Towards practical application of sensors for monitoring animal health; design and validation of a model to detect ketosis.

The objective of this study was to design and validate a mathematical model to detect post-calving ketosis. The validation was conducted in four commercial dairy farms in Israel, on a total of 706 multiparous Holstein dairy cows: 203 cows clinically diagnosed with ketosis and 503 healthy cows. A logistic binary regression model was developed, where the dependent variable is categorical (healthy/diseased) and a set of explanatory variables were measured with existing commercial sensors: rumination duration, activity and milk yield of each individual cow. In a first validation step (within-farm), the model was calibrated on the database of each farm separately. Two thirds of the sick cows and an equal number of healthy cows were randomly selected for model validation. The remaining one third of the cows, which did not participate in the model validation, were used for model calibration. In order to overcome the random selection effect, this procedure was repeated 100 times. In a second (between-farms) validation step, the model was calibrated on one farm and validated on another farm. Within-farm accuracy, ranging from 74 to 79%, was higher than between-farm accuracy, ranging from 49 to 72%, in all farms. The within-farm sensitivities ranged from 78 to 90%, and specificities ranged from 71 to 74%. The between-farms sensitivities ranged from 65 to 95%. The developed model can be improved in future research, by employing other variables that can be added; or by exploring other models to achieve greater sensitivity and specificity.

Towards practical application of sensors for monitoring animal health: the effect of post-calving health problems on rumination duration, activity and milk yield.

Three sources of sensory data: cow's individual rumination duration, activity and milk yield were evaluated as possible indicators for clinical diagnosis, focusing on post-calving health problems such as ketosis and metritis. Data were collected from a computerised dairy-management system on a commercial dairy farm with Israeli Holstein cows. In the analysis, 300 healthy and 403 sick multiparous cows were studied during the first 3 weeks after calving. A mixed model with repeated measurements was used to compare healthy cows with sick cows. In the period from 5 d before diagnosis and treatment to 2 d after it, rumination duration and activity were lower in the sick cows compared to healthy cows. The milk yield of sick cows was lower than that of the healthy cows during a period lasting from 5 d before until 5 d after the day of diagnosis and treatment. Differences in the milk yield of sick cows compared with healthy cows became greater from 5 to 1 d before diagnosis and treatment. The greatest significant differences occurred 3 d before diagnosis for rumination duration and 1 d before diagnosis for activity and milk yield. These results indicate that a model can be developed to automatically detect post-calving health problems including ketosis and metritis, based on rumination duration, activity and milk yield.