Prediction of weekly goat milk yield using autoregressive models

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 = 0.42 25 kg/d) and bias (mean error, ME = 0.004 4 kg/d).