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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Validation and inter-comparison of three rnethodologies for interpolating daily precipitation and temperature across Canada


The use of daily climate data in agriculture has increased considerably over the past two decades due to the rapid development of information technology and the need to better assess impacts and risks from extreme weather and accelerating climate change. While daily station data is now regularly used as an input to biophysical and biogeochemical models for the study of climate, agriculture, and forestry, questions still remain on the level of uncertainty in using daily data, especially for predictions made by spatial interpolation models. We evaluate the precision of three models (i.e., spline, weighted-truncated Gaussian filter, and hybrid inverse-distance/natural-neighbor) for interpolating daily precipitation and temperature at 10 km across the Canadian landmass south of 60 degrees latitude (encompassing Canada's agricultural region). We compute daily, weekly, and monthly-aggregated bias and root-mean-square (RMSE) validation statistics, examining how error varies with orography and topography, and proximity to large water. Our findings show the best approach for interpolating daily temperature and precipitation across Canada requires a mixed-model/Bayesian approach. Further application of interpolation methods that consider non-stationary spatial covariance, alongside measurement of spatial correlation range would aid considerably in reducing interpolation prediction uncertainty. Copyright (C) 2010 Crown in Right of Canada.

  • CA
  • AAFC_Agr_&_Agri_Food_Canada (CA)
Data keywords
  • information technology
Agriculture keywords
  • agriculture
Data topic
  • 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
  • AAFC_Agr_&_Agri_Food_Canada (CA)
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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.