Data-Driven Feed Optimization

Data-driven feed optimization adjusts ration size, pellet size, and feeding timing continuously using live inputs such as biomass estimates, water temperature, dissolved oxygen, feeding camera response, and historical FCR, instead of fixed feeding tables. The core logic is simple but powerful: appetite and growth rate vary daily, and a fish eats and converts feed best within a narrow temperature-oxygen window, so feeding exactly what the stock will consume at that moment minimizes waste and maximizes growth. Typical gains reported on well-instrumented farms are 5-15% feed savings and FCR improvements of 0.1-0.4, worth tens of thousands of dollars per cycle on large sites where feed is the dominant cost. The method requires trustworthy data plumbing: frequent biomass updates from hydroacoustics or sampling, calibrated oxygen sensors, and honest feed delivery records, because the model is only as good as its inputs. Overfeeding risk shifts from visible waste to model error, so farms keep conservative safety margins and camera verification. Advanced systems incorporate weather forecasts to pre-adjust feeding before storms, and machine learning to detect appetite decline as an early disease signal. The discipline generalizes: the same data pipeline that optimizes feeding also improves harvest planning, oxygen budgeting, and cost forecasting.