MODELING DAILY MILK-YIELD IN HOLSTEIN COWS USING TIME-SERIES ANALYSIS

Time series analysis of milk yield data collected three times daily was used to create stochastic models for short-term forecasting of milk production. These models can be used in systems for automated registration of daily milk weights. Analyses were carried out on partial or complete lactation yield data from 513 lactations. Time series analysis showed that the exponential smoothing function was most appropriate to model both individual milking and daily yield data. Model parameters were influenced by parity, stage of lactation, occurrence of missed milkings, and treatment for diseases. The model to forecast daily total yield performed equally well as the model to forecast individual-milking yield, since the variance of the residual errors of the forecast daily production was similar to the sum of the variances of the forecast errors of the individual milkings. Average parameter values from heifers and multiparous cows not treated for diseases, and without missing milk weights, were estimated and used to forecast next day milk yield. Average forecast errors preceding disease diagnosis showed a sudden production decline in the case of clinical mastitis and a gradual production decrease in the case of clinical ketosis. Therefore, separate screening tests are needed when screening for these diseases with daily milk weight systems. © 1990, American Dairy Science Association. All rights reserved.