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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Predicting environmental drivers for prawn aquaculture production to aid improved farm management


A wide range of aquaculture industries are exposed to environmental conditions and variability which can impact on production. Information about future climate conditions on a range of time scales can improve risk management, buffer production against unfavourable environmental conditions, and allow maximised production during opportune times. Seasonal forecasting, providing information beyond weather forecasting up to several months into the future, can aid such decision making. In north-east Australian coastal pond-based prawn farms environmental stresses influence timing of farming periods, animal growth and survival. Here we describe the development, packaging and provision of regional and local forecast products, derived from the Predictive Ocean Atmosphere Model for Australia (POAMA; version 2), to the Queensland prawn industry. Product development followed a three-stage process; (i) assessment of management needs and critical timescales, (ii) development and evaluation of the forecast products, and (iii) forecast implementation and refinement of the forecast products. Prawn farm managers identified the critical forecast variables as minimum and maximum air temperatures and rainfall at lead times of up to 2-3 months. The POAMA forecast skill for all three variables was evaluated, with three spatially averaged regional environmental indexes derived from POAMA showing a strong relationship with large-scale observed conditions. Forecast accuracy was assessed using model hindcast data together with historical observations, and was similar in all three regions, higher for temperature than rainfall, and declined with lead time in all cases. Forecast indices were then scaled using local weather station information for a subset of prawn farms in the study. Discussion with prawn farm managers helped refine the format and visualisation of the forecasts. Tailored forecast packages were then delivered through a web-based system and directly by email. These forecasts could aid a range of management decisions, and user feedback led to further refinement. This approach has great potential to be extended to other coastal aquaculture industries using these and other environmental variables. Information about future conditions, such as provided by seasonal forecasting, can assist aquaculture managers in development of production plans that will be more robust to short-term environmental variability and represents the first adaptation step on a pathway to coping with longer term climate change. Crown Copyright (C) 2015 Published by Elsevier B.V. All rights reserved

  • AU
  • CSIRO (AU)
  • Bureau_Meteorol (AU)
Data keywords
  • web based system
Agriculture keywords
  • farm
  • farming
Data topic
  • modeling
  • decision support
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.