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
Multiple concerns over the impact of wide scale changes in land management have motivated comprehensive analyses of environmental sustainability of food and biofuel production. These call for high-resolution land management tools that enable comprehensive analyses of natural resources for decision-making. The agroecosystem simulation models with the most biophysical detail are point models, which often have a user interface that allows users to provide inputs and examine results for agricultural field scale analyses. These are not able to meet the needs of high-resolution regional or national simulations. We describe an efficient computational approach for deployment of the Environmental Policy Integrated Climate (EPIC) model at high-resolution spatial scales using high performance computing (HPC) techniques. We developed an integrated procedure for executing the millions of simulations required for high-resolution, regional studies, and also address building databases for model initialization, model forcing data, and model outputs. We first ported EPIC from Windows to an HPC platform and validated output from both platforms. We then developed methods of packaging simulations for efficient, unattended parallel execution on the HPC cluster. The job queuing system, Portable Batch System (PBS) is employed to control job submission. Simulation outputs are extracted to PostgreSQL database for analysis. In a case study covering four counties in central Wisconsin using HPC-EPIC, we finished over 140 K simulations in a total of 10 h on an HPC cluster using 20 nodes. This is a speedup of 40 times. More nodes could be used to achieve larger speedups. The HPC-EPIC model developed in this study is anticipated to provide information useful for high-resolution land use management and decision making. The framework for high-performance computing can be extended to other traditional, point-based biophysical simulation models. (C) 2011 Elsevier B.V. All rights reserved.
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