Predicting the excretion of feces, urine and nitrogen (N) from dairy cows is an effective way to prevent and control the environmental pollution caused by scaled farming. The traditional prediction methods such as pollutant generation coefficient (PGC) and mathematical model based on linear regression (LR) may be limited by prediction range and regression function assumption, and sometimes may deviate from the actual condition. In order to solve these problems, the support vector regression (SVR) was applied for predicting the cows' feces, urine and N excretions, taking Holstein dry cows as a case study. SVR is a typical non-parametric machine learning model that does not require any specific assumptions about the regression function in advance and only by learning the training sample data, and also it can fit the function closest to the actual in most cases. To evaluate prediction accuracy effectively, the SVR technique was compared with the LR and radial basis function artificial neural network (RBF-ANN) methods, using the required sample data obtained from actual feeding experiments. The prediction results indicate that the proposed technique is superior to the other two conventional (especially LR) methods in predicting the main indicators of feces, urine, and N excretions of Holstein dry cows.
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