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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Concept Development and Use of an Automated Food Intake and Eating Behavior Assessment Method
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Predicting feed intake using modelling based on feeding behaviour in finishing beef steers.

C Davison1, J M Bowen2, C Michie1

  • 1Department Electronic and Electrical Engineering, University of Strathclyde, 204 George Street, Glasgow G1 1XW, UK.

Animal : an International Journal of Animal Bioscience
|June 11, 2021
PubMed
Summary

Predicting individual feed intake in cattle using feeding behavior is crucial for farm efficiency. While simpler models showed higher correlation, advanced techniques had lower errors, but overall prediction accuracy remains insufficient for practical farm application.

Keywords:
Beef cattleDM intakeFeed efficiencyFinishing steersMachine learning

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Area of Science:

  • Animal Science
  • Agricultural Engineering
  • Data Science

Background:

  • Accurate measurement of individual feed intake in housed cattle is essential for optimizing production efficiency.
  • Current methods for measuring feed intake are expensive and time-consuming, limiting their use in commercial settings.
  • Developing practical methods to estimate individual intake using easily obtainable farm data is a key objective.

Purpose of the Study:

  • To predict individual animal feed intake in cattle using feeding behavior, liveweight, and age.
  • To evaluate the performance of different modeling techniques, including group-based, regression, random forests, and support vector regression.
  • To assess the suitability of these prediction models for commercial farm applications.

Main Methods:

  • Eighty steers were assigned to two diets (mixed and concentrate).
  • Individual daily fresh weight intake (FWI) and dry matter intake (DMI) were recorded using electronic feeders over 56 days.
  • Feeding behavior variables (visits, time at feeder, time consuming feed) were extracted from electronic feeder data.
  • Four modeling approaches were tested: group-based (GRP), multiple linear regression (REG), random forests (RF), and support vector regressor (SVR).

Main Results:

  • Regression, Random Forests, and Support Vector Regressor models predicted FWI with R²_RM values ranging from 0.1 to 0.36 and DMI with R²_RM values from 0.13 to 0.19.
  • Group-based models demonstrated higher R²_RM values (0.42-0.49 for FWI, 0.32-0.44 for DMI) compared to regression and machine learning techniques.
  • Despite higher correlations, group-based models exhibited larger errors, indicating they did not capture individual feeding patterns effectively.
  • Regression and machine learning techniques yielded lower errors but overall prediction precision was insufficient for practical farm use.

Conclusions:

  • Simpler group-based models provide a higher correlation for predicting feed intake but are less precise due to not accounting for individual feeding behaviors.
  • Advanced regression and machine learning techniques offer lower prediction errors for individual intake but still lack the necessary precision for practical application on commercial farms.
  • Further research is needed to develop more accurate and practical methods for estimating individual feed intake in housed cattle to support efficient farm management.