Comparison of forecast models of production of dairy cows combining animal and diet parameters
2020
Nguyễn, Quốc Thông | Fouchereau, Rémy | Frénod, Emmanuel | Gérard, Christine | Sincholle, Vincent
We study the effect of nutritional diet characteristics on the lactating Holstein–Friesian dairy cows in Brittany, France from 36 individuals. An analysis of the relations between fat/protein content and milk yield was implemented for our dataset. The fat and protein production increase at a slower rate as milk yield increases. The importance of chemical composition on milk production is studied using the linear model. The data analysis confirms the importance of Starch, crude fiber, and protein which have a positive effect on milk production. This analysis also confirms the previous study on the effect of parity on the production. After that, the milk production forecasting is investigated using both linear models and machine learning approaches (support vector machine, random forest, neural network). We study the performance of multiple linear regression and machine learning-based models in both non-autoregressive and autoregressive cases at the individual level. The autoregressive models, which take into account the previously observed milk yield, have proven to significantly outperform the non-autoregressive approaches. Moreover, the computational cost of each approach is presented in the paper. While the random forest algorithm gives the best performance in both non-autoregressive and autoregressive approaches. The support vector machine algorithm gives a very close performance with a substantial less computing time. The support vector machine is shown to be the best compromise between accuracy and computational cost.
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