The Egyptian Insurance Union has stressed that integrating environmental, biological, operational, and climatic data is crucial for enhancing the efficiency of risk management in aquaculture projects. This integration allows for the early detection of unusual changes and the identification of their potential causes before they develop into losses affecting stock or production continuity. Effective risk management cannot rely on a single source of information, as reading water quality alone is insufficient to determine the cause of a risk.
The union highlighted that water quality data is a primary source of information, including monitoring dissolved oxygen, temperature, acidity, salinity, turbidity, and levels of ammonia and nitrite. Continuous measurements help detect trends and rates of change, rather than relying on a single reading that does not explain what happened between measurement periods. The accuracy of this data depends on the correct selection and calibration of sensor locations, reflecting the actual conditions faced by the stock.
A 2026 review of 32 studies found an increasing use of Internet of Things technologies and machine learning in monitoring oxygen levels, turbidity, and predicting water quality. However, challenges persist, including sensor calibration, data availability, and the ability of models to work with the same efficiency in different production environments. The union also noted that surface and underwater cameras provide data on fish movement, distribution, and response to feed.
The use of computer vision techniques analyzes swimming patterns, aggregation, and detects dead fish, estimating biomass and monitoring feeding processes. Integrating this information with data on feed quantities, timing, and uneaten feed helps detect changes in appetite and reduce waste. However, relying solely on fish behavior to diagnose causes of risk is not recommended, as a single sign can be linked to multiple environmental or health factors.
Stock data includes the number of cultured organisms, their weights, density, age stages, and size distribution, used to estimate feed and oxygen needs, determine the value of stock at risk, and record mortality cases. Operational data covers the status of pumps, ventilation and oxygenation devices, filters, control units, feeding systems, and backup power sources. Monitoring these indicators can reveal equipment performance degradation before complete failure.
The union emphasized the importance of health records, including the source of seed, examination results, isolation and treatment procedures, laboratory analyzes, and stock movement between units. Climatic and spatial data, such as air and water temperatures, rainfall, wind, waves, and tides, are also crucial. Utilizing satellite imagery, geographic information systems, and drones helps determine cage locations, study water movement, and monitor environmental changes.
The union concluded that the true value of these sources is realized through their integration into a single system, enabling a comprehensive picture of incidents and their potential causes. By identifying the most relevant indicators to stock health and operational continuity, and ensuring their accuracy and calibration, data can be transformed into a practical tool for risk location, cause identification, and intervention before losses escalate.
Key points
- Integrating environmental, biological, operational, and climatic data enhances risk management in aquaculture projects.
- Accurate and calibrated data is crucial for effective risk management.
- A comprehensive data system enables early detection of risks and informed decision-making.
