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
Agroecosystem models that can incorporate management practices and quantify environmental effects are necessary to assess sustainability-associated food and bioenergy production across spatial scales. However, most agroecosystem models are designed for a plot scale. Tremendous computational capacity on simulations and datasets is needed when large scales of high-resolution spatial simulations are conducted. We used the message passing interface (MPI) parallel technique and developed a master slave scheme for an agroecosystem model, EPIC on global food and bioenergy studies. Simulation performance was further enhanced by applying the Vampir framework. On a Linux-based supercomputer, Cray XT7 Titan, we used 2048 cores and successfully shortened the running time from days to 30 min for a global 30 years of modeling of a bioenergy crop at the resolution of half-degree (62,482 grids) with the message passing interface based EPIC (mpi_EPIC). The results illustrate that mpi_EPIC using parallel design can balance simulation workloads and facilitate large-scale, high-resolution analyses of agricultural production systems, management alternatives and environmental effects. (C) 2014 Elsevier B.V. All rights reserved.
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