Joseph C. Dalton
Heat detection errors severely limit reproduc-tive performance on many dairies. Ovulation is tied to the onset of heat; therefore, there is limited opportunity to maximize conception to AI. Consequently, a successful AI program must include accurate heat detection and timely AI relative to ovulation.
The primary sign of heat is when a cow stands to allow a herdmate to mount. Secondary signs include attempting to mount cows, increased activity, clear mucous discharge and swelling and redness of the vulva.
Progesterone, produced by the corpus luteum, is also associated with events of the estrous cycle. Milk and blood progesterone concentration is low for two days before heat and remains low for two to three days after heat. Low milk or blood progesterone is not an indicator of heat, but high milk or blood progesterone is a confirmation a cow is not in heat.
10 DIFFERENT SIGNS
Following milk progesterone analyses, researchers reported the proportion of cows not in heat when inseminated varied from zero to 60% among herds, signifying a specific, individual herd problem. A limitation of past studies, however, includes the decision to use up to 10 producer-identified signs of heat, ranging from standing (presumably as determined by visual observation) to blood on the vulva.
Visual heat observation almost never occurs on large dairies. Consequently, labor efficient management strategies such as once-daily heat detection via tail chalk or paint application and subsequent identification of ruffled hair on the tailhead or lost chalk or paint, and once-daily AI are more common. Therefore, current management strategies require cows are restrained in headlocks daily, during which time tail chalk or paint is applied and read in a few seconds as AI personnel walk behind the cows. This system not only allows for heat detection of numerous animals in a time-efficient manner but also allows for heat detection accuracy to be monitored periodically via blood samples taken from animals identified in heat and receiving AI.
A study is underway to determine heat detection accuracy as measured by blood progesterone of AI technicians using once-daily tail chalk on lactating dairy cows housed in open lots or freestalls. On the day of AI, one blood sample was collected from lactating cows detected in natural or prostaglandin-induced heat. No blood samples were obtained from cows receiving timed AI. Samples were analyzed for progesterone concentration. High blood progesterone (> 1 ng/mL) was confirmation cows were not in heat when AI occurred.
The percentage of cows not in heat at AI varied between herds, from zero to 13%, signifying a specific, individual herd problem. Overall, average heat detection accuracy across the herds was 95%. The study is on-going; however, the data suggests AI technicians can use tail chalk and detect heat with accuracy, yet individual herd problems do exist.