The NSW RAC intention is to utilize each sectors R&D Strategic Plans to form a component of the NSW RAC Plan. The NSW Commercial Fishing R&D Strategic Plan is outdated and no longer relevant.

Strategic planning is central to the ensuring funding of research, develop and extension is prioritized and meets industry needs. A five year plan will guide investment in R,D & E, and each year the Fisheries Research Advisory Committee needs to scan its business environment and review its RD&E activities to ensure its portfolio is balanced, well directed and meets the needs of the NSW commercial fishing industry.

To ensure that the FRDC's RD&E investment is undertaken with reference to the priorities of the NSW commercial fishing industry a strategic plan must be developed through extensive consultation with key stakeholders and the PFA.

The PFA has identified that a industry workshop should be held to understand the research and development needs for the NSW commercial fishing industry and information gaps. The PFA proposes that a R&D Strategic Plan is needed for the NSW commercial fishing industry to ensure relevant and strategic priorities are set for the industry.

During a recent national Fisheries Statistics Working Group meeting, data managers from all Australian states highlighted and discussed the likely high prevalence of inaccurate or fraudulent data supplied by fishers and accrued through data-entry errors. Current data quality control measures in each jurisdiction are largely heterogeneous, undocumented and often rely on manual checks by clerks or analysts that are labour intensive and costly and not routinely executed. Because many of these checks occur during manual data entry of paper-based records, these are likely to become obsolete as reliance on electronic reporting increases, with data entered directly by fishers through online portals or mobile applications.

There is a need to develop automated data cleansing and diagnostic procedures that can be applied post-hoc or retrospectively to large fisheries databases to detect and flag errors and outliers and provide subsets of reliable catch and effort data for stock assessments and other analyses. This project will contribute towards addressing these issues, by developing automated processes to routinely assess newly entered fisheries catch and effort data for errors, retrospectively quantify error rates in existing data and assess their likely influence on the outputs of stock assessment analyses. The outcomes will help improve the quality and accuracy of catch and effort data used in routine stock assessments, and in turn lead to more sustainable management of wild capture fisheries resources.