Data Sheet 1_Activity-based first estrus detection in grazing heifers and first-lactation dairy cows for use as novel fertility phenotypes at scale.pdf

Here, we evaluated whether leg-mounted triaxial accelerometers could generate activity-based first estrus phenotypes under these constraints by relying on extended onboard data storage. In this proof-of-concept study, IceQube triaxial accelerometers were fitted to 1,880 nulliparous Holstein-Friesian (HF) and HF × Jersey crossbred heifers in 18 herds in 2019 and 1,786 primiparous HF and HF × J crossbred cows in 17 herds in 2020 (66% heifers from the previous year and 34% new animals) managed in pasture-based, seasonal calving systems.

Signal-processing methods were used to identify periods of high step numbers or low lying time that predicted estrus events. Twenty-four-hour moving averages of the activity data were first calculated for each animal to remove diurnal patterns, and the herd mean was then subtracted at each time step to correct for whole herd activity. The resulting deviation was standardized for each animal to a mean of zero and a standard deviation of one, and high activity events were indicated when the standardized deviation remained above a threshold of 2.2 for at least 12 hours during a two-day period.

Approaches to handling missing data, spurious detection spikes and censored results were also proposed. High activity events were detectable in both the heifer and cow step data. They were also highly correlated (F1 statistic in the range 64.1-75.9%) with estrus observations in reference datasets; these being (1) a single herd of heifers where visual observation aided by tail paint was used to record estrus, and (2) recorded insemination dates with and without subsequent positive pregnancy tests for 14 herds of first-lactation cows.

The algorithm was then used to predict first estrus for each animal during the peripubertal and postpartum periods, to derive novel activity-based fertility phenotypes. We conclude that data from wearable activity sensors can be used to derive activity-based estrus phenotypes at scale, although further validation across commercial heifer herds would strengthen application in replacement animals. The approach supports the use of sensors at scale in generating useful fertility phenotypes where sensor connectivity is a challenge, although longer monitoring periods and improved data retrieval infrastructure will be needed to make data collection feasible and reduce censoring in extensively grazed heifers.</p>