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Science Applied to Dairy Management - Estrous Detection Strategies

September 24, 2020

There are a few different, effective ways to detect estrus in dairy cows, ranging from visual detection to the use of automated systems. Though efficiency and accuracy in accomplishing the task varies from herd to herd, anestrous cows (those frustrating ones that do not show good signs of estrus) present a constant challenge for estrus detection.

A research group in Florida designed a study to compare two different estrous detection strategies. Cows in a large Holstein herd were divided into two treatment groups. Both groups were routinely observed by farm personnel for estrous activity, including evaluation of mounting indicator devices attached to the tail head. Cows in the second treatment group also wore collars with automated activity monitors to measure movement and rumination, providing data which was used to help make insemination decisions.

Figure adapted from Marques et al., 2020

Following a 48-day voluntary waiting period, the aim was to inseminate cows after detection of estrus. However, to account for anestrous cows, those not detected in estrus by 68±3 days in milk (DIM) were enrolled in a timed AI (TAI) protocol. After first insemination, estrous detection was again prioritized until the time of pregnancy diagnosis, at which time a TAI protocol was initiated for open cows.

A significant improvement (significant differences highlighted in red in the figure) in the rate of estrous detection was observed at second insemination for those cows wearing automated activity monitors, with 7.1% more open cows being serviced before pregnancy diagnosis and initiation of a TAI protocol. Interestingly, when researchers factored in milk production levels, it was discovered that the use of automated activity monitors significantly increased the overall rate at which pregnancies were established for the high-producing cows, but there was no apparent benefit for the low-producing group (data not shown).

When comparing fertility for the two treatment groups after first service, there was a 5.2% advantage for the group of cows wearing automated activity monitors at the time of the second pregnancy diagnosis (approximately 95 days after insemination). Again evaluating cows based on milk production, it was discovered that use of automated activity monitors improved conception rates at both first and second service for the high-producing cows, but there was no fertility improvement for low-producing cows (data not shown).


Estrous detection is always a challenge. Automated activity monitoring systems are costly, but they can be an effective tool for accelerating the rate at which inseminations are accomplished across a group of cows and may help improve accuracy with the timing of insemination. These benefits may be especially apparent for high-producing cows which may otherwise have been in the “anestrous cow” category because of decreased intensity or duration of estrus. Timed AI is oftentimes the best option for the remaining cows which are not cycling or showing any estrous activity.

Ultimately, the extent to which a herd may benefit from an automated activity monitoring system for reproductive management will depend largely on two major factors – how committed personnel are to using the system and how much room there is for improved reproductive performance. Struggling herds stand to benefit the most.



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