Recently, the following case was presented to me:
A newly-expanded sow herd with a “green” herd manager is struggling to produce quality weaned pigs. The genetic supplier provided the owners with a list of areas to improve upon. The consulting veterinarian decided the first priority would be to reduce the current rate of stillborns by half a pig.
Up to this point, the herd had been inducing sows to farrow on day 116 of gestation—and loading them on the same day. After reviewing the induction protocol, the consulting veterinarian decided to “run a trial” to see if inducing the sows at day 115 would reduce the stillborn rate. Therefore, he instructed the farm manager to use the new induction protocol for two months, after which they would look at stillborn rates to see if the new protocol generated enough improvement. The manager was also instructed to load sows on day 113.
This ‘trial’ is more of a ‘try-it-and-see’ approach, and it is far too common in pig production. More importantly, however, when important questions aren’t asked up front, these ‘trials’ are apt to generate incorrect conclusions.
So what are the questions that should be asked? What is the variability in stillborn rate? Are there substantial differences in stillborn rates across parities, such that a possible parity-effect needs to be incorporated into the trial design? What will the outcome variable be: stillborn rate or percent of total born weaned (i.e., if decreasing stillborns results in more low viability piglets, is there any gain)? And, from a statistical perspective, what difference in stillborn rate would warrant a change in protocol? Answers to these questions would allow the veterinarian and herd manager to make a reasonable decision about the number of sows over which the protocols would need to be compared.
Here the farm was planning to use historical information as “control” for the new protocol. This is where most on-farm trials go awry—fair trials are not as simple as comparing data from two different time periods. Too often multiple changes are instituted, yet only a single change is considered in the analysis. How can one reasonably conclude that any difference observed would be attributable to a single item?
In this case, two management changes were being made—loading day and induction day—while only one change will be attributed to having an effect. In order to draw a reasonable conclusion about the effectiveness of the induction protocol, the trial needs to be compared across sows loaded by the same day of gestation.
I am firm believer in the value of numbers and using them to make decisions—but numbers have to be used appropriately. Part-prospective, part-retrospective ‘trials’ lend themselves to errant conclusions. They can be used to justify a gut-level approach to a skeptical audience. (And how many times are new protocols instituted under the guise of a trial and never removed?) Or, they could miss a real effect that actually exists.
For herds wanting to make management decisions on the basis of statistical evidence, it is necessary to do due diligence of developing a design that permits comparison of practices or products in the setting in which it would be used [which, I might add, is less cumbersome than it would seem].
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