Associations of Wearable Sensor Measures With Feed Intake, Production Traits, Lactation, and Environmental Parameters Impacting Feed Efficiency in Dairy Cattle - Siberski–Cooper, Cori J.; Mayes, Mary S.; Healey, Mary; Goetz, Brady M.; Baumgard, Lance H.; Koltes, James E. (Frontiers in Animal Science, 2022)
Feed efficiency is an important trait to dairy production because of its impact on sustainability and profitability. Measuring individual cow feed intake on commercial farms would be unfeasibly costly. Thus, developing cheap and portable indicators of feed intake would be highly beneficial for genetic selection and precision feeding management tools. Given the growing use of automated sensors on dairy farms, the objective of this study was to determine the relationship between measurements recorded from multiple wearable sensors and feed intake. A total of three different wearable sensors were evaluated for their association with dry mater intake (DMI). The sensors measured activity (sensors = 3), rumination (sensors = 1), ear temperature (sensors = 1), rumen pH (sensors = 1) and rumen temperature (sensors = 1). A range of 56-340 cows with assorted sensors from 24-313 days in milk (DIM) were modeled to evaluate associations with DIM, parity, and contemporary group (CG; comprised of pen and study cohort). Models extending upon these variables included known energy sinks (i.e., milk production, milk fat/protein and metabolic body weight), to characterize the association of sensors measures and DMI. Statistically significant (i.e., P < 0.05) regression coefficients for individual sensor measures with DMI ranged from 9.01E-07 to -3.45 kg DMI/day. When integrating all measures from a single sensor in a model, estimated regression coefficients ranged 8.83E-07 to -3.48 kg DMI/day. Significant associations were also identified for milk production traits, parity, DIM and CG. Associations tended to be highest for timepoints around the time of feeding and when multiple measurements within a sensor were integrated in a single model. The findings of this study indicate sensor measures are associated with feed intake and other energy sink traits and variables impacting feed efficiency. This information would be helpful to improve feed and feeding efficiency on commercial farms as a proxy measurement of feed intake.