title: Comparison of modelling techniques for milk-production forecasting.
abstract: The objective of this study was to assess the suitability of 3 different modeling techniques for the prediction of total daily herd milk yield from a herd of 140 lactating pasture-based dairy cows over varying forecast horizons. A nonlinear auto-regressive model with exogenous input, a static artificial neural network, and a multiple linear regression model were developed using 3 yr of historical milk-production data. The models predicted the total daily herd milk yield over a full season using a 305-d forecast horizon and 50-, 30-, and 10-d moving piecewise horizons to test the accuracy of the models over long- and short-term periods. All 3 models predicted the daily production levels for a full lactation of 305 d with a percentage root mean square error (RMSE) of ≤ 12.03%. However, the nonlinear auto-regressive model with exogenous input was capable of increasing its prediction accuracy as the horizon was shortened from 305 to 50, 30, and 10 d [RMSE (%)=8.59, 8.1, 6.77, 5.84], whereas the static artificial neural network [RMSE (%)=12.03, 12.15, 11.74, 10.7] and the multiple linear regression model [RMSE (%)=10.62, 10.68, 10.62, 10.54] were not able to reduce their forecast error over the same horizons to the same extent. For this particular application the nonlinear auto-regressive model with exogenous input can be presented as a more accurate alternative to conventional regression modeling techniques, especially for short-term milk-yield predictions.
authors.affiliation: Animal and Grassland Research Innovation Centre, Teagasc Moorepark, Co. Cork, Ireland; Department of Mechanical Engineering, Cork Institute of Technology, Co. Cork, Ireland.
authors.forename: M D
authors.initials: MD
authors.lastname: Murphy
authors.affiliation: Department of Mechanical Engineering, Cork Institute of Technology, Co. Cork, Ireland.
authors.forename: M J
authors.initials: MJ
authors.lastname: O'Mahony
authors.affiliation: Animal and Grassland Research Innovation Centre, Teagasc Moorepark, Co. Cork, Ireland.
authors.forename: L
authors.initials: L
authors.lastname: Shalloo
authors.affiliation: Animal and Grassland Research Innovation Centre, Teagasc Moorepark, Co. Cork, Ireland.
authors.forename: P
authors.initials: P
authors.lastname: French
authors.affiliation: Animal and Grassland Research Innovation Centre, Teagasc Moorepark, Co. Cork, Ireland. Electronic address: john.upton@teagasc.ie.
authors.forename: J
authors.initials: J
authors.lastname: Upton
mesh_terms: D000818:Animals; D002417:Cattle; D003612:Dairying; D005260:Female; D007774:Lactation; D016014:Linear Models; D008892:Milk / Q000378:metabolism*; D016571:Neural Networks, Computer
keywords: dairy production; milk-production forecasting; modelling