Genetic and phenotypic relationships among milk urea nitrogen, fertility, and milk yield in holstein cows

The aims of the study were to evaluate the relationships among milk urea nitrogen and nonreturn rates at the phenotypic scale, and to estimate genetic parameters among milk urea nitrogen, milk yield, and fertility traits in the early period of lactation. Milk yield, protein percentage, the interval from calving to first service, and 56- and 90-d nonreturn rates were available from 73,344 Holstein cows from 2,178 different herds located in a region in northwestern Germany. Generalized linear models with a logit link function were applied to assess the phenotypic relationships. Bivariate threshold-threshold, linear-threshold, and linear-linear models, fitted in a Bayesian framework, were used to estimate genetic correlations among traits. Milk yield, protein percentage, and milk urea nitrogen were means from test-day 1 (on average 20.8 d in milk) and test-day 2 (on average 53.1 d in milk) after calving. An increase in milk urea nitrogen was associated with decreasing 56-d nonreturn rates on the phenotypic scale. At fixed levels of milk urea nitrogen, greater values of protein percentage, indicating a surplus of energy in the feed, were positively associated with nonreturn rates. Heritabilities were 0.03 for 56- and 90-d nonreturn rates, 0.07 for interval from calving to first service, 0.13 for milk urea nitrogen, and 0.19 for milk yield. Service sire explained a negligible part (below 0.15%) of the total variance for nonreturn rates. Genetic correlations between the interval from calving to first service and nonreturn rates were close to zero. The genetic correlation between nonreturn rates was 0.94, suggesting that a change from nonreturn after 90 d to nonreturn after 56 d in the national genetic evaluation would not result in any loss of information. The genetic correlation between milk yield and nonreturn after 56 d was −0.31, and between milk yield and calving to first service was 0.14, both indicating an antagonistic relationship between production and reproduction. The genetic correlation between milk yield and milk urea nitrogen was 0.44, reflecting an energy deficiency in early lactation. The genetic correlations between milk urea nitrogen and nonreturn rates were too weak (−0.19 for 56-d nonreturn rate, and −0.23 for 90-d nonreturn rate) to justify the use of milk urea nitrogen as an additional trait in genetic selection for fertility, as demonstrated by selection index calculations.     

Modeling nuisance variables for prediction of service sire fertility

The purpose of this research was to determine which (available) nuisance variables should be included in a model for phenotypic evaluation of US service sire conception rate (CR), based on field data. Alternative models were compared by splitting data into records for estimation and set-aside records, computing predictions using the estimation data, and then comparing predictions to bulls’ average CR in the set-aside data. Breedings for estimation were from January 1, 2003, to June 30, 2005, and set-aside records spanned July 1, 2005, to June 30, 2006. Only matings with known outcomes were included in either data set. Correlations and mean differences were the main statistics used to compare models. Nuisance variables considered were management groups based on herd-year-season-parity-registry (HYSPR) classes, year-state-month, cow age, DIM, and various combinations of lactation, service number, and milk yield. Preliminary analyses led to 1) selection of standardized lactational milk yield as the production variable for consideration and 2) modeling quantitative independent variables as categorical factors rather than linear and quadratic covariates. Two general strategies for management groups were tested, one where HYSPR groups were required to have an absolute specified minimum number of records and a second where groups were combined across registry, season, and parity subclasses until a minimum group size was achieved. Combining groups to a target size of 20 and including a herd-year into the evaluation provided it had a minimum of 10 breedings maximized correlation with future year CR and was chosen as the management grouping strategy for implementation. Combining groups implied that some groups had multiple seasons as well as parities, which was the reason for consideration of year-state-month and lactation as additional factors. The final nuisance variables selected for inclusion in the model for prediction of service sire CR were, in addition to HYSPR, year-state-month, lactation, service number, milk yield, cow age at breeding, an interval between breedings variable to account for lower CR following short estrus cycles, and the cow effect, partitioned as permanent environment and breeding value. This model maximized correlation with future year CR (55.14%), minimized mean square error (3.255), and had a mean difference of essentially 0.     

Genetic analysis of days from calving to first insemination and days open in Danish Holsteins using different models and censoring scenarios

The objectives of this study were to estimate genetic parameters and evaluate models for genetic evaluation of days from calving to first insemination (ICF) and days open (DO). Data including 509,512 first-parity records of Danish Holstein cows were analyzed using 5 alternative sire models that dealt with censored records in different ways: 1) a conventional linear model (LM) in which a penalty of 21 d was added to censored records; 2) a bivariate threshold-linear model (TLM), which included a threshold model for censoring status (0, 1) of the observations, and a linear model for ICF or DO without any penalty on censored records; 3) a right-censored linear model (CLM); 4) a Weibull proportional hazard model (SMW); and 5) a Cox proportional hazard model (SMC) constructed with piecewise constant baseline hazard function. The variance components for ICF and DO estimated from LM and TLM were similar, whereas CLM gave higher estimates of both additive genetic and residual components. Estimates of heritability from models LM, TLM, and CLM were very similar (0.102 to 0.108 for ICF, and 0.066 to 0.069 for DO). Heritabilities estimated using model SMW were 0.213 for ICF and 0.121 for DO in logarithmic scale. Using SMC, the estimates of heritability, defined as the log-hazard proportional factor for ICF and DO, were 0.013 and 0.009, respectively. Correlations between predicted transmitting ability from different models for sires with records from at least 20 daughters were far from unity, indicating that different models could lead to different rankings. The largest reranking was found between SMW and SMC, whereas negligible reranking was found among LM, TLM, and CLM. The 5 models were evaluated by comparing correlations between predicted transmitting ability from different data sets (the whole data set and 2 subsets, each containing half of the whole data set), for sires with records from at least 20 daughters, and χ² statistics based on predicted and observed daughter frequencies using a cross validation. The model comparisons showed that SMC had the best performance in predicting breeding values of the 2 traits. No significant difference was found among models LM, TLM, and CLM. The SMW model had a relatively poor performance, probably because the data are far from a Weibull distribution. The results from the present study suggest that SMC could be a good alternative for predicting breeding values of ICF and DO in the Danish Holstein population.     

Genetic analysis of fertility in the Italian Brown Swiss population using different models and trait definitions

The aim of this study was to estimate genetic parameters for fertility and production traits in the Brown Swiss population reared in the Alps (Bolzano-Bozen province, Italy). Fertility indicators were interval from parturition to first service, interval from first service to conception (iFC), and interval from parturition to conception, either expressed as days and as number of potential 21-d estrus cycles (cPF, cFC, and cPC, respectively); number of inseminations to conception; conception rate at first service; and non-return rate at 56 d post-first service. Production traits were peak milk yield, lactation milk yield, lactation length, average lactation protein percentage, and average lactation fat percentage. Data included 71,556 lactations (parities 1 to 9) from 29,582 cows reared in 1,835 herds. Animals calved from 1999 to 2007 and were progeny of 491 artificial insemination bulls. Gibbs sampling and Metropolis algorithms were implemented to obtain (co)variance components using both univariate and bivariate censored threshold and linear sire models. All of the analyses accounted for parity and year-month of calving as fixed effects, and herd, permanent environmental cow, additive genetic sire, and residual as random effects. Heritability estimates for fertility traits ranged from 0.030 (iFC) to 0.071 (cPC). Strong genetic correlations were estimated between interval from parturition to first service and cPF (0.97), and interval from parturition to conception and cPC (0.96). The estimate of heritability for cFC (0.055) was approximately double compared with iFC (0.030), suggesting that measuring the elapsed time between first service and conception in days or potential cycles is not equivalent; this was also confirmed by the genetic correlation between iFC and cFC, which was strong (0.85), but more distant from unity than the other 2 pairs of fertility traits. Genetic correlations between number of inseminations to conception, conception rate at first service, non-return rate at 56 d post-first service, cPF, cFC, and cPC ranged from 0.07 to 0.82 as absolute value. Fertility was unfavorably correlated with production; estimates ranged from −0.26 (cPC with protein percentage) to 0.76 (cPC with lactation length), confirming the genetic antagonism between reproductive efficiency and milk production. Although heritability for fertility is low, the contemporary inclusion of several reproductive traits in a merit index would help to improve performance of dairy cows.     

Analysis of fertility and dystocia in Holsteins using recursive models to handle censored and categorical data

A method based on the analysis of recursive multiple-trait models was used to 1) estimate genetic and phenotypic relationships of calving ease (CE) with fertility traits and 2) analyze whether dystocia negatively affects reproductive performance in the next reproductive cycle. Data were collected from 1995 through 2002, and contained 33,532 records of CE and reproductive data of 17,558 Holstein cows distributed across 560 herds in official milk recording from the Basque Country Autonomous Community (Spain). The following fertility traits were considered: days open (DO), days to first service, number of services per pregnancy (NINS), and outcome of first insemination (OFI). Four bivariate sire and sire-maternal grandsire models were used for the analyses. Censoring existed in DO (26.49% of the data) and NINS (12.22% of the data) because of cows having been sold or culled before reaching the next parturition. To avoid bias, a data augmentation technique was applied to censored data. Threshold models were used for CE and OFI. To consider that CE affects fertility and the genetic determination of CE and fertility traits, recursive models were applied, which simultaneously considered CE as a fixed effect on fertility performance and the existence of a genetic correlation between CE and fertility traits. The effects of CE score 3 (difficult birth) with respect to score 1 (no problem) for days to first service, DO, NINS, and OFI were 8 d, 31 d, 0.5 services, and - 12% success at first insemination, respectively. These results showed poorer fertility after dystocia. Genetic correlations between genetic effects of fertility traits and CE were close to zero, except for the genetic correlations between direct effects of DO and CE, which were positive, moderate, and statistically different from 0 (0.47 ± 0.24), showing that genes associated with difficult births also reduce reproductive success.     

Genetic parameters for body weight from birth to calving and associations between weights with test-day,health, and female fertility traits

A data set including 57,868 records for calf birth weight (CABW) and 9,462 records for weight at first insemination (IBW) were used for the estimation of direct and maternal genetic effects in Holstein Friesian dairy cattle. Furthermore, CABW and IBW were correlated with test-day production records and health traits in first-lactation cows, and with nonreturn rates in heifers. Health traits considered overall disease categories from the International Committee for Animal Recording diagnosis key, including the general disease status, diarrhea, respiratory diseases, mastitis, claw disorders, female fertility disorders, and metabolic disorders. For single-trait measurements of CABW and IBW, animal models with maternal genetic effects were applied. The direct heritability was 0.47 for CABW and 0.20 for IBW, and the direct genetic correlation between CABW and IBW was 0.31. A moderate maternal heritability (0.19) was identified for CABW, but the maternal genetic effect was close to zero for IBW. The correlation between direct and maternal genetic effects was antagonistic for CABW (?0.39) and for IBW (?0.24). In bivariate animal models, only weak genetic and phenotypic correlations were identified between CABW and IBW with either test-day production or health traits in early lactation. Apart from metabolic disorders, there was a general tendency for increasing disease susceptibilities with increasing CABW. The genetic correlation between IBW and nonreturn rates in heifers after 56 d and after 90 d was slightly positive (0.18), but close to zero when correlating nonreturn rates with CABW. For the longitudinal BW structure from birth to the age of 24 mo, random regression models with the time-dependent covariate “age in months” were applied. Evaluation criteria (Bayesian information criterion and residual variances) suggested Legendre polynomials of order 3 to modeling the longitudinal body weight (BW) structure. Direct heritabilities around birth and insemination dates were slightly larger than estimates for CABW and IBW from the single-trait models, but maternal heritabilities were exactly the same from both models. Genetic correlations between BW were close to 1 for neighboring age classes, but decreased with increasing time spans. The genetic correlation between BW at d 0 (birth date) and at 24 mo was even negative (?0.20). Random regression model estimates confirmed the antagonistic relationship between direct and maternal genetic effects, especially during calfhood. This study based on a large data set in dairy cattle confirmed genetic parameters and (co)variance components for BW as identified in beef cattle populations. However, BW records from an early stage of life were inappropriate early predictors for dairy cow health and productivity.     

Genetic analysis of atypical progesterone profiles in Holstein-Friesian cows from experimental research herds

The objective of this study was to quantify the genetic variation in normal and atypical progesterone profiles and investigate if this information could be useful in an improved genetic evaluation for fertility for dairy cows. The phenotypes derived from normal profiles included cycle length traits, including commencement of luteal activity (C-LA), interluteal interval, luteal phase length. and interovulatory interval. In total, 44,977 progesterone test-day records were available from 1,612 lactations on 1,122 primiparous and multiparous Holstein-Friesian cows from Ireland, the Netherlands, Sweden, and the United Kingdom. The atypical progesterone profiles studied were delayed cyclicity, prolonged luteal phase, and cessation of cyclicity. Variance components for the atypical progesterone profiles were estimated using a sire linear mixed model, whereas an animal linear mixed model was used to estimate variance components for the cycle length traits. Heritability was moderate for delayed cyclicity (0.24 ± 0.05) and C-LA (0.18 ± 0.04) but low for prolonged luteal phase (0.02 ± 0.04), luteal phase length (0.08 ± 0.05), interluteal interval (0.08 ± 0.14), and interovulatory interval (0.03 ± 0.04). No genetic variation was detected for cessation of cyclicity. Commencement of luteal activity, luteal phase length, and interovulatory interval were moderately to strongly genetically correlated with days from calving to first service (0.35 ± 0.12, 0.25 ± 0.14, and 0.76 ± 0.24, respectively). Delayed cyclicity and C-LA are traits that can be important in both genetic evaluations and management of fertility to detect (earlier) cows at risk of compromised fertility. Delayed cyclicity and C-LA were both strongly genetically correlated with milk yield in early lactation (0.57 ± 0.14 and 0.45 ± 0.09, respectively), which may imply deterioration in these traits with selection for greater milk yield without cognizance of other traits.     

Prediction of dairy bull fertility from field data: use of multiple services and identification and utilization of factors affecting bull fertility

The objectives of this research were to assess the utility of multiple services, rather than first service only, and an expanded service sire term for prediction of bull conception rate (CR) by artificial insemination in the United States. The intent with the expanded service sire term was to determine whether accuracy could be improved by estimating factors affecting the bull's CR explicitly in the model and then formulating the bull's prediction as the sum of his own service sire solution along with the solutions for the other factors. Factors considered for the expanded service sire term included age of the bull at the time of mating, stud, inbreeding of the service sire, inbreeding of the mating (potential embryo), and an additive genetic effect. Both simulated and field data were used to study the objectives. In simulation, predictions were compared with true values, whereas with real data, predictions were compared with the bulls’ average CR in set-aside data. Field data, using lactations 1 to 5, included 3,312,998 breedings of 737,626 Holstein cows in 1,419 herds distributed over 43 states and across 12 yr (1995 to 2006). The use of both multiple services and an expanded service sire term improved the accuracy of predictions. Multiple services contributed a 7 to 9% increase in accuracy, whereas the expanded service sire term improved accuracy by an estimated 12%. The amount of improvement in accuracy depends on the number of services available, but even for bulls with at least 500 matings, the combination of multiple services and an expanded service sire term can be expected to result in an overall increase in accuracy of at least 20%. Mean differences between predictions and bulls’ average CR in set-aside data indicated that this improvement in accuracy can be brought about without introducing bias into the evaluations. Heritability estimates for artificial-insemination bull CR were essentially zero. Thus, use of an additive genetic effect for the service sire will not be of assistance in predicting bull fertility. All 4 of the other factors used in the expanded service sire term contributed to improved accuracy, although age of the bull at the time of mating was, by far, the major factor (correlation of 55.2% with future-year CR when included, 44.0% when not included). Allowing the stud effect to vary by year and using only the stud's most recent year solution in prediction were shown to be superior to using stud alone.     

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.     

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 for fertility of dairy heifers and cows at different parities and relationships with production traits in first lactation

The objectives of this study were to estimate genetic parameters for fertility of Brown Swiss cattle, considering reproductive measures in different parities as different traits, and to estimate relationships between production traits of first lactation and fertility of heifers and first-parity and second-parity cows. Reproductive indicators were interval from parturition to first service, interval from first service to conception, interval from parturition to conception, number of inseminations to conception, conception rate at first service, and nonreturn rate at 56 d after first service. Production traits were peak milk yield (pMY), lactation milk yield, and lactation length (LL). Data included 37,546 records on heifers, and 24,098 and 15,653 records on first- and second-parity cows, respectively. Cows were reared in 2,035 herds, calved from 1999 to 2007, and were progeny of 527 AI bulls. Gibbs sampling was implemented to obtain (co)variance components using both univariate and bivariate threshold and censored linear sire models. Estimates of heritability for reproductive traits in heifers (0.016 to 0.026) were lower than those in first-parity (0.017 to 0.142) and second-parity (0.026 to 0.115) cows. Genetic correlations for fertility in first- and second-parity cows were very high (>0.920), whereas those between heifers and lactating cows were moderate (0.348 to 0.709). The latter result indicates that fertility in heifers is a different trait than fertility in lactating cows, and hence it cannot be used as robust indicator of cow fertility. Heifer fertility was not related to production traits in first lactation (genetic correlations between −0.215 and 0.251). Peak milk yield exerted a moderate and unfavorable effect on the interval from parturition to first service (genetic correlations of 0.414 and 0.353 after first and second calving, respectively), and a low and unfavorable effect on other fertility traits (genetic correlations between −0.281 and 0.295). Infertility after first calving caused a strong elongation of the lactation, and LL was negatively correlated with fertility of cows after second calving, so that LL can itself be regarded as a measure of fertility. Lactation milk yield depends on both pMY and LL, and, as such, is a cause and consequence of (in)fertility.     

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.     

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.     

Genetic and environmental parameters for milk production, udder health, and fertility traits in Mexican Holstein cows

Genetic and phenotypic parameters for Mexican Holstein cows were estimated for first- to third-parity cows with records from 1998 to 2003 (n = 2,971-15,927) for 305-d mature equivalent milk production (MEM), fat production (MEF), and protein production (MEP), somatic cell score (SCS), subsequent calving interval (CAI), and age at first calving (AFC). Genetic parameters were obtained by average information matrix-REML methodology using 6-trait (first-parity data) and 5-trait (second- and third-parity data) animal models. Heritability estimates for production traits were between 0.17 ± 0.02 and 0.23 ± 0.02 for first- and second-parity cows and between 0.12 ± 0.03 and 0.13 ± 0.03 for third-parity cows. Heritability estimates for SCS were similar for all parities (0.10 ± 0.02 to 0.11 ± 0.03). For CAI, estimates of heritability were 0.01 ± 0.05 for third-parity cows and 0.02 ± 0.02 for second-parity cows. The heritability for AFC was moderate (0.28 ± 0.03). No unfavorable estimates of correlations were found among MEM, MEF, MEP, CAI, and SCS. Estimates of environmental and phenotypic correlations were large and positive among production traits; favorable between SCS and CAI; slightly favorable between MEM, MEF, and MEP and SCS, between AFC and SCS, and between SCS and CAI; and small but unfavorable between production traits and CAI. Estimates of genetic variation and heritability indicate that selection would result in genetic improvement of production traits, AFC, and SCS. Estimates of both heritability and genetic variation for CAI were small, which indicates that genetic improvement would be difficult.     

Sire contribution to fertilization failure and early embryo survival in cattle

Despite passing routine laboratory tests of semen quality, bulls used in artificial insemination (AI) exhibit a significant range in field fertility. The objective of this study was to determine whether subfertility in AI bulls is due to issues of sperm transport to the site of fertilization, fertilization failure, or failure of early embryo or conceptus development. In experiment 1, Holstein-Friesian bulls (3 high fertility, HF, and 3 low fertility, LF) were selected from the national population of AI bulls based on adjusted fertility scores from a minimum of 500 inseminations (HF: +4.37% and LF: ?12.7%; mean = 0%). Superovulated beef heifers were blocked based on estimated number of follicles at the time of AI and inseminated with semen from HF or LF bulls (n = 3–4 heifers per bull; total 19 heifers). Following slaughter 7 d later, the number of corpora lutea was counted and the uteri were flushed. Recovered structures (oocytes/embryos) were classified according to developmental stage and stained with 4′,6-diamidino-2-phenylindole to assess number of cells and accessory sperm. Overall recovery rate (total structures recovered/total corpora lutea) was 52.6% and was not different between groups. Mean (± standard error of the mean) number of embryos recovered per recipient was 8.7 ± 5.2 and 9.4 ± 5.5 for HF and LF, respectively. Overall fertilization rate of recovered structures was not different between groups. However, more embryos were at advanced stages of development (all blastocyst stages combined), reflected in a greater mean embryo cell number on d 7 for HF versus LF bulls. Number of accessory sperm was greater for embryos derived from HF than for LF bulls. The aim of experiment 2 was to evaluate the effect of sire fertility on survival of bovine embryos to d 15. Day 7 blastocysts were produced in vitro using semen from the same HF (n = 3) and LF (n = 3) bulls and transferred in groups of 5–10 to synchronized heifers (n = 7 heifers per bull; total 42 heifers). Conceptus recovery rate on d 15 was higher in HF (59.4%,) versus LF (45.0%). Mean length of recovered conceptuses for HF bulls was not affected by fertility status. In conclusion, while differences in field fertility among AI sires used in this study were not reflected in fertilization rate, differences in embryo quality were apparent as early as d 7. These differences likely contributed to the higher proportion of conceptuses surviving to d 15 in HF bulls.     

Infrequent milk progesterone measurements in daughters enable bull selection for cow fertility

The interval from calving to first luteal activity (CLA) has been suggested as an unbiased and, therefore, preferable measure for selection on female fertility in dairy cattle. However, measurement of this interval for individual cows is not feasible for reasons of cost and labor associated with the necessary frequent (milk) progesterone measurements. The objective of this study was to test the hypothesis that mean sire progesterone profiles based on individual progesterone measurements of daughters at 3- to 6-wk intervals have prospects as a measure for female fertility when selecting sires in a progeny testing scheme. In this study, progesterone concentrations were measured in milk samples collected at routinely performed milk recordings during the first 100 d of lactation of daughters of 20 test bulls. It is demonstrated that a) mean progesterone profiles can be used to calculate the earliest stage of lactation at which at least 50% of the daughters of a test bull has a milk progesterone level >3 ng/mL (indicating luteal activity) and that b) this stage, at which 50% of the daughters of a bull have an active corpus luteum (CLA50%), varies largely between test bulls. We conclude that selecting sires based on daughter CLA50% may improve female fertility.