Automatic milking systems (AMS) are increasingly popular throughout the world. Our objective was to analyze 635 North American dairy farms with AMS for (risk) factors associated with increased milk production per cow per day and milk production per robot per day. We used multivariable generalized mixed linear regressions, which identified several significant risk factors and interactions of risk factors associated with milk production. Free traffic was associated with increased production per cow and per robot per day compared with forced systems, and the presence of a single robot per pen was associated with decreased production per robot per day compared with pens using 2 robots. Retrofitted farms had significantly less production in the first 4 yr since installation compared with production after 4 yr of installation. In contrast, newly built farms did not see a significant change in production over time since installation. Overall, retrofitted farms did not produce significantly more or less milk than newly constructed farms. Detailed knowledge of factors associated with increased production of AMS will help guide future recommendations to producers looking to transition to an AMS and maximize their production.

Currently, cows with poor metabolic adaptation during early lactation, or poor metabolic adaptation syndrome (PMAS), are often identified based on detection of hyperketonemia. Unfortunately, elevated blood ketones do not manifest consistently with indications of PMAS. Expected indicators of PMAS include elevated liver enzymes and bilirubin, decreased rumen fill, reduced rumen contractions, and a decrease in milk production. Cows with PMAS typically are higher producing, older cows that are earlier in lactation and have greater body condition score at the start of lactation. It was our aim to evaluate commonly used measures of metabolic health (input variables) that were available [i.e., blood β-hydroxybutyrate acid, milk fat:protein ratio, blood nonesterified fatty acids (NEFA)] to characterize PMAS. Bavarian farms (n = 26) with robotic milking systems were enrolled for weekly visits for an average of 6.7 wk. Physical examinations of the cows (5-50 d in milk) were performed by veterinarians during each visit, and blood and milk samples were collected. Resulting data included 790 observations from 312 cows (309 Simmental, 1 Red Holstein, 2 Holstein). Principal component analysis was conducted on the 3 input variables, followed by K-means cluster analysis of the first 2 orthogonal components. The 5 resulting clusters were then ascribed to low, intermediate, or high PMAS classes based on their degree of agreement with expected PMAS indicators and characteristics in comparison with other clusters. Results revealed that PMAS classes were most significantly associated with blood NEFA levels. Next, we evaluated NEFA values that classify observations into appropriate PMAS classes in this data set, which we called separation values. Our resulting NEFA separation values [<0.39 mmol/L (95% confidence limits = 0.360-0.410) to identify low PMAS observations and ≥0.7 mmol/L (95% confidence limits = 0.650-0.775) to identify high PMAS observations] were similar to values determined for Holsteins in conventional milking settings diagnosed with hyperketonemia and clinical symptoms such as anorexia and a reduction in milk yield, as reported in the literature. Future studies evaluating additional clinical and laboratory data, breeds, and milking systems are needed to validate these finding. The aim of future studies would be to build a PMAS prediction model to alert producers of cows needing attention and help evaluate on-farm metabolic health management at the herd level.

No abstract

ObjectiveTo develop transparent and reproducible methods for imputing missing data on disease incidence at national-level for the year 2005.MethodsWe compared several models for imputing missing country-level incidence rates for two foodborne diseases – congenital toxoplasmosis and aflatoxin-related hepatocellular carcinoma. Missing values were assumed to be missing at random. Predictor variables were selected using least absolute shrinkage and selection operator regression. We compared the predictive performance of naive extrapolation approaches and Bayesian random and mixed-effects regression models. Leave-one-out cross-validation was used to evaluate model accuracy.FindingsThe predictive accuracy of the Bayesian mixed-effects models was significantly better than that of the naive extrapolation method for one of the two disease models. However, Bayesian mixed-effects models produced wider prediction intervals for both data sets.ConclusionSeveral approaches are available for imputing missing data at national level. Strengths of a hierarchical regression approach for this type of task are the ability to derive estimates from other similar countries, transparency, computational efficiency and ease of interpretation. The inclusion of informative covariates may improve model performance, but results should be appraised carefully.

Digital dermatitis (DD) is the most important infectious claw disease in the cattle industry causing outbreaks of lameness. The clinical course of disease can be classified using 5 clinical stages. M-stages represent not only different disease severities but also unique clinical characteristics and outcomes. Monitoring the proportions of cows per M-stage is needed to better understand and address DD and factors influencing risks of DD in a herd. Changes in the proportion of cows per M-stage over time or between groups may be attributed to differences in management, environment, or treatment and can have impact on the future claw health of the herd. Yet trends in claw health regarding DD are not intuitively noticed without statistical analysis of detailed records. Our specific aim was to develop a mobile application (app) for persons with less statistical training, experience or supporting programs that would standardize M-stage records, automate data analysis including trends of M-stages over time, the calculation of predictions and assignments of Cow Types (i.e., Cow Types I-III are assigned to cows without active lesions, single and repeated cases of active DD lesions, respectively). The predictions were the stationary distributions of transitions between DD states (i.e., M-stages or signs of chronicity) in a class-structured multi-state Markov chain population model commonly used to model endemic diseases. We hypothesized that the app can be used at different levels of record detail to discover significant trends in the prevalence of M-stages that help to make informed decisions to prevent and control DD on-farm. Four data sets were used to test the flexibility and value of the DD Check App. The app allows easy recording of M-stages in different environments and is flexible in terms of the users' goals and the level of detail used. Results show that this tool discovers trends in M-stage proportions, predicts potential outbreaks of DD, and makes comparisons among Cow Types, signs of chronicity, scorers or pens. The DD Check App also provides a list of cows that should be treated augmented by individual Cow Types to help guide treatment and determine prognoses. Producers can be proactive instead of reactive in controlling DD in a herd by using this app. The DD Check App serves as an example of how technology makes knowledge and advice of veterinary epidemiology widely available to monitor, control and prevent this complex disease.