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We aimed to estimate genetic parameters for semen quality and quantity traits as well as for within-boar variation of these traits to evaluate their inclusion in breeding goals. Genetic parameters were estimated within line using a multiple-trait (4 × 4) repeatability animal model fitted for 5 pig lines, considering 4 semen traits: sperm motility (MOT), sperm progressive motility (PROMOT), log-transformed number of sperm cells per ejaculate (lnNcells), and total morphological abnormalities (ABN). The within-boar variation of these traits was analyzed based on a multiple-trait (2 × 2) approach for SD and average (AVG) and a single-trait analysis for CV. The average heritabilities across the 5 lines estimated by multiple-trait analysis were 0.18 ± 0.07 (MOT), 0.22 ± 0.08 (PROMOT), 0.16 ± 0.04 (lnNcells), and 0.20 ± 0.04 (ABN). The average genetic correlations were favorable between MOT and PROMOT (0.86 ± 0.10), between MOT and ABN (−0.66 ± 0.25), and between PROMOT and ABN (−0.65 ± 0.25). As determined by within-boar variation analysis, AVG exhibited the greatest heritabilities followed by SD and CV, respectively, for the traits MOT and ABN. For PROMOT, average SD heritability was lower than CV heritability, whereas for lnNcells, they were the same. The average genetic correlations between AVG and SD were favorable for MOT (−0.60 ± 0.13), PROMOT (−0.79 ± 0.14), and ABN (0.78 ± 0.17). The moderate heritabilities indicate the possibility of effective selection of boars based on semen traits. Average and SD are proposed as appropriate traits for selection regarding uniformity.

Life starts with fertilisation. Variation in fertility is caused by both farm and sow related parameters and boar and semen related parameters. Therefore, achieving high fertility is not obvious. Predominantly, artificial insemination (AI) is used for breeding pigs. The advantage of AI is that you can dilute semen from high fertile breeding boars and in this way inseminate many sows. The last years, the pig industry is increased in scale and evaluated in specialisation. Unfortunately there is no golden standard concerning the requirements for qualitative good semen. This thesis handles different semen quality characteristics in relation to fertility, showing that the effect of semen quality characteristics on the variation in fertility is relatively small. These effects seems small, but the economic value in a high producing pig industry is large, with the objective to minimise the variation between farms. Assessing semen quality characteristics objectively and relate them to field fertility leads to an efficient production of insemination doses, which results in an efficient spreading of the genetic genes. Semen motility assessed with CASA (Computer Assisted Semen Analysis) and analysing DNA damage are semen quality parameters which show a relation with fertility, in high fertile breeding boars. The value of these tests is possibly even higher in a population in which less selection is performed, added with other tested parameters from this thesis. The conclusions in this thesis contribute to the development of semen quality assessments which improve the prediction of porcine male fertility.

Sperm quality is often evaluated through computer-assisted semen analysis (CASA) and is an indicator of boar fertility. The aim of this research was to study the relationship between CASA motility parameters and fertility results in pigs. Insemination records and semen parameters from a total of 45,532 ejaculates collected over a 3-yr period were used. The statistical model for analysis of fertility data from these inseminations included factors related to sow productivity. The boar- and semen-related variance (direct boar effect) were corrected for the effects of individual boar, genetic line of the boar, age of the boar, days between ejaculations, number of sperm cells in an ejaculate, number of sperm cells in an insemination dose, and AI station. The remaining variance was analyzed if semen motility parameters had a significant effect. This analysis revealed significant (P 0.05) were observed between effects of AI stations on fertility outcome, underscoring the objectivity of the CASA system used. Motility parameters can be measured with CASA to assess sperm motility in an objective manner. On the basis of the motility pattern, CASA enables one to discriminate between the fertilizing capacity of ejaculates, although this depends on the genetic line of the boar used in AI stations.

In order to obtain a more standardised semen motility evaluation, Varkens KI Nederland has introduced a computer assisted semen analysis (CASA) system in all their pig AI laboratories. The repeatability of CASA was enhanced by standardising for: 1) an optimal sample temperature (39 °C); 2) an optimal dilution factor; 3) optimal mixing of semen and dilution buffer by using mechanical mixing; 4) the slide chamber depth, and together with the previous points; 5) the optimal training of technicians working with the CASA system; and 6) the use of a standard operating procedure (SOP). Once laboratory technicians were trained in using this SOP, they achieved a coefficient of variation of < 5% which was superior to the variation found when the SOP was not strictly used. Microscopic semen motility assessments by eye were subjective and not comparable to the data obtained by standardised CASA. CASA results are preferable as accurate continuous motility dates are generated rather than discrimination motility percentage increments of 10% motility as with motility estimation by laboratory technicians. The higher variability of sperm motility found with CASA and the continuous motility values allow better analysis of the relationship between semen motility characteristics and fertilising capacity. The benefits of standardised CASA for AI is discussed both with respect to estimate the correct dilution factor of the ejaculate for the production of artificial insemination (AI) doses (critical for reducing the number of sperm per AI doses) and thus to get more reliable fertility data from these AI doses in return.