Across the span of human history, understanding of infertility has progressed from a ‘blame-the-female’ problem to an issue that affects males, too. And these same issues with fertility hold true in animal production. However, recognition of male-associated infertility—and maybe more importantly male sub-fertility—has likely been complicated by the use of artificial insemination.
Historically, boar studs have employed visualization tests to assess semen quality at the time of collection—either computer-assisted or manually estimated. The logic behind this approach is that only progressively motile sperm would be able to “find” and attach to an oocyte. Some boar studs additionally look at acrosomes (the “caps” on sperm cells that contain enzymes which allow for penetration of the oocyte). However, in the absence of any viable oocytes [be they in a laboratory or in a sow], these assessments of semen quality are only proxies for semen fertility.
In practice, there are two measures of boar fertility: farrowing rate and the total number of piglets born per litter (totalborn). Yet, because the sow can also influence these outcomes, and because of timeline of genetic improvement [and, therefore, boar replacement], we’ve not been in the habit of using field-based fertility measures to judge a boar’s value in stud. Furthermore, some studs are in the practice of pooling semen, a practice that has the potential to mask subfertile boars.
At the end of the day, failure to consider boar fertility in the field may be short-sighted. Small scale studies have found that subfertility does not necessarily change farrowing rate but does predictably decrease litter size. Though for some subfertile boars, the effect on totalborn could be mitigated by substantially increasing sperm count in each semen dose.
The implications for boar studs and sow farms are not small. For studs, increasing semen count per dose can reduce dose output per boar by 25-40%. For sow farms, elimination of subfertile boars from their semen sourcing has the potential to improve totalborn per sow per year. Suppose a unit breeds 140 sows per week to achieve 125 litters farrowed. If the average number of doses of semen per sow is 2.0 and the average number of doses produced per boar per week is between 25 and 26, then 11 boars would be needed to supply semen. If one of those 11 was subfertile, generating only 10% fewer piglets per litter, it would have the same effect as reducing the number of litters farrowed by one (average totalborn assumed to be 12). If two of the 11 boars were affected, this would translate to a reduction of approximately two litters born. And of course, such a simplified calculation would not consider any impact on sow longevity—i.e., blame the sow for the litter size.
Use of field data to measure boar fertility has great merit, but it is not without complications. First, only single-sire matings can be used in any evaluation. Second, data integrity is critical (i.e., accurate recording of all information, including sow, semen ID, etc.). And third, because fertility is a two-sided equation—i.e., the male side and the female side—sample size is critical. Analysis of large datasets from the field allows for separation of the herd- and sow-level effects from the boar’s effect. But before any field-level analysis can occur, studs and sow units must be willing and able to have their data linked.
No matter how great his genetic value, a subfertile boar is costly endeavor, especially left undetected. The current visualization methods used in stud to identify poor-quality semen lack the power of data gathered in the field—fertility measures from living, breathing, inseminated sows. Yet, since the price of boar subfertility is borne by the sow units, doesn’t it only make sense for boar studs and sow units to develop cooperative methods to quickly and reliably identify subfertile boars?