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:
- Evolution of the number of publications between 2005 and 2015
- Map of most publishing countries between 2005 and 2015
- Network of country collaborations
- Network of institutional collaborations (+10 publications)
- Network of keywords relating to data - Link
Xu, H., De Jong, R., Gameda, S. and Qian, B. 2010. Development and evaluation of a Canadian agricultural ecodistrict climate database. Can. J. Soil Sci. 90: 373-385. Spatially representative climate data are required input in various agricultural and environmental modelling studies. An agricultural ecodistrict climate database for Canada was developed from climate station data using a spatial interpolation procedure. This database includes daily maximum and minimum air temperatures, precipitation and incoming global solar radiation, which are necessary inputs for many agricultural modelling studies. The spatial interpolation procedure combines inverse distance squared weighting with the nearest neighbour approach. Cross-validation was performed to evaluate the accuracy of the interpolation procedure. In addition to some common error measurements, such as mean biased error and root mean square error, empirical probability distributions and accurate rates of precipitation occurrence were also examined. Results show that the magnitude of errors for this database was similar to those in other studies that used similar or different interpolation procedures. The average root mean square error (RMSE) was 1.7 degrees C, 2.2 degrees C and 3.8 mm for daily maximum and minimum temperature, and precipitation, respectively. The RMSE for solar radiation varied from 16 to 19% of the climate normal during April through September and from 21 to 28% of the climate normal during the remainder of the year.
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