MilkHub Mastitis Performance

The bottom line for mastitis

The first order of business in managing mastitis is to ensure bulk milk somatic cell count (BMSCC) is maintained below the penalty downgrade level. Other mastitis related costs include reduced yield, cow treatment and lost yield due to cow withholding. These costs can rapidly multiply with a contagious form of mastitis spreading throughout the herd.

Key to controlling these costs is the ability to detect major contributors to the BMSCC before downgrades occur. Treatment of the detected cows will limit the other mastitis related costs.

How to find the infected cows

Skilled staff may be able to detect clinical signs of mastitis but lack of suitable staff and production throughput pressure gives little opportunity for this. Staff cannot detect sub-clinical mastitis.

Herd tests give a snap shot of the infection symptom measured by SCC. This measurement is valid at the time of the herd test but can quickly become out of date as some infected cows spontaneously cure while other cows develop new symptoms or have symptoms that persist even after treatment.

An ideal situation would be to have frequent herd tests to track the persistence and severity of infections but the associated costs would be unsustainable. A practical alternative is an in-line detector that takes account of severity and time to select cows to check.

The MilkHub automatically measures combined symptoms of infection for every cow every milking. These measurements are stored as an individual cow database that is analysed to give a prediction of mastitis. In this way in the MilkHub provides a continuous monitoring system that takes account of severity and time.

The MilkHub system ranks the individual cow prediction across all cows. The milking staff can then use the MilkHub system to screen cows for mastitis and draft the highest ranked cows to perform confirmation testing and treatment as required.

How good is the MilkHub?

The performance of the MilkHub system is illustrated in the following graphs based on a single herd test of about 600 cows. The first graph shows the distribution to BMSCC of cows ranked from smallest to largest contributor. It can be seen that only a relatively small number of cows contribute to most of the BMSCC.

The second graph shows the effect of using the MilkHub to draft out identified cows. It can be seen that the BMSCC is rapidly reduced from near the downgrade value to a safe value.

The MilkHub does not necessarily identify cows in the same order as the herd test. This is because the herd test and MilkHub use different symptoms of infection, the herd test is a snap shot whereas the MilkHub uses a time history, and the effect of combined limitations in both approaches.

Overall the MilkHub system is very effective in managing the BMSCC. Identifying only 10 likely infected cows reduces the BMSCC by more than 75,000 well away from the downgrade value. If 50 cows are identified the BMSCC is almost halved.