e-infrastructure Roadmap for Open Science in Agriculture

A bibliometric study

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.

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Consistent Climate Scenarios: projecting representative future daily climate from global climate models based on historical climate data


As part of the Commonwealth Department of Agriculture, Fisheries and Forestry's (DAFF's) 'Australia's Farming Futures Climate Change Research Program' (CCRP), the Queensland Government undertook a project to support the climate data requirements for nine climate adaptation studies. The project, known as Consistent Climate Scenarios (CCS), delivered climate change projections data, in consistent model-ready formats, enabling project teams to undertake climate change adaptation studies for various primary industries across Australia, in particular within the grazing, cropping and horticultural sectors. Statistical approaches were developed to transform historical climate data from the Queensland Government's SILO climate database using climate projections modelling from the Intergovernmental Panel on Climate Change (IPCC), Fourth Assessment Report (AR4). All IPCC AR4 models from the Third Climate Model Intercomparison Project (CMIP3) were ranked by an Expert Panel overseeing the CCS project. Ranking was based on model performance over the Australian region using, as a guide, methods developed by Suppiah, et al. (2007) and Smith & Chandler (2010). Of 23 available models, four were omitted as underperforming, and the remaining models were used to develop the CCS projections data. Over 1 million data files were delivered to the CCRP project teams. These projections data are now available to the wider research community as an adjunct to SILO. Registered users can obtain 'CCS data' at http://longpaddock.qld.gov.au/climateprojections. Two different techniques are used to modify the daily observed climate values extracted from the SILO database (http://longpaddock.qld.gov.au/silo) using trends obtained from global climate models (GCMs). The two techniques are monthly change factors (CF) derived by pattern scaling from GCMs, and quantile matching (QM). The CF technique projects trends in mean values whereas the QM technique projects both the mean and internal variability within climate sequences. The initial CF trend data were obtained from CSIRO and constituted the monthly trends interpolated to 25 km grids by OzClim (TM) (http://www.csiro.au/ozclim). This set included trends in maximum and minimum temperatures for only seven required GCMs, and did not include specific humidity for five GCMs, or solar radiation for two. Estimation techniques, using the combination of machine learning and regression techniques (Ricketts&Carter 2011) were used to estimate missing variables. The UK Met-Office has also made available maximum and minimum temperature, and specific humidity files for the HadCM3 and HadGEM1 models, which had not been available to CSIRO from the IPCC's repository at PCMDI (http://www-pcmdi.llnl.gov/). The QM methodology (Li, Sheffield & Wood 2010, Kokic, Jin & Crimp 2012, Kokic, Jin & Crimp 2013) was developed in conjunction with CSIRO. Two variations of QM are described in these papers, one which requires daily data from the GCM (which is only available from a very small subset of GCMs) and one which uses monthly GCM data. Data generated by the methods described may be downloaded after registration, currently at no additional cost from the web site. Users may request up to ten datasets at a time, selected from SILO's 4759 available patched point stations, projected to either 2030 or 2050, based on six SRES scenarios and two stabilization scenarios, and three different climate sensitivities. They receive projection files in a choice of two formats, plus additional data (e.g. CO2 concentrations, diagnostic plots and a comprehensive user guide). In addition to the nine CCRP projects, more than 120,000 files have been downloaded from this web site in the 2012/13 financial year to eight Australian universities and a number of state bodies and consultancies.

  • AU
  • CSIRO (AU)
Data keywords
  • machine learning
Agriculture keywords
  • agriculture
  • farming
Data topic
  • modeling
Document type

Inappropriate format for Document type, expected simple value but got array, please use list format

Institutions 10 co-publis
  • CSIRO (AU)
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e-ROSA - e-infrastructure Roadmap for Open Science in Agriculture has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 730988.
Disclaimer: The sole responsibility of the material published in this website lies with the authors. The European Union is not responsible for any use that may be made of the information contained therein.