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
The grey-brown alluvial soil is a typical soil in the Northern China. It is selected as research object to reveal feasibility and possibility of real-time analyzing soil parameter with NIR spectroscopic techniques. 150 samples are collected from a winter wheat farm. And then the NIR absorbance spectra are rapidly measured under the original conditions by a Nicolet Antaris FT-NIR analyzer. Two soil parameters, soil total nitrogen content (TN) and soil total phosphorus content (TP), are analyzed. 2262 nm and 1888 nm wavelengths were selected in the TN estimated model. Estimation model of soil TN content by using the first spectral derivation was established. After noises was eliminated by the first spectral deviation, the correlation coefficient of the model (R(C)(2)) was 0.767. For TP, a partial least square regression model was available, using 2258 nm, 2040 nm, 1810 nm, 1490 nm, 1332 nm and 996 nm wavelengths with correlation coefficient of 0.818. The results showed that soil total nitrogen content and soil total phosphorus content could be evaluated by using NIR absorbance spectra of soil samples.
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