Prediction of weekly goat milk yield using autoregressive models

Author: C. Fernandez, J. Gomez, P. Sanchez-Seiquer, L. Gomez-Chova, E. Soria-Olivas, L. Mocé and C Garcés
Year: 2004
Issue: 5
Volume: 34
Page: 169 - 172

This paper proposes the use of autoregressive models to predict weekly milk yield in a goat farm. Twenty-eight goats were used to build the model and eight goats were used to validate it. The best models obtained were those in which the prediction was directly related to the present milk yield and previous milk yield (both observed and predicted by the model). This emphasises the strong correlation in terms of time series which exists between consecutive values (weekly in our case) of milk production. The best model provided the best results in terms of accuracy (root mean square error, RMSE = 0.4225 kg/d) and bias (mean error, ME = 0.0044 kg/d).

Keywords: autoregressive models, Goat milk, milk yield, time series prediction
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