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
India accounts for more than fifty percent of sericulture production in the world. The modern Sericulture methods that have evolved demand, accurate classification of soil suitable for Mulberry crop productivity. But the most prevalent method adopted currently in soil testing is manual, which often fails to give the correct prescription to make soil suitable for Mulberry crop. A scientific approach of soil testing could aid farmers in dynamic decision-making, which would significantly increase Mulberry crop productivity. Such analysis is possible with the help of data analysis, thanks to the advent of modern computer technology. Due to significant advances in the area of Information Technology and agriculture, there is scope of interdisciplinary work, application thereof to solve agricultural problems. Hence effort was made to explore and develop an automated system for the analysis of range of soil characteristic suitable for Mulberry crop production, which in turn contribute to increase in Cocoon productivity. The experiment was carried out by collecting soil samples from different irrigated regions of Karnataka, India, to deduce the range of soil parameters supporting the healthy growth of Mulberry crop. Further, different classification technique was applied on parameters of soil suitable for Mulberry crop using Hunt's algorithm, and J48 Decision tree was more applicable in decision making. The statistical information obtained from data mining technique were validated through mathematical model for developing a forewarning predictive system for crop productivity. (C) 2015 The Authors. Published by Elsevier B.V.
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