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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.

This study was conducted to evaluate the relationship between boar and semen related parameters and the variation in field fertility results. In 8 years time semen insemination doses from 110 186 ejaculates of 7429 boars were merged to fertility parameters of inseminations of 165 000 sows and these records were used for analysis. From all ejaculates boar and semen related data were recorded at the artificial insemination (AI) centers. Fertility parameters, such as farrowing rate (FR), ranging between 80.0% and 84.0%, and the total number of piglets born (TNB), ranging between 12.7 and 13.1, were recorded and from these the least square means per ejaculate were calculated. Only 5.9% of the total variation in FR was due to boar and semen variability of which 21% (P = 0.0001) was explained by genetic line of the boar, 11% (P = 0.047) was explained by laboratory technician, and 7% (P = 0.037) was explained by the AI center. For TNB the total variation was 6.6% boar and semen related of which 28% (P < 0.0001) was explained by genetic line of the boar and 7% (P = 0.011) was explained by the AI center. Only 4% of the boar and semen related variation was caused by sperm motility (microscopically assessed at collection, ranging from 60% to 90%). Other variation in FR and TNB was explained by management and semen related parameters (age of boar, 3%; P = 0.009; and 8%; P = 0.031, respectively), days between ejaculations (1%; P < 0.0001 of FR), number of cells in ejaculate (1%; P = 0.042 of TNB), year (9%; P = 0.032), and 13%; P = 0.0001, respectively), and month (11%; P = 0.0001; and 5%; P = 0.0001, respectively). Although semen motility is considered an important parameter to validate the quality of the ejaculate processed, it only minimally relates to fertility results under the current Dutch AI practice. Other boar and semen related parameters, like genetic line of the boar, are more relevant factors to select boars for AI purposes.