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


Managing soil-borne crop diseases using precision agriculture in Australia


Field experiments in southern Australia examined the spatial distribution of soil-borne disease inoculum within paddocks using DNA-based soil assays. Paddocks were divided into zones using cluster analysis for a range of combinations of digital data layers. Inoculum levels differed among zones in 33-64% of the 108 cases examined, depending on the zone model used. It was concluded that zone models used for precision agriculture (PA) most commonly in Australia (viz. zones based on cluster analysis of grain yield maps, ECa, and elevation, and zones based on satellite biomass imagery) were most suitable for partitioning inoculum distribution within paddocks. Generally there was a correlation between pre-sowing levels of inoculum and crop root damage and shoot biomass; however, there was not always a strong correlation between inoculum level and grain yield. There was some evidence that damage/unit soil inoculum varied among zones, but difficulties in predicting this a priori suggest that the damage rate should be assumed to be equal among zones. It is suggested that crop managers divide paddocks into yield or management zones and test each zone before every crop, using an appropriate soil sampling protocol. The disease risk and yield potential for each zone should then be considered to decide whether differential management is feasible or warranted. A soil test based on one composite paddock sample gives a paddock average only, which in many cases gives insufficient information about varying inoculum levels for robust zone management. If testing of every zone is not possible, then zones with the highest risk to profit from disease damage should be tested, to minimise risk. As PA technologies and biological understanding of disease behaviour improve, crop managers will have greater opportunities to exploit the non-random spatial distribution of soil-borne disease inoculum in new and imaginative ways at the zone level.

  • AU
  • SARDI_S_Australian_Res_&_Dev_Inst (AU)
Data keywords
  • digital data
Agriculture keywords
  • agriculture
Data topic
  • sensors
Document type

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

Institutions 10 co-publis
    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.