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Prediction of growth and feed efficiency in mink using machine learning algorithms.

A Shirzadifar1, G Manafiazar2, P Davoudi2

  • 1Department of Animal Science and Aquaculture, Dalhousie University, Truro, Nova Scotia B2N 5E3, Canada; Biosystems Engineering Department, Shiraz University, Shiraz, Iran.

Animal : an International Journal of Animal Bioscience
|January 25, 2025
PubMed
Summary

Machine learning accurately predicts mink feed efficiency using basic measurements like sex and age, reducing costly individual monitoring. This approach enhances competitiveness in the mink industry by simplifying data collection.

Keywords:
Extreme gradient boostingFeed conversion ratioMachine learningPrecision livestock managementResidual feed intake

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Area of Science:

  • Animal Science
  • Machine Learning Applications
  • Agricultural Economics

Background:

  • Feed efficiency is critical for mink industry profitability, but traditional measurement methods are costly and labor-intensive.
  • Accurate feed efficiency metrics are essential for improving mink farming competitiveness.
  • Current methods for measuring feed intake and body weight in mink are impractical for widespread adoption.

Purpose of the Study:

  • To evaluate machine learning algorithms for predicting mink average daily gain (ADG), feed conversion ratio (FCR), and residual feed intake (RFI).
  • To identify key features (sex, color, age, body weight, length) for accurate prediction of feed efficiency metrics.
  • To determine the most significant features influencing ADG, FCR, and RFI in mink.

Main Methods:

  • Utilized machine learning algorithms to predict ADG, FCR, and RFI over a 15-week period.
  • Collected data on sex, color type, age, body weight (BW), and body length at 3-week intervals.
  • Compared prediction accuracy using various combinations of features, with initial measurements (August 1st) proving most effective.

Main Results:

  • The Extreme Gradient Boosting (XGB) algorithm demonstrated high accuracy in predicting ADG (R²=0.71), FCR (R²=0.74), and RFI (R²=0.76).
  • Including all initial features (sex, color, age, BW, length on August 1st) yielded the most accurate predictions.
  • Sex was identified as the most significant predictor for ADG, FCR, and RFI.

Conclusions:

  • Machine learning, particularly XGB, can accurately predict mink feed efficiency metrics without direct measurement of daily feed intake.
  • Initial measurements of sex, color, age, BW, and length are sufficient for reliable feed efficiency prediction.
  • The findings offer a cost-effective solution for evaluating feed efficiency, enhancing mink industry competitiveness.