Case Study: Early Detection of Missing Fish Improves Efficiency

Introduction

Fish farms rely on accurate biomass data to guide feeding and optimize production. In this case, discrepancies between modeled and actual growth revealed potential missing stock. By analyzing the situation, the farm gained insights into how early detection prevents waste and improves profitability.

Analysis

Graph 1: Model Growth vs Actual Growth
Shows the difference between expected (model) and actual fish growth.
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Graph 2: Feed Compared to the Model with Early Detection ("Correct Feeding")
Illustrates how feed matches biomass more closely when missing fish are detected early.
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Graph 3: Relative Feed without Early Detection ("Overfed")
Demonstrates overfeeding that occurs if missing fish are not identified.
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Graph 4: Daily Relative Feed – Early vs Late Detection
Daily comparison of feed use under early detection versus no detection.
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Graph 5: Accumulated Feed – Early vs Late Detection
Highlights how early detection avoids unnecessary feed use and results in significant savings.
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Results

  • Estimated missing stock: 30,000 fish

  • Feed savings with early detection: 18,814 kg

  • Cost savings with early detection: €23,142

  • Likely stage of loss: Input

  • Likely cause: Stock not loaded at supplier stage

Recommendations

  • Conduct verification sampling at input stage.

  • Implement automated sensor checks to monitor stock discrepancies.

  • Audit supplier loading procedures and improve traceability.

Conclusion

This case study shows that early detection of missing fish leads to measurable improvements in feed efficiency and cost control. Farms adopting proactive monitoring can reduce losses and increase production reliability.

→ Contact us to learn how we can help your farm achieve similar results.