e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

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:

Discover all records
Home page


A Predictive Model Construction for Mulberry Crop Productivity


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.

  • IN
  • Jain_Univ (IN)
Data keywords
  • information technology
Agriculture keywords
  • agriculture
Data topic
  • big data
  • modeling
Document type

Inappropriate format for Document type, expected simple value but got array, please use list format

Institutions 10 co-publis
    Powered by Lodex 8.20.3
    logo commission europeenne
    e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
    Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.