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When I was a teenager, our farm had a 1966 International pickup truck that had a faulty fuel gauge. It only read two things: E or F. The dial always said F until right before the truck ran out of gas, at which time the needle rapidly headed towards E. My older brother and I both became pretty good at getting home before the truck ran out of gas, but occasionally one or both of us was stranded at a neighbor's. The fuel gauge on this vehicle was just not sensitive enough to operate properly.
Unfortunately, many businesses, including farms often choose to monitor things that while convenient, are not really sensitive enough either. For example, a farmer calls the veterinarian because too many calves are dying from diarrhea. He might be more specific and say that four of the last twelve calves born have died. He might monitor death rate in young calves, which would certainly be a good idea. However, choosing death as a monitor is not sensitive enough because unless miracles are possible, there is nothing he can to change the outcome of the process of raising baby calves once they are dead.
Examples of more sensitive monitors in this case might be: monthly percent failure of passive transfer, monthly average total serum protein, monthly average Brix colostrum readings, monthly percent of calves fed colostrum replacer, monthly percent of calves fed more than four hours after birth, monthly bacteria counts of pasteurized milk, and monthly percent morbidity for scours and pneumonia. By knowing some of these numbers, the dairyman might be able to make changes in the process that result in less sick and dead calves. Below are some more examples of insensitive monitors and suggestions for more sensitive monitors on dairies.
Percent of calvings resulting in displaced abomasum is relatively insensitive, although most can be fixed. More sensitive monitors might be: monthly or weekly percent elevated blood BHBA in fresh cows, average dry matter intake of the prefresh and fresh cow pens, weekly or monthly percent of fresh cows with clinical metritis, or perhaps weekly or monthly average urine pH in the prefresh pen.
Percent of herd that died, either monthly or annually, while certainly important to monitor, is too insensitive, just as in the case of baby calves. It might make more sense to monitor dead cows by reason for death. Reasons might be categorized into groups that can help you change practices so that less cows die. For example, deaths resulting from fatty liver, or milk fever might be categorized as due to failures of transitions, while deaths due to hemorrhagic bowel syndrome or late-lactation abomasal torsions might be categorized as failures of nutritional management, for example. If deaths due to transition are too high, or increase, one could monitor post calving BHBA, precalving NEFA, prefresh dry matter intake, overcrowding in the prefresh pen and fresh pen, for example. Injuries are one of the most common reasons for deaths on some farms, yet farms often do not monitor this.
Somatic cell count seems to be a good thing to measure, and it probably is, since it directly affects your milk check. However, there are a lot of inputs that go into the process of producing milk with a low somatic cell count. For example, most farms can reduce their somatic cell count significantly by calling the trucker, but this method has almost nothing to do with actually controlling mastitis, and is often very expensive. Instead, how about monitoring monthly new infection rate, or better yet, new infection risk? This metric is directly related to what processes are performed daily and how well they are performed on your farm. You might also want to measure monthly new cases or clinical mastitis, as percent of herd size. New means the first case this lactation. Farms with a lot of clinical cases might be really good at detecting mastitis, and might have a relatively low somatic cell count, but have a relatively high clinical case rate. If you culture mastitis cases, you can probably also monitor cure rates of treated cows, which will tell you if your treatment strategies are working. Monitoring the chronic infections as percent of herd will help you determine if your somatic cell count is elevated because there are too many new infections, or because cows that become infected do not cure.
Milk production is something everyone wants to measure, and for a lot of good reasons. To be more sensitive one might measure milk production by lactation, or by stage of lactation. However, there are things that are much more sensitive that could be easily monitored. For example, we know that the growth rate of calves in the first 60 days of life explains about 50 percent of the difference in milk production between herd mates. (Contrast this to genetics, which explains only about 25 percent.) Growth rate will also explain a portion or the difference within older cow groups as well, although perhaps not as much as 50 percent. So, instead of waiting for 2-3 years to get enough data to monitor milk production for that new heifer, why not monitor monthly average growth rates? Better yet, why not look at the data, find the top and bottom performers, and change how you manage them? Perhaps you want to sell the bottom performers and breed the top performers to sexed semen as you might do when monitoring genomics of young calves.
Hopefully, everyone monitors number of milk or meat residues, and the results are always zero. However, why not monitor the percent of cows sold or milked that were sold or milked before the prescribed withdrawal? You might be surprised how often this can happen, even on a well-managed farm. How about monitoring the percent of treatments that are not administered according to documented farm protocols? Lack of compliance is one of the most common reasons for milk and meat residues. Why not measure compliance instead of residues?
Once, my brother actually drove the old International into the garage and shut it off, and then the next day dear old dad could not start it because it was actually out of gas. (Dad was not happy about this.) While this might be an example of excellent forecasting, it is more likely just being lucky, and it is really no way to operate a vehicle. Dairy farming is a competitive business, and just being lucky is seldom good enough, and this is not really the way to operate the business. Perhaps it is time to sit down with your farm's veterinarian, nutritionist, and other key advisors and determine what you need to monitor to keep things running smoothly.[[In-content Ad]]

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