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
With the growing international food crisis, grain production and supply have become an important task. Information technology, (IT) can help developing countries forecast their grain production. The Generalized Regression Neural Network (GRNN) model is a good technique for predicting grain production in rural areas. Using real agricultural data from Tianjin Binhai area, the agricultural production value has been predicted. The GRNN method is suitable for non-linear, multi-objectives, and multivariable forecasting. Improving local crop production and demand prediction in developing countries will help to improve food supply.
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