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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Estimates of Soil Organic Carbon Stocks in Zhejiang Province of China Based on 1:50 000 Soil Database Using the PKB Method


Soil organic carbon (SOC) is an important component of farming systems and global carbon cycle. Accurately estimating SOC stock is of great importance for assessing soil productivity and modeling global climate change. A newly built 1:50 000 soil database of Zhejiang Province containing 2154 geo-referenced soil profiles and a pedological professional knowledge-based (PKB) method were used to estimate SOC stock up to a depth of 100 cm for the Province. The spatial patterns of SOC stocks stratified by soil types, watershed (buffer analysis), topographical factors, and land use types were identified. Results showed that the soils in Zhejiang covered an area of 100 740 km(2) with a total SOC stock of 831.49 x 10(6) t and a mean SOC density of 8.25 kg m(-2), excluding water and urban areas. In terms of soil types, red soils had the highest SOC stock (259.10 x 10(6) t), whereas mountain meadow soils contained the lowest (0.15 x 10(6) t). In terms of SOC densities, the lowest value (5.11 kg m(-2)) was found in skel soils, whereas the highest value (45.30 kg m(-2)) was observed in mountain meadow soils. Yellow soils, as a dominant soil group, determined the SOC densities of different buffer zones in Qiantang River watershed because of their large area percentage and wide variation of SOC density values. The area percentages of various soil groups significantly varied with increasing elevation or slope when overlaid with digital elevation model data, thus influencing the SOC densities. The highest SOC density was observed under grassland, whereas the lowest SOC density was identified under unutilized land. The map of SOC density (0-100 cm depth) and the spatial patterns of SOC stocks in the Province would be helpful for relevant agencies and communities in Zhejiang Province, China.

  • CN
  • US
  • Zhejiang_Univ_ZJU (CN)
  • Michigan_State_Univ (US)
Data keywords
  • knowledge
  • knowledge based
Agriculture keywords
  • farming
Data topic
  • information systems
  • semantics
Document type

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

Institutions 10 co-publis
  • Zhejiang_Univ_ZJU (CN)
  • Michigan_State_Univ (US)
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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.