Feeding is a cornerstone of successful aquaculture, as it directly influences fish growth, feed conversion efficiency, and overall profitability. While feeding frequency is a critical factor, its impact on fish performance can vary widely depending on species, farming conditions, and feeding rates.
To better understand this complex relationship, researchers from Ocean University of China, Sun Yat-sen University, and Qingdao Agricultural University conducted a comprehensive meta-analysis of existing studies to examine the impact of increased feeding frequency on fish growth and feed utilization rates with both fixed and non-fixed feeding amounts.
The Economic Importance of Feed Management
Feed costs represent a substantial portion of aquaculture expenses, making efficient feed management a critical factor in profitability. By optimizing feeding strategies, producers can reduce feed waste, improve growth rates, and enhance overall economic performance.
The Role of Feeding Frequency
Feeding frequency is a key determinant of fish growth and feed utilization. While it is generally understood that increasing feeding frequency can benefit growth, the optimal frequency may vary depending on various factors. Understanding the relationship between feeding frequency and fish performance is crucial for developing effective feeding strategies.
Key Findings of the Meta-Analysis
The main results of the study, published in the scientific journal Aquaculture, are as follows:
- No significant impact on average daily gain (ADG) with fixed daily feed amounts: Increasing feeding frequency did not lead to an increase in average daily weight gain (ADG) when the total daily feed amount was kept constant.
- Improved ADG with satiation feeding: However, when fish were fed to satiation at each feeding, increasing the frequency resulted in higher ADG.
- Quadratic relationship between feeding frequency and ADG: The relationship between feeding frequency and ADG was found to be quadratic, suggesting that there is an optimal feeding frequency for most fish species.
- Challenge to the traditional gastric evacuation approach: The traditional approach of determining optimal feeding intervals based on gastric evacuation time was found to be insufficient for all fish species.
A New Model to Predict Fish Performance
To address the limitations of existing methods, the researchers developed a Gradient Boosting Machine (GBM) model. This model can accurately predict both the feed conversion ratio (FCR) (R2 = 0.9303) and ADG (R2 = 0.7249) based on various factors, such as environmental variables, fish characteristics, culture period, and feeding frequency.
Key Features of the GBM Model
- Prediction of average daily gain (ADG) and feed conversion ratio (FCR): The GBM model can accurately predict average daily weight gain (ADG) and feed conversion ratio (FCR) based on various factors.
- Consideration of environmental variables: The model incorporates environmental factors, fish characteristics, feeding frequency, and culture period to provide more comprehensive predictions.
- Support for data-driven decisions: The GBM model can help aquaculture producers make data-driven decisions regarding feeding strategies.
Implications for Aquaculture Practices
The findings of this study provide valuable insights for aquaculture professionals. By understanding the optimal feeding frequency for different fish species and considering factors such as environmental conditions and fish characteristics, farmers can:
- Improve feed efficiency: Reduce feed waste and improve feed conversion ratios.
- Enhance growth rates: Maximize fish growth and production.
- Optimize feeding strategies: Develop customized feeding plans that cater to the specific needs of their aquaculture operations.
- Reduce environmental impact: Minimize aquaculture’s environmental footprint by reducing feed inputs and associated waste.
Conclusion
This meta-analysis provides a scientific foundation for optimizing feeding strategies in aquaculture. By leveraging the GBM model and considering the factors identified in the study, farmers can make data-driven decisions to improve fish performance, reduce costs, and promote sustainable aquaculture practices.
Contact
Qin-Feng Gao
Key Laboratory of Mariculture, Ministry of Education, Ocean University of China
Qingdao, Shandong Province 266003, China
Email: qfgao@ouc.edu.cn
Reference
Huang, M., Zhou, Y., Yang, X., Gao, Q., Chen, Y., Ren, Y., & Dong, S. (2025). Optimizing feeding frequencies in fish: A meta-analysis and machine learning approach. Aquaculture, 595, 741678. https://doi.org/10.1016/j.aquaculture.2024.741678