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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Modeling the Relationship between Catchment Attributes and In-stream Water Quality


The physical attributes of catchments have a significant influence on the chemistry and physical features of in-stream water quality. Consequently, modeling this relationship is important for informing management strategies aimed at improving regional water quality. This study used a machine learning approach (Artificial Neural Networks or ANNs) to model the relationship between land use/cover, associated with other physical attributes of the catchment such as geological permeability and hydrologic soil groups, and in-stream water quality parameters (e.g., K+, Na+, Mg2+, Ca2+, SO42-, Cl-, HCO3-, SAR, pH, EC, TDS). Eighty-eight catchments in the southern basins of the Caspian Sea were explored. To enhance the architecture of ANNs, the study applied backward elimination-based multiple linear regression, through which the optimum input nodes of ANNs can be determined amongst the most relevant variables. A transformation approach was also applied to qualify the performance of ANNs in four quality classes, ranging from unsatisfactory to very good. According to the findings, ANN based TDS model performance improved from unsatisfactory to very good. However, the linear regression-based pH model resulted in a decrease in performance, from "very good" to satisfactory. Moreover, among all catchment attributes, urban areas had the greatest impact on K+, Na+, Mg2+, Cl-, SO42-, EC and SAR concentration values. K+, TDS and EC were influenced by agricultural area (%). Bare land areas (%) had the largest impact on Na+, Ca2+ and HCO3-. Assessing the performance of the ANN-based models developed in this study indicates that 10 out of 11 models had "very good" quality ratings and can be reliably used in practice.

  • IR
  • CA
  • Univ_Tehran (IR)
  • McGill_Univ (CA)
Data keywords
  • machine learning
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
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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.