When optimization rather than prevention is the goal, the strategies are different.
We know the old truth, ‘it’s the exception that makes the rule.’ Isn’t that why we find ourselves taking off our shoes at airports and able to carry only small containers of toothpaste in our luggage? In fact, this approach represents the ultimate prevention strategy—as long as the threats and causes are known.
So too in pig production, we find ourselves using exceptions to make rules. In walking pens, we look for the exceptional pigs that show signs of illness to determine which ones to treat. In production records, we look for exceptional patterns, as with statistical process control (i.e., trends of three or more points of decline, one point more than three standard deviations from the mean, etc.), to determine when interventions may be warranted.
In many ways, it makes good production sense to tune into the exceptions. The exceptionally good groups or farms may hold the keys to improving the system. The exceptionally poor groups or farms may be in need of interventions.
However, there are also times when we make ‘rules’ based on selective observations of ‘exceptions.’ Although these rules may afford some confidence in the practices we employ, they may be short-sighted with regard to the bigger economic and production picture.
It is likely that many of us have had the experience of walking through a farrowing house, seeing nice litter after nice litter and then walk by a sow with six piglets. The question may arise, “What happened here?” In an effort to justify the circumstance and prevent its recurrence, the farm manager may pull the sow’s history and report that her previous litter (e.g., parity 4) was only seven live-born piglets. Through this action, a mental note was made: cull all parity 4 sows with seven piglets or less lest they return with fewer pigs in the next litter. Yet, we rarely look at histories for sows with acceptably sized litters. Therefore, we miss the sows that had small litters and then went on to have acceptably sized litters.
In reality, a sow effect explains less than 20% of the variability in litter size. This shouldn’t be surprising. After all, many factors, including season, semen quality, insemination technique, and sow plane of nutrition during follicular development, have all been demonstrated to affect conception rates and/or litter size.
Just as there is a tendency to look at the records of sows with small litters, there is also a tendency to look at the histories of sows that are repeat breeders and note the failures rather than find the successes. Normally, we would expect that healthy sows would have higher conception rates on their first estrus than on their second. Similarly, for sows that fail to conceive on their second estrus, we would expect a lower conception rate on the third estrus, and so on. However, the magnitude of difference among conception rates of healthy sows bred on their first, second, and third estruses is often less than people in the barn would expect. After all, several factors can affect conception as well.
Suppose the probability of conception is 90 percent for the first estrus, 80 percent for the second estrus and 70 percent for the third estrus. We would expect that at least 99 of 100 sows would conceive if insemination were performed up through the third estrus. Additionally, if a herd is struggling with fertility and fertility of all populations is affected, it is unreasonable to expect that, over the long run, the replacement gilt would have a more favorable outcome than the sow that has already conceived and carried a litter to term.
When prevention is the goal, it is hard to argue with the philosophy that the exception should make the rule. However, when optimization is the goal, numerical exceptions need to be considered in their greater statistical context. That is, some exceptional performance (i.e., exceptionally poor at the level of the individual) needs to be tolerated in order to achieve the best economic outcome for the herd, farm, or system as a whole.
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