The dairy industry has faced constant change over time. Driven by economic challenges, technological innovation, consumer expectations, demanding regulations, etc., the global industry has refused to be stagnant.
Most of these changes have had a significant impact on one thing: the health and welfare of dairy cows.
As consolidation continues, and the number of dairy farms continues to decline while the average herd size increases, herd health is becoming an increasingly hot topic. The association between herd size and animal health and welfare is complex, affected by many factors. The organizational skills of the farmer, rate of herd expansion, quality of facilities, training and experience of personnel, are all factors that influence a herd’s health.
A possible explanation for the rise in disease rates we are seeing in larger herds might be the fact that as dairy herd sizes increase, many producers have become removed from routine herd health checks. Hired laborers are becoming more responsible for managing animal health, and they often lack the experience needed to implement the best management practices. The lack of routine monitoring, experience and feedback have contributed to a progressive failure to efficiently monitor and manage herd health, and frequently lead to protocol drift.
Even though 93% of operations with more than 500 cows have implemented a computerized record-keeping system, herd health data is not captured with accuracy or consistency. The collection, recording and evaluation of data regarding milk production, reproduction and somatic cell count (SCC) has been standardized across the U.S.; however, this is not true for health information that is a user-defined event (UDE). According to research in 2012, it is very common that health records lack the accuracy and consistency needed to be useful for evaluating and informing herd-level health management decisions.
As an example, consider one of the most prevalent diseases in dairy cows – metritis. This single disease can be recorded using multiple different event entries. For instance, metritis can be recorded as “METR.” But it can also be paired with many different terminologies, such as SICK, ILL, MISC, HOSP, RED, DIRTY, UT INF and so on. Even within the same herd, three to four different names can be incorrectly used to record metritis. To avoid confusion and improve the evaluation of your herd’s health, consider the following Golden Rules for better health record-keeping.
Record all disease episodes
Each disease should have a single, specific event, and every diagnosed disease episode should be recorded, regardless of the severity or duration of treatment. These additional entries can be recorded in the event remark. Some dairies record only severe cases or specific situations – for instance, when the animals are treated with antibiotics that require a withdrawal period. However, improving health evaluation requires complete records. Meaning, all cases and treatments on the farm should be recorded.
Reports with the monthly counts of disease episodes provided by the management software can be more accurate if the user-defined event for a disease is recorded one time per episode. The main advantage of recording all relevant information is that the comparison of past diseases and treatments will help management make decisions in the future. Implementing processes to capture more complete disease records will allow reliable comparisons, in the same way we currently are able to compare fertility and milk production information.
Be accurate when entering diseases
Record episodes using a single event for each disease. Information that cannot be measured cannot be managed. So rather than recording a uterine infection as SICK, DIRTY, ILL, EXNL, INFUSE or TREATED, record it as METR or METRITIS. Creating and recording specific disease diagnoses will allow you to get a better picture of your herd’s health. By avoiding unspecified codes such as ILL, HOSP, TREATED, you are able to evaluate your herd’s health in better detail. This rule applies to all common diseases. For instance, a cow with clinical mastitis should be recorded as MAST, not INF, HOSP, ECOLI, MYCO or STAPH. Specific information of that MAST event can be recorded in the remark.
When a cow needs to be re-treated, use a different event so that you have an accurate count of disease episodes. This also makes it possible to identify cows with repeat episodes or treatments that failed to solve the first event. A key part in establishing the accuracy of your records is picking the terminology you wish to use for each event and treatment, and training your team to use standard terminology correctly.
Be consistent with your data
Record remarks with the same information, in the same order, using the same abbreviations for every episode of a disease. Be as detailed as you think is necessary, but at minimum record the following:
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Treatment. Record how the event was managed and be sure to always include the meat or milk withhold time for the protocol. It is also important to offer “no treatment (NT)” as an option. Many research studies have shown that around 50% of clinical milk samples would be qualified as a “no treatment” decision because they are negative for bacteria or it is a gram-negative pathogen. Indicating that the standard treatment for the disease was not given removes the possibility for confusion of whether the cow was not treated or if it just was not recorded.
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Episode location (i.e., quarter with mastitis, foot that is lame). Recording where the event occurred is critical to evaluating where management may have failed. A re-treatment of mastitis in the same quarter suggests treatment failure. Another clinical episode in a different quarter suggests prevention failure.
- Cow location (pen when diagnosed). Recording this information allows for the evaluation of a pen as a risk factor for disease. Use the same abbreviations for every episode of disease recorded.
Usually, the user-defined health records lead to inaccurate and inconsistent data evaluation, making any management decision less reliable. Utilizing these three rules can help avoid the gap in the accuracy and consistency of user-defined health data that is needed for better management decisions on the dairy. Training dairy personnel to enter standardized health data should be a priority to allow efficient evaluation and facilitate the decision-making process in the dairy. An effective solution, bringing the dairy herds closer to accurate, consistent health records, would be for management software to control entry of all health data in the same way reproduction data are currently handled.
Until health data inputs are prompted for and controlled by the system to ensure quality, it will be necessary for dairies to establish and follow standard health data recording protocols.
