Short communication: Genetic analysis of dairy bull fertility from field data of Brown Swiss cattle

The aim of this study was to estimate heritability and repeatability of dairy bull fertility in Italian Brown Swiss cattle. Bull fertility indicators were calving per service and nonreturn rate at 56 d after service. Data included 124,206 inseminations carried out by 86 technicians on 28,873 heifers and cows in 1,400 herds. Services were recorded from 1999 to 2008 and were performed with semen from 306 AI Brown Swiss bulls. Data were analyzed with a threshold animal model, which included the fixed effects of parity by class of days in milk of the inseminated cow (age at insemination for heifers), year-season of insemination, and status of the service bull at the time of insemination (progeny testing or proven), and the random effects of herd, technician, additive genetic, and permanent environment of inseminated heifer/cow and service bull, and residual. Also, genetic covariance between heifer/cow and service bull effects was considered in the model. Heritability and repeatability were 0.0079 and 0.0100 for nonreturn rate at 56 d after service, and 0.0153 and 0.0202 for calving per service, respectively. The low estimates obtained in the present study indicate that selection for male fertility using field data is hardly pursuable.     

Genetic parameters of semen quality traits and genetic correlations with service sire nonreturn rate in Nordic Holstein bulls

Despite the importance of the quality of semen used in artificial insemination to the reproductive success of dairy herds, few studies have estimated the extent of genetic variability in semen quality traits. Even fewer studies have quantified the correlation between semen quality traits and male fertility. In this study, records of 100,058 ejaculates collected from 2,885 Nordic Holstein bulls were used to estimate genetic parameters for semen quality traits, including pre- and postcryopreservation semen concentration, sperm motility and viability, ejaculate volume, and number of doses per ejaculate. Additionally, summary data on nonreturn rate (NRR) obtained from insemination of some of the bulls (n = 2,142) to cows in different parities (heifers and parities 1–3 or more) were used to estimate correlations between the semen quality traits and service sire NRR. In the study, low to moderate heritability (0.06–0.45) was estimated for semen quality traits, indicating the possibility of improving these traits through selective breeding. The study also showed moderate to high genetic and phenotypic correlations between service sire NRR and some of the semen quality traits, including sperm viability pre- and postcryopreservation, motility postcryopreservation, and sperm concentration precryopreservation, indicating the predictive values of these traits for service sire NRR. The positive moderate to high genetic correlations between semen quality and service sire NRR traits also indicated that selection for semen quality traits might be favorable for improving service sire NRR.     

Evaluation of bull fertility in Italian Brown Swiss dairy cattle using cow field data

Dairy bull fertility is traditionally evaluated using semen production and quality traits; however, these attributes explain only part of the differences observed in fertility among bulls. Alternatively, bull fertility can be directly evaluated using cow field data. The main objective of this study was to investigate bull fertility in the Italian Brown Swiss dairy cattle population using confirmed pregnancy records. The data set included a total of 397,926 breeding records from 1,228 bulls and 129,858 lactating cows between first and fifth lactation from 2000 to 2019. We first evaluated cow pregnancy success, including factors related to the bull under evaluation, such as bull age, bull inbreeding, and AI organization, and factors associated with the cow that receives the dose of semen, including herd-year-season, cow age, parity, and milk yield. We then estimated sire conception rate using only factors related to the bull. Model predictive ability was evaluated using 10-fold cross-validation with 10 replicates. Interestingly, our analyses revealed that there is a substantial variation in conception rate among Brown Swiss bulls, with more than 20% conception rate difference between high-fertility and low-fertility bulls. We also showed that the prediction of bull fertility is feasible as our cross-validation analyses achieved predictive correlations equal to 0.30 for sire conception rate. Improving reproduction performance is one of the major challenges of the dairy industry worldwide, and for this, it is essential to have accurate predictions of service sire fertility. This study represents the foundation for the development of novel tools that will allow dairy producers, breeders, and artificial insemination companies to make enhanced management and selection decisions on Brown Swiss male fertility.     

Systematic environmental, direct, and service sire effects on conception rate in artificially inseminated Holstein cows

Result of insemination was verified for 329,314 artificial inseminations by 882 service sires to 97,245 Holstein cows in 1,075 herds between May 1970 and December 1983. Estimates of systematic environmental and genetic components of cow and service sire components of conception rate were obtained preliminary to development of a fertility monitoring system. Conception rate was 2.9% higher in stanchion than loose housed herds and 2.3% higher in grade than registered cows. Fall months were superior to winter months, the greatest difference being 6.1% between October and January. Conception rate increased with herd milk production, decreased with both increased cow age, and increased herd size in mature and old cows. Regions and inseminators within regions were highly variable. Conception may be influenced by semen price; however, week day of insemination and duration of semen storage had no effect. Conception rate decreased for semen by bulls 8 yr and older, was lowest for semen harvested in June, but no season of collection effect was detected. No genetic trends for cow and service sire conception rate were found; heritability and repeatability were .08 and .06, respectively. Genetic correlations between cow and service sire conception rate and these components with first lactation production and semen output measures were all near zero. Therefore, the relationship between sire fertility and daughter fertility is near zero.     

A field study on fertility and purity of sex-sorted cattle sperm

The study assessed the fertility and purity of sexed semen used for inseminating Holstein-Friesian heifers in commercial dairy herds. Sex-sorted semen from 4 proven Holstein-Friesian bulls and available under commercial conditions was used on nulliparous Holstein heifers reared on 61 dairy farms of northern Italy. Data from 536 artificial inseminations with pregnancy diagnosis and 258 calvings were analyzed using the logistic regression procedure. The effects of year and season of insemination or calving, age at insemination or calving, heifer inbreeding, and the sperm dose used for insemination on the probability of a positive pregnancy diagnosis or of the birth of a female calf, respectively, were studied. The overall pregnancy rate for sexed semen was 51% and was affected by year of insemination and bull. Heifers inseminated with sexed semen from 2 bulls had lower pregnancy rates than heifers inseminated with sexed semen from other bulls. Purity of the sexed sperm, based on the proportion of female calves, was 87% and this percentage was not affected by explanatory variables included in the logistic regression. The results demonstrate that bulls differ in terms of fertility of their sexed semen. Careful selection of the insemination sires used for sorted semen is advisable for avoiding low fertility inseminations.     

Fertility of fresh and frozen sex-sorted semen in dairy cows and heifers in seasonal-calving pasture-based herds

Our objective in this study was to evaluate the reproductive performance of dairy heifers and cows inseminated with fresh or frozen sex-sorted semen (SS) in seasonal-calving pasture-based dairy herds. Ejaculates of 10 Holstein-Friesian bulls were split and processed to provide (1) fresh conventional semen at 3 × 10⁶ sperm per straw (CONV); (2) fresh SS at 1 × 10⁶ sperm per straw (SS-1M); (3) fresh SS semen at 2 × 10⁶ sperm per straw (SS-2M); and (4) frozen SS at 2 × 10⁶ sperm per straw (SS-FRZ). Generalized linear mixed models were used to evaluate the effect of semen treatment and other explanatory variables on pregnancy per artificial insemination (P/AI) in heifers (n = 3,214) and lactating cows (n = 5,457). In heifers, P/AI was greater for inseminations with CONV (60.9%) than with SS-FRZ (52.8%) but did not differ from SS-1M (54.2%) or SS-2M (53.5%). Cows inseminated with CONV had greater P/AI (48.0%) than cows inseminated with SS, irrespective of treatment (SS-1M, SS-2M, and S-FROZEN; 37.6, 38.9, and 40.6%, respectively). None of the SS treatments differed from each other with regard to P/AI in either heifers or cows. The relative performance of SS compared with CONV was also examined [i.e., relative P/AI = (SS P/AI)/(CONV P/AI) × 100]. Frozen SS achieved relative P/AI >84%. Bull affected P/AI in both heifers and cows, but no bull by semen treatment interaction was observed. In heifers, P/AI increased with increasing predicted transmitting ability for milk protein percentage. In cows, P/AI increased with increasing Economic Breeding Index (EBI) and with days in milk (DIM) at AI but decreased with increasing EBI milk subindex, parity and with DIM². Cows in parity ≥5 had the lowest P/AI and differed from cows in parities 1, 2, or 3. Dispatch-to-AI interval of fresh semen did not affect P/AI in lactating cows, but a dispatch-to-AI interval by bull interaction was detected whereby P/AI was constant for most bulls but increased with greater dispatch-to-AI intervals for 2 bulls. In conclusion, frozen SS achieved greater P/AI relative to conventional semen than was previously reported in lactating cows. Fresh SS did not achieve greater P/AI than frozen SS, regardless of whether the sperm dose per straw was 1 × 10⁶ or 2 × 10⁶. A bull effect for all semen treatments, as well as a dispatch-to-AI interval by bull interaction for fresh semen, highlights the importance of using a large team of bulls for breeding management.     

Comparisons of cows and herds in two progeny testing programs and two corresponding states

Data were USDA genetic evaluations of cows and DHI herd profiles from 4154 Holstein progeny-test herds from two artificial insemination organizations. 21st Century Genetics (Shawano, WI) and Genex (Ithaca, NY), and from 6361 additional herds from Minnesota and New York. We grouped herds into four categories: 21st Century Genetics herds, other Minnesota herds, Genex herds, and other New York herds. Herds were eliminated if they contributed fewer than 10 cows with genetic evaluations and birth dates from January 1989 to March 1995. Data included 83 and 74%, respectively, of first-crop daughters of 21st Century Genetics and Genex progeny-test bulls with genetic evaluations from January 1995 to February 1997. Herds were characterized by DHI profile and cow evaluation data. Daughters of progeny-test bulls with extreme production records (outside of 3 SD) relative to herd mean and variance did not appear in disproportionate numbers among the progeny of bulls likely culled or considered for further use. The two organizations appear to have selected larger, genetically superior, and better managed herds from within their respective regions for progeny-testing purposes. We were not able to predict whether a bull in the progeny-testing programs of these two organizations was going to exceed or fail to meet the pedigree prediction from characteristics of herds in which his daughters performed. Differences between parent average and daughter yield deviations for typical young sires appear to result from Mendelian segregation of genes.     

Breeding practices on Illinois Holstein farms

Questionnaires requesting breeding information were mailed to 1,148 Illinois Holstein herds in the Dairy Herd Improvement program. A total of 591 questionnaires (51%) were returned. Dairy producers with herds producing over 7,100 kg of milk returned 64% of their questionnaires whereas 34% of dairy producers with herds producing less than 5,900 kg returned their questionnaires. State average and standard errors were: 23 +/- 1 bulls used per 100 cows, 78 +/- 2% dairy producers select the bulls, 96 +/- 1% herds use artificial insemination, 2.1 +/- .1 artificial insemination organizations per herd, 29 +/- 2% herds participate in young sire testing programs, 87 +/- 1% farmers consider calving ease indicators in mating heifers, and 17 +/- 2% farmers consider calving ease indicators in mating cows. Breeding practices positively associated with increasing rolling herd average milk production were number of bulls per herd and per 100 cows, self as bull selector, use of artificial insemination, number of artificial insemination organizations, and participation in young sire testing programs. Three breeding practices were negatively associated with increasing rolling herd average milk production: artificial-insemination technician as bull selector and consideration of calving ease for mating heifers and cows. Dairy producers also were asked to rate the emphasis placed on traits in bull and cow selection. For bull selection, udder conformation and Predicted Difference milk were most important. In cow selection, milk production, followed by udder conformation, feet and legs, and fat percentage, was the most important trait.     

Genetic analysis of semen characteristic traits in Norwegian Red bulls at the artificial insemination center

Compared with cow fertility, genetic analyses of bull fertility are limited and based on relatively few animals. The aim of the present study was to estimate genetic parameters for semen characteristics of Norwegian Red bulls at the artificial insemination (AI) center (Geno AI station, Stange, Norway) and to estimate genetic correlations between some of these traits and andrology traits measured at the performance test station. The data from the AI center consisted of records from 137,919 semen collections from 3,145 bulls with information on semen weight, sperm concentration, motility before and after cryopreservation, motility change during cryopreservation, and number of accepted straws made. Data from the performance test station included 12,522 observations from 3,219 bulls on semen volume, concentration, and motility (%) when fresh and after storing for 24 and 48 h. Genetic parameters were estimated using linear animal repeatability models that included fixed effects of year-month of observation, age of bull, interaction between semen collection number, and interval between collections for all traits and type of diluter for postcryopreservation traits. The random effects included test-day, permanent environmental, and additive genetic effects of the bull. Based on records from the AI center, we found that semen weight, sperm concentration, and number of straws were moderately heritable (0.18–0.20), whereas motility had a lower heritability (0.02–0.08). Heritability of motility (%) was higher after cryopreservation than before. Genetic correlations among the semen characteristics ranged from unfavorable (−0.35) to favorable (0.93), with standard errors ranging from 0.02 to 0.22. Among the most precise genetic correlation estimates, number of straws made from a batch correlated favorably with semen weight (0.62 ± 0.06) and sperm concentration (0.44 ± 0.08), whereas sperm concentration was negatively correlated with weight (−0.33 ± 0.09). The genetic correlation between motility (%) before and after cryopreservation was 0.64 ± 0.14, and motility change during cryopreservation had a strong favorable genetic correlation with motility after cryopreservation (−0.93 ± 0.02). The estimated genetic correlation (standard error) between the traits volume, concentration, and motility when fresh measured at the performance test station and their respective corresponding traits at the AI center were 0.83 (0.05), 0.78 (0.09), and 0.49 (0.31). The final product at the AI center (number of accepted straws) correlated genetically favorably with all semen characteristic traits recorded at the performance test station (ranging from 0.51 to 0.67). Our results show that the andrology testing done at the performance test station is a resource to identify the genetically best bulls for AI production.     

Evaluations for service-sire conception rate for heifer and cow inseminations with conventional and sexed semen

Service-sire conception rate (SCR), a phenotypic fertility evaluation based on conventional (nonsexed) inseminations from parities 1 through 5, was implemented for the United States in August 2008. The SCR model contains the categorical fixed effects of parity for lactations 1 to 5; state-year-month of insemination group; 6 standardized milk yield groups; service number for inseminations 1 to 7; cow age; and herd-year-season-parity-registry status class. Covariate effects for service-sire and mating inbreeding coefficients were linear regressions fit as deviations from the overall mean. Random effects included service-sire age group; AI organization-insemination year group; individual service sire; cow's genetic ability to conceive; cow's permanent environmental effect; and residual. Using insemination data from 2005 through 2009, the SCR procedure was applied separately for nulliparous heifer inseminations with conventional semen (SCRHconv), cow inseminations with conventional semen (SCRCconv), nulliparous heifer inseminations with sexed semen (SCRHsexed), and cow inseminations with sexed semen (SCRCsexed). Holstein and Jersey bulls with ≥300 and ≥200 artificial inseminations, respectively, in ≥10 herds and with ≥100 breedings during the 12 mo before evaluation were examined. The number of bulls evaluated for SCR in January 2010 was 270 Holsteins and 16 Jerseys for SCRHconv, 2,309 Holsteins and 214 Jerseys for SCRCconv, 114 Holsteins and 6 Jerseys for SCRHsexed, and 25 Holsteins and 7 Jerseys for SCRCsexed. The mean SCR for each evaluation category was set to 0; Holstein standard deviations were 2.55% for SCRHconv, 2.21% for SCRCconv, 4.29% for SCRHsexed, and 2.39% for SCRCsexed. The mean Holstein reliabilities were 82, 79, 75, and 73%, respectively. Correlations for Holstein SCR between conventional and sexed semen averaged near zero (−0.21 to 0.18). Predicted correlations between true SCR were −0.27 to 0.24. In contrast, correlations between Holstein heifers and cows were high (0.66 to 0.76), and predicted true correlations averaged near 1.0 (0.82 to 1.03). Correlations for Jerseys were often larger, although based on fewer inseminations and service sires compared with Holsteins. Some rankings for SCR could benefit from combining cow and heifer data but should be kept separate for conventional and sexed semen inseminations.     

The impact of 3 strategies for incorporating polled genetics into a dairy cattle breeding program on the overall herd genetic merit

Dehorning in cattle has been associated with behavioral, physiological, and neuroendocrine responses indicative of pain. Unaddressed, the pain associated with a routine production procedure could contribute to a negative public perception of livestock production practices. Alternative considerations of dehorning include the selection of polled cattle within herds, thereby avoiding pain and production loss. As polledness results from an autosomal dominant pattern of inheritance, genetic selection for polled cattle could reduce the prevalence of the horned trait. Herein we discuss 3 strategies to incorporate polled genetics into a cow herd and the estimated impact on the overall genetic merit of the herd. Furthermore, the availability and genetic merit of polled artificial insemination bulls in the United States is summarized. Both Holstein and Jersey dairy bulls registered with the National Association of Animal Breeders from December 2010 through April 2013 were queried. Polled bulls were identified as either being homozygous (PP) or heterozygous (Pp) and the average net merit (NM) predicted transmitting ability (PTA) of each sire group was calculated. The percentage of polled calves born each year over a 10-yr period was calculated for the following 3 scenarios: (A) various percentages of horned cows were randomly mated to Pp bulls, (B) various percentages of horned cows were preferentially mated to Pp bulls, and (C) horned cows were selectively mated to PP bulls, heterozygous cows to Pp bulls, and homozygous polled cows to horned bulls. Additionally, the change in NM PTA of the cow herd was calculated over the same period. The highest percentage of polled animals (87%) was achieved in scenario C. An evaluation of the herd NM PTA highlights the trade-offs associated with increasing polled genetics. Given the current genetic merit of horned and polled bulls, increasing the percentage of polled calves will decrease the NM PTA in Holstein, but may have minimal impact in Jersey herds. Decisions regarding selective breeding to increase polled genetics will need to be evaluated in the context of production objectives, cost of dehorning, and impact on overall genetic merit.     

Genetic analysis of male and female fertility using longitudinal binary data

A longitudinal Bayesian threshold analysis of insemination events during the first 250 d after calving of first-parity Holsteins was carried out. The outcome of an insemination event was treated as a binary response of either a success (1) or a failure (0). Thus, all breeding information for a cow, including all service sires, was included, thereby allowing for a joint evaluation of male and female fertility. An edited data set of 297,823 insemination records from 151,758 first lactation cows was used. On the liability scale, the model included the systematic effects of herd-year of insemination, technician, month of insemination, and regressions on age of service sire, 3 test days in the first 100 d of lactation (early milk yield), and days in milk at insemination. The random effects in the model were the additive breeding value, the permanent effect of the cow, and the service sire effect. Posterior mean (standard deviation) of the dispersion parameters in the model were 0.034 (0.006), 0.009 (0.001), and 0.171 (0.013) for the additive, service sire, and permanent environmental variances, respectively. The residual variance was fixed at 1, as a result of the nonidentifiability of the threshold model. The posterior mean (standard deviation) of heritability was 0.028 (0.005). This point estimate of heritability is well within the range of available estimates for the trait. Thus, these estimates suggest that some genetic variation exists that can potentially be used to improve reproductive performance or at least avoid its further deterioration. The estimate of the regression coefficient on age of service sire was 0.001, indicating better fertility among older bulls. However, this result has to be interpreted with caution given the preferential use of proven bulls on well-managed cows (as opposed to problem breeders). The estimate of the regression coefficient was negative (-0.005) for early milk yield, as expected, and positive (0.003) for days in milk at insemination. This suggests that high-producing cows are less likely to conceive at the beginning of lactation.     

Sperm viability,reactive oxygen species,and DNA fragmentation index combined can discriminate between above- and below-average fertility bulls

The accurate prediction of bull fertility is of major economic importance in the dairy breeding industry. Sperm fertilizing potential is determined by their ability to reach the oocyte, complete fertilization, and sustain embryogenesis, which is partly determined by the quality of sperm DNA. In the present study, we analyzed several sperm functions required for fertility, including DNA damage, in frozen-thawed spermatozoa of breeding bulls with different adjusted nonreturn rates (NRR56), and identified a suitable combination of parameters that could be used to predict bull fertility. Based on the NRR56, bulls were classified into below- and above-average fertility, a total of 37 characteristics of spermatozoa were evaluated for each bull, and their relationship with bull fertility was studied. Of the different sperm functional attributes, differences were observed in sperm viability, acrosomal integrity, reactive oxygen species, and DNA fragmentation index (%DFI) among below-average, average, and above-average fertility bulls. Principal component analysis also revealed that sperm viability, acrosome status, reactive oxygen species, and %DFI were the important variables, having highest correlation with NRR56. Our results indicated that the proportion of live [correlation coefficient (r) = 0.53] and live acrosome-reacted spermatozoa (r = 0.50) were significantly positively related to NRR56, whereas the proportion of dead spermatozoa (r = ?0.53) and %DFI (r = 0.61) were significantly negatively related to NRR56 in bulls. Linear regression analysis indicated that a combination of live [coefficient of determination (R²) = 0.72], dead (R² = 0.72), live hydrogen peroxide-negative spermatozoa (R² = 0.64), and %DFI (R² = 0.56) could differentiate below-average and above-average fertility bulls, and thus were considered for development of a fertility prediction model. The accuracy of the developed model for fertility prediction in bulls was high (R² = 0.83). We concluded that flow cytometric detection of sperm viability, hydrogen peroxide status, and %DFI could discriminate below- from above-average fertility bulls.     

Effect of synchronization and semen sorting on artificial insemination bull fertility

Field data were collected over a period of 2 yr by artificial insemination technicians for the purpose of evaluating differences among bulls in their fertility when synchronization and semen sorting were involved. First, main effects of synchronization and semen sorting were found to reduce bull fertility by 1.5 and 12.7%, respectively. Second, the interaction of both factors with bull fertility significantly enhanced the evaluation models. Differences between 2 sets of adjusted conception rates for synchronized and nonsynchronized services ranged from 0.5 to 2.9%, whereas differences between 2 sets of adjusted conception rates for sorted and conventional semen ranged from −1.8 to 15.2%. This implies that using conventional fertility models that ignore these effects may not be sufficiently accurate in situations where synchronization or semen sorting are involved. Accounting for synchronization and especially for semen sorting to evaluate bulls on their fertility and the production of separate sets of conception rates under each situation are essential.     

Genetic parameters for fertility of dairy bulls

Genetic parameters for male fertility and fertility ratings of AI bulls were obtained by analyzing 298,013 service records of cows with successive calving records. Cows were mated to 746 service bulls, which were progeny of 126 sires. The model for variance component estimation accounted for fixed effects of herd-year-seasons, sire of the service bull, age of mates, and random effects of service bull and residual error. Estimates of variances of service bulls and residual error components for bull fertility indicated almost 10% of the phenotypic variation for fertility is among AI bulls. Best linear unbiased prediction of fertility ratings of individual bulls with inclusion of sire and maternal grandsire relationships on these data permitted the evaluation of 886 AI bulls for bull fertility. Heritability for bull fertility computed as twice the regression of son on sire was .158. Differences in fertility ratings of AI bulls ranged from -.29 to .19. Prediction of fertility of young AI bulls and more accurate rating of proven bulls might be useful to the industry.     

Selection of bulls for progeny testing using pedigree indices and characteristics of potential bull-dams' herds

A total of 209 bulls selected from herds in the northeastern US by Eastern AI Coop., Inc. from 1978 to 1981 were identified. The DHI data were obtained for the 145 herds from which these bulls were sampled. Also acquired were evaluations from both Modified Contemporary Comparison and animal model on these bulls and their ancestors and on cows and their sires in the bull-dam herds. From evaluation by animal model, animals appeared to have contributed information to each other effectively through relationship matrix, and thus the accuracy of cow evaluation has been improved. Bulls selected from herds of high genetic level were genetically superior to those from herds of low genetic level. However, there was no evidence that bulls from low intraherd milk variation herds were superior to those from high variation herds in the northeastern population, as was the case in Michigan herds. Parent indices were greater than bull PTA in herds of lower genetic level but less than bull PTA in herds of higher genetic level. The correlation between herd yield average and herd genetic level and that between herd yield average and intraherd yield SD were moderate but significantly different from zero. Other correlations between phenotypic and genetic measures of bull-dam herds were negligible. None of the herd characteristics showed promise in characterizing herds that were more successful in having their sampled bulls returned by AI organization after progency test.     

Economic impact of different strategies to use sex-sorted sperm for reproductive management in seasonal-calving,pasture-based dairy herds

To maximize efficiency, profitability, and societal acceptance of modern dairy production, it is important to minimize the production of male dairy calves with poor beef merit. One solution involves using sex-sorted sperm (SS) to generate dairy replacements and breeding all other cows to an easy-calving, short-gestation bull with good beef merit. We used the Pasture Based Herd Dynamic Milk Model to investigate the effect of herd fertility and use of SS on farm net profit in a herd of 100 cows. This was completed by simulating herds with differing fertility performance (good, average, poor), and differing farm reproductive management [conventional semen (CONV) or SS with varying pregnancy per artificial insemination (P/AI) relative to CONV (i.e., relative P/AI 100%, 85%, and 70%)]. As an additional consideration, the method of allocating SS to cows was also examined. The first option used SS on random heifers and cows (S). The second option used SS on heifers and targeted high-fertility cows (S_Sel). The final option was similar to S_Sel, but used a fixed-time artificial insemination (AI) protocol to facilitate AI on the farm mating start date (S_Sync). For CONV, dairy breed semen was used for AI until 50 animals were pregnant (50% chance of a female calf), whereas for S, S_Sel, or S_Sync the target number of animals successfully conceiving with SS was set at 28 (based on assumed 90% chance of a female calf from pregnancies derived from SS). Beef breed semen was used on all other dams. The results indicated that the biggest effect on farm net profit was not based on whether or not SS was used, but instead was most affected by the overall fertility performance of the herd. Total farm profit decreased by 10% between the good and average fertility herds, and decreased by a further 12% between the average and poor fertility herds. In almost all situations, when the relative P/AI with SS was ≥85%, use of SS led to an overall increase of the farm net profit. There was an economic benefit of using either S_Sel or S_Sync compared with S for the average and poor fertility herds but not for the good fertility herd, highlighting an interaction between SS P/AI and overall herd fertility as well as management practices. If the relative P/AI with SS was <70%, the use of SS led to a decrease in profitability in all simulations except for S_Sync, highlighting the importance of a good management strategy for use of SS. The findings in this study indicated that SS has significant potential to help facilitate greater integration between the dairy and beef production sectors, as well as increase farm profitability when used appropriately.     

Genetic change in milk yield estimated from simultaneous genetic evaluation of bulls and cows

Genetic trend for milk yield in the artificially sired Holstein population in the northeastern United States was estimated from solutions for simultaneous genetic evaluations of bulls and cows for an animal model using mixed model equations and including all known relationships among the population by years of birth of 1960 to 1980 (1978 for bulls). First lactation milk records of 1,074,971 artificially sired cows sired by 6000 bulls in 20,065 herds were used. Averages of estimated genetic value by year of birth were similar from solutions after 10, 20, and 30 rounds of iteration of the mixed model equations. The trend in genetic value of bulls that entered artificial insemination was marked by slight negative change for three periods totaling 9 yr and substantial positive change averaging 105 kg/yr for the other 9 yr, which suggests that the dairy industry sacrificed milk yield in genetic value of bulls put into artificial insemination during those periods to selection criteria other than milk yield. There was little change in average genetic value of registered cows from 1960 to 1970. Gain in nonregistered cows totaled 177 kg for the same period. Gain from 1970 to 1980 was similar for both registered and nonregistered cows, 39.5 and 38.1 kg/yr, respectively. Average genetic value of nonregistered cows exceeded that of registered cows each year except 1960.     

Comprehensive functional analysis reveals that acrosome integrity and viability are key variables distinguishing artificial insemination bulls of varying fertility

In vitro methods of assessing bull semen quality in artificial insemination (AI) centers are unable to consistently detect individuals of lower fertility, and attempts to reliably predict bull fertility are still ongoing. This highlights the need to identify robust biomarkers that can be readily measured in a practical setting and used to improve current predictions of bull fertility. In this study, we comprehensively analyzed a range of functional, morphological, and intracellular attributes in cryopreserved spermatozoa from a selected cohort of Holstein Friesian AI bulls classified as having either high or low fertility (n = 10 of each fertility phenotype; difference of 11.4% in adjusted pregnancy rate between groups). Here, spermatozoa were assessed for motility and kinematic parameters, morphology, acrosome integrity, plasma membrane lipid packing, viability (or membrane integrity), superoxide production, and DNA integrity. In addition, spermatozoa were used for in vitro fertilization to evaluate their capacity for fertilization and successful embryo development. The information collected from these assessments was then used to phenotypically profile the 2 groups of bulls of divergent fertility status as well as to develop a model to predict bull fertility. According to the results, acrosome integrity and viability were the only sperm attributes that were significantly different between high- and low-fertility bulls. Interestingly, although spermatozoa from low-fertility bulls, on average, had reduced viability and acrosome integrity, this response varied considerably from bull to bull. Principal component analysis revealed a sperm phenotypic profile that represented a high proportion of ejaculates from low-fertility bulls. This was constructed based on the collective influence of several sperm attributes, including the presence of cytoplasmic droplets and superoxide production. Finally, using the combined results as a basis for modeling, we developed a linear model that was able to explain 47% of the variation in bull field fertility in addition to a logistic predictive model that had a 90% chance of distinguishing between fertility groups. Taken together, we conclude that viability and acrosome integrity could serve as fertility biomarkers in the field and, when used alongside other sperm attributes, may be useful in detecting low-fertility bulls. However, the variable nature of low-fertility bulls suggests that additional, in-depth characterization of spermatozoa at a molecular level is required to further understand the etiology of low fertility in dairy bulls.     

Genetic evaluation of fertility traits of dairy cattle using a multiple-trait animal model

A genetic evaluation system was developed for 5 fertility traits of dairy cattle: interval from first to successful insemination and nonreturn rate to 56 d of heifers, and interval from calving to first insemination, nonreturn rate to 56 d, and interval first to successful insemination of cows. Using the 2 interval traits of cows as components, breeding values for days open were derived. A multiple-trait animal model was applied to evaluate these fertility traits. Fertility traits of later lactations of cows were treated as repeated measurements. Genetic parameters were estimated by REML. Mixed model equations of the genetic evaluation model were solved with preconditioned conjugate gradients or the Gauss-Seidel algorithm and iteration on data techniques. Reliabilities of estimated breeding values were approximated with a multi-trait effective daughter contribution method. Daughter yield deviations and associated effective daughter contributions were calculated with a multiple trait approach. The genetic evaluation software was applied to the insemination data of dairy cattle breeds in Germany, Austria, and Luxembourg, and it was validated with various statistical methods. Genetic trends were validated. Small heritability estimates were obtained for all the fertility traits, ranging from 1% for nonreturn rate of heifers to 4% for interval calving to first insemination. Genetic and environmental correlations were low to moderate among the traits. Notably, unfavorable genetic trends were obtained in all the fertility traits. Moderate to high correlations were found between daughter yield-deviations and estimated breeding values (EBV) for Holstein bulls. Because of much lower heritabilities of the fertility traits, the correlations of daughter yield deviations with EBV were significantly lower than those from production traits and lower than the correlations from type traits and longevity. Fertility EBV were correlated unfavorably with EBV of milk production traits but favorably with udder health and longevity. Integrating fertility traits into a total merit selection index can halt or reverse the decline of fertility and improve the longevity of dairy cattle.