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
Parallelization and optimization of spatial analysis for large scale environmental model data assembly
Spatial-temporal modelling of environmental systems such as agriculture, forestry, and water resources requires high resolution input data. Assembling and summarizing this data in the appropriate format for model input often requires a series of spatial analyses which can be extremely time-consuming, especially when many large data sets are involved. In this paper we investigated the ability of high-performance computing techniques to improve the efficiency of spatial analysis for model data assembly. We implemented an array-based algorithm to calculate summary statistics for long time-series daily grid climate data sets for 11,575 climate-soil zones across the Australian wheat-growing regions for input into a crop simulation model. We developed a zonal statistics algorithm using Python's Numpy module then parallelized it and processed it using a shared memory, multi-processor system. We assessed algorithm performance with a varying number of CPU cores, and assessed the influence of load balancing on the efficiency of parallel processing. Compared with traditional desktop GIS software, the serial and parallel (32 cores) implementation achieved about 180 and 1440 times speed-up, respectively. We also found that the most efficient computation occurred when not all of the available CPU cores were used, and the chunk size of jobs also had an important influence on computing efficiency. The algorithm and the parallel processing scheme provides a useful approach to address computing challenges posed by spatial analysis of numerous large data sets for large scale environmental modelling. (C) 2012 Elsevier B.V. All rights reserved.
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