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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Problems in correlation of Czech national soil classification and World Reference Base 2006


The use of legacy data in international soil mapping projects entails the demand for the harmonisation of soil data. Accurate correlation between national and international soil classification units is an important prerequisite in global soil mapping and acquisition of harmonised soil data usable in environmental applications. The correlation of soil units at different taxonomic levels was undertaken to relate the Czech national soil classification system with the World Reference Base (WRB) and evaluate the effectiveness of a semantic approach (analogical soil units provided by expert knowledge), and quantitative approach in the correlation. For the quantitave approach, a set of 433 soil profiles randomly selected from the Large-scale mapping of agricultural soils in Czechoslovakia was classified according to WRB using available analytical and morphological soil data. The study showed the necessity for an analytical approach and quantitative data use for reliable correlation between the two classification schemes. The general level of correlability at the higher taxonomic level can be considered as high (88%), whilst there is a significant variability of correlation accuracy between soil types. Conversion of some soil units, e.g. Rankers, Rendzinas, Pararendzinas, Cernice, Cernozems, Podzols or Luvizems requires analytical and morphological data of corresponding profiles. Relatively low correlability is caused by various factors. Different concepts of the soil unit and different setting of the criteria of the diagnostic soil properties are the most important. Some units such as Glejs, Fluvizems or Hnedozems can be correlated with a high probability of accurate assignment. High incompatibility was shown at the lower taxonomic level. Correlation of lower taxonomic units should be subject to analytical processing. (C) 2011 Elsevier B.V. All rights reserved.

  • CZ
  • IT
  • Czech_Univ_Life_Sci_Prague_CZU (CZ)
  • European_Commission (IT)
Data keywords
  • knowledge
  • semantic
Agriculture keywords
  • agriculture
Data topic
  • modeling
Document type

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

Institutions 10 co-publis
  • Czech_Univ_Life_Sci_Prague_CZU (CZ)
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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.