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.

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A Low Complexity Data Driven Model of Environmental Discharge Dynamics for Wireless Sensor Network Applications


Poor water quality is a global concern, with agricultural practices the major contributor to reduced water quality with emissions of nutrient fluxes in to water systems. Using a collaborative framework to support catchment-scale water quality monitoring, control and management (WQMCM), individual sub-networks can learn and predict the impact of catchment events on their locality[1], allowing dynamic decision making for local irrigation strategies. Since resource constraints on network nodes (e.g. battery life, computing power etc) require a simplified predictive model, low-dimensional model parameters are derived from the existing National Resource Conservation Method (NRCS). An M5 decision tree algorithm is then used to develop predictive models for total discharge volume (Q), response start and duration (t(1) & t(d)). Evaluation of these models demonstrates high accuracy (84-94%) even for a small training set of under 100 samples for Q and t(d). However, for t(1), 300 samples are required to give adequate performance. (C) 2014 The Authors. Published by Elsevier Ltd.

  • GB
  • Univ_Southampton (UK)
Data keywords
  • machine learning
Agriculture keywords
  • agriculture
Data topic
  • big data
  • information systems
  • modeling
  • sensors
Document type

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

Institutions 10 co-publis
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    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.