When talking about litter size in practice, the sow is the focus – rightly so. Maternal line genetics, body condition, heat management, feeding, health and farrowing management determine most of the outcome. Nevertheless, it is worth taking a second look: the AI boar used can also have a measurable influence on the number of piglets born alive. This so-called service sire effect is not a “miracle lever”, but an additional building block that modern breeding programs can use in a targeted manner.
What does “sire effect” mean in practice?
By sire effect is meant not only “pregnant or not”, but an influence of the boar on characteristics around farrowing – in particular the number of piglets born alive. Biologically, this can work in several ways: quality and stability of fertilisation, early embryonic development or factors that only manifest themselves after fertilisation. Important for the practitioner: The sire effect is small compared to the sow effect, but can become visible per boar through many matings.
Practical data is worth its weight in gold – if you read it correctly
Today, more fertility data are being generated than ever before – not only in breeding farms, but in many practice farms. AI studs and producer coops contribute to the fact that mating and farrowing data are systematically recorded, checked for plausibility and made usable for evaluations. This is a big step forward: the more data from different farms come together, the more likely it is to detect small effects.
However, especially in the case of small effects, the data structure determines how resilient conclusions are. In practice, distributions are often unequal: Piétrain has been the dominant sire line for years, while the increased use of Duroc genetics in many places has only been increasing significantly for a few years. In addition, there are evaluation conditions that further thin out the data – for example, if only farms that use several sire breed in parallel are taken into account in order to make comparisons more fair. And finally, the data is often distributed among different sire lines from different genetic companies, female lines, parities and management levels. Without a clean correction, an apparent line or sire effect can quickly be over-interpreted.
The consequence is pragmatic: practical data shows what happens under real conditions. However, breeding decisions require models that separate the sire effect from the sow effect, farm influence and time trends – so that observations become reliable selection descisions.
What do large data sets from the PIC breeding program show?
It is precisely this step that PIC researchers have implemented in an extensive scientific evaluation. The sire effect was estimated as a separate genetic building block – based on around 80,000 litter information, around 40,000 sows and around 1,600 KB boars from three pure-bred terminal sire lines from the period 2020 to 2024. The decisive factor is not only the number of litters, but the depth of the genetic information behind them: for the AI boars considered, pedigrees with around 8 million animals recorded in the database are available; of these, around 450,000 have been genotyped.
This explains why a small effect can be harnessed at all: If you have large, cleanly linked databases (operating data + pedigree + genotypes) and the right expertise, you can reliably recognise small genetic differences – and then take them into account in the breeding programme.
The PIC study revealed:
- Heritability of the service sire effect on live-born piglets/litter: 0.01 – 0.03
- Heritability sow effect on live born piglets/litter: 0.09 – 0.15
- Genetic correlations between boar and sow effect: from slightly negative to moderately positive (varies per terminal sire line)
- Differences of up to 2% in the number of piglets born alive were observed.
Translated for practical purposes: Yes, the paternal effect is present in breeding – but it is small. This is precisely why it is predestined for systematic, data-driven selection: many litters per boar, clear correction for operation/month/parity and clean differentiation from environmental influences.
Sperm quality: important, but not the whole explanation
In practice, it makes sense to explain differences between boars, especially with regard to sperm quality. Traits such as motility or morphological abnormalities can be recorded very well in stud data sets and can be used for genetic improvement.
The sperm characteristics themselves are moderately heritable:
- motility approx. 0.10 – 0.12;
- morphological abnormalities approx. 0.17 – 0.28).
However, the connection to the service sire effect on the NBA is small, albeit in a favorable direction (motility slightly positive, abnormalities slightly negative).
However, the following applies to litter size: Sperm quality is important (and easily selectable), but it only explains the “sire effect” on litter size to a small extent. If you only look at motility/morphology, you potentially overlook boar families that appear reproductively “inconspicuous”, but on average deliver piglets that are born a little less alive. The added value arises when stud data and farrowing data are combined and evaluated genetically correctly.
No “switch gene” – consistent breeding work
Fertility traits are usually polygenic: many genes each contribute only small parts. This is typical – and precisely the reason why modern breeding programs work via genetic evaluation and genomics. The path to implementation therefore does not lead through “one gene” or “one boar myth”, but through consistent measurement, modelling, selection and monitoring over many generations.
Implementation in the PIC breeding program
The difference between “talking about it” and “putting it into practice” is the consequence in a breeding program. If the sire effect is valued as a separate building block, boar families with an unfavourable influence can be identified and specifically eliminated in the breeding programme – while favourable lines are systematically used. This is the core of modern breeding work: many small, safe steps add up over time to measurable progress.
The “sperm quality” trait has been part of the index for over 10 years. In the annual index update in summer 2025, PIC has now included the new trait “sire effect (on litter size)” in the weekly breeding value estimation routine.
Sire effect – what does this mean for the sow farmer?
- The biggest lever remains the sow (genetics + management). However, the boar makes an additional, measurable contribution.
- Deceive individual cases: With small effects, it takes a lot of litters – otherwise, chance wins.
- Documentation makes the difference: recording parity, live/stillbirths, fostering, pre-wean mortality and health events cleanly.
- Returning data: The better farms and studs structure data, the better evaluations will be – and the more precise breeding decisions will be.
Conclusion
The sire effect on litter size is not a substitute for good management and strong dam lines – but an additional breeding building block that becomes visible in large, cleanly linked datasets. Where data, pedigrees, genotypes and evaluation know-how come together, this effect can not only be described, but consistently implemented in selection and breeding programs. For the farmer, this means: less discussion about individual cases – more robust decisions based on data.