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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Data Analysis, Quality Indexing and Prediction of Water Quality for the Management of Rawal Watershed in Pakistan


In contrast to managing the water quality only at the command level (where water is being consumed), one should also give importance to the water quality in the areas where water is being produced i.e. the watersheds. The failure to do so deteriorates the water quality for down streams and poses serious challenges for the water managers in order to meet the water quality requirements on sustainable basis. In order to have an effective water management in command areas, it is essential to assess different aspects of water quality. Rawal watershed is a relatively small watershed area which is being affected by the anthropogenic activities e.g. urbanization, deforestation etc. In this paper, we present the last four years (2009 - 2012) trends of water quality related parameters along with month-wise as well as source-wise parametric satisfactory analysis against WHO quality standards. Moreover, we applied regression models to check the seasonal water quality trends. The quality indices were analyzed by the combination of supervised and unsupervised machine learning techniques. Different sources of fecal coliforms contamination were also identified. Lastly the possible reasons for high contamination were identified by studying the watershed land covers. Our research suggests that in order to find the quality index of water, Average Linkage (Within Groups) method of Hierarchical Clustering using Euclidean distance is an accurate unsupervised learning technique. Similarly, for classifications, Multi-Layer Perceptron (MLP) has been found to be more accurate supervised learning technique. Higher values of fecal coliforms were found in the months of March, June, July, and October. Some of the possible reasons are land-covers especially scrub forest and rain-fed agriculture areas, poultry farms, and population settled around the streams.

  • PK
    Data keywords
    • machine learning
    Agriculture keywords
    • agriculture
    • farm
    Data topic
    • information systems
    • modeling
    • semantics
    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.