21 results

Australian Agrifood Data Exchange (OzAg Data Exchange): Deliver an interconnected data highway for Australia's AgriFood value chain - Proof of concept

Project number: 2020-126
Project Status:
Completed
Budget expenditure: $344,500.00
Principal Investigator: Irene Sobotta
Organisation: Meat and Livestock Australia (MLA)
Project start/end date: 23 Sep 2021 - 30 May 2024
Contact:
FRDC

Need

Although the use of data and analytics is becoming more widespread across agricultural industries and institutions, the sector is held back by the lack of a consolidated data platform that combines multiple data sets from multiple data sources in real time. Other technology
and data challenges compromising the strength of the Australian agriculture industry include:

Businesses often need to access multiple data systems/datasets which are stored across various platforms and functions and are not well integrated. Aggregating and reconciling these datasets require manual intervention, is rife with errors/duplication and require significant effort to ensure uptake across the business in order to lead to tangible analytics outcomes. This interoperability challenge is commonplace across the industry today.

Data is not shared between the various stakeholders within the industry at times leading to analysis been taken place with incomplete datasets and other times for duplication of efforts with varying results. Data sharing/collaborating culture which would be backed by an established data governance framework including protocols/policies for data access, privacy, definition and standards, would uplift the industry analytical capabilities.

Challenges in understanding where to prioritise efforts to best support the industry. With significant opportunities for data-driven use cases across the value chain, defining the prioritisation of funding and efforts to build capabilities is a critical challenge for industry bodies and governments. The OzAg DX could enable consolidated, integrated and standardised data, to help reduce the labour intensive effort of collecting and analysing data to make better informed prioritisation decisions on deployment of limited support resources and capabilities.

A slow take up of digital technologies is slowing agricultural productivity growth. As Australia looks to achieve the target of $100 billion farm gate output by 2030, digital agriculture is expected to contribute up to an additional $20 billion annually to the gross value of agricultural production.

Objectives

1. Exchange data efficiently on agreed terms with trusted service providers or other interested parties such as government and researchers
2. Enable Australia's agrifood sector to access and take full advantage of the huge amounts of data that is being generated and efficiently transfer their data across the value chain
3. Reduce costly inefficiencies, poor collaboration, wasteful use of critical managerial time and loss of opportunities caused by disparate, siloed and proprietary data systems

Presentation

Presentation • 14.10 MB
Experiment 4 Demo – compliance and traceability for rock lobster quota in Western Australia by Telstra IBM.pdf

Summary

Pain point:
The delay in exchange and reconciliation of catch data by fishers and processors means that there is a delay in quota accounting which impacts planning due to lack of timely information. Furthermore, with no access to pre-fishing information data to the processors means they are unable to plan logistics for efficient transportation. In addition, longer term ambitions of an end-to-end product traceability system will require a reliable data exchange between inputs, production and logistics.

Experiment:
To demonstrate the timely flow of pre-fishing information, quota accounting data, and product (catch) data from WA DPIRD (Fisheries management agency) to Fishers and Processors in a secure and permissioned manner to allow for better logistics planning, and data from Fishers and Processors to DPIRD to enable timely quota consumption accounting. 

Project products

Presentation • 14.10 MB
Experiment 4 Demo – compliance and traceability for rock lobster quota in Western Australia by Telstra IBM.pdf

Summary

Pain point:
The delay in exchange and reconciliation of catch data by fishers and processors means that there is a delay in quota accounting which impacts planning due to lack of timely information. Furthermore, with no access to pre-fishing information data to the processors means they are unable to plan logistics for efficient transportation. In addition, longer term ambitions of an end-to-end product traceability system will require a reliable data exchange between inputs, production and logistics.

Experiment:
To demonstrate the timely flow of pre-fishing information, quota accounting data, and product (catch) data from WA DPIRD (Fisheries management agency) to Fishers and Processors in a secure and permissioned manner to allow for better logistics planning, and data from Fishers and Processors to DPIRD to enable timely quota consumption accounting. 
Presentation • 14.10 MB
Experiment 4 Demo – compliance and traceability for rock lobster quota in Western Australia by Telstra IBM.pdf

Summary

Pain point:
The delay in exchange and reconciliation of catch data by fishers and processors means that there is a delay in quota accounting which impacts planning due to lack of timely information. Furthermore, with no access to pre-fishing information data to the processors means they are unable to plan logistics for efficient transportation. In addition, longer term ambitions of an end-to-end product traceability system will require a reliable data exchange between inputs, production and logistics.

Experiment:
To demonstrate the timely flow of pre-fishing information, quota accounting data, and product (catch) data from WA DPIRD (Fisheries management agency) to Fishers and Processors in a secure and permissioned manner to allow for better logistics planning, and data from Fishers and Processors to DPIRD to enable timely quota consumption accounting. 
Presentation • 14.10 MB
Experiment 4 Demo – compliance and traceability for rock lobster quota in Western Australia by Telstra IBM.pdf

Summary

Pain point:
The delay in exchange and reconciliation of catch data by fishers and processors means that there is a delay in quota accounting which impacts planning due to lack of timely information. Furthermore, with no access to pre-fishing information data to the processors means they are unable to plan logistics for efficient transportation. In addition, longer term ambitions of an end-to-end product traceability system will require a reliable data exchange between inputs, production and logistics.

Experiment:
To demonstrate the timely flow of pre-fishing information, quota accounting data, and product (catch) data from WA DPIRD (Fisheries management agency) to Fishers and Processors in a secure and permissioned manner to allow for better logistics planning, and data from Fishers and Processors to DPIRD to enable timely quota consumption accounting. 
Presentation • 14.10 MB
Experiment 4 Demo – compliance and traceability for rock lobster quota in Western Australia by Telstra IBM.pdf

Summary

Pain point:
The delay in exchange and reconciliation of catch data by fishers and processors means that there is a delay in quota accounting which impacts planning due to lack of timely information. Furthermore, with no access to pre-fishing information data to the processors means they are unable to plan logistics for efficient transportation. In addition, longer term ambitions of an end-to-end product traceability system will require a reliable data exchange between inputs, production and logistics.

Experiment:
To demonstrate the timely flow of pre-fishing information, quota accounting data, and product (catch) data from WA DPIRD (Fisheries management agency) to Fishers and Processors in a secure and permissioned manner to allow for better logistics planning, and data from Fishers and Processors to DPIRD to enable timely quota consumption accounting. 
Presentation • 14.10 MB
Experiment 4 Demo – compliance and traceability for rock lobster quota in Western Australia by Telstra IBM.pdf

Summary

Pain point:
The delay in exchange and reconciliation of catch data by fishers and processors means that there is a delay in quota accounting which impacts planning due to lack of timely information. Furthermore, with no access to pre-fishing information data to the processors means they are unable to plan logistics for efficient transportation. In addition, longer term ambitions of an end-to-end product traceability system will require a reliable data exchange between inputs, production and logistics.

Experiment:
To demonstrate the timely flow of pre-fishing information, quota accounting data, and product (catch) data from WA DPIRD (Fisheries management agency) to Fishers and Processors in a secure and permissioned manner to allow for better logistics planning, and data from Fishers and Processors to DPIRD to enable timely quota consumption accounting. 
Presentation • 14.10 MB
Experiment 4 Demo – compliance and traceability for rock lobster quota in Western Australia by Telstra IBM.pdf

Summary

Pain point:
The delay in exchange and reconciliation of catch data by fishers and processors means that there is a delay in quota accounting which impacts planning due to lack of timely information. Furthermore, with no access to pre-fishing information data to the processors means they are unable to plan logistics for efficient transportation. In addition, longer term ambitions of an end-to-end product traceability system will require a reliable data exchange between inputs, production and logistics.

Experiment:
To demonstrate the timely flow of pre-fishing information, quota accounting data, and product (catch) data from WA DPIRD (Fisheries management agency) to Fishers and Processors in a secure and permissioned manner to allow for better logistics planning, and data from Fishers and Processors to DPIRD to enable timely quota consumption accounting. 
Presentation • 14.10 MB
Experiment 4 Demo – compliance and traceability for rock lobster quota in Western Australia by Telstra IBM.pdf

Summary

Pain point:
The delay in exchange and reconciliation of catch data by fishers and processors means that there is a delay in quota accounting which impacts planning due to lack of timely information. Furthermore, with no access to pre-fishing information data to the processors means they are unable to plan logistics for efficient transportation. In addition, longer term ambitions of an end-to-end product traceability system will require a reliable data exchange between inputs, production and logistics.

Experiment:
To demonstrate the timely flow of pre-fishing information, quota accounting data, and product (catch) data from WA DPIRD (Fisheries management agency) to Fishers and Processors in a secure and permissioned manner to allow for better logistics planning, and data from Fishers and Processors to DPIRD to enable timely quota consumption accounting. 
Presentation • 14.10 MB
Experiment 4 Demo – compliance and traceability for rock lobster quota in Western Australia by Telstra IBM.pdf

Summary

Pain point:
The delay in exchange and reconciliation of catch data by fishers and processors means that there is a delay in quota accounting which impacts planning due to lack of timely information. Furthermore, with no access to pre-fishing information data to the processors means they are unable to plan logistics for efficient transportation. In addition, longer term ambitions of an end-to-end product traceability system will require a reliable data exchange between inputs, production and logistics.

Experiment:
To demonstrate the timely flow of pre-fishing information, quota accounting data, and product (catch) data from WA DPIRD (Fisheries management agency) to Fishers and Processors in a secure and permissioned manner to allow for better logistics planning, and data from Fishers and Processors to DPIRD to enable timely quota consumption accounting. 
Presentation • 14.10 MB
Experiment 4 Demo – compliance and traceability for rock lobster quota in Western Australia by Telstra IBM.pdf

Summary

Pain point:
The delay in exchange and reconciliation of catch data by fishers and processors means that there is a delay in quota accounting which impacts planning due to lack of timely information. Furthermore, with no access to pre-fishing information data to the processors means they are unable to plan logistics for efficient transportation. In addition, longer term ambitions of an end-to-end product traceability system will require a reliable data exchange between inputs, production and logistics.

Experiment:
To demonstrate the timely flow of pre-fishing information, quota accounting data, and product (catch) data from WA DPIRD (Fisheries management agency) to Fishers and Processors in a secure and permissioned manner to allow for better logistics planning, and data from Fishers and Processors to DPIRD to enable timely quota consumption accounting. 
Presentation • 14.10 MB
Experiment 4 Demo – compliance and traceability for rock lobster quota in Western Australia by Telstra IBM.pdf

Summary

Pain point:
The delay in exchange and reconciliation of catch data by fishers and processors means that there is a delay in quota accounting which impacts planning due to lack of timely information. Furthermore, with no access to pre-fishing information data to the processors means they are unable to plan logistics for efficient transportation. In addition, longer term ambitions of an end-to-end product traceability system will require a reliable data exchange between inputs, production and logistics.

Experiment:
To demonstrate the timely flow of pre-fishing information, quota accounting data, and product (catch) data from WA DPIRD (Fisheries management agency) to Fishers and Processors in a secure and permissioned manner to allow for better logistics planning, and data from Fishers and Processors to DPIRD to enable timely quota consumption accounting. 
Presentation • 14.10 MB
Experiment 4 Demo – compliance and traceability for rock lobster quota in Western Australia by Telstra IBM.pdf

Summary

Pain point:
The delay in exchange and reconciliation of catch data by fishers and processors means that there is a delay in quota accounting which impacts planning due to lack of timely information. Furthermore, with no access to pre-fishing information data to the processors means they are unable to plan logistics for efficient transportation. In addition, longer term ambitions of an end-to-end product traceability system will require a reliable data exchange between inputs, production and logistics.

Experiment:
To demonstrate the timely flow of pre-fishing information, quota accounting data, and product (catch) data from WA DPIRD (Fisheries management agency) to Fishers and Processors in a secure and permissioned manner to allow for better logistics planning, and data from Fishers and Processors to DPIRD to enable timely quota consumption accounting. 
Presentation • 14.10 MB
Experiment 4 Demo – compliance and traceability for rock lobster quota in Western Australia by Telstra IBM.pdf

Summary

Pain point:
The delay in exchange and reconciliation of catch data by fishers and processors means that there is a delay in quota accounting which impacts planning due to lack of timely information. Furthermore, with no access to pre-fishing information data to the processors means they are unable to plan logistics for efficient transportation. In addition, longer term ambitions of an end-to-end product traceability system will require a reliable data exchange between inputs, production and logistics.

Experiment:
To demonstrate the timely flow of pre-fishing information, quota accounting data, and product (catch) data from WA DPIRD (Fisheries management agency) to Fishers and Processors in a secure and permissioned manner to allow for better logistics planning, and data from Fishers and Processors to DPIRD to enable timely quota consumption accounting. 
Presentation • 14.10 MB
Experiment 4 Demo – compliance and traceability for rock lobster quota in Western Australia by Telstra IBM.pdf

Summary

Pain point:
The delay in exchange and reconciliation of catch data by fishers and processors means that there is a delay in quota accounting which impacts planning due to lack of timely information. Furthermore, with no access to pre-fishing information data to the processors means they are unable to plan logistics for efficient transportation. In addition, longer term ambitions of an end-to-end product traceability system will require a reliable data exchange between inputs, production and logistics.

Experiment:
To demonstrate the timely flow of pre-fishing information, quota accounting data, and product (catch) data from WA DPIRD (Fisheries management agency) to Fishers and Processors in a secure and permissioned manner to allow for better logistics planning, and data from Fishers and Processors to DPIRD to enable timely quota consumption accounting. 

Data management and governance framework development for fishing and aquaculture

Project number: 2021-088
Project Status:
Completed
Budget expenditure: $50,000.00
Principal Investigator: Andrew Skinner
Organisation: More Than Machines Pty Ltd (MTM)
Project start/end date: 31 Oct 2021 - 30 Dec 2021
Contact:
FRDC

Need

FRDC requires mechanisms to assess and govern the data for which it is custodian or may become custodian of. FRDC requires a data governance framework that builds on the concepts of the NFF Farm Data Code and other Agricultural data best practices for use by FRDC data stakeholders. A data governance framework will ensure that FRDC BAU and project data is captured, managed and distributed with accountability, consistency, security and meets defined standards throughout the data lifecycle. As a coordinating industry body, it is essential that FRDC leads the way with a robust, considered approach to data management. This will place FRDC as a best practice example, it will enable consistent discussion and guidance to stakeholders and data partners and will provide a consistent foundation for overall trust and capability in the use of data as well as providing a foundation for the FRDC to maximise the value of data created through the Australian innovation system. It is expected that subsets of the FRDC data governance framework will be developed in the future to extend support to FRDC stakeholderss.

Objectives

1. Development of a Data Governance Framework for use by FRDC and for FRDC to use to provide data governance advice and support to stakeholders
2. Identification of the processes, roles and policies required to ensure data quality, management and security
3. A documented FRDC data lifecycle
4. A documented approach to monitoring and review of the framework
5. Recommendations for training opportunities for future FRDC investment
6. 1-2 Written use cases to support the framework and aid adoption

DAFF National Agriculture Traceability Regulatory Technology Research and Insights Grant: Australian AgriFood Data Exchange - Ag sector traceability transformation delivered through an interoperable data platform and exchange

Project number: 2022-197
Project Status:
Current
Budget expenditure: $500,000.00
Principal Investigator: Irene Sobotta
Organisation: Meat and Livestock Australia (MLA)
Project start/end date: 18 Jun 2023 - 29 Jun 2025
Contact:
FRDC

Need

Regulatory efficiency and compliance across agricultural supply chains is hindered by inefficient, incompatible or unavailable data and systems that prevent creation of robust, interoperable traceability solutions. The Australian AgriFood Data Exchange (AAFDX) will solve this challenge by creating a secure, cloud-based platform enabling government, industry and other participants to share, re-use and merge data from disparate systems in a secure, controlled manner. The AAFDX will be a modern, efficient, internationally recognised data infrastructure enabling regulators and industry to better manage compliance, stimulate innovation and supply chain performance, assure consumers, coordinate biosecurity and export market access, through enhanced traceability. The funding will build the minimal viable product, with expansion to specific traceability and compliance applications. The AAFDX will endure beyond the funding period with partner co-investment and a user pays revenue stream

Objectives

1. Deliver a minimum viable product (MVP) of the Australian Agrifood Data Exchange services
2. Develop a platform that facilitates applications/solutions that increase traceability, productivity, compliance, profitability
3. Develop governance arrangements to ensure that data security, and in turn users trust in ag-tech is not compromised
4. Build digital maturity of the fisheries and aquaculture sectors to engage in the potential, permissioned shared data offers
Environment
Environment
PROJECT NUMBER • 2009-067
PROJECT STATUS:
COMPLETED

Tactical Research Fund: Nutrient and phytoplankton data from Storm Bay to support sustainable resource planning

This project has provided preliminary data on environmental conditions in Storm Bay that is assisting managers and marine industries to better understand effects of climate change and climate variability on fisheries and aquaculture in the region, including changing currents and primary...
ORGANISATION:
University of Tasmania (UTAS)
Adoption
PROJECT NUMBER • 2016-272
PROJECT STATUS:
COMPLETED

Love Australian Prawns evaluation using consumer research, sales data and market insights

Having commissioned Brand Council to review Love Australian Prawns (LAP) strategy and outputs and the University of Sunshine Coast to compare LAP consumer perception and awareness to previous years, the Australian Council of Prawn Fisheries Ltd (ACPF) and the Australian Prawn Farmers’...
ORGANISATION:
Australian Council of Prawn Fisheries Ltd (ACPF)

Reducing the Number of Undefined Species in Future Status of Australian Fish Stocks Reports: Phase Two - training in the assessment of data-poor stocks

Project number: 2017-102
Project Status:
Completed
Budget expenditure: $188,995.00
Principal Investigator: Paul Burch
Organisation: CSIRO Oceans and Atmosphere Hobart
Project start/end date: 4 Feb 2018 - 29 Sep 2018
Contact:
FRDC

Need

The Status of Australian Fish Stocks project is increasing the number of species/stocks to be included but many of these new stocks may fall into the "undefined" category and, because they suggest a lack of assessment and management, they lower the overall impression of the state of fisheries management within Australia. The FRDC National Priority 1 has two targets relating to the "undefined" category. By 2020, the target is to increase the number of species covered in SAFS to 200, and at the same time, to reduce the percentage of stocks classified as undefined to less than 10%. Most major commercial species by value are already included in SAFS, so increasing that number to 200 will mean including many data-poor fisheries making achieving both targets by 2020 difficult. An earlier project (2016-135) disarticulated the undefined category into sub-groups at least one of which should be amenable to data-poor assessment techniques. There is thus a need, within each jurisdiction, to identify which of the new species selected for inclusion in SAFS are likely to be classed as undefined and yet still amenable to a data-poor assessment method. There have been many recent developments with data-poor stock assessment methods and there is thus also a need in all jurisdictions for staff training to develop more local expertise in these new methods and to transfer suitable custom software for conducting such analyses. With the agreement of all parties involved there is a need to then apply and document the particular assessment method used that permits a status determination for each species selected, thereby reducing the number of undefined species.

Objectives

1. Of the species proposed for inclusion in the 2018 and 2020 SAFS reports, identify those which may be deemed 'undefined' in each jurisdiction and yet potentially amenable to a data-poor stock assessment.
2. In each jurisdiction with potentially ‘undefined’ species, arrange a training workshop for local staff using the candidate species from objective 1 to act as case studies for the application of suitable data-poor stock assessment methods.
3. Include the 15 potentially assessable species from SAFS 2016, as identified in Phase one of this project (FRDC Project 2016-135).
4. Ensure that at least the local scientists involved with SAFS assessments understand how to use the illustrated data-poor assessment methods to develop a defensible stock status report and, if required, associated management advice.

Final report

ISBN: 978-1-4863-1288-7
Authors: Malcolm Haddon Paul Burch Natalie Dowling Rich Little
Final Report • 2019-07-01 • 1.93 MB
2017-102-DLD.pdf

Summary

Seven data-poor assessment method training workshops were run in seven different jurisdictions (Tasmania, Victoria, South Australia, Western Australia, Northern Territory, Queensland, and New South Wales). Originally the workshops were to have been undertaken from March to the end of May 2018. However, the availability of project staff combined with the availability of people within the jurisdictions meant that time-table had to be extended into June.
Two open source R packages, simpleSA and cede, were used in the workshops, with additional development of the software contained in each one continuing as experience in the different jurisdictions expanded. cede contained software to assist with data exploration (simple mapping and data summary functions) and with illustrating and comparing different catch-effort standardization techniques. simpleSA contained three main data-poor stock assessment techniques (catch-MSY, surplus-production modelling, and age-structured surplus production modelling) plus functions to assist with catch-curve analysis.
The workshops consisted of an introduction to the problems of assessing data-poor fisheries, potential solutions, and their implications for management. The workshops included an introductory lecture and then live demonstrations of the software with expla-nations of the limitations and assumptions of each approach, followed by hands-on use by participants using either data sets included in the packages or, ideally, their own datasets prepared before the workshops.
Initially the workshops were designed around the idea of being two days long, but after the first two workshops, this was altered to become three days (for all but the Tasmanian workshop, which required only 2 days). This allowed time for participants to more fully explore their data, to make brief presentations of analyses they had conducted, and to receive feedback on these from the workshop presenters and their own colleagues.
Final Report • 2019-07-01 • 1.93 MB
2017-102-DLD.pdf

Summary

Seven data-poor assessment method training workshops were run in seven different jurisdictions (Tasmania, Victoria, South Australia, Western Australia, Northern Territory, Queensland, and New South Wales). Originally the workshops were to have been undertaken from March to the end of May 2018. However, the availability of project staff combined with the availability of people within the jurisdictions meant that time-table had to be extended into June.
Two open source R packages, simpleSA and cede, were used in the workshops, with additional development of the software contained in each one continuing as experience in the different jurisdictions expanded. cede contained software to assist with data exploration (simple mapping and data summary functions) and with illustrating and comparing different catch-effort standardization techniques. simpleSA contained three main data-poor stock assessment techniques (catch-MSY, surplus-production modelling, and age-structured surplus production modelling) plus functions to assist with catch-curve analysis.
The workshops consisted of an introduction to the problems of assessing data-poor fisheries, potential solutions, and their implications for management. The workshops included an introductory lecture and then live demonstrations of the software with expla-nations of the limitations and assumptions of each approach, followed by hands-on use by participants using either data sets included in the packages or, ideally, their own datasets prepared before the workshops.
Initially the workshops were designed around the idea of being two days long, but after the first two workshops, this was altered to become three days (for all but the Tasmanian workshop, which required only 2 days). This allowed time for participants to more fully explore their data, to make brief presentations of analyses they had conducted, and to receive feedback on these from the workshop presenters and their own colleagues.
Final Report • 2019-07-01 • 1.93 MB
2017-102-DLD.pdf

Summary

Seven data-poor assessment method training workshops were run in seven different jurisdictions (Tasmania, Victoria, South Australia, Western Australia, Northern Territory, Queensland, and New South Wales). Originally the workshops were to have been undertaken from March to the end of May 2018. However, the availability of project staff combined with the availability of people within the jurisdictions meant that time-table had to be extended into June.
Two open source R packages, simpleSA and cede, were used in the workshops, with additional development of the software contained in each one continuing as experience in the different jurisdictions expanded. cede contained software to assist with data exploration (simple mapping and data summary functions) and with illustrating and comparing different catch-effort standardization techniques. simpleSA contained three main data-poor stock assessment techniques (catch-MSY, surplus-production modelling, and age-structured surplus production modelling) plus functions to assist with catch-curve analysis.
The workshops consisted of an introduction to the problems of assessing data-poor fisheries, potential solutions, and their implications for management. The workshops included an introductory lecture and then live demonstrations of the software with expla-nations of the limitations and assumptions of each approach, followed by hands-on use by participants using either data sets included in the packages or, ideally, their own datasets prepared before the workshops.
Initially the workshops were designed around the idea of being two days long, but after the first two workshops, this was altered to become three days (for all but the Tasmanian workshop, which required only 2 days). This allowed time for participants to more fully explore their data, to make brief presentations of analyses they had conducted, and to receive feedback on these from the workshop presenters and their own colleagues.
Final Report • 2019-07-01 • 1.93 MB
2017-102-DLD.pdf

Summary

Seven data-poor assessment method training workshops were run in seven different jurisdictions (Tasmania, Victoria, South Australia, Western Australia, Northern Territory, Queensland, and New South Wales). Originally the workshops were to have been undertaken from March to the end of May 2018. However, the availability of project staff combined with the availability of people within the jurisdictions meant that time-table had to be extended into June.
Two open source R packages, simpleSA and cede, were used in the workshops, with additional development of the software contained in each one continuing as experience in the different jurisdictions expanded. cede contained software to assist with data exploration (simple mapping and data summary functions) and with illustrating and comparing different catch-effort standardization techniques. simpleSA contained three main data-poor stock assessment techniques (catch-MSY, surplus-production modelling, and age-structured surplus production modelling) plus functions to assist with catch-curve analysis.
The workshops consisted of an introduction to the problems of assessing data-poor fisheries, potential solutions, and their implications for management. The workshops included an introductory lecture and then live demonstrations of the software with expla-nations of the limitations and assumptions of each approach, followed by hands-on use by participants using either data sets included in the packages or, ideally, their own datasets prepared before the workshops.
Initially the workshops were designed around the idea of being two days long, but after the first two workshops, this was altered to become three days (for all but the Tasmanian workshop, which required only 2 days). This allowed time for participants to more fully explore their data, to make brief presentations of analyses they had conducted, and to receive feedback on these from the workshop presenters and their own colleagues.
Final Report • 2019-07-01 • 1.93 MB
2017-102-DLD.pdf

Summary

Seven data-poor assessment method training workshops were run in seven different jurisdictions (Tasmania, Victoria, South Australia, Western Australia, Northern Territory, Queensland, and New South Wales). Originally the workshops were to have been undertaken from March to the end of May 2018. However, the availability of project staff combined with the availability of people within the jurisdictions meant that time-table had to be extended into June.
Two open source R packages, simpleSA and cede, were used in the workshops, with additional development of the software contained in each one continuing as experience in the different jurisdictions expanded. cede contained software to assist with data exploration (simple mapping and data summary functions) and with illustrating and comparing different catch-effort standardization techniques. simpleSA contained three main data-poor stock assessment techniques (catch-MSY, surplus-production modelling, and age-structured surplus production modelling) plus functions to assist with catch-curve analysis.
The workshops consisted of an introduction to the problems of assessing data-poor fisheries, potential solutions, and their implications for management. The workshops included an introductory lecture and then live demonstrations of the software with expla-nations of the limitations and assumptions of each approach, followed by hands-on use by participants using either data sets included in the packages or, ideally, their own datasets prepared before the workshops.
Initially the workshops were designed around the idea of being two days long, but after the first two workshops, this was altered to become three days (for all but the Tasmanian workshop, which required only 2 days). This allowed time for participants to more fully explore their data, to make brief presentations of analyses they had conducted, and to receive feedback on these from the workshop presenters and their own colleagues.
Final Report • 2019-07-01 • 1.93 MB
2017-102-DLD.pdf

Summary

Seven data-poor assessment method training workshops were run in seven different jurisdictions (Tasmania, Victoria, South Australia, Western Australia, Northern Territory, Queensland, and New South Wales). Originally the workshops were to have been undertaken from March to the end of May 2018. However, the availability of project staff combined with the availability of people within the jurisdictions meant that time-table had to be extended into June.
Two open source R packages, simpleSA and cede, were used in the workshops, with additional development of the software contained in each one continuing as experience in the different jurisdictions expanded. cede contained software to assist with data exploration (simple mapping and data summary functions) and with illustrating and comparing different catch-effort standardization techniques. simpleSA contained three main data-poor stock assessment techniques (catch-MSY, surplus-production modelling, and age-structured surplus production modelling) plus functions to assist with catch-curve analysis.
The workshops consisted of an introduction to the problems of assessing data-poor fisheries, potential solutions, and their implications for management. The workshops included an introductory lecture and then live demonstrations of the software with expla-nations of the limitations and assumptions of each approach, followed by hands-on use by participants using either data sets included in the packages or, ideally, their own datasets prepared before the workshops.
Initially the workshops were designed around the idea of being two days long, but after the first two workshops, this was altered to become three days (for all but the Tasmanian workshop, which required only 2 days). This allowed time for participants to more fully explore their data, to make brief presentations of analyses they had conducted, and to receive feedback on these from the workshop presenters and their own colleagues.
Final Report • 2019-07-01 • 1.93 MB
2017-102-DLD.pdf

Summary

Seven data-poor assessment method training workshops were run in seven different jurisdictions (Tasmania, Victoria, South Australia, Western Australia, Northern Territory, Queensland, and New South Wales). Originally the workshops were to have been undertaken from March to the end of May 2018. However, the availability of project staff combined with the availability of people within the jurisdictions meant that time-table had to be extended into June.
Two open source R packages, simpleSA and cede, were used in the workshops, with additional development of the software contained in each one continuing as experience in the different jurisdictions expanded. cede contained software to assist with data exploration (simple mapping and data summary functions) and with illustrating and comparing different catch-effort standardization techniques. simpleSA contained three main data-poor stock assessment techniques (catch-MSY, surplus-production modelling, and age-structured surplus production modelling) plus functions to assist with catch-curve analysis.
The workshops consisted of an introduction to the problems of assessing data-poor fisheries, potential solutions, and their implications for management. The workshops included an introductory lecture and then live demonstrations of the software with expla-nations of the limitations and assumptions of each approach, followed by hands-on use by participants using either data sets included in the packages or, ideally, their own datasets prepared before the workshops.
Initially the workshops were designed around the idea of being two days long, but after the first two workshops, this was altered to become three days (for all but the Tasmanian workshop, which required only 2 days). This allowed time for participants to more fully explore their data, to make brief presentations of analyses they had conducted, and to receive feedback on these from the workshop presenters and their own colleagues.
Final Report • 2019-07-01 • 1.93 MB
2017-102-DLD.pdf

Summary

Seven data-poor assessment method training workshops were run in seven different jurisdictions (Tasmania, Victoria, South Australia, Western Australia, Northern Territory, Queensland, and New South Wales). Originally the workshops were to have been undertaken from March to the end of May 2018. However, the availability of project staff combined with the availability of people within the jurisdictions meant that time-table had to be extended into June.
Two open source R packages, simpleSA and cede, were used in the workshops, with additional development of the software contained in each one continuing as experience in the different jurisdictions expanded. cede contained software to assist with data exploration (simple mapping and data summary functions) and with illustrating and comparing different catch-effort standardization techniques. simpleSA contained three main data-poor stock assessment techniques (catch-MSY, surplus-production modelling, and age-structured surplus production modelling) plus functions to assist with catch-curve analysis.
The workshops consisted of an introduction to the problems of assessing data-poor fisheries, potential solutions, and their implications for management. The workshops included an introductory lecture and then live demonstrations of the software with expla-nations of the limitations and assumptions of each approach, followed by hands-on use by participants using either data sets included in the packages or, ideally, their own datasets prepared before the workshops.
Initially the workshops were designed around the idea of being two days long, but after the first two workshops, this was altered to become three days (for all but the Tasmanian workshop, which required only 2 days). This allowed time for participants to more fully explore their data, to make brief presentations of analyses they had conducted, and to receive feedback on these from the workshop presenters and their own colleagues.
Final Report • 2019-07-01 • 1.93 MB
2017-102-DLD.pdf

Summary

Seven data-poor assessment method training workshops were run in seven different jurisdictions (Tasmania, Victoria, South Australia, Western Australia, Northern Territory, Queensland, and New South Wales). Originally the workshops were to have been undertaken from March to the end of May 2018. However, the availability of project staff combined with the availability of people within the jurisdictions meant that time-table had to be extended into June.
Two open source R packages, simpleSA and cede, were used in the workshops, with additional development of the software contained in each one continuing as experience in the different jurisdictions expanded. cede contained software to assist with data exploration (simple mapping and data summary functions) and with illustrating and comparing different catch-effort standardization techniques. simpleSA contained three main data-poor stock assessment techniques (catch-MSY, surplus-production modelling, and age-structured surplus production modelling) plus functions to assist with catch-curve analysis.
The workshops consisted of an introduction to the problems of assessing data-poor fisheries, potential solutions, and their implications for management. The workshops included an introductory lecture and then live demonstrations of the software with expla-nations of the limitations and assumptions of each approach, followed by hands-on use by participants using either data sets included in the packages or, ideally, their own datasets prepared before the workshops.
Initially the workshops were designed around the idea of being two days long, but after the first two workshops, this was altered to become three days (for all but the Tasmanian workshop, which required only 2 days). This allowed time for participants to more fully explore their data, to make brief presentations of analyses they had conducted, and to receive feedback on these from the workshop presenters and their own colleagues.
Final Report • 2019-07-01 • 1.93 MB
2017-102-DLD.pdf

Summary

Seven data-poor assessment method training workshops were run in seven different jurisdictions (Tasmania, Victoria, South Australia, Western Australia, Northern Territory, Queensland, and New South Wales). Originally the workshops were to have been undertaken from March to the end of May 2018. However, the availability of project staff combined with the availability of people within the jurisdictions meant that time-table had to be extended into June.
Two open source R packages, simpleSA and cede, were used in the workshops, with additional development of the software contained in each one continuing as experience in the different jurisdictions expanded. cede contained software to assist with data exploration (simple mapping and data summary functions) and with illustrating and comparing different catch-effort standardization techniques. simpleSA contained three main data-poor stock assessment techniques (catch-MSY, surplus-production modelling, and age-structured surplus production modelling) plus functions to assist with catch-curve analysis.
The workshops consisted of an introduction to the problems of assessing data-poor fisheries, potential solutions, and their implications for management. The workshops included an introductory lecture and then live demonstrations of the software with expla-nations of the limitations and assumptions of each approach, followed by hands-on use by participants using either data sets included in the packages or, ideally, their own datasets prepared before the workshops.
Initially the workshops were designed around the idea of being two days long, but after the first two workshops, this was altered to become three days (for all but the Tasmanian workshop, which required only 2 days). This allowed time for participants to more fully explore their data, to make brief presentations of analyses they had conducted, and to receive feedback on these from the workshop presenters and their own colleagues.
Final Report • 2019-07-01 • 1.93 MB
2017-102-DLD.pdf

Summary

Seven data-poor assessment method training workshops were run in seven different jurisdictions (Tasmania, Victoria, South Australia, Western Australia, Northern Territory, Queensland, and New South Wales). Originally the workshops were to have been undertaken from March to the end of May 2018. However, the availability of project staff combined with the availability of people within the jurisdictions meant that time-table had to be extended into June.
Two open source R packages, simpleSA and cede, were used in the workshops, with additional development of the software contained in each one continuing as experience in the different jurisdictions expanded. cede contained software to assist with data exploration (simple mapping and data summary functions) and with illustrating and comparing different catch-effort standardization techniques. simpleSA contained three main data-poor stock assessment techniques (catch-MSY, surplus-production modelling, and age-structured surplus production modelling) plus functions to assist with catch-curve analysis.
The workshops consisted of an introduction to the problems of assessing data-poor fisheries, potential solutions, and their implications for management. The workshops included an introductory lecture and then live demonstrations of the software with expla-nations of the limitations and assumptions of each approach, followed by hands-on use by participants using either data sets included in the packages or, ideally, their own datasets prepared before the workshops.
Initially the workshops were designed around the idea of being two days long, but after the first two workshops, this was altered to become three days (for all but the Tasmanian workshop, which required only 2 days). This allowed time for participants to more fully explore their data, to make brief presentations of analyses they had conducted, and to receive feedback on these from the workshop presenters and their own colleagues.
Final Report • 2019-07-01 • 1.93 MB
2017-102-DLD.pdf

Summary

Seven data-poor assessment method training workshops were run in seven different jurisdictions (Tasmania, Victoria, South Australia, Western Australia, Northern Territory, Queensland, and New South Wales). Originally the workshops were to have been undertaken from March to the end of May 2018. However, the availability of project staff combined with the availability of people within the jurisdictions meant that time-table had to be extended into June.
Two open source R packages, simpleSA and cede, were used in the workshops, with additional development of the software contained in each one continuing as experience in the different jurisdictions expanded. cede contained software to assist with data exploration (simple mapping and data summary functions) and with illustrating and comparing different catch-effort standardization techniques. simpleSA contained three main data-poor stock assessment techniques (catch-MSY, surplus-production modelling, and age-structured surplus production modelling) plus functions to assist with catch-curve analysis.
The workshops consisted of an introduction to the problems of assessing data-poor fisheries, potential solutions, and their implications for management. The workshops included an introductory lecture and then live demonstrations of the software with expla-nations of the limitations and assumptions of each approach, followed by hands-on use by participants using either data sets included in the packages or, ideally, their own datasets prepared before the workshops.
Initially the workshops were designed around the idea of being two days long, but after the first two workshops, this was altered to become three days (for all but the Tasmanian workshop, which required only 2 days). This allowed time for participants to more fully explore their data, to make brief presentations of analyses they had conducted, and to receive feedback on these from the workshop presenters and their own colleagues.
Final Report • 2019-07-01 • 1.93 MB
2017-102-DLD.pdf

Summary

Seven data-poor assessment method training workshops were run in seven different jurisdictions (Tasmania, Victoria, South Australia, Western Australia, Northern Territory, Queensland, and New South Wales). Originally the workshops were to have been undertaken from March to the end of May 2018. However, the availability of project staff combined with the availability of people within the jurisdictions meant that time-table had to be extended into June.
Two open source R packages, simpleSA and cede, were used in the workshops, with additional development of the software contained in each one continuing as experience in the different jurisdictions expanded. cede contained software to assist with data exploration (simple mapping and data summary functions) and with illustrating and comparing different catch-effort standardization techniques. simpleSA contained three main data-poor stock assessment techniques (catch-MSY, surplus-production modelling, and age-structured surplus production modelling) plus functions to assist with catch-curve analysis.
The workshops consisted of an introduction to the problems of assessing data-poor fisheries, potential solutions, and their implications for management. The workshops included an introductory lecture and then live demonstrations of the software with expla-nations of the limitations and assumptions of each approach, followed by hands-on use by participants using either data sets included in the packages or, ideally, their own datasets prepared before the workshops.
Initially the workshops were designed around the idea of being two days long, but after the first two workshops, this was altered to become three days (for all but the Tasmanian workshop, which required only 2 days). This allowed time for participants to more fully explore their data, to make brief presentations of analyses they had conducted, and to receive feedback on these from the workshop presenters and their own colleagues.
Final Report • 2019-07-01 • 1.93 MB
2017-102-DLD.pdf

Summary

Seven data-poor assessment method training workshops were run in seven different jurisdictions (Tasmania, Victoria, South Australia, Western Australia, Northern Territory, Queensland, and New South Wales). Originally the workshops were to have been undertaken from March to the end of May 2018. However, the availability of project staff combined with the availability of people within the jurisdictions meant that time-table had to be extended into June.
Two open source R packages, simpleSA and cede, were used in the workshops, with additional development of the software contained in each one continuing as experience in the different jurisdictions expanded. cede contained software to assist with data exploration (simple mapping and data summary functions) and with illustrating and comparing different catch-effort standardization techniques. simpleSA contained three main data-poor stock assessment techniques (catch-MSY, surplus-production modelling, and age-structured surplus production modelling) plus functions to assist with catch-curve analysis.
The workshops consisted of an introduction to the problems of assessing data-poor fisheries, potential solutions, and their implications for management. The workshops included an introductory lecture and then live demonstrations of the software with expla-nations of the limitations and assumptions of each approach, followed by hands-on use by participants using either data sets included in the packages or, ideally, their own datasets prepared before the workshops.
Initially the workshops were designed around the idea of being two days long, but after the first two workshops, this was altered to become three days (for all but the Tasmanian workshop, which required only 2 days). This allowed time for participants to more fully explore their data, to make brief presentations of analyses they had conducted, and to receive feedback on these from the workshop presenters and their own colleagues.

Australian Fisheries and Aquaculture Statistics 2022

Project number: 2023-082
Project Status:
Completed
Budget expenditure: $60,000.00
Principal Investigator: Robert Curtotti
Organisation: Department of Agriculture, Fisheries and Forestry (DAFF) ABARES
Project start/end date: 17 Dec 2023 - 29 Jun 2024
Contact:
FRDC

Need

Statistics on Australian fisheries production and trade seeks to meet the needs of the fishing and aquaculture industry, fisheries managers, policymakers and researchers. It can assist in policy decisions, industry marketing strategies and the allocation of research funding or priorities. The gross value of production for specific fisheries are used for determining the research and development levies collected by government.

The neutrality and integrity of GVP estimates is therefore important due to their forming the basis for research levies for each fishery. At the international level, the Department of Agriculture through the Australian Bureau of Agricultural and Resource Economics and Sciences (ABARES) contributes to a number of international databases. These include databases managed by the Food and Agriculture Organisation (FAO) and the Organisation for Economic Cooperation and Development (OECD). Information at the international level can assist in international negotiations on issues such as trans-boundary fisheries and analysis of trade opportunities.

Objectives

1. To maintain and improve the data base of production, gross value of production and trade statistics for the Australian fishing industry, including aquaculture.
2. To provide these data in an accessible form.

Profiling and tracking change in Australia's seafood workforce: establishing a baseline workforce dataset

Project number: 2022-034
Project Status:
Current
Budget expenditure: $259,342.00
Principal Investigator: Stephane M. Mahuteau
Organisation: University of Adelaide
Project start/end date: 30 Sep 2022 - 4 Sep 2025
Contact:
FRDC

Need

The project developed to address the call for EOI recognises that the seafood workforce is diverse and operates within a changing natural, technological, and socioeconomic environment, providing unique challenges and opportunities. The seafood workforce also, however, operates within the wider Australian economy where rural and regional employment, small-medium business operations, and increasing value-adding opportunities are common topics of interest. The project proposes to provide a comprehensive assessment of the current data framework, make recommendations for improving it, and develop a baseline workforce dataset. The focus will be on the potential to use existing sources of data (particularly administrative data collected by government institutions and data that is required to be collected) and how and when those need to be effectively complemented with additional data. Administrative data are confidential and access limited as is the variety of seafood industry data often collected. Accessing administrative data is explicitly part of this proposal and identifying the sources of, and the type of data available, from industry surveys.

Objectives

1. To establish a baseline workforce dataset to address the lack of accessible, accurate workforce data
2. To identify how to overcome the shortcomings of official classifications to better align data information with how the seafood industry and its workforce operate.
3. To determine how using whole of population statistical data may provide a more accurate picture of the seafood industry workforce
4. To use available literature and expert input to provide an understanding of the true diversity of employment in the seafood sector.
5. To undertake a comprehensive stock-take of the relevant current data sources recording information on the seafood industry workforce.
6. To undertake a comprehensive analysis of the existing data sources and investigate the usefulness of large administrative data such as BLADE/MADIP.
7. To closely involve seafood industry participants through an effective stakeholder engagement strategy and promote a co-design element to the project
8. To provide recommendations to address data gaps and improve the utility of current data, and support the FRDC in meeting the objectives of its Capability and Capacity Building Strategy.