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Establishment of a feed intake prediction model based on eating time, ruminating time and dietary composition

The individual feed intake of dairy cows is an indicator of oestrus and health status and can be used as an ab­ normality warning in health monitoring. However, existing methods for monitoring individual feed intake are limited by the feeding environment and equipment and are difficult to apply in commercial production. In this experiment, a feed intake monitoring system for dairy cows was used to determine the daily feed intake of 10 dairy cows. The dairy cows were fed 8 diets with different concentration-to-forage ratios according to different phases or time intervals. The adaptation period of the cows to each diet was 7 days, and the trial period was 7 days. Smart collars were used to monitor the daily eating time and ruminating time of the dairy cows, which were used along with dietary components as input parameters to establish five feed intake prediction models: a linear regression model (LRM), an artificial neural network model, a support vector machine model, a K-nearest neighbour model and a chi-square automatic interaction detector model. The research results showed that R2 was 0.73 for the LRM and ≥0.82 for the other four models, which indicated that the relationship between feed intake and the predictors may not be a simple linear relationship. The five models had root mean square errors (RMSEs) ≤ 1.5 kg/d and standard deviations (SDs) ≤ 0.87 kg/d, and in external verification, the five models achieved R2 values ≥ 0.80, RMSEs ≤ 0.75 kg/d, and SDs ≤ 0.90 kg/d. This study shows that adding dietary ingredient data to collar-collected data can improve prediction model accuracy.

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