AI Stations Research Articles

Novel interpretation of sperm stress test and morphology for maturity assessment of young Norwegian Red bulls

The use of genomic selection significantly reduces the age of dairy bulls entering semen production compared to progeny testing. The study aimed to identify early indicators that could be used for screening bulls during their performance testing period and could give us insight into their future semen production performance, acceptance for the AI station, and prediction of their future fertility. The study population consisted of 142 young Norwegian Red bulls enrolled at the performance test station, followed until we received semen production data, semen doses, and, subsequently, non-return rates (NR56) from the AI station. A range of semen quality parameters were measured with computer-assisted sperm analysis and flow cytometry from ejaculates collected from 65 bulls (9–13 months). The population morphometry of normal spermatozoa was examined, showing that Norwegian Red bulls at 10 months of age have homogenous sperm morphometry. Norwegian Red bulls could be separated into 3 clusters according to their sperm's reaction patterns to stress test and cryopreservation. Results of semi-automated morphology assessment of young Norwegian Red bulls showed that 42% of bulls rejected for the AI station and 18% of bulls accepted had ejaculates with abnormal morphology scores. For the youngest age group at 10 months, the mean (SD) proportion of spermatozoa with normal morphology was 77.5% (10.6). Using novel interpretation of sperm stress test combined with sperm morphology analysis and consecutive cryopreservation at a young age allowed identification of the candidate's sperm quality status. This could help breeding companies introduce young bulls earlier to the AI stations.

Reaction time as a libido indicator and its relation to pre-freeze and post-thaw sperm quality in bulls.

Libido and sperm quality output relationship is already not clear in farm animals. The present study compared reaction time (RT) as a libido indicator and the pre-freeze and post-thaw sperm quality of AI bulls. Before the collection of ejaculates (n = 53, from 22 AI bulls [4.2 ± 1 years of age]), RTs were collected using a chronometer as the interval between the bull's arrival at the semen collection area and his first false mount (FM) on another male. The ejaculates were examined for their volume, concentration and motility. Subsequently, all aliquots were diluted with a commercial semen extender and equilibrated for 3 h before freezing. Frozen semen samples were thawed and examined for sperm kinematics using CASA, plasma membrane and acrosome integrity of sperm (PMAI) by flow cytometry. Additionally, the temperature humidity index (THI) values were assessed during the study. Multiple linear regression analysis was used to analyse the data. The results indicated that THI had a significant effect on libido (p < .001). However, libido had no effect on either pre- or post-thaw sperm quality parameters except for the velocity of the average pathway (VAP) (p < .05). Therefore, relying solely on RT -libido- as an indicator of bull sperm quality at AI stations may not be reliable, as it is a complex behavioural assessment.

Dissection of variance for semen quality traits to assist boar AI stations

Dissection of variance for semen quality traits to assist boar AI stations

Boar genotype as a factor shaping age-related changes in semen parameters and reproduction longevity simulations

The aim of this study was a detailed analysis of the boar genotypes used in AI stations with an indication of their production capacity, including age and a precise analysis of their culling time and reason. The study included 334 boars: 81 Polish Large White (PLW), 108 Polish Landrace (PL), 49 Pietrain (P), 56 Duroc × Pietrain (D × P) and 40 Hampshire × Pietrain (H × P). Semen volume, spermatozoa concentration, total number of spermatozoa, number of motile spermatozoa, and number of insemination doses were analyzed. Quadratic regression was used to illustrate the selected sperm parameters at specific ages. Among all the studied boars the lowest motilities of spermatozoa were identified in white breeds PLW and PL, and the difference between motility extremes was 3.53% (P ≤ 0.01). The highest number of insemination doses were produced from D × P crossbreed boars: about 0.7 portions more compared to PL, 1.13 to PLW, 1.18 to H × P and 1.8 to P (all differences P ≤ 0.01). It has been shown in the case of ejaculate volume that for PLW and H × P boars the culling moment was far too early in terms of production capacity and differences were, respectively, 16.35 ml for PLW and 12.61 ml for H × P. Based on the developed regression equations, the earliest maximum number of motile sperm (73.82 × 109) was obtained by H × P crossbreed boars as early as at age 24 months. The highest values for this parameter were achieved, however, by other D × P crossbreed boars: 74.30 × 109 at the later age of 32 months. A consequence of the high number of motile sperm in young H × P boars was that the theoretical maximum value of the number of AI doses was produced as early as the 14th month (25.59 portions). Curves of similar shape were obtained for PL and D × P boars; the difference in maximal values was 0.54 portions in favor of crossbreeds, at a later age of 7 months. It was noted that for PLW and D × P boars the highest number of cullings were due to reduced demand (24.47% and even 31.19%, respectively). Other boars - PL, P and H × P - were most frequently culled because of low semen values. Regardless of the genotype, high survival probabilities (over 0.96) were noted for the age of 12 months. The highest probabilities (still over 0.96) were noted in the longest time period (up to 18 months) for P boars. The results of our study can be used in AI stations as reference points for the exploitation and culling of boars with different genotypes.

The effect of age, interval collection and season on selected semen parameters and prediction of AI boars productivity

The possibility of planning the production level has a special importance for AI stations, and its inaccuracy should be minimized by eliminating the factors differentiating the quantity and quality of the ejaculate. The aim of the study was to determine the effect of age, collection interval and season on selected quantitative and qualitative semen parameters and to develop equations for estimating production capacity for AI stations. The study included 48 117 ejaculates. Experimental groups were created according to: age of boar (8–9, 10–12, 13–18, 19–24, 25–30, >30 months), collection interval (≤2, 3, 4, 5, 6, 7, ≥8 days), and season (Winter, Spring, Summer, Autumn). Selected quantitative and qualitative parameters were analyzed: semen volume (ml), spermatozoa concentration (×106ml−1), total number of spermatozoa in ejaculate (×109), number of motile spermatozoa in ejaculate (×109), and number of insemination doses obtained from one ejaculate (n). The most important role in shaping the results was played by interactions. The highest semen volumes were achieved for the oldest boars after 7-day interval collection during Autumn (about 300ml). Interestingly, the lowest semen volumes were also noted during Autumn from the youngest boars after 1–2 and 7-day interval collection (a little over 160ml). The highest spermatozoa concentrations (515×106ml−1) were observed in Autumn for 10–12 month-old boars with the longest interval collection, which clearly demonstrates the positive impact of long resting on young boars. The lowest spermatozoa concentrations – below 300×106ml−1 – were reported for the oldest boars after 1–2 and 3-day collection intervals. The highest total number of spermatozoa in ejaculate and motile spermatozoa were noted during the longest collection interval in Winter at the ages of 13–18 months and 25–30 months, while the lowest occurred in the same season with a short interval from the 10–12 month boars. Almost all correlations were statistically proven at the level of P≤0.05 and P≤0.01, except for the correlation between the number of motile spermatozoa and the age of boars. Developed regression equations enable, with a high degree of accuracy, the estimation of the selected ejaculate parameters on the basis of age, collection interval and season. The solution proposed in the article is a useful tool, especially for AI stations or large farms with a high rate of boars producing semen for AI. AI stations assessing the long-term production capacity of boars should take into account the factors examined in this study. Underestimation of the interaction may, in fact, reduce the number of insemination doses by up to 5 portions of a single ejaculate.

Genetic parameters for male fertility and its relationship to skatole and androstenone in Danish Landrace boars1

Concerns have been raised regarding selection against the boar taint compounds, androstenone and skatole, due to potential unfavorable genetic correlations with important male fertility traits (i.e., selection of boars with low levels of these boar taint compounds might also reduce male fertility). Hence, the objective of this investigation was to study the genetic association between direct measures of male fertility and the boar taint compounds in Danish Landrace pigs. Concentrations of skatole and androstenone in the back fat were available for approximately 6,000 and 1,000 Landrace boars, respectively. The litter size traits, such as total number born, live piglets at d 5, and piglet survival until d 5 on relatives of the slaughter boars, were extracted from the Danish Landrace breeding program, yielding 35,715 records. Semen volume, sperm concentration, subjective sperm quality score, and total number of sperm were available from 95,267 ejaculates. These ejaculates were collected between 2005 and 2012 and originated from 3,145 Landrace boars from 12 AI stations in Denmark. The traits were analyzed using single and multitrait animal models including univariate random regression models. Skatole and androstenone concentrations were moderate to highly heritable (i.e., 0.33 and 0.59, respectively). The genetic correlation between the two compounds was moderate (0.40). Genetic variance of sperm production per ejaculate increased during the productive life of the boar, resulting in heritability estimates increasing from 0.18 to 0.31. Genetic correlations between sperm production per ejaculate at different ages were high and generally larger than 0.8, indicating that later genetic merit can be predicted from records at an early age. The heritability (based on service-sire genetic component) of both total number of piglets born and survival to d 5 were 0.02, and the correlation between these effects and the additive genetic effect on boar taint ranged from 0.05 to -0.40 (none of these correlations were significantly different from zero). Most importantly, the genetic correlations between skatole and androstenone and the different semen traits tended to be more favorable with increase in age of the boars. In conclusion, these data suggest that concentrations of skatole and androstenone can be reduced through genetic selection without negatively affecting important male fertility traits in Danish Landrace pigs.

178 MODELLING THE EFFECT OF ELEVATED TESTICULAR TEMPERATURE OF HOLSTEIN FRIESIAN BULLS IN A MODERATE CLIMATE ON REJECTION RATES OF EJACULATES IN SEMEN PROCESSING

Heat stress can cause bulls to start producing sub- or even infertile semen. This is usually not noticed immediately during quality control while processing semen. Sometimes, a heat period is already over before an increase of rejection ejaculates is noticed in the laboratory. This delayed effect, but also because a heat period is often not discrete or clear-cut, makes it difficult to relate heat stress with semen quality. The recent study of Rahman et al. (Theriogenology, in press) is therefore of interest, given that heat stress was mimicked by elevation of testicular temperature (∼2°C, 48 h) of 2 Holstein Friesian bulls in a controlled experiment. Analyses of semen, collected 1 week before until 9 weeks after the experiment, revealed that specific stages in spermatogenesis, meiosis and spermiogenesis are susceptible to heat stress. During these stages, the percentages of morphologically normal, progressive motile and membrane-intact cells reduced by approximately 33, 25 and 20%, respectively. The objective of this study was to model how these reductions would affect rejection rates for these parameters in a moderate climate, such as the Netherlands. The standards of the 3 evaluated quality parameters, which are used by many AI stations, are per ejaculate more than 75% morphologically normal cells, more than 35% progressive motile and 50% membrane intact cells after thawing. Normal rejection rates were determined from original scores of at least 2 years from 2 AI stations (CRV, the Netherlands). That is, 11 453 morphology scores of fresh semen, 10 805 progressive motility and 3741 membrane integrity scores of thawed semen from 2065, 492 and 1488 bulls, respectively. To estimate rejection rates as if ejaculates were affected by heat stress, each morphology, progressive motility and membrane integrity score was reduced by 33, 25 and 20%, respectively. Normal vs estimated heat stress rejection rates, based on original and modeled scores, respectively, were 50 vs 100% for morphology, 2 vs 17% for progressive motility and 9 vs 61% for membrane integrity. Rahman showed that already a short period of heat stress can lead to reduced sperm quality for over a month. This study indicates that this effect can be very important for AI stations. In the case of progressive motility, which in the Netherlands is scored for each ejaculate, the estimated higher rejection rate of 17% could lead to a considerable increase of production costs. Membrane integrity is occasionally scored (when requested for export of semen), but morphology is always scored when bulls start to produce semen until ejaculates are produced in succession with more than 75% normal cells. High rejection rates for morphology are typical when bulls (∼11 months old) start, but this decreases quickly for most bulls to 0% within a few months as they mature. When these young bulls suffer heat stress before production, it is possible that the rejection rate will stay at 100% for a long time. When this is suspected, a prolonged test period should be considered to avoid wrongful culling of bulls.

Application of computer-assisted semen analysis to explain variations in pig fertility1

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) effects of progressive motility, velocity curvilinear, and beat cross frequency on farrowing rate (FR). Total motility, velocity average path, velocity straight line, and amplitude of lateral head displacement affected (P < 0.05) total number of piglets born (TNB). Boar- and semen-related parameters explained 5.3% of the variation in FR and 5.9% of the variation in TNB. Motility parameters, measured by CASA, explained 9% of the boar- and semen-related variation in FR and 10% of the boar- and semen-related variation in TNB. Individual boar and genetic line of the boar affected (P < 0.0001) the variation in FR and TNB. No differences (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.

Can external quality control improve pig AI efficiency?

External quality control programmes carried out by central laboratories have been long established in human andrology with the aim of enhancing the accuracy and reproducibility of semen assessment. Compared to human, demands on boar semen assessment in AI stations are more complex, with the need both to identify boars with poor ejaculate quality and to monitor individual boar differences for semen storage. Additionally, appropriate assessment serves as a control instrument to ensure the security and efficiency of semen processing. Despite current limitations regarding the ability of sperm assays to estimate the potential fertility of males, it is evident that boar fertility is related to certain conventional semen tests, e.g. sperm morphology. In central studies carried out on stored semen from 11 AI stations, flow cytometric assessment of plasma and acrosome membrane integrity proved to be more sensitive in detecting sperm damage associated with ageing and temperature stress as compared to light microscopy. Membrane integrity of stored semen differed between AI stations indicating significant influences of semen processing on sperm quality. Thus external control of semen quality in reference laboratories may be useful to monitor the efficiency of internal semen quality control in individual AI stations, to identify males with lower semen quality and/or poor response to semen storage, and to verify the precision of sperm counting. The possibility that central laboratories with sufficient resources may be able to identify functionally different responding sperm subpopulations for better estimation of fertility is discussed. Ideally, external quality control schemes for AI stations would comprise application of validated tests with high relevance for fertility (including bacterial status), analysis of semen processing on the AI station, and training courses for laboratory personnel.

Chromosome C-band polymorphism in relation to fertility in boars of the Duroc breed

In a study of the variation of fertility in boars of the Duroc breed from Danish AI stations, chromosome investigation has been conducted on 11 boars with high and 11 with low fertility, respectively, out of a total of 98 boars having normal semen quality according to AI standards. All 22 boars had the normal chromosome number. C-band polymorphism at chromosome 16 and 17 was more frequent in the low fertility boars compared with high fertile ones (P less than 0.01), and on average a C-band polymorphic boar sired 0.99 piglet less per litter than a non-polymorphic. More investigations are warranted before application of the results can be brought into practical swine breeding.

Seasonal effects of semen collection and artificial insemination on dairy cow conception

The effects of four seasons of semen collection and of artificial insemination on conception in dairy cows were studied. The solstices and equinoxes (December, March, June and September) defined the beginning and/or end of each season. Semen was collected from 973 progeny-test bulls over 8 years at the two Norwegian AI stations at 60.8°N and 63.4°N where artificial light was used to provide a minimum photoperiod of 10 h/day. The effect of using semen of elite bulls during progeny testing and after selection as elite sires also was investigated. Norwegian Red (NRF) cows were inseminated over a 7-year period using progeny test semen and over the last 4 years of the same period using the semen of the elite sires. The probability of conception to only first inseminations for cows up to, and including, the fifth lactation was assessed by 56-day non-return rate (56d NRR) and calving rate. Two data sets were analysed which excluded cows culled within 270 days of AI or included such cows as non-calving. The reasons for culling were categorised as those for fertility problems or all other reasons. Semen was used for AI irrespective of the season in which it had been collected. Season of semen collection did not affect 56d NRR but calving rate was significantly higher (by 0.5–0.8%, approximately; P < 0.01) for semen collected in the December–March period, when photoperiod was increasing, than at other times of the year. The season in which AI was performed showed a peak of 56d NRR in spring for heifers ( P < 0.01) and in summer for parous animals ( P < 0.01). For calving rate, however, no seasonal peak was found in heifers, whereas pluriparous cows had much higher calving rates in summer and autumn/early winter than late winter and spring ( P < 0.01). Semen of elite sires resulted in higher calving rates by 0.5 (NS) to 1.9% ( P < 0.01) when used after selection than when used during progeny testing. The difference between the calving rate achieved when the semen from elite sires was used during progeny testing and after selection indicates that farmers select different classes of cows for submission to AI by progeny test bulls and sires. The 56d NRR was not as good as calving rate for assessing seasonal and other effects on conception rates.

Viruses in boar semen: detection and clinical as well as epidemiological consequences regarding disease transmission by artificial insemination

Many viruses have been reported to be present in boar semen, particularly during the viremic phase of the diseases. Some of them, such foot-and-mouth disease virus, porcine reproductive and respiratory syndrome virus, swine vesicular disease virus, porcine parvovirus, picornaviruses, adenoviruses, enteroviruses, Japanese encephalitis virus, pseudorabies virus, African swine fever virus and reoviruses are of particular importance and accurate monitoring prior to and during the presence of boars in AI stations is essential. Various methods may be used to detect these viruses in the animals, or even directly in batches of semen. Cell culture, ELISA and PCR are the most accurate and widely used. Because of the high risk of dissemination of disease via AI, the absolute goal is to provide pathogen-free semen and this is feasible with the adequate measures that are discussed briefly in this paper.

Management of disease control and epidemics in AI in Denmark

The author's presentation constituted a survey of the control of contagious diseases at Danish AI stations. The control is performed in accordance with EU regulations and requires documented freedom from Aujeszky's disease, Classical Swine Fever and Brucellosis (based on blood testing) before transfer to quarantine sections and to AI stations. The Danish regulations regarding Porcine Reproductive and Respiratory Syndrome (PRRS) are described for both sero-positive and sero-negative stations. At some stations, all boars are vaccinated against both American PRRS (Ingelvac PRRS vet.) and European PRRS (Progressis vet.), and are subsequently sero-positive for PRRS. The period from vaccination until the time that semen can be used is described, and the reason for its length is explained. Furthermore, the process applied when distinguishing Yersinia from Brucellosis reactions is described. The purpose of this control is to prevent Yersinia-positive herds that supply boars from compromising supplies from Yersinia-negative herds through cross-reactions with Brucellosis. The vaccinations required before transfer to isolation and AI stations (Glässer's disease, pleuropneumonia caused by Actinobacillus pleuropneumoniae, erysipelas and PPV) as well as vaccinations done regularly at the AI stations (erysipelas and PPV) are described. Finally, a survey is presented of other diseases found at Danish AI stations (primarily pneumonia and leg problems) and their medical treatment.

Prevention of disease transmission by semen in cattle

To test the safety of semen two approaches can be applied: checking the end product, or continuous surveillance of the bulls before and after semen production. The first method is examination of semen for the presence of infectious agents. This method depends completely on a single investigation and therefore relies only on the sensitivity of the test method. The second method is testing the bulls for diseases before and after semen collection, based on sequential investigations for the absence of either pathogens or antibodies against infectious agents. The EU-Directive 88/407 prescribes that bulls in AI stations must be monitored for the absence of diseases, but only at 12-monthly intervals, which is a severe disadvantage. Furthermore, the directive is specific neither in the tests to be carried out nor in the specification of some pathogens (e.g. Campylobacter foetus). A programme is presented based on monthly testing of a limited number of bulls for the absence of endemic diseases only, on the basis of Hazard Analysis of Critical Control Points (HACCP). This method only applies to diseases with high transmission rates. Testing some 20% of the animals on a monthly basis can monitor these highly contagious diseases (e.g. IBR). To monitor BVDv infections, however, monthly testing of all negative animals and semen culture or semen PCR of animals that have seroconverted for this virus seems necessary. Endemic diseases with slow transmission rates in bulls do not suit such a system and can only be monitored on an individual basis.

Genetics and Disease in Cattle

There are many bovine congenital defects of unknown etiology, suspected etiology and known etiology. Congenital defects of known environmental etiology such as bovine virus diarrhea-induced cerebellar atrophy, or plant-induced deformity such as crooked-calf diseases caused by lupine, require changes in management procedures. Defects caused by genetic factors are of considerable concern to the AI stations, cattle breeders, and cattle breed associations. Initial diagnosis of congenital defects is frequently required of practicing veterinarians. Some of the genetic diseases of current concern to the cattle industry of the United States include congenital defects of the skin such as epitheliogenesis imperfecta, hypotrichosis, fragile skin, and protoporphyria. Of the internal organs, the reproductive system is of particular concern. Defects of bone are numerous and may affect single structures such as adactyly, tibial hemimelia, and syndactyly. Generalized skeletal defects of importance include dwarfism and osteopetrosis. Muscular defects of significance are doublemuscling and arthrogryposis. Furthermore, genetically-caused defects of the central nervous system need further attention, particularly internal hydrocephalus and mannosidosis. Many defects encountered of suspected etiology need close attention. In this category, as well as those of unknown etiology, practicing veterinarians remain the main source of information to guide further study.