Using animal activity to detect disease

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Metabolic diseases in the transition period continue to cause substantial economic losses to dairy producers. With the advancement in activity monitors for dairy cows, researchers have begun to examine ways to utilize animal behavior data to detect disease prior to the onset of clinical signs. 

The Virginia Tech Dairy Center was given the new AfiFarm pedometer system called “PedometerPlus©”.  These new monitors record total rest time (time spent lying), rest bouts (number of times the animals went from lying to standing), rest time per bout and traditional step activity. 

With this data, researchers can now compare the behavior of healthy animals compared to those who are sick. Last year, scientists evaluated this with respect to experimental mastitis and more recently; Emily Yeiser (Master’s student) examined this in relation to metabolic disease. In her study, activity measures were collected during the periparturient period to determine the likelihood of disease occurrence.

Primiparous and multiparous Holstein, Jersey, and crossbred dairy cows were monitored for rest bouts, rest duration, rest time, and average daily steps throughout the pre and postpartum periods from -21 days to +30 days relative to calving.

The researchers were able to analyze the data related to dystocia, subclinical ketosis, clinical mastitis, and milk fever. Results show that on the day of calving, rest bouts increased in animals that experienced dystocia over those who did not experience dystocia.

Further, cows experiencing subclinical ketosis displayed increased rest bouts on the day prior to disease and decreased daily steps six days before the disease was diagnosed. Additionally, cows experiencing clinical mastitis had decreased rest times beginning three days prior to the onset of disease as compared to animals without mastitis. Cows with milk fever displayed more rest bouts with decreased daily steps on the day prior to and the day after disease diagnosis and increased overall rest duration and time after the clinical diagnosis of disease compared to cows that were not diseased.

As you can see from this study, there are certainly changes in activity both prior to the onset of metabolic disease, as well as afterwards. These deviations may allow producers to identify animals at risk for disease and more proactively manage herd health in the future. 

However, we still do not have the research to know what to do with these cows once they are identified as “at-risk”. Therefore, future studies will be designed to examine whether early intervention may be appropriate.

Source: Emily Yeiser, Graduate Student, Department of Dairy Science milyy18@vt.edu; Christina Petersson-Wolfe, Extension Dairy Scientist, Milk Quality & Milking Management (540) 231-4767; cspw@vt.edu



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