Effect of parity weighting on milk production forecast models.

Highlights • The NARX model and Ali-Schaeffer model were compared at individual cow level. • Six input treatments including parity weight combinations were tested and compared. • The NARX Model was more accurate than the Ali and Schaeffer model. • The effectiveness of the parity weight treatment varied between cow groups. • Parity weight trends were a determining factor in the success of the treatments. Abstract 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 cows' historical milk production relative to parity and also the prediction year. These results highlighted 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 weight were shown to be the two most successful input treatments. The results showed that historical parity weighting trends had a substantial effect on the success rate of the treatments for both milk production forecast models. [ABSTRACT FROM AUTHOR]