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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Mapping the Risk of Sudden Oak Death in Oregon: Prioritizing Locations for Early Detection and Eradication


Phytophthora ramorum was first discovered in forests of southwestern Oregon in 2001. Despite intense eradication efforts, disease continues to spread from initially infested sites because of the late discovery of disease outbreaks and incomplete detection. Here we present two GIS predictive models of sudden oak death (SOD) establishment and spread risk that can be used to target monitoring and eradication activities in western Oregon. Model predictions were based on three primary parameters: weather and climate variability, host vegetation susceptibility and distribution, and dispersal (force of infection). First, a heuristic model using multi-criteria evaluation (MCE) method was developed to identify the areas at potential risk. We mapped and ranked host susceptibility using new geospatial vegetation data available from the U.S. Depaihnent of Agriculture, Forest Service (USDA FS)/Oregon State University (OSU) Landscape, Ecology, Modeling, Mapping, and Analysis project (LEMMA). Precipitation and temperature conditions derived from PRISM climate database were parameterized in accordance to their epidemiological importance in the SOD disease system. The final appraisal scores were calculated and summarized to represent a cumulative spread risk index, standardized into five risk categories from very low risk to very high risk. Second, we used the machine-learning method, maximum entropy (MAXENT) to predict the current distribution of SOD infections. Here, probability of infection was calibrated based on the correlation between 500 field observations of disease occurrence and several predictor variables including climate variability, host susceptibility and abundance, topographic variables, and a dispersal constraint. The dispersal constraint estimates the force of infection at all locations and thus predicts the actual or current distribution of the pathogen rather than its potential distribution. Numerous forests across the western region of Oregon appear to be susceptible to SOD. Areas at greatest risk of disease spread are concentrated in the southwest region of Oregon where the highest densities of susceptible host species exist, in particular tanoak (Lithocarpus densiflorus). These models provide a better picture of threatened forest resources across the state and are being actively used to prioritize early detection and eradication efforts.

  • US
  • USDA_Forest_Serv (US)
  • Oregon_State_Univ (US)
  • Univ_N_Carolina_Charlotte (US)
  • Oregon_Dept_Forestry (US)
Data keywords
  • machine learning
Agriculture keywords
  • agriculture
Data topic
  • big data
  • information systems
  • modeling
  • sensors
Document type

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

Institutions 10 co-publis
  • Oregon_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.