Automated heat detection

Automated heat detection can be used for identifying pre-mating heats, cows that need to be submitted for artificial insemination in the mating period, and those that aborted and resumed cycling later.

Why use automated heat detection technologies?

Heat detection is a key driver of reproductive performance in the dairy herd. Achieving good performance manually requires a high degree of skill, effort and organisation. Farmer fatigue and limited skills among staff are the most common challenges to getting a good result.

An accurate automated heat detection technology offers the potential to apply an acceptable, consistent level of performance, irrespective of staff skills. Potential benefits include:

• Reduced dependence on the skills of staff.
• Greater consistency of detection.
• Less staff time spent on heat detection.

The above points can facilitate a move to an all-AB mating, which offers the benefit of using the non-return rate as a more accurate indicator for conception rate. This allows for timely feedback and adjustments to mating (e.g., treatments, extended mating) if necessary.

Many factors can cause poor reproductive performance, such as a high proportion of non-cycling cows leading in to mating, poor heat detection, and a low conception rate. Reasons for reproductive failure can be identified using the In-Calf Fertility Focus Report which sets out the key performance indicators of good reproductive performance and calculates your farm performance against industry targets.

Activity-based detection

Wearables use an accelerometer inside a device mounted on a neck collar, leg bracelet or ear tag of each cow. Typically, the cow’s level of activity is compared with an ‘activity baseline’ or reference, e.g. her last seven days or the herd average. If activity changes above a pre-determined threshold, an alert is generated.

The threshold set for activity-based alerts is critical to the performance of this technology:

• If the activity threshold is set too high, a lower proportion of the cows will be identified by alert and cows in heat could be missed.
• If the activity threshold is set too low, many of these cows may not be in heat (i.e. more false alerts).

A heat detection back-up system, such as tail painting, could be used as well to mitigate the risk of the system failing (e.g. due to a base station malfunction or loss of communication).

Some wearables can also measure rumination eating or temperature, which may be used to improve the heat detection ability or indicate health events.

How well do these technologies perform?

The performance of all systems rely on the quality and frequency of the data recorded, the sophistication of software algorithms (calculations) that generate alerts, and regular maintenance of the associated devices and databases (i.e. keeping cow records up to date).

Auto-drafting is a useful option to automate the separation of alerted cows, further reducing manual effort.

Achieving and maintaining best performance from the system will require:

• Good installation (e.g. well positioned EID readers).
• Regular maintenance checks (e.g. identifying and replacing defective activity devices).
• Up-to-date record keeping where the system relies on daily data (e.g. cows changing herds that may affect walking distances).

What does the research say?

There has been little independent evidence available to farmers on expected changes to reproductive performance using wearables.

A study in 2024 by DairyNZ has analysed the available data to assess reproductive performance before and after herds adopt wearables compared to a reference group. The analysis used data from 141 self-selected herds with wearables and 1,158 reference herds. The herds were filtered using various criteria, and the reference herds were matched to the wearable herds on location, herd size, production and calving dates to compare like with like. Reference herds with a wearable integration with MINDA were removed.

After adopting wearables, farmers extended the duration of their artificial breeding (AB) periods. Some transitioned to all-AB in the first year, eliminating any natural mating periods with bulls. By the second year, most had shifted to all-AB, with the delay most likely to be while they built confidence in the technologies.

The analysis did not provide evidence to support improved reproductive performance. Although herds with wearables demonstrated better reproductive performance, they already showed better performance prior to adopting the technology. Herds using wearables had higher performance measures, such as 3-week submission and 6-week calving rates compared to those without wearables, both before and after adopting the technology.

Wearable herds had lower non-return rates, likely because they tended to have longer AB periods. However, after accounting for mating length, there was no significant difference. When herds were segmented by low or high 3-week submission rate prior to adoption (above or below 80%) there was also no difference in reproductive performance after adopting wearables. Their relative results to the reference group remained the same before and after using wearables.

In summary, the study showed that if farmers had used wearables to automate their mating, then they had done so without compromising reproductive performance. However, farmers adopting wearables should be conservative when assessing the potential benefits for reproductive performance in investment analyses. While the technology facilitates increased AB mating duration, which could have potential benefits to reproductive performance with accompanying changes to management, these appear not to have been realised by this group of farmers (at least during the first 2 years post-adoption).

What are the investment costs?

The investment costs for heat detection can include a cost per cow, fixed costs for electronic identification (EID) readers and computer systems, and ongoing maintenance and replacement. Payback on investment is highly dependent on initial costs, and the difference between current manual detection and performance of the new system. While there are a number of potential tangible and intangible benefits to adopting wearables, currently, there is no evidence to suggest that reproductive performance will improve, so farmers should be cautious in factoring in improved reproduction when assessing return on investment.

What are the key considerations before purchase?

Consider your reasons for investing in an automated system and evaluate the technologies to understand the trade-offs for you, your team, and your business.

If your motivation is to improve reproductive performance through improved heat detection, you first need to be sure that heat detection performance is your key problem;

• Use information from the Fertility Focus Report to review all areas of reproductive performance.
• Know your current heat detection performance and the potential for improvement.
• Review whether you can achieve your target through better procedures and/or more motivated staff, as this is the lowest-cost solution.
• Seek reliable information on whether the automated systems can help you achieve your target.

If your goal is to maintain a high heat detection performance, but with reduced dependence on key skilled people, be mindful that technologies do not replace the need for skilled staff;

• Training will be needed to use the heat detection system; for entering data, setting alert thresholds and confirming heats, and performing maintenance. Discuss with your technology supplier what training and support will be provided and whether the software is ‘user-friendly’ (simple).
• Skilled handlers will still need to make the final decision on cows selected for artificial insemination.

Prepare a budget using realistic costs and returns so that you know the projected annual costs, the number of years until break-even is reached and the longer-term return on investment.