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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Can gamma-radiometrics predict soil textural data and stoniness in different parent materials? A comparison of two machine-learning methods


The use of gamma-radiometrics for soil proximal sensing is strongly site specific, because of the influence of parent material mineralogy on the gamma-rays emitted from the soil. The work wants to propose a non-linear and multivariate computational approach to predict soil textural data and surficial stoniness based on gamma-spectroscopy. The gamma-spectroscopy survey was performed in heterogeneous soils in terms of parent material, pedogenesis, morphology, coarse material and moisture content. The gamma-radiometrics survey was performed by "The Mole" sensor (The Netherlands) and gamma-ray spectra were analysed by a Full Spectrum Analysis. 70 experimental points were described and classified according to parent material and surficial stoniness. The 70 experimental sites were also sampled during the gamma-ray survey and analysed for soil texture and moisture content An explorative PCA in the experimental points, based on the gamma-ray data and the elevation, showed 3 groups of cases, relating to the three groups of bedrock (i) calcareous flysch, (ii) feldspathic sandstone, and (iii) other lithologies, namely marly-shales, marine and fluvio-lacustrine deposits. Two machine learning models were used to predict sand, clay and surficial stoniness. The models were Support Vector Machines (SVM) and Artificial Neural Networks (ANN). An independent validation set of 20 soil samples was used to check the accuracy of the prediction models. Both SVM and ANN showed good prediction accuracy for sand and clay, although SVM showed the lowest errors. Both models showed lower accuracy for stoniness prediction, mainly due to high prediction errors in several sampling points. Probably, the high errors in stoniness prediction were due the strong heterogeneity of rock types and mineralogy. However, prediction model of stoniness spatial variability is very important in order to an adequate farming management (C) 2014 Elsevier B.V. All rights reserved.

  • IT
  • CREA_Council_Agr_Res_&_Agr_Economics (IT)
Data keywords
  • machine learning
Agriculture keywords
  • farming
Data topic
  • big data
  • modeling
  • sensors
Document type

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

Institutions 10 co-publis
  • CREA_Council_Agr_Res_&_Agr_Economics (IT)
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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.