Summary

Currently, boars selected for commercial use as AI sires are evaluated on grow-finish performance and carcass characteristics. If AI sires were also evaluated and selected on semen production, it may be possible to reduce the number of boars required to service sows, thereby improving the productivity and profitability of the boar stud. The objective of this study was to evaluate breeding value estimates for semen traits in the boar: total sperm cells (TSC), total concentration (TC), volume collected (SV), number of extended doses (ND), and acceptance rate of ejaculates (AR). Semen collection records and performance data for 843 boars and two generations of pedigree data were provided by NPD USA. Breeding values were predicted from five four-trait and one five-trait animal models using MTDFREML. Breeding value estimates for the various semen traits would indicate that there is an opportunity to select for genetically superior boars that would produce ejaculates that are more acceptable and would yield more extended doses. The range in breeding values shows that from best to worst there is an 18% difference in acceptance rate and nearly 22 more extended doses per ejaculate.

Introduction

The production of a large quantity of high quality semen is important to pork producers since most sows are artificially inseminated (Singleton, 2001). The adoption of artificial insemination (AI) has had a significant impact on the structure of the swine genetics industry. It has been reported that AI now accounts for more than 60 percent of the total swine mating in the United States (Singleton, 2001). This effectively reduces the number of boars required in the U.S. swine breeding herd and at the same time increases the importance of high fertility and genetic merit for each boar. While genetic evaluation procedures (BLUP) to select the top boars for AI are commonplace, the genetic control of semen traits has not been extensively studied. Currently, boars selected for commercial use as AI sires are evaluated on grow-finish performance and carcass characteristics. If AI sires were also evaluated and selected on semen production, it may be possible to reduce the number of boars required to service sows, thereby improving the productivity and profitability of the boar stud. In the past, male fertility traits were not analyzed due to loss of data during natural mating (Brandt and Grandjot, 1998). However, a larger data set can be obtained due to adoption of artificial insemination techniques. The objective of this study was to evaluate breeding value estimates for semen traits in the boar such as semen volume collected (SV), total sperm cells (´ 10⁹) (TSC), total concentration of sperm per mL (´ 10⁶) (TC), number of extended doses (ND), and acceptance rate of ejaculates (AR).

Materials and Methods

data source

Semen collection records and performance data for 843 boars selected for artificial insemination were provided by NPD USA (Roanoke Rapids, NC). A total of 1,736 individuals were included in the pedigree file. Boars represented three breeds and were housed in two farms. Each farm was similar in numbers of boars of each breed.

Traits were ADG, BF, MD, SV, TSC, TC, ND, and AR. Backfat thickness and MD were measured longitudinally by real-time ultrasound using Aloka 500 (Corometrics; Ithaca, NY). Semen traits wererecorded as repeated records. Semen volume was measured as the weight of the ejaculate volume. Total concentration was measured using a self-calibrating photometer. Total sperm cells were determined by multiplying SV and TC. Acceptance rate of ejaculates is based on the subjective evaluation of technicians and for an individual collection is binomial. The acceptance rate was calculated over the lifetime of the boar as the number of accepted collections divided by the total collections placing this data on a more normal scale. Technicians discarded ejaculates when blood or urine was present in the collection, when an evaluation of semen morphology presented a large number of abnormal sperm cells or when motility of sperm cells was low. Number of extended doses was calculated using total sperm cells divided by desired number of sperm cells per dose. For these data, each dose averaged 2.7 billion sperm with 100 ml fluid. For these analyses the arithmetic mean of each semen trait for each individual was calculated to perform the multiple trait analyses with production traits. Therefore, our estimates for semen production traits are repeatabilities since permanent environmental effects were not separated in the model. This also resulted in averaging out the effects of collector, year-season and age of boar.

Breeding values were estimated from five four-trait and one five-trait animal models using MTDFREML (Boldman et al., 1995). Five different combinations of four multiple traits were (1) ADG, BF, MD and SV (2) ADG, BF, MD and TSC (3) ADG, BF, MD and TC (4) ADG, BF, MD and ND (5) ADG, BF, MD and AR, respectively. The five-trait analysis consisted of all semen traits, SV, TSC, TC, ND, and AR.Correlations between individual breeding values for each trait were obtained from the different multiple traits analyses. Pearson correlation coefficients using SAS 8.01 were calculated and tests of significance were performed under H₀: ρ = 0.

Results and Discussion

Statistics of breeding value for each reproductive trait from the five-multiple traits analysis are in Table 1. Breeding value estimates for the various semen traits would indicate that there is an opportunity to select for genetically superior boars that would produce ejaculates that are more acceptable and would yield more extended doses. The range in breeding values shows that from best to worst there is an 18% difference in acceptance rate and nearly 22 more extended doses per ejaculate.

Table 1. Statistics of breeding value estimates for semen trait (SV, TSC, TC, ND, and AR) ¹

Table 2 shows correlations between breeding values from different multiple traits analysis for each semen trait. Pearson correlations for SV, TSC, TC, ND, and AR were .76, .75, .60, .77, and .45, respectively. All were significantly different from zero (P < .0001), but not approaching one. This result is expected due to the genetic correlations between BF and MD and the semen traits and genetic correlations among semen traits. Therefore to implement genetic selection for semen traits the most efficient evaluation procedure needs to be determined. This may be a separate genetic evaluation of semen traits and then appropriate weightings with BF and MD in the development of breeding objectives.

Table 2. Pearson correlations between breeding values estimated from multiple trait analyses with different combinations of traits

Implications

Genetic selection for semen traits is possible. Breeding value estimates for the various semen traits would allow for selection of boars that would produce ejaculates that are more acceptable and would yield more extended doses Additional work is needed to understand the relative economic importance of semen traits in the development of breeding objectives.

References

Boldman, K. G., L. A. Kriese, L. D. Van Vleck, C. P. Van Tassell and S. D. Kachman. (1995). A Manual for Use of MTDFREML. A Set of Programs to Obtain Estimates of Variances and Covariances [Draft]. U.S. Department of Agriculture, Agricultural Research Service.

Brandt, H. and G. Grandjot. (1998). Genetic and environmental effects on male fertility of AI boars. Proc. 6^th World Congress on Genetics Applied to Livestock Production, Armidale, Australia. 23:527-530.

Singleton, W. L. (2001). State of the art in artificial insemination of pigs in the United States. Theriogenology. 56:1305.