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
Utilisation of agricultural databases for statistical evaluation of yields of barley and wheat in relation to soil variables and management practices
The objective of this study was to utilise existing databases for evaluating effects of soil and management factors on the yields of barley and wheat in Norway. In a multiple regression model, 17% and 19% of the variation of barley and wheat yield, respectively, was explained by 15 significant predictors. The highest-ranking predictors for barley yield were percent area of wheat ( indicating more favourable climatic condition), irrigation, pH and silt. Other significant predictors were agricultural education, CV pH, man-hours on farm, ( pH)(2), loam, 1n P-AL, 1n area of farm, number of fields, ( 1n P-AL)(2), ( 1n area)(2\) and percent grass area. For wheat yield, the highest-ranking predictors were irrigation, percent winter wheat, pH and ln area of farm. Other significant predictors were CV pH, 1n P-AL, ( pH)(2), loam, number of fields, ( 1n P-AL)(2), man-hours per ha, percent grass area, agricultural education, other grain and ( 1n area)(2).
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