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
Agriculture is a complex process of air, water, weather, soil, plants, animals and micro-organisms, which are uneven in distribution. Because of the uncertainty and risk associated with agricultural production - due to vulnerability in weather, variability in soil, infestation of pests and insects - some most important decisions based on "what if" depends on the existing knowledge base of current and future physical conditions like soil and climate, yield and prices, crop and area. If the past pattern and exact impact of agriculture-associated resources are known, one can predict the likely occurrences of certain crop-related attributes in advance, so that farmers can put together safeguards by tiding over the controllable attributes, which are likely to have serious impact on crop growth and yield. Thus the quality of decision-making can play an important role in complex and uncertain situations. Empirical evidence reveals that human judgement and decision-making can be far from optimal and could even deteriorate further with added complexity and stress. Therefore, aiding the deficiencies of human judgement and quality decision-making has been a major focus of research throughout the history particularly with the advancement in electronic processing of data and design of decision_support System (DSS). DSS can help to reduce uncertainty and improve the decision-making process by providing access to data through procedures and analytical reasoning. Computerized decision_support for sustainable agriculture is not new. These systems have been designed to address complex tasks involving agronomic, economic, regulatory, climate change and pollution control, enabling us to match the biological requirements of crop to the physical characteristics of land so that the objective specified by the user is obtained. Most agricultural DSSs aim to help stakeholders realize their strategic aim of securing a competitive advantage through timely decision-making. While addressing the limitations in current decision_support technologies, visionaries and researchers throughout tithes have talked of exploiting our mass of information to produce new knowledge automatically, build intelligent DSSs and eliminate human burdens associated with information-seeking and problem-solving activities. DSSs are widely used and known with agriculture and they have proved to be important tools in the decision-making process from farm to fork. Unfortunately, ease of use, low adoption, failure to show cost benefits, complexity and user inputs, distrust for the output, lack of field testing, lack of integration among heterogeneous components, success measurements, non-involvement of end-user before and after development stages and under-definition of beneficiaries are some issues that need to be addressed. But keeping the amount of input data required as small as possible; keeping the system itself as flexible as possible; providing users with default values; ensured involvement of users from basic development stage; greater user involvement through participatory learning approaches; interactive prototyping as well as keeping DSS development manageable and small in scope can provide avenues for improvement in DSS research and development.
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