World Aquaculture 2023

May 29 - June 1, 2023

Darwin, Northern Territory, Australia

ENHANCING FARM MANAGEMENT PRACTICES: A CASE STUDY OF IMPROVING DECISION SUPPORT TOOLS FOR MANAGING THE RISK OF SEA LICE INFESTATION IN SALMON FARMS WITHIN FARM MANAGEMENT AREAS

Emma McCall*, Harrison Carmody, Viknesh Sangaraju, Cemlyn Barlow, David Vale and Louise Bruce

BMT Commercial Australia,

Level 5/348 Edward Street, Brisbane, QLD 4000, Australia

Emma.McCall@bmtglobal.com

 



Aquaculture is a major industry worldwide and is a significant contributor to the global seafood supply. However, the industry is facing several challenges, one of which is sea lice infestation, which affects the health and growth of farmed fish resulting in significant economic losses for the industry and negative environmental impacts. Greater understanding of sea lice infestation rates and response to treatment will enhance farm management practices to reduce losses and costs to the industry.

The study was conducted in a salmon farming region in Scotland, where sea lice infestation is a significant problem. The goal of the study was to develop a decision support system (DSS) designed for aquaculture operators to enhance farm management practices and reduce levels of sea lice infestation (Figure 1). Data from the Scottish Environmental Protection Agency (SEPA), including sea lice monitoring data and farm management data including treatment records were incorporated into the DSS with the aim of assist farmers in making informed decisions about sea lice management. The DSS also incorporated results from a 3D hydrodynamic model (TUFLOW FV) that was coupled with a biologically responsive particle transport model (PTM) and calibrated using farm data from SEPA.

The DSS was designed to provide farm managers with up-to-date information on the risk of sea lice infestation, based on environmental and farm management data within the wider domain and encompassing all farm operators. The DSS additionally provided model output data from management scenarios by adjusting the timing and use of specific treatments (both physical and chemical) and assessing the impact on predicted sea lice counts.

Several challenges associated with predicting infestation risk in areas with high connectivity between farms could be improved with further refinement of the DSS tool to include higher frequency and refined data. Despite these challenges, the proof-of-concept model demonstrated the potential of the DSS in enhancing farm management practices and reducing the risk of sea lice infestation, and identified the need for collaboration between farmers, researchers, and regulatory authorities to address these challenges.