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.

You can access and play with the graphs:

Discover all records
Home page


Temporal stability in soil water content patterns across agricultural fields


When a field or a small watershed is repeatedly surveyed for soil water content, locations can often be identified where soil water contents are either consistently larger or consistently less than the study area average. This phenomenon has been called temporal stability, time stability, temporal persistence, or rank stability in spatial patterns of soil water contents. Temporal stability is of considerable interest in terms of facilitating upscaling of observed soil water contents to obtain average values across the observation area, improving soil water monitoring strategies, and correcting the monitoring results for missing data. The objective of this work was to contribute to the existing knowledge base on temporal stability in soil water patterns using frequent multi-depth measurements with Multisensor Capacitance Probes (MCPs) installed in a coarse-texture soil under multi-year corn production. Water contents at 10, 30, 50, and 80 cm depths were measured every 10 min for 20 months of continuous observation from May 200 1 to December 2002. The MCPs revealed temporal stability in soil water content patterns. Temporal stability was found to increase with depth. The statistical hypothesis could not be rejected (P<0.0001) that data collected each 10 min, each 2 h, each day, and each week had the same temporal stability. The locations that were best for estimating the average water contents were different for different depths. The best three locations for the whole observation period were the same as the best locations for a month of observations in about 60% of the cases. Temporal stability for a specific location and depth could serve as a good predictor of the utility of this location for estimating the area-average soil water content for that depth. Temporal stability could be efficiently used to correct area-average water contents for missing data. Soil water contents can be upscaled and efficiently monitored using the temporal stability of soil water content patterns. Published by Elsevier B.V.

  • US
  • Univ_Calif_Riverside (US)
  • USDA_ARS_Agr_Res_Serv (US)
Data keywords
  • knowledge
  • knowledge based
Agriculture keywords
  • agriculture
Data topic
  • information systems
  • sensors
Document type

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

Institutions 10 co-publis
  • USDA_ARS_Agr_Res_Serv (US)
Powered by Lodex 8.20.3
logo commission europeenne
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.