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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Effective map scales for soil transport processes and related process domains - Statistical and spatial characterization of their scale-specific inaccuracies


Digital Soil Mapping (DSM) aims at the creation of reliable, reproducible and dynamic spatial soil information according to specific users' requests and demands. Positional and temporal inaccuracies as well as the question of an optimal resolution of digital elevation models (DEMs) indicate scale-related issues which represent typical challenges for DSM. In this study, the effective map scale (EMS) approach is presented which enables the detection of operational scales where soil-related processes take place, the localization of corresponding process domains, as well as the statistical and spatial visualization of their scale-specific inaccuracies. The underlying algorithm can be considered as a test procedure for predictive efficiency where measurements, characterizing a soil-related process as well as a proxy variable and its scale-specific variation, are optimized. In doing so, positional and semantic inaccuracies of legacy data can be detected. The EMS approach is applied to the example of an agricultural parcel where soil erosion by tillage is assumed. Auger samples have been taken in order to quantify the amount of soil loss and accumulation during the last 80 years in a German landscape with a complex topography and dominating loess parent material. The measurements have been related to the terrain attribute Mass Balance Index (MBI), which acts as an indicator for tillage erosion and has been varied according to both scale and soil surface complexity. The indicator MBI is derived from a high resolution digital elevation model and combines the basic terrain attributes slope, curvature and vertical distance to channel network due to their importance for tillage erosion processes. Different scale levels have been created by a region-growing segmentation algorithm. Each scale level contains discrete soil-terrain objects, represented by polygons. The scale-related analysis of MBI variations and measurements has revealed a range of EMSs where process domains are visible. Their accuracies are characterized from various perspectives: (1.) The analysis of single MBI variants of scale and complexity by linear regression expresses the spatial and statistical variance of EMSs. (2.) The application of the data mining algorithm random forest on all the MBI variants of complexity per scale level leads to a spatial and statistical suppression of uncertain process domains and an emphasis of process domains, which could be predicted with a higher reliability. In this study, the process domains of accumulation could be identified on a range of operational scale levels. Due to the positional inaccuracies of auger samples and temporal inaccuracies based on overlaying long-term soil transport processes, the process domains of soil loss could not be sufficiently located. (C) 2015 Elsevier B.V. All rights reserved.

  • DE
  • Univ_Halle_Wittenberg (DE)
  • Helmholtz_Assoc (DE)
Data keywords
  • semantic
Agriculture keywords
  • agriculture
Data topic
  • big data
  • 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
  • Helmholtz_Assoc (DE)
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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.