Inline Reticulorumen pH as an Indicator of Cows Reproduction and Health Status Our study hypothesis is that the interline registered pH of the cow reticulum can be used as an indicator of health and reproductive status. The main objective of this study was to examine the relationship of pH, using the indicators of the automatic milking system (AMS), with some parameters of cow blood components. The following four main groups were used to classify cow health status: 15xe2x80x9330 d postpartum, 1xe2x80x9334 d after insemination, 35 d after insemination (not pregnant), and 35 d (pregnant). Using the reticulum pH assay, the animals were categorized as pH   6.62 (10.5% of cows). Using milking robots, milk yield, fat protein, lactose level, somatic cell count, and electron conductivity were registered. Other parameters assessed included the temperature and pH of the contents of reticulorumens. Assessment of the aforementioned parameters was done using specific smaXtec boluses. Blood gas parameters were assessed using a blood gas analyzer (EPOC (Siemens Healthcare GmbH, Erlangen, Germany). The study findings indicated that pregnant cows have a higher pH during insemination than that of nonpregnant ones. It was also noted that cows with a low fatprotein ratio, lactose level, and high SCC had low reticulorumen pH. They also had the lowest blood pH. It was also noted that, with the increase of reticulorumen pH, there was an increased level of blood potassium, a high hematocrit, and low sodium and carbon dioxide saturation.

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.