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. 2025 Jun 16;15(12):1773.
doi: 10.3390/ani15121773.

Machine Learning-Based Prediction of Feed Conversion Ratio: A Feasibility Study of Using Short-Term FCR Data for Long-Term Feed Conversion Ratio (FCR) Prediction

Xidi Yang  1   2   3 , Liangyu Zhu  1   2   3 , Wenyu Jiang  1   2   3 , Yiting Yang  1   2   3 , Mailin Gan  1   2   3 , Linyuan Shen  1   2   3 , Li Zhu  1   2   3
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Machine Learning-Based Prediction of Feed Conversion Ratio: A Feasibility Study of Using Short-Term FCR Data for Long-Term Feed Conversion Ratio (FCR) Prediction

Xidi Yang et al. Animals (Basel). .

Abstract

Feed conversion ratio (FCR) is a critical indicator of production efficiency in livestock husbandry. Improving FCR is essential for optimizing resource utilization and enhancing productivity. Traditional methods for FCR optimization rely on experience and long-term data collection, which are time-consuming and inefficient. This study explores the feasibility of predicting long-term FCR using short-term FCR data based on machine learning techniques. We employed nineteen machine learning algorithms, including Linear Regression, support vector machines (SVMs), and Gradient Boosting, using historical datasets to train and validate the models. The results show that the Gradient Boosting model demonstrated superior performance, achieving a coefficient of determination (R2) of 0.72 and a correlation of 0.85 between predicted and actual values when the testing interval exceeded 40 kg. Therefore, we recommend a minimum feeding measurement interval of 40 kg. Furthermore, when the testing interval was set to 40 kg and further refined to the range of 50-90 kg, the model achieved an R2 of 0.81 and a correlation of 0.90 for FCR prediction in the 30-105 kg range. Among the 19 machine learning algorithms tested, Gradient Boosting, LightGBM, and CatBoost showed superior performance, with Gradient Boosting achieving the best results. Considering practical production requirements, the 50-90 kg feeding stage proved to be the most ideal for FCR testing. This study provides a more effective method for predicting feed efficiency and offers robust data support for precision livestock farming.

Keywords: data prediction; deep learning; feed conversion ratio (FCR); machine learning; precision livestock farming.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure A1
Figure A1
Model Training Parameter Settings.
Figure 1
Figure 1
FCR experimental flowchart.
Figure 2
Figure 2
R2 curves for Gradient Boosting, LightGBM, and CatBoost models.
Figure 3
Figure 3
Predicted scatter plots of Gradient Boosting, LightGBM, and CatBoost models.
See this image and copyright information in PMC

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