Optimizing feeding frequencies in fish: A meta-analysis and machine learning approach

Abstract

Feeding is a critical factor in aquaculture, directly impacting cultivation success and profitability. Feeding frequency determines feed intake and regulates fish appetite and nutrient utilization. However, the effects of increased feeding frequency on fish growth and feed conversion ratio (FCR) can vary depending on species, cultivation conditions, feeding rates. This variability can lead to inconsistencies in interpretations, with individual studies often lacking in statistical power. To address this issue, we analyzed data from 145 study cases across 53 publications to quantify the impact of feeding frequency on average daily weight gain (ADG) and FCR. Our meta-analysis revealed that increasing feeding frequency could reduce FCR, but it did not enhance ADG when daily feed amounts were fixed. Only when each feeding reached satiation did increased feeding frequency boost ADG. We found that the relationship between feeding frequency and ADG in fish is quadratic, with most fish species achieving optimal ADG at four feedings per day. Traditional approaches suggest that fish appetite recovery is linked to gastric evacuation time, defining an optimal feeding interval at 80 % gastric evacuation. However, our research indicates that this approach is not suitable for all fish species. Therefore, we developed a Gradient Boosting Machine (GBM) model that predicts FCR (R2 = 0.9303) and ADG (R2 = 0.7249) based on environmental variables, fish characteristics, culture period, and feeding frequency. This study provides a scientific basis for formulating more efficient and sustainable aquaculture feeding strategies, optimizing growth and feed utilization across different fish species.