Prediction of the diet energy digestion using kernel extreme learning machine: A case study with Holstein dry cows Abstract   In order to effectively evaluate the diet nutritional and feeding value, it is essential to accurately predict the indicators of diet energy digestion of dairy cows, thus further optimizing feed formulation and improving feeding management. Traditional mathematical models used to predict the main indicators of diet energy digestion of dairy cows are usually based on linear regression (LR) method. However, as a typical parametric model, the LRbased method is limited by regression function assumption, and the inaccurate assumed function sometimes leads to the learned regression model biased from the groundtruth one. In this study, we propose a kernel extreme learning machine (KELM) technique to predict the indicators of Holstein dry cowsu0027 diet digestible energy (DE) and energy digestibility (ED). KELM is a typical nonparametric machine learning model, which does not require any specific assumptions about the regression function in advance. The learned modal by KELM can well fit the actual one only by learning the training sample data in most cases. To evaluate prediction accuracy effectively, we compared the KELM technique with traditional parametric prediction LR model and other commonly used nonparametric models such as radial basis function artificial neural network, support vector machine and standard extreme learning machine methods. The required sample data is obtained from actual feeding and digestion experiments. The prediction results indicate that the proposed KELMbased prediction technique is superior to other methods in most performance metrics for prediction of the DE and ED indicators of diet energy digestion of Holstein dry cows. In particular, it has higher prediction accuracy than traditional LRbased prediction method, and can predict DE and ED better under small samples conditions. The developed KELMbased prediction model may be utilized to provide key decision support information to animal nutrition experts, livestock farmers and feed suppliers. The presented model is also exploited as a potential tool for accurate evaluation of dairy cows diet feeding effect.

A machine learning based decision aid for lameness in dairy herds using farmbased records Abstract   Lameness is a common welfare issue in dairy farms and can negatively influence productivity and farm profitability. Onfarm measurements are time consuming and continuous monitoring of lameness can be thus challenging. A predictive model that is suitable for routine field applications can be thus an efficient strategy to improve lameness status in dairy herds. We explored the use of a machine learning approach based on decision tree induction to detect and monitor the risk level of dairy herds for lameness based on 20 routinely precollected farmbased records related to herd management and housing, milk production performance, reproduction performance, longevity, and genetic merit. The risk of lameness was determined for 229 dairy herds based on herd prevalence. The results based on 10fold repeated crossvalidation suggest that there was little difference between a simple decision tree and more advanced machine learning algorithms such as random forests or boosting algorithms with an area under the receiver operating characteristic curve (AUC) of 0.73xe2x80x930.75, but machine learning performed better than classical multivariate logistic regression (AUC of 0.28). Model sensitivity based on machine learning was highest at 0.58, whereas model specificity was highest at 0.89 among all models tested. Stacking the individual machine learning algorithms to a higherorder model slightly lifted model performance (AUC of 0.76) at a model sensitivity of 0.54 and a model specificity of 0.94 but at a loss in model interpretability. Additional information more closely related to lameness are required to further improve model performance. Nevertheless, decision tree induction was found to be useful in analysing small data sets that frequently occur in various livestock farming disciplines. Overall, the machine learning approach described in our study may be an appropriate and powerful decision support and monitoring tool, and can be implemented in a computerized information system to detect herds with potential deficiencies in lameness.