The cost of being wrong: Why cost-benefit analyses are replacing guesswork on swine farms
By Derald Holtkamp, MS, DVM
Iowa State University, College of Veterinary Medicine
James Alfred Wight, better known by his pen name James Herriot, epitomized the practice of veterinary medicine in a bygone era. The British television series, “All Creatures Great and Small,” was adapted from his books and embodied the classic perception of veterinary medicine in the mid-20th century. While his intentions were aslways good, I doubt Dr. Wight ever paused during his daily practice to conduct a cost-benefit analysis before dispensing his recommendation to a farmer.
Much has changed in the food animal industry since Dr. Herriott’s time. And remarkably, the biggest one over the last 50 years isn’t related to advancements in science. It has more to do with the consolidation of industries. We now have fewer and bigger farms, which in turn means fewer and more specialized decision makers. A single animal health decision may now involve millions of animals. The cost of being wrong has gotten so large that it’s no longer acceptable to rely on an educated guess.
A cost-benefit analysis is the process of weighing all costs and benefits of an intervention for the purpose of making an informed decision. To do that, we must measure both the costs and benefits in common units, which practically speaking means dollars.
Cost-benefit analyses may be conducted prospectively to decide whether to implement an intervention or retrospectively to determine if an animal-health intervention was successful. Either way, they’re invaluable.
For many animal health interventions, the cost is relatively easy to calculate. For example, if we choose to vaccinate 10,000 pigs with one dose of a vaccine at $1 per dose, the total cost of the intervention will be $10,000. We can be certain that the cost won’t vary considerably from that figure.
On the other hand, the benefit of animal health interventions is usually much more difficult to calculate. There’s nearly always uncertainty about the estimate, and it may vary considerably from what we expect.
The primary expected benefit is improved productivity. However, there are others that might include reduced treatment costs or reduced weight variation, which can help producers market hogs in a tighter weight range to capture higher sort premiums. The value of reduced variation is more challenging to calculate. Furthermore, we seldom measure things such as individual pig weights, which would give us the value of that reduced variation.
When the benefit an animal health intervention is improved productivity, estimating the value of the benefit for a cost-benefit analysis is a two-step process (Figure 1).
The first step is to estimate the changes in productivity. To do this, we must select the appropriate key productivity indicators (KPIs). In the breeding herd, the KPIs we choose to include for a cost-benefit analysis will depend on what we expect to improve and how much detail we need to capture that improvement.
For example, if we implement an intervention where the primary benefit is an improvement in pre-weaning mortality, we will estimate the improvement in that KPI, then calculate the changes in pigs weaned per litter, pigs weaned per breeding female per year and total pigs weaned per year; that’s assuming the litters per breeding female per year doesn’t change.
In the growing pig herd, KPIs we typically include for a cost-benefit analysis are mortality, average daily gain and feed conversion.
The second step is to estimate how changes in productivity will change revenues, costs and profit for the operation. Many producers and veterinarians think this step is difficult, but it’s really relatively easy, especially if you have the necessary tools such as budgeting models.
Rules of thumb are often wrong
A common approach is to rely on rules of thumb. For example, there’s a rule of thumb that a 1% reduction in wean-to-market mortality is worth $2 per pig marketed. However, rules of thumb fail to capture the impact of the timing of mortality and how the value of mortality varies as market-hog and feed prices change. Rules of thumb are like broken clocks — they’re occasionally right, but most of the time they’re wrong. That’s why a budgeting model is preferable.
The budgeting models we use are built with spreadsheets and include equations based on data to calculate costs, revenue and profit. Data that may be entered typically includes values for KPIs, space and flow parameters, output prices, and input prices or costs.
An example of a budgeting model for a single group of pigs in the wean-to-market phase of production is shown in Figure 2. The model is set up to calculate the value of improved productivity with the intervention compared to a baseline without the intervention. Yellow-highlighted cells are values that can be entered by the user and changed to try out various scenarios such as changes in KPIs and market-pig and diet prices. Cells with the blue background are KPIs.
In this case, the benefit of the intervention is the value of improvements entered for wean-to-market mortality and average daily gain, which is calculated as the increase in profit ($6,578) between the baseline and the intervention scenarios.
The outcome of a cost-benefit analysis is a benefit:cost ratio. A ratio greater than 1 implies the benefit of an intervention exceeds the cost. The budgeting model in Figure 2 is set up to also calculate the direct cost of a vaccine intervention — $2,040 for the vaccine and labor to administer it, and a benefit:cost ratio to complete the cost-benefit analysis.
There are several advantages to using budgeting models like the one in Figure 2 with built-in spreadsheets. First, there’s the ability to enter alternative values to evaluate how they affect the cost:benefit ratio. The models can also be reused or modified to include some additional detail necessary to evaluate other animal-health interventions.
The stakes are high. In a recent retrospective analysis conducted after a change was made to vaccine protocols for pigs from a flow of 30,000 sows, the estimated impact on income was a $1.3 million annual increase in net profit. The analysis gave the producer confidence the change was yielding the results expected. I imagine that Dr. Wight would have considered the analysis time well spent.