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Effect of Parity Weighting on Milk Production Forecast Models
- Zhang, F., Upton, J., Shalloo, L., Murphy, M.D.
- Computers and electronics in agriculture 2018
- Holstein, cows, herds, lactation, milk, milk yield, models, prediction, Ireland
- The objectives of this study were to compare the prediction accuracy of two milk prediction models at the individual cow level and to develop, compare and evaluate six input data preprocessing treatments designed to factor parity information into the milk prediction model configuration process. The two models were a nonlinear auto-regressive model with exogenous input and a polynomial curve fitting model. These were tested using six different parity data input treatments. Different combinations of static parity weight, dynamic parity weight and removal of the first lactation data were selected as input treatments. Lactation data from 39 individual cows were extracted from a sample herd of pasture-based Holstein-Friesian cattle located in the south of Ireland and situated in close proximity. The models were trained using three years of historical milk-production data and were employed for the prediction of the total daily milk yield of the fourth lactation for each individual cow using a 305-day forecast horizon. The nonlinear auto-regressive model with exogenous input was found to provide higher prediction accuracy than the polynomial curve fitting model for individual cows using each input treatment. An improvement in forecast accuracy was observed in 62% of test cows (24 of 39). However, on average across the entire population, only part of the treatments delivered an increase in accuracy and the success rate varied between test groups. Prediction performance was strongly influenced by the cow’s historical milk production relative to parity and also the prediction year. These results highlight the importance of examining the accuracy of milk prediction models and model training strategies across multiple time horizons. Removal of the first lactation and applying static parity weigh were shown to be the two most successful input treatments. The results showed that historical parity weighting trends have a substantial effect on the success rate of the treatments for both milk production forecast models.