We all experience unexpected expenses from time to time. Even the best financial analytic teams suggest their past performance is no indication of the future. What we often forget when budgeting is every prediction has a confidence variation surrounding it. A good example is weather predictions, each channel tries 3 times a day to predict the same thing, but I suspect each of you have a different confidence in the prediction depending on the channel.
A recent paper in the Journal of Animal Science by Claudia Blakebrough-Hall and her Australian co-workers highlights some novel methods to confidently diagnose respiratory disease.
The initial method for bovine respiratory disease (BRD) diagnosis used in the experiment was not all that different from what you consider normal, depression, nasal discharge, or coughing. There were two additional components to the visual diagnosis used in the experiment. First a visually healthy pen mate was also pulled with each visual BRD diagnosis. Second, the initial visual BRD diagnosis was confirmed by evaluating lung damage at harvest.
Using this treatment and diagnosis “protocol” calves were sorted into 5 groups: healthy, visually healthy pen mate treated due to high temperature or lung sounds with no severe lung damage, visually diagnosed and treated but no lung damage, visually healthy and untreated but damaged lungs, and confirmed BRD due to visual symptoms accompanied by treatment and damaged lungs.
Not surprising, the calves with visual symptoms and severe lung damage were least profitable, slowest growing and yielded the lowest quality carcasses. As expected the greater the number of BRD treatments the poorer the cattle performed.
There were 145 calves (18%) treated at least once for BRD due to visual diagnosis. Therefore 145 visually healthy pen mates were pulled for comparison. Of these randomly-selected, healthy appearing pen mates, 63 head or 7.2% of all calves in the experiment, exhibited elevated temperature or lung sounds consistent with BRD when evaluated at the chute.
This diagnosis and treatment process occurred before the calves exhibited any visual symptoms resulting in comparable growth and carcass performance to the 67.5% of cattle that remained healthy. This suggests two possible outcomes, these randomly selected calves were actually sick and responded to early treatment or they were healthy and have a higher than “normal” temperature and/or noisy lungs.
The idea that over 40% of healthy appearing, randomly selected pen mates were in early stages of BRD demonstrates an opportunity for technology using process control, animal monitoring or even health history to assist with quantifying each animal’s normal behavior patterns and potentially limiting unnecessary treatments.
The calves visually diagnosed and treated during the feeding period who didn’t exhibit lung damage at harvest (10%) were considered a treatment success. Despite having clear lungs, these calves were unable to perform as well as healthy calves but did exceed the 6.7% of clinically ill calves who exhibiting lung damage in addition to visual symptoms.
All cattle were sourced via auction markets so no previous health history accompanied calves at arrival. Without history, the researchers could not determine the cause of lung damage at harvest, could be from sub-clinical BRD, a previous BRD infection or a combination. A 951 pound placement weight suggests the opportunity for previous respiratory disease certainly existed.
With this in mind there were 8.4% of the cattle who were never pulled due to visual symptoms yet showed severe lung damage at harvest. These calves were described as sub-clinical, due to lack of visual symptoms. Performance and carcass merit of the sub-clinical group was less than healthy but better than the clinically ill.
When the research team evaluated the financials there was a wide range in net returns per head due to health, -$16 for clinically ill to $127 for the healthy calves. For many of you this is not an unexpected difference. The unexpected expense the authors highlighted was the $45 per head opportunity cost of diagnosing the sub-clinical group.
When considering technology cost and use we often consider the obvious saving or revenue opportunities. This data is an example where the unexpected opportunity lies in the middle, preventing unnecessary treatment or finding calves we didn’t know were ill.
Process control and advanced diagnosis technology aside, imagine a simple solution where we spend part of the $45 to prevent subclinical disease with management and vaccines and then digitally communicate health history beyond the ranch gate.