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Health data can only be helpful if translated into clear language

JoDee Sattler for Progressive Dairyman Published on 28 February 2019
Automated health monitoring systems hold potential to expedite metabolic disease diagnosis

The science of monitoring milk yield and deviations from the expected lactation curve dates back more than half a century – when Dairy Herd Improvement testing began.

However, today’s science allows dairy producers to monitor so much more than milk yield and deviations, which still remain strong indicators of cattle health and fertility.



Current technologies provide data encompassing somatic cell count, bodyweight, locomotion score, body condition score, beta-hydroxybutyrate concentration in milk, feed intake, feeding time, rumination time, lying time and temperature (skin, vagina, rumen).

While this information can be helpful in improving cattle health, productivity and reproductive success, it can also be overwhelming.

Health monitoring systems and their generated data will only positively influence dairies when translated into clear language that allows for prompt, proactive measures to solve challenges or make decisions.

When it comes to health, the transition period remains the most precarious time in a cow’s reproductive cycle. Approximately one-third of cows experience at least one clinical disease in the first three weeks of lactation, representing 60 to 80 percent of all clinical cases.

Increased disease susceptibility in early postpartum stems from cows’ reduced immunocompetence. Cows’ nutritional status and associated metabolic scenario impair immune cell function and increase susceptibility to microbial infections.


In addition, the enlarged uterus contains placenta remnants that favour uterine infections. Postpartum disease reduces oocyte fertilization and zygote survival during the first week of development.

Fighting infection expends energy

What leads to immunocompetence? In general, it’s reduced appetite, increased bodyweight loss and altered nutrient partition.

By mounting a response against infection, inflammation increases energy expenditure and partitions resources away from production and reproduction.

Multiple mechanisms with additive effects impair fertility, including reduced developmental competence of oocytes and altered uterine environment. Inflammatory disease, suboptimal uterine conditions and less competent embryos cause most reproductive failures.

Controlling inflammation may mitigate its effects on reproduction. Researchers gave a nonsteroidal, anti-inflammatory drug (meloxicam) to treat clinical mastitis and improve subsequent fertility.

Cows treated with meloxicam had better conception in first postpartum A.I., and more cows became pregnant by day 120 after calving compared with the control.


A nutraceutical alternative for inflammation control reduces the ratio of omega-6 to omega-3 fatty acids in the postpartum diet. This strategy could also minimize the effects of inflammation on reproduction.

Use health monitoring to prevent disease

We’ve painted a pretty dismal picture for cows getting through the transition period and delivering another healthy calf in a timely manner.

Preventing postpartum diseases is the best strategy to reduce embryonic mortality and increase reproductive efficiency. Thus, it’s important to establish health programs for early disease detection and rapid intervention when necessary.

Automated health monitoring systems hold potential to expedite metabolic disease diagnosis, compared with clinical evaluation alone or cowside tests.

Treating cows identified as abnormal by a health monitoring system with supportive therapy, despite no other clinical symptoms, should be considered and evaluated as a means to minimize future clinical disease and production losses.

In facilities other than tiestall barns, individual feed consumption is nearly impossible to measure. Yet dry matter intake strongly correlates with transition cow health, productivity and fertility.

Some health monitoring systems can record rumination time, which may serve as a proxy for changes in dry matter intake over time.

Several studies showed cows that developed postpartum metabolic and infectious diseases, retained placenta, displaced abomasum or indigestion have altered prepartum and postpartum rumination patterns.

A study demonstrated using the Data Flow II software health index, activity and days in milk as a diagnostic tool resulted in sensitivities (percentage of healthy cows properly diagnosed as healthy) between 86 and 100 percent for metabolic diseases, 55 to 89 percent for clinical mastitis and 49 to 78 percent for metritis.

Specificity of using the health index (percentage of sick cows properly diagnosed as sick) as a diagnostic tool for postpartum disease was 98 percent. Herds with sub-par transition cow monitoring could benefit from using an automated rumination activity monitoring system.

Evaluating AEDM systems

Should a dairy producer invest in an automated estrous detection monitoring (AEDM) system? While the systems offer several advantages, the “correct” answer most likely depends on the dairy’s reproductive performance.

Let’s review a couple studies. Researchers demonstrated a program of breeding via A.I. when estrus is detected by an AEDM system resulted in a shorter interval from calving to pregnancy establishment (80 versus 90 days) than a reproductive strategy based on fixed-time A.I. following the Presynch-Ovsynch protocol.

However, other researchers did not observe differences when cows were submitted to fixed-time A.I. at 79 days postpartum compared with cows inseminated at estrus detected by an AEDM system.

In another experiment, it was demonstrated pregnancy per A.I. of cows inseminated in estrus detected by AEDM was dependent on the cows’ cyclic status.

Cyclic cows’ pregnancy per A.I. (cows inseminated in estrus detected by AEDM and inseminated at fixed time) were 33.9 and 34.2 percent, respectively, whereas anovular cows’ pregnancy per A.I. (cows inseminated in estrus detected by AEDM and fixed time) were 16.4 and 29.7 percent, respectively.

The interval from calving to pregnancy establishment was largest for cows that were anovular at 50 days postpartum.

AEDM can help with properly timing A.I. According to research, the ideal interval from onset of estrus to insemination with conventional semen is four to 12 hours for lactating cows.

With AEDM, it was demonstrated among multiparous cows inseminated with conventional semen, the ideal interval was zero to 12 hours. For primiparous cows inseminated with conventional semen, the ideal interval was 13 to 16 hours.

Consider current, potential estrous detection rates

When evaluating an AEDM system’s value, consider current estrous detection rate, potential estrous detection efficiency, system’s lifetime expectancy and labour efficiency.

In general, if a herd’s estrous detection rate is less than 50 percent, an AEDM system should provide value. Herds with an estrous detection rate of more than 65 percent may not find this technology beneficial.

Today, many dairy managers collect a massive amount of animal health data. This presents an exciting opportunity for improved management – leading to improved health, production and reproduction.

Ultimately, this can make a huge impact on dairy farm profitability. The benefit is largely dependent on the technology’s ability to easily convey a message to dairy managers so they can implement proactive solutions.

Bottom line: Dairy managers must understand what the technology offers and whether or not current systems and protocols are not sufficient to maximize health, productivity and reproductive efficiency.  end mark

PHOTO: Automated health monitoring systems hold potential to expedite metabolic disease diagnosis, compared with clinical evaluation alone or cowside tests. Photo by Peggy Coffeen.

References omitted but are available upon request. Click here to email an editor.

Source: Ricardo Chebel and Eduardo Ribeiro, 2017 Dairy Cattle Reproduction Council Annual Meeting presentations

JoDee Sattler is the Marketing and Communications Director for the Dairy Cattle Reproduction Council.