Comparative study of three prediction models in predicting the milk yields of Yunnan Holstein cows

Abstract

The polynomial model and wood model have been extensively applied to predict the milk yield of cows, which aims to measure and partially explain the uncertainty under single factor condition. However, different sample data would affect the goodness of fit of the models. To investigate the milk yield regularities of the Chinese Holstein cows in Yunnan Standardized Pasture (YSP), data of cows with 1–3 parities using different observation periods were derived from the 401,497 records of 1,826 cows collected from YSP, and fitted in the BP neural network (BPNN) model, the Quadrinomial model and the Wood model. Prediction results of three models were compared. The results show that, for group data (mean), the Quadrinomial model was likely to over-fit under the single factor condition with small sample data, while the Wood model had a better goodness of fit; the BPNN model was more suitable for multi-factor and large sample data analysis.