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Heat detection accuracy and A.I. technician evaluation

Joseph C. Dalton and Amin Ahmadzadeh Published on 03 February 2010

Reproductive performance has declined over the last few decades in U.S. dairy herds.

The downward trend in reproductive performance is disturbing and has eroded the profitability of dairies. The loss of potential income for each day a cow remains non-pregnant over 100 days in milk has been estimated at $0.42 to $4.95 per day, depending on stage of lactation. In 2008, the average days open for Southwestern, Northwestern and Eastern dairy herds that processed records at DHI-Provo was 149, 154, and 165 days, respectively.



Pregnancy rate is defined as the percentage of eligible cows that become pregnant within a given time frame. Pregnancy rate (PR) is a timely measurement, and as such, is a more sensitive and valuable indicator of reproductive performance than average days open. Nationally, average 21-day pregnancy rates have been reported to range between 12 to 16 percent. According to Overton, the optimal 21-day pregnancy rate approaches 30 percent.

What’s the value of an increase (or decrease) in pregnancy rate? Depending upon milk price and milk yield, each 1 percent increase (or decrease) in pregnancy rate results in the gain (or loss) of approximately $12 to $25 per cow per year. Why? Because as pregnancy rate increases, over time the average days in milk for the milking herd will decrease, leading to higher average milk production per day of lactation, more time per lifetime spent in the most profitable portion of lactation, and less veterinary and breeding costs. As pregnancy rate decreases, average days in milk increases, leading to increased management, feed, and veterinary costs for cows in the least- profitable portion of lactation.

Insemination risk (IR) and conception risk (CR) are components of PR. Insemination risk (formerly known as heat detection rate) is the percentage of eligible cows that are inseminated within a given time frame (including animals inseminated following a detected heat or a timed A.I.), while conception risk (formerly known as conception rate) is the total number of pregnant cows divided by the total number of inseminated cows with known outcomes. Thus, an increase (or decrease) in PR may be traced to an increase (or decrease) in IR, CR, or both components. In order to troubleshoot low PR in a herd, factors that impact IR and CR must be identified, and a plan must be made to alleviate problems associated with the factors identified.

Factors affecting conception risk

Heat detection accuracy is defined as the proportion of detected periods of heat in which cows were truly in heat, as evidenced by low progesterone concentration in milk or blood. Simply stated, if a cow is inseminated when not in heat, there’s little to no chance of a pregnancy resulting from that particular insemination. Consequently, low heat detection accuracy can reduce CR and ultimately decrease PR.

Other than heat detection accuracy, what else impacts CR and ultimately PR? Other factors may include fertility and general health of the cow, timing of insemination relative to heat or ovulation, semen handling, A.I. technique, semen quality, increased environmental temperature and compliance with synchronization protocols.


Previous heat detection accuracy research

The failure to accurately detect heat is a common and costly problem of A.I. programs and a major limiting factor of reproductive performance on many dairies. Published literature provides evidence that heat detection accuracy varies widely. As previously mentioned, heat detection accuracy is defined as the proportion of detected periods of heat in which cows were truly in heat, as evidenced by low progesterone concentration in milk or blood. Progesterone concentration in blood and milk is associated with events of the estrous cycle, as concentration is low for two days prior to heat and remains low for approximately two to three days. Low milk or blood progesterone alone is not an indicator of heat; however, high milk or blood progesterone is considered a confirmation that a cow is not in heat.

Using milk progesterone analyses, Reimers et al. reported the proportion of cows not in or near heat when inseminated varied from 0 to 60 percent among dairy herds, signifying a specific individual herd problem. Nebel et al. also reported highly variable heat detection accuracy among A.I. personnel and argued that errors in heat detection should be considered “a significant cause of low conception rates.” Nevertheless, one limitation of previous studies is the use of small herds and the inclusion of up to 10 producer-identified signs of heat, ranging from “standing” (presumably as determined by visual observation) to “blood on the vulva.”

Visual observation (defined as specifically watching a group of cows for a period of time without performing another duty) almost never occurs on large dairies. Consequently, labor-efficient management strategies such as once-daily heat detection, via daily tail chalk or paint application and subsequent identification of ruffled hair on the tailhead or lost chalk or paint, and once-daily A.I., are more common. Therefore, current management strategies on large dairies require that cows are restrained in headlocks daily, during which time tail chalk or paint is applied and read in a matter of seconds as A.I. personnel walk behind the cows.

Current heat detection accuracy study

Preliminary data provides evidence that A.I. technicians can use tail chalk and detect heat with high accuracy, although specific individual herd problems appear to exist.

A.I. technician evaluation

There are A.I. technicians that can detect heat with high accuracy. Further technician evaluation can provide valuable insight into the success or inadequacies of a reproductive program. Nevertheless, how do we evaluate technicians fairly?

Start by considering the number of inseminations necessary to draw meaningful conclusions. Realistically, a minimum of 250 observations per insemination code or technician is recommended.


When possible, go behind the numbers and stratify by insemination code or lactation number. Ask the questions: Are the A.I. technicians breeding the same types of cows? Are all technicians breeding throughout the year or only during a specific season?

Compare like groups, or “apples to apples” and not “apples to oranges” and try to determine if perceived differences in CR are real. Lastly, keep in mind that CR is a component of PR, and that PR is really the important monitor of success.

Technician management: Semen handling and site of semen deposition

Conception risk is most likely to be maximized when personnel:

  • Accurately identify and administer the appropriate treatments to all cows to synchronize heat or ovulation.
  • Accurately identify cows in heat.
  • Follow the A.I. stud’s recommendations for thawing semen.
  • Prevent direct straw-to-straw contact during thawing to avoid decreased post-thaw sperm viability as a result of straws freezing together.
  • Use appropriate hygienic procedures.
  • Maintain thermal protection of straws during A.I. gun assembly and transport to the cow.
  • Deposit semen in the uterus of the cow within approximately 10 to 15 minutes after thawing.

A.I. technicians must pay close attention to details, whether it is in the areas of heat detection, semen handling or site of semen deposition. In addition to evaluation of A.I. technicians, it is imperative that dairy producers and A.I. companies provide continuing education to their technicians, so they can acheive greater succees, which will offer the dairy a greater opportunity to accumulate pregnant cows quickly and increase profitability. PD

References omitted due to space but are available upon request by emailing .

—Excerpts from 2009 Western Dairy Management Conference Proceedings

Joseph C. Dalton
  • Joseph C. Dalton

  • Extension Dairy University of Idaho
  • Email Joseph C. Dalton