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
iGreen: A ubiquitous dynamic network to enable manufacturer independent data exchange in future precision farming
In order to master the future challenges concerning increased demand for food and energy it is obvious that new methods are needed to boost productivity in a sustainable way. One key aspect to optimize agricultural processes and decision making is to derive effective means to acquire and share information along the value chain. In the past, data management in agriculture was dominated by proprietary, mostly Original Equipment Manufacturer (OEM) driven solutions with limited scope. To overcome the shortcomings associated with this, the German national joint research project iGreen was initiated in 2009 to enable convenient and efficient data sharing in a holistic and OEM independent way. As a project partner, John Deere focused on developing concepts and components for interconnecting machines among each other and with infrastructure nodes. In this paper the achieved results such as the infrastructure component referred to as the machine connector, the onboard data management and integration of mobile devices will be presented and evaluated through experiments focusing on data sharing in wheat and forage harvesting. The overall promising results indicate that in the future new applications focusing on optimizing processes can be enabled that will greatly improve the effectiveness and ease of agricultural production, especially fleet management and resource planning. (C) 2013 Elsevier B.V. All rights reserved.
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