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An Efficient Single—Person Technique for Milk Sampling from Laboratory Mice
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Comparison of modelling techniques for milk-production forecasting.

M D Murphy1, M J O'Mahony2, L Shalloo3

  • 1Animal and Grassland Research Innovation Centre, Teagasc Moorepark, Co. Cork, Ireland; Department of Mechanical Engineering, Cork Institute of Technology, Co. Cork, Ireland.

Journal of Dairy Science
|April 16, 2014
PubMed
Summary

A nonlinear auto-regressive model accurately predicts daily herd milk yield, outperforming artificial neural networks and multiple linear regression, especially for short-term forecasts. This milk production modeling offers improved accuracy for dairy farm management.

Keywords:
dairy productionmilk-production forecastingmodelling

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Area of Science:

  • Dairy Science
  • Agricultural Engineering
  • Data Science

Background:

  • Accurate prediction of daily herd milk yield is crucial for efficient dairy farm management.
  • Traditional modeling techniques may have limitations in capturing dynamic production patterns.
  • Evaluating advanced modeling approaches is essential for optimizing dairy operations.

Purpose of the Study:

  • To assess the suitability of three distinct modeling techniques for predicting total daily herd milk yield.
  • To compare the accuracy of nonlinear auto-regressive, artificial neural network, and multiple linear regression models.
  • To evaluate model performance across varying forecast horizons, from short-term to a full lactation period.

Main Methods:

  • Developed and compared a nonlinear auto-regressive model with exogenous input (NARX), a static artificial neural network (ANN), and a multiple linear regression (MLR) model.
  • Utilized three years of historical milk production data from a herd of 140 pasture-based dairy cows.
  • Tested model accuracy using forecast horizons of 305 days, and moving piecewise horizons of 50, 30, and 10 days.

Main Results:

  • All three models achieved a percentage root mean square error (RMSE) of ≤ 12.03% for the full 305-day lactation prediction.
  • The NARX model demonstrated improved accuracy with shorter forecast horizons (RMSE reduced from 8.59% to 5.84% for 305-day to 10-day horizons).
  • ANN and MLR models showed less improvement in prediction accuracy as forecast horizons were shortened.

Conclusions:

  • The nonlinear auto-regressive model with exogenous input (NARX) is a more accurate alternative for predicting daily herd milk yield compared to ANN and MLR.
  • NARX modeling is particularly advantageous for short-term milk yield predictions in pasture-based dairy herds.
  • These findings support the adoption of advanced time-series models for enhanced dairy production forecasting.