging knowledge on how to best use technology and data in farming systems, with DSSs acting as a boundary object for learning. Externalisation of tacit knowledge into an explicit form suitable for DSSs was a major focus of this social learning. Co-construction of DSS knowledge in the emerging network was impeded by the absence of potentially important agents, in addition to the incomplete links between existing agents such as technology retailers and farmers. A technological innovation systems perspective is used to propose an improved framework to make greater use of translators and intermediaries. It is aimed at improving links amongst the community to more effectively aid farmers in creating new knowledge in agricultural DSS use. (C) 2012 Elsevier Ltd. All rights reserved.
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