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
Can computer models stimulate learning about sustainable land use? Experience with LUPAS in the humid (sub-)tropics of Asia
In many rice-cultivating regions of east and south-east Asia, competition for land and water resources is strong and increasing. This calls for exploration of future technology and policy options in support of sustainable land (and water) use. Sustainable land use is a complex issue, that involves uncertainties about the dynamics of the biophysical system and the social system, as well as multiple perspectives. The capacity to identify options for sustainable and equitable development depends on the acquisition of knowledge and skills for (a) holistic analysis of the biophysical system dynamics, (b) analysis of the multiple positions, perceptions, values, beliefs and interests of the relevant stakeholders, (c) contemplation of the action needed to fill the gap between the desired socio-technical system and the perceived real-world situation. Learning is contextual and gradual: historically, agricultural scientists have moved from a reductionist to a holistic hard system perspective, while now slowly embracing the interpretive system perspective. Under the Ecoregional Initiative for the humid tropics of Asia, SysNet (1996-2000) trained scientists at four National Agricultural Research Systems (NARS) in trans-disciplinary analysis through development and application of the LUPAS (Land Use Planning and Analysis System) modelling framework. Aim was to develop a holistic land use analysis methodology for four different rice-cultivating regions. To ensure relevance for land use decision making, scientists organized meetings with planners and other stakeholders. Four NARS teams of 12-24 scientists acquired and/or increased expertise and skills outside their own disciplines, in modelling and improved data management, and learned to perform agro-ecological analyses. Regional planners were presented with region-specific data in support of formulation of area-specific technical and policy recommendations, and agricultural experts used LUPAS tools to extrapolate research results to other areas. Much learning was acquired within the technical-economic domain, but the exchange with stakeholders did not yet lead to a critical learning system approach. Nevertheless, NARS highly appreciated the new tools and knowledge, and made a significant step forward from mono-disciplinary to holistic agro-ecological analysis. Application of the LUPAS methodology has been expanded following finalization of the project, despite practical problems such as diminishing long-term funds and the high demand for IT people outside agriculture. NRM decision making demands reflexivity and skilful facilitation. In these processes, LUPAS modelling has a specific added value: it enhances long-term strategic thinking about sustainable land use and rural development issues. (c) 2006 Elsevier Ltd. All rights reserved.
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