Combining reproductive outcomes predictors and automated estrus alerts recorded during the voluntary waiting period identified subgroups of cows with different reproductive performance potential.

The objective was to compare differences in reproductive performance for dairy cows grouped based on the combination of data for predictors available during the prepartum period and before the end of the VWP, automated estrus alerts (AEA) during the VWP, and the combination of both factors. In a cohort study, data for AEA and potential predictors of the percentage of cows that receive insemination at detected estrus (AIE) and pregnancies per AI (P/AI) for first service, and the percentage of cows pregnant by 150 DIM (P150) were collected from -21 to 49 DIM for lactating Holstein cows (n = 886). The association between each reproductive outcome with calving season (cool, warm), calving-related events (yes, no), genomic daughter pregnancy rate (gDPR; high, medium, low), days in the close-up pen (ideal, not ideal), health disorder events (yes, no), rumination time (high or low CV prepartum and high or low increase rate postpartum), and milk yield (MY) by 49 DIM (high, medium, low) were evaluated in univariable and multivariable logistic regression models. Individual predictors (health disorders, gDPR, and MY) associated with the 3 reproductive outcomes in all models were used to group cows based on risk factors (RF; yes, n = 535 or no, n = 351) for poor reproductive performance. Specifically, cows were included in the RF group if any of the following conditions were met: the cow was in the high MY group, had low gDPR, or had at least one health disorder recorded. Cows were grouped into estrus groups during the VWP based on records of AEA (E-VWP, n = 476 or NE-VWP, n = 410). Finally, based on the combination of levels of AEA and RF cows were grouped into an estrus and no RF (E-NoRF, n = 217), no estrus and RF (NE-RF, n = 276), no estrus and no RF (NE-NoRF, n = 134), and estrus and RF (E-RF, n = 259) groups. Cows received AIE up to 31 d after the end of the VWP, and if did not receive AIE, received timed AI after an Ovsynch plus progesterone protocol. Logistic and Cox proportional hazard regression compared differences in reproductive outcomes for different grouping strategies. The NoRF (AIE:76.9%; P/AI:53.1%; P150:84.5%) and E-VWP (AIE:86.8%; P/AI:44.8%; P150:82.3%) groups had more cows AIE, P/AI, and P150 than the RF (AIE:64.5%; P/AI:34.9%; P150:72.9%) and NE-VWP (AIE:50.0%; P/AI:38.9%; P150:72.1%) groups, respectively. When both factors were combined, the largest and most consistent differences were between the E-NoRF (AIE:91.3%; P/AI:58.7%; P150:88.5%) and NE-RF groups (AIE:47.3%; P/AI:35.8%; P150:69.5%). Compared with the whole population of cows or cows grouped based on a single factor, the E-NoRF and NE-RF groups had the largest and most consistent differences with the whole cow cohort. The E-NoRF and NE-RF group also had statistically significant differences of a large magnitude when compared with the remaining cow cohort after removal of the respective group. We conclude that combining data for AEA during the VWP with other predictors of reproductive performance could be used to identify groups of cows with larger differences in expected reproductive performance than if AEA and the predictors are used alone.

The ovarian function and endocrine phenotypes of lactating dairy cows during the estrous cycle were associated with genomic-enhanced predictions of fertility potential.

The objectives of this prospective cohort study were to characterize associations among genomic merit for fertility with ovarian and endocrine function and the estrous behavior of dairy cows during an entire, non-hormonally manipulated estrous cycle. Lactating Holstein cows entering their first (n = 82) or second (n = 37) lactation had ear-notch tissue samples collected for genotyping using a commercial genomic test. Based on genomic predicted transmitting ability values for daughter pregnancy rate (gDPR) cows were classified into a high (Hi-Fert; gDPR > 0.6 n = 36), medium (Med-Fert; gDPR -1.3 to 0.6 n = 45), and low fertility (Lo-Fert; gDPR < -1.3 n = 38) group. At 33 to 39 DIM, cohorts of cows were enrolled in the Presynch-Ovsynch protocol for synchronization of ovulation and initiation of a new estrous cycle. Thereafter, the ovarian function and endocrine dynamics were monitored daily until the next ovulation by transrectal ultrasonography and concentrations of progesterone (P4), estradiol, and FSH. Estrous behavior was monitored with an ear-attached automated estrus detection system that recorded physical activity and rumination time. Overall, we observed an association between fertility group and the ovarian and hormonal phenotype of dairy cows during the estrous cycle. Cows in the Hi-Fert group had greater circulating concentrations of P4 than cows in the Lo-Fert group from d 4 to 13 after induction of ovulation and from day -3 to -1 before the onset of luteolysis. The frequency of atypical estrous cycles was 3-fold greater for cows in the Lo-Fert than the Hi-Fert group. We also observed other modest associations between genomic merit for fertility with the follicular dynamics and estrous behavior. There were several associations between milk yield and parity with ovarian, endocrine, and estrous behavior phenotypes as cows with greater milk yield and in the second lactation were more likely to have unfavorable phenotypes. These results demonstrate that differences in reproductive performance between cows of different genomic merit for fertility classified based on gDPR may be partially associated with circulating concentrations of P4, the incidence of atypical phenotypes during the estrous cycles, and to a lesser extent the follicular wave dynamics. The observed physiological and endocrine phenotypes might help explain part of the differences in reproductive performance between cows of superior and inferior genomic merit for fertility.

Effects of targeted clinical examination based on alerts from automated health monitoring systems on herd health and performance of lactating dairy cows.

Our objectives were to compare the proportion of lactating dairy cows diagnosed with health disorders (HD) and herd performance when using a health monitoring program designed to rely primarily but not exclusively on alerts from automated health monitoring (AHM) systems or a health monitoring program based primarily on systematic clinical examinations, milk yield monitoring, and visual observation of cows. In a clinical trial, at ∼30 d before expected parturition, nulliparous and parous Holstein cows, stratified by parity and days in gestation, were randomly assigned to the high-intensity clinical monitoring (HIC-M; n = 625) or automated monitoring (AUT-M; n = 624) treatment. Cows were fitted with a neck-attached rumination and physical activity monitoring tag, and individual daily milk yield data were collected from parlor milk meters. For cows in HIC-M, clinical examination was conducted daily until 10 d in milk (DIM) and then in response to milk yield reduction alerts or visual observation of clinical signs of HD over the course of 21 DIM. For cows in AUT-M, clinical examination until 21 DIM was because of health index (HI) score alerts and reduced milk yield alerts. The HI score alerts used were generated based on the manufacturer's settings for the system for the last 2-h period before cows were selected for examination. Visual observation of clinical signs of HD was used for identifying cows potentially missed by automated alerts. Binomial and quantitative data were analyzed by logistic regression and ANOVA with repeated measures, respectively. The percentage of cows diagnosed with at least 1 HD during the experimental treatments risk period tended to be greater and the incidence rate ratio of HD diagnosed was greater in the HIC-M than in the AUT-M treatment. We found no difference between treatments for cows that exited the herd up to 60 or 150 DIM, but more cows tended to exit the herd from 61 to 150 DIM in the HIC-M than in the AUT-M treatment. No differences were detectable between treatments in daily or total milk yield to 21 DIM or in weekly mean milk yield and total milk yield to 150 DIM. More cows were inseminated in estrus for first service if in the HIC-M treatment and had no HD diagnosed than if in the HIC-M treatment but with HD diagnosed, or in the AUT-M treatment and had no HD diagnosed. Cows in the AUT-M treatment with HD diagnosed did not differ from other groups. No differences between treatments were observed in pregnancies per artificial insemination or pregnancy loss for first service. Despite a reduction in the risk of diagnosis of HD, no evidence indicated that a health monitoring program that relied on AHM system alerts to select cows for clinical examination reduced herd performance compared with a more intensive program that included systematic clinical examinations of all cows for the first 10 DIM, reduced milk yield alerts, and visual observation. However, to obtain the same herd performance as with the HIC-M treatment, the AUT-M treatment required use of visual observation. In conclusion, a health monitoring program designed to rely primarily on targeted clinical examination based on alerts from automated health monitoring systems might be a feasible alternative to programs that rely more on clinical examination, provided that visual observation is used to identify cows not detected by automated alerts.

Metabolic-digestive clinical disorders of lactating dairy cows were associated with alterations of rumination, physical activity, and lying behavior monitored by an ear-attached sensor.

The objective of this observational cohort study was to characterize the pattern of rumination time (RT), physical activity (PA), and lying time (LT) monitored by an automated health monitoring system, based on an ear-attached sensor, immediately before, during, and after clinical diagnosis (CD) of metabolic-digestive disorders. Sensor data were collected from 820 lactating Holstein cows monitored daily from calving up to 21 DIM for detection of health disorders (HD). Cows were grouped retrospectively in the no-clinical health disorder group (NCHD; n = 616) if no HD were diagnosed, or the metabolic-digestive group (METB-DIG; n = 58) if diagnosed with clinical ketosis or indigestion only. Cows with another clinical health disorder within -7 to +7 d of CD of displaced abomasum, clinical ketosis, or indigestion were included in the metabolic-digestive plus one group (METB-DIG+1; n = 25). Daily RT, PA, and LT, and absolute and relative changes within -7 to +7 d of CD were analyzed with linear mixed models with or without repeated measures. Rumination time and PA were smaller, and LT was greater for the METB-DIG and METB-DIG+1 group than for cows in the NCHD group for most days from -7 to +7 d of CD of HD. In general, daily RT, PA, and LT differences were larger between the METB-DIG+1 and NCHD groups than between the METB-DIG and NCHD groups. In most cases, RT and PA decreased to a nadir and LT increased to a peak immediately before or after CD of HD, with a return to levels similar to the NCHD group within 7 d of CD. Absolute values and relative changes from 5 d before CD to the day of the nadir for RT and PA or peak for LT were different for cows in the METB-DIG and METB-DIG+1 group than for the NCHD group. For PA, the METB-DIG+1 group had greater changes than the METB-DIG group. For cows affected by metabolic-digestive disorders, RT, PA, and LT on the day of CD and resolution of clinical signs were different than for cows in the NCHD group, but an increase in RT and PA or a decrease in LT was observed from the day of CD to the day of resolution of clinical signs. We conclude that dairy cows diagnosed with metabolic-digestive disorders including displaced abomasum, clinical ketosis, and indigestion presented substantial alterations in the pattern of RT, PA, and LT captured by an ear-attached sensor. Thus, automated health monitoring systems based on ear-attached sensors might be used as an aid for identifying cows with metabolic-digestive disorders. Moreover, RT, PA, and LT changes after CD might be positive indicators of recovery from metabolic-digestive disorders.

Metritis and clinical mastitis events in lactating dairy cows were associated with altered patterns of rumination, physical activity, and lying behavior monitored by an ear-attached sensor.

Understanding changes in parameters recorded by automated health monitoring systems based on ear-attached sensors on the days immediately before and after diagnosis of metritis and clinical mastitis can help develop dairy cow health monitoring strategies. The objective of this observational cohort study was to characterize rumination time, physical activity, and lying time monitored by an ear-attached sensor before, during, and after clinical diagnosis (CD) of metritis and clinical mastitis. Lactating Holsteins monitored daily for 21 d in milk for detection of health disorders were retrospectively included in the no clinical health disorder group (NCHD; n = 616) if no disorders were diagnosed. Cows were included in the metritis (MET; n = 69) or clinical mastitis (MAST; n = 36) group if diagnosed only with nonsevere metritis (watery, reddish, and fetid uterine discharge with or without pyrexia) or nonsevere clinical mastitis (visibly abnormal milk secretion with or without signs of udder inflammation, with no pyrexia and no systemic signs of disease), respectively. Cows diagnosed with severe metritis (signs of metritis plus systemic signs of disease) or severe clinical mastitis (signs of mastitis plus pyrexia and systemic signs of disease), and cows diagnosed with nonsevere metritis or clinical mastitis plus another disorder within -7 to +7 d of CD of metritis or clinical mastitis diagnosis, were included in the metritis plus (MET+; n = 25) or the clinical mastitis plus (MAST+; n = 15) group, respectively. Cows were fitted with an ear-attached accelerometer to measure rumination time, physical activity, and lying time. Mean daily values, mean value absolute change, and relative change for the mean daily value from 3 or 5 d before CD to the nadir for cows with metritis and clinical mastitis, respectively, were analyzed with linear mixed models with or without repeated measures. Rumination time and physical activity were lesser, and lying time was greater for the MET and MET+ groups than for the NCHD group for most days from -4 to +7 d of CD of metritis. Generally, daily rumination time, physical activity, and lying time differences were greater and more prolonged between the MET+ and NCHD than between the MET and NCHD groups. Similarly, cows in the MAST and MAST+ groups had lesser rumination time and physical activity than cows in the NCHD group for several days before diagnosis. Lying time was greater for the MAST+ than the NCHD group on d -1 and 0 relative to CD. Absolute values and relative changes from 3 d before CD to the day of the nadir for rumination time and physical activity, or peak for lying time, were different for cows in the MET and MET+ groups than for the NCHD group. Similar results were observed for the MAST and MAST+ groups compared with the NCHD group. For cows with metritis, either an increase in rumination time and physical activity or a decrease in lying time was observed from the day of CD to resolution of clinical signs, but no changes were observed for the NCHD. Cows with clinical mastitis and the NCHD group had different rumination times, physical activity, and lying times on the day of CD and resolution of clinical signs, but cows with clinical mastitis had no significant changes from the day of CD to resolution of clinical signs. We conclude that cows affected by metritis and clinical mastitis presented substantial alterations of the patterns of rumination time, physical activity, and lying time captured by an ear-attached sensor. Thus, automated health monitoring systems based on ear-attached sensors might be used as an aid for identifying cows with metritis and clinical mastitis. Moreover, behavioral parameter changes after CD might be good indicators of resolution of clinical signs of metritis but not mastitis.

Effect of reproductive management programs that prioritized artificial insemination at detected estrus or timed artificial insemination on the reproductive performance of primiparous Holstein cows of different genetic merit for fertility.

Our objective was to compare reproductive outcomes of primiparous lactating Holstein cows of different genetic merit for fertility submitted for insemination with management programs that prioritized artificial insemination (AI) at detected estrus (AIE) or timed AI (TAI). Moreover, we aimed to determine whether subgroups of cows with different fertility potential would present a distinct response to the reproductive management strategies compared. Lactating primiparous Holstein cows (n = 6 commercial farms) were stratified into high (Hi-Fert), medium (Med-Fert), and low (Lo-Fert) genetic fertility groups (FG) based on a Reproduction Index value calculated from multiple genomic-enhanced predicted transmitting abilities. Within herd and FG, cows were randomly assigned either to a program that prioritized TAI and had an extended voluntary waiting period (P-TAI; n = 1,338) or another that prioritized AIE (P-AIE; n = 1,416) and used TAI for cows, not AIE. Cows in P-TAI received first service by TAI at 84 ± 3 d in milk (DIM) after a Double-Ovsynch protocol, were AIE if detected in estrus after a previous AI, and received TAI after an Ovsynch-56 protocol at 35 ± 3 d after a previous AI if a corpus luteum (CL) was visualized at nonpregnancy diagnosis (NPD) 32 ± 3 d after AI. Cows with no CL visualized at NPD received TAI at 42 ± 3 d after AI after an Ovsynch-56 protocol with progesterone supplementation (P4-Ovsynch). Cows in P-AIE were eligible for AIE after a PGF2α treatment at 53 ± 3 DIM and after a previous AI. Cows not AIE by 74 ± 3 DIM or by NPD 32 ± 3 d after AI received P4-Ovsynch for TAI at 74 ± 3 DIM or 42 ± 3 d after AI. Binary data were analyzed with logistic regression, count data with Poisson regression, continuous data by ANOVA, and time to event data by Cox's proportional hazard regression. Pregnancy per AI (P/AI) to first service was greater for cows in the Hi-Fert (59.8%) than the Med-Fert (53.6%) and Lo-Fert (47.7%) groups, and for the P-TAI (58.7%) than the P-AIE (48.7%) treatment. Overall, P/AI for all second and subsequent AI combined did not differ by treatment (P-TAI = 45.2%; P-AIE = 44.5%) or FG (Hi-Fert = 46.1%; Med-Fert = 46.0%; Lo-Fert = 42.4%). The hazard of pregnancy after calving was greater for the P-AIE than the P-TAI treatment [hazard ratio (HR) = 1.27, 95% CI: 1.17 to 1.37)], and for the Hi-Fert than the Med-Fert (HR = 1.16, 95% CI: 1.05 to 1.28) and Lo-Fert (HR = 1.34, 95% CI: 1.20 to 1.49) groups. More cows in the Hi-Fert (91.2%) than the Med-Fert (88.4%) and Lo-Fert (85.8%) groups were pregnant at 200 DIM. Within FG, the hazard of pregnancy was greater for the P-AIE than the P-TAI treatment for the Hi-Fert (HR = 1.41, 95% CI: 1.22 to 1.64) and Med-Fert (HR = 1.28, 95% CI: 1.12 to 1.46) groups but not for the Lo-Fert group (HR = 1.13, 95% CI: 0.98 to 1.31). We conclude that primiparous Holstein cows of superior genetic merit for fertility had better reproductive performance than cows of inferior genetic merit for fertility, regardless of the type of reproductive management used. In addition, the effect of programs that prioritized AIE or TAI on reproductive performance for cows of superior or inferior genetic merit for fertility depended on the outcomes evaluated. Thus, programs that prioritize AIE or TAI could be used to affect certain outcomes of reproductive performance or management.

Effect of reproductive management programs that prioritized artificial insemination at detected estrus or timed artificial insemination on the economic performance of primiparous Holstein cows of different genetic merit for fertility.

The objective of this randomized controlled experiment was to evaluate the effect of reproductive management programs that prioritized artificial insemination (AI) at detected estrus (AIE) or timed AI (TAI) during the first lactation on the economic performance of dairy cows of different genomically enhanced predicted transmitting ability for fertility. Lactating primiparous Holstein cows from 6 commercial farms were stratified into high, medium, and low fertility groups based on a reproduction index value calculated from multiple genomically enhanced predicted transmitting abilities to predict the number of days to achieve pregnancy. Within herd and fertility group, cows were randomly assigned either to a program that prioritized AIE (P-AIE; n = 1,416) and used TAI for cows not AIE for all AI services or another that prioritized TAI and had an extended voluntary waiting period for first service and prioritized TAI for second and greater AI services (P-TAI; n = 1,338). Cash flow (CF) per cow accumulated for the experimental (first) and second calving interval (CIN) and cash flow per slot per 28 mo after calving in the experimental lactation were calculated. Market and rearing heifer cost values were used for estimating CF. For cows in the high fertility group, a positive effect of delayed pregnancy on milk income during the first lactation was observed (+$248 for P-TAI) but was insufficient to generate significant differences in CF between treatments mainly because of milk income compensation in the second lactation (+$125 for P-AIE) and minor reductions in reproductive cost and gains in calf value for the P-AIE treatment. In this regard, CF for 2 CIN was greater for the P-TAI treatment by $61 and $86 for market and rearing replacement heifer cost, respectively. Similarly, CF per slot was favorable to the P-TAI treatment but only by $13 and $47 for market and rearing replacement heifer cost, respectively. For cows in the low fertility group, CF was numerically in favor of the P-AIE treatment due to a pregnancy and herd exit dynamics that resulted in gains in milk income over feed cost during the first ($29) and second ($113) lactation. Differences in CF for the 2 CIN were $58 and $47 for market or rearing heifer value, respectively, and $77 and $19 for market and rearing heifer values, respectively for the slot analysis. Differences in CF between cows of different genetic merit for fertility were consistent across treatment and estimation method. Of note, cows in the low fertility group had greater CF than cows in the high fertility group in all comparisons, ranging from $198 per cow for 2 CIN to as much as $427 per slot. For the low fertility group, greater milk production contributed directly (milk income over feed cost) and indirectly (reduced culling) to increased CF. We concluded that genetic merit for fertility and CF are associated because cows of inferior genetic potential for fertility had greater CF than cows of superior genetic for fertility despite some increased costs and reduced revenues. Also, the magnitude of the CF differences observed for cows of different genetic merit for fertility managed with the P-AIE or P-TAI program may be valuable to commercial dairy farms but did not allow to conclusively support the choice of a type of reproductive management strategy for cows of different genetic merit for fertility.

Erratum to "An automated controlled-release device for intravaginal hormone delivery" (JDS Commun. 1:15-20).

[This corrects the article DOI: 10.3168/jdsc.2020-18816.].

Effect of a targeted reproductive management program designed to prioritize insemination at detected estrus and optimize time to insemination on the reproductive performance of lactating dairy cows.

The primary objective of this randomized controlled experiment was to evaluate the insemination dynamic and reproductive performance of cows managed with a targeted reproductive management (TRM) program designed to prioritize artificial insemination (AI) at detected estrus (AIE) and optimize timing of AI by grouping cows based on detection of estrus during the voluntary waiting period (VWP). Our secondary objective was to evaluate reproductive outcomes for cows with or without estrus during the VWP. Lactating Holstein cows fitted with an ear-attached sensor for detection of estrus were randomly assigned to a TRM treatment that prioritized AIE based on detection of estrus during the VWP (TP-AIE; n = 488), a non-TRM treatment that prioritized AIE (P-AIE; n = 489), or an all timed AI (TAI) treatment with extended VWP (ALL-TAI; n = 491). In TP-AIE, cows with or without automated estrus alerts (AEA) recorded during the VWP received AIE if detected in estrus for at least 31 ± 3 or 17 ± 3 d after a 49 d VWP, respectively. Cows not AIE with or without AEA during the VWP received TAI after Ovsynch with progesterone supplementation and 2 PGF2α treatments (P4-Ov) at 90 ± 3 or 74 ± 3 d in milk (DIM), respectively. In P-AIE, cows received AIE if detected in estrus for 24 ± 3 d after a 49 d VWP, and if not AIE received TAI at 83 ± 3 DIM after P4-Ov. In ALL-TAI, cows received TAI at 83 ± 3 DIM after a Double-Ovsynch protocol. Data were analyzed by logistic and Cox's proportional hazard regression. The proportion of cows AIE did not differ for TP-AIE (71.0%) and P-AIE (74.6%). Overall P/AI at 39 d after first service was greater for the ALL-TAI (47.6%) than for the P-AIE (40.2%) and TP-AIE (39.5%) treatments. The hazard of pregnancy up to 150 DIM was greater for cows in TP-AIE (hazard ratio = 1.2; 95% confidence interval: 1.1-1.4) and P-AIE (hazard ratio = 1.2; 95% confidence interval: 1.1-1.4) than for cows in the ALL-TAI treatment which resulted in median time to pregnancy of 89, 89, and 107 d. Conversely, the proportion of cows pregnant at 150 DIM did not differ (ALL-TAI 78.5%, P-AIE 76.3%, TP-AIE 76.0%). Except for a few outcomes for which no difference was observed, cows detected in estrus during the VWP had better performance than cows not detected in estrus. Cows with AEA during the VWP were more likely to receive AIE, had greater P/AI, and greater pregnancy rate up to 150 DIM regardless of first service management. We conclude that a TRM program designed to prioritize AIE by grouping cows based on detection of estrus during the VWP was an effective strategy to submit cows for first service resulting in similar or improved performance than a non-TRM program that prioritized AIE or an all-TAI program with extended VWP. Also, AEA recorded during the VWP might be used as a strategy for identifying subgroups of cows with different reproductive performance.

Symposium review: Use of multiple biological, management, and performance data for the design of targeted reproductive management strategies for dairy cows.

As the reproductive efficiency of dairy cattle continues to improve in response to better management and use of technology, novel reproductive management approaches will be required to improve herd performance, profitability, and sustainability. A potential approach currently being explored is targeted reproductive management. This approach consists of identifying cows with different reproductive and performance potential using multiple traditional and novel sources of biological, management, and performance data. Once subgroups of cows that share biological and performance features are identified, reproductive management strategies specifically designed to optimize cow performance, herd profitability, or alternative outcomes of interest are implemented on different subgroups of cows. Tailoring reproductive management to subgroups of cows is expected to generate greater gains in outcomes of interest than if the whole herd is under similar management. Major steps in the development and implementation of targeted reproductive management programs for dairy cattle include identification and validation of robust predictors of reproductive outcomes and cow performance, and the development and on-farm evaluation of reproductive management strategies for optimizing outcomes of interest for subgroups of cows. Predictors of cow performance currently explored for use in targeted management include genomic predictions; behavioral, physiological, and performance parameters monitored by sensor technologies; and individual cow and herd performance records. Once the most valuable predictive sources of variation are identified and their effects quantified, novel analytic methods (e.g., machine learning) for prediction will likely be required. These tools must identify groups of cows for targeted management in real time and with no human input. Despite some encouraging research evidence supporting the development of targeted reproductive management strategies, extensive work is required before widespread implementation by commercial farms.

Evaluation and characterization of estrus alerts and behavioral parameters generated by an ear-attached accelerometer-based system for automated detection of estrus.

Our objectives were to evaluate the performance of an ear-attached automated estrus detection (AED) system (Smartbow; Zoetis) that monitored physical activity and rumination time, and to characterize AED system estrus alert features (i.e., timing and duration). Lactating Holstein cows (n = 216) commenced a protocol for the synchronization of estrus at 50 ± 3 DIM or 18 ± 3 d after artificial insemination. For 7 d after induction of luteolysis with PGF2α (d 0), we used visual observation of estrous behavior (30 min, 2 times per day) and data from an automated mounting behavior monitoring system based on a pressure-activated tail-head sensor (HeatWatch; Cowchips LLC) as a reference test (RTE) to detect behavioral estrus. Concomitantly, estrus alerts and their features were collected from the AED system. Progesterone levels confirmed luteal regression, and transrectal ultrasonography confirmed the occurrence and timing of ovulation. Performance metrics for the AED system were estimated with PROC FREQ in SAS, using the RTE or ovulation only as a reference. Performance was also estimated after the removal of cows with a discrepancy between the RTE and ovulation. Continuous outcomes with or without repeated measurements were evaluated by ANOVA using PROC MIXED in SAS. Based on the RTE, 86.6% (n = 187) of the cows presented estrus and ovulated; 1.4% (n = 3) presented estrus and did not ovulate; 6.4% (n = 14) did not present estrus but ovulated; and 5.6% (n = 12) did not present estrus or ovulation. We found no difference in the proportion of cows detected in estrus and with ovulation for the AED system (83.4%) and the RTE (86.6%). Compared with estrus events as detected by the RTE, sensitivity for the AED was 91.6% (95% CI: 87.6-95.5) and specificity was 69.2% (95% CI: 51.5-87.0). Using ovulation as reference, sensitivity was 89.6% (95% CI: 85.3-93.8) and specificity was 86.7% (95% CI 69.5-100). For all cows with agreement between the RTE and ovulation, sensitivity was 92.5% (95% CI: 88.7-96.3) and specificity was 91.7% (95% CI: 76.0-100). The mean (±SD) interval from induction of luteolysis to estrus alerts, estrus alert duration, and the onset of estrus alerts to ovulation interval were 72.2 ± 18.1, 13.5 ± 3.8, and 23.8 ± 7.1 h, respectively. We concluded that an ear-attached AED system that monitored physical activity and rumination time was effective at detecting cows in estrus and generated few false positive alerts when accounting for ovulation, cow physiological limitations, and the limitations of the RTE.

Effect of reproductive management programs for first service on replacement dairy heifer economics.

Our objective was to evaluate cash flow for dairy heifers managed for first service with programs that relied primarily on insemination at detected estrus (AIE), timed AI (TAI), or a combination of both. Holstein heifers from 2 commercial farms were randomized to receive first service with sexed semen after the beginning of the AI period (AIP) at 12 mo of age with 1 of 3 treatments: (1) PGF+AIE (n = 317): AIE after PGF2α injections every 14 d (up to 3) starting at the beginning of the AIP; heifers not AIE 9 d after the third PGF2α were enrolled in the 5d-Cosynch (5dCP) protocol; (2) ALL-TAI (n = 315): TAI after ovulation synchronization with the 5dCP protocol; and (3) PGF+TAI (n = 334): AIE after 2 PGF2α injections 14 d apart (second PGF2α at beginning of AIP). If not AIE 9 d after the second PGF2α, the 5dCP protocol was used for TAI. After first service heifers were AIE or received TAI after the 5dCP with conventional semen. Individual heifer cash flow (CF) for up to a 15-mo period (d 0 = beginning of AIP) was calculated using reproductive cost (rearing only), feed cost (rearing only), income over feed cost (lactation only), calf value, operating cost, and with or without replacement cost. A stochastic analysis with Monte Carlo simulation was used to estimate differences in CF for a range of market values for inputs and outputs. Time to pregnancy for up to 100 d after the beginning of the AIP was analyzed by Cox's proportional regression, binary data with logistic regression, and continuous outcomes by ANOVA. Time to pregnancy (hazard ratio and 95% CI) was reduced for the ALL-TAI versus the PGF+AIE treatment (1.20; 1.02-1.42), but it was similar for ALL-TAI and PGF+TAI (1.13; 0.95-1.33) and the PGF+AIE and PGF+TAI treatments (1.07; 0.91-1.25). The proportion of heifers not pregnant by 100 d did not differ (PGF+AIE = 7.0%; PGF+TAI = 6.5%; ALL-TAI = 6.8%). When including replacement cost, CF ($/slot per 15 mo) differences were $51 and $42 in favor of the PGF+TAI and ALL-TAI compared with the PGF+AIE treatment, and $9 in favor of the PGF+TAI compared with the ALL-TAI treatment but did not differ statistically. Excluding heifers that were replaced to evaluate the effect of timing of pregnancy differences only, the difference in CF between the PGF+AIE with the PGF+TAI and ALL-TAI treatment was the same (i.e., $15) and favored the programs that used more TAI, but also did not differ statistically. Stochastic simulation results were in line with those of the deterministic analysis confirming the benefit of the programs that used more TAI. We concluded that submission of heifers for first service with TAI only or TAI in combination with AIE generated numerical differences in CF of potential value to commercial dairy farms. Reduced rearing cost and increased revenue during lactation increased CF under fixed (not statistically significant) or simulated variable market conditions.

Lactating dairy cows managed for second and greater artificial insemination services with the Short-Resynch or Day 25 Resynch program had similar reproductive performance.

The objective of this randomized controlled experiment was to evaluate reproductive performance and reproductive physiological outcomes of lactating Holstein cows managed for second and greater artificial insemination (AI) services with the Short-Resynch or Day 25 Resynch program. Cows from 2 commercial farms were randomly assigned after first service to the Short-Resynch (SR; n = 870) or Day 25 Resynch (D25R; n = 917) program in which they remained until 210 d after first service or left the herd. Cows in D25R received GnRH 25 ± 3 d after AI, whereas cows in SR did not. Cows not reinseminated at detected estrus (AIE) by 32 ± 3 d after AI underwent nonpregnancy diagnosis (NPD) through transrectal ultrasonography (TUS). Nonpregnant cows from both treatments with a corpus luteum (CL) ≥15 mm and an ovarian follicle ≥10 mm (hereafter, CL cows) received 2 PGF2α treatments 24 h apart, GnRH 32 h after the second PGF2α, and timed AI 16 to 18 h later. Cows that did not meet the criteria to be included in the CL group (NoCL cows) received a modified Ovsynch protocol with progesterone (P4) supplementation [P4-Ovsynch; GnRH and controlled internal drug-release device (CIDR) in, 7 d later CIDR removal and PGF2α, 24 h later PGF2α, 32 h later GnRH, and 16 to 18 h later timed AI]. In a subgroup of cows, blood samples were collected and TUS conducted at each treatment to evaluate ovarian responses to resynchronization. Binary data were analyzed with logistic regression, continuous data by ANOVA, and time-to-event data by Cox's proportional hazard regression. A greater proportion (mean; 95% CI) of cows were AIE before NPD in the SR (60.5%; 57.0-63.8; n = 3,416) than the D25R (50.1%; 46.5-53.7; n = 3,177) treatment, whereas pregnancy per AI (P/AI) at 32 d for AIE services before NPD was greater for the D25R (41.3%; 38.8-43.8; n = 1,560) than the SR (37.6%; 35.5-39.8; n = 1,961) treatment. At NPD, a greater proportion of cows in the D25R (84.3%; 82.2-86.2) than the SR (77.0%; 74.4-79.4) treatment were considered CL cows. Pregnancy per AI at 32 d was greater for the D25R than the SR treatment for all timed AI services (D25R = 43.0%; 40.2-45.9 vs. SR = 36.8%; 33.8-39.8) and for CL cows (D25R = 42.8%; 39.7-45.9 vs. SR = 33.8%; 30.6-37.2) but did not differ for NoCL cows (D25R = 39.4%; 32.1-47.3 vs. SR = 44.0%; 36.8-51.4). The hazard ratio for time to pregnancy (1.03; 0.93-1.14) and the proportion of cows not pregnant at the end of the observation period (D25R = 5.9%; 4.4-7.8 vs. SR = 6.7%; 5.0-8.7) did not differ between SR and D25R treatments. The GnRH treatment 25 d after AI resulted in more cows with P4 >1 ng/mL (D25R = 80.5%; 75.3-84.9 vs. SR = 63.6%; 57.3-69.4) and smaller follicle diameter at NPD 32 ± 3 d after AI for D25R (16.2 ± 0.4 mm) than for SR (17.5 ± 0.4 mm); however, it did not affect follicle diameter and luteal regression risk (CL cows only) before TAI. We concluded that the use of reproductive management programs including SR and D25R for CL cows and the P4-Ovsynch protocol for NoCL cows resulted in similar hazard of pregnancy and proportion of nonpregnant cows for up to 210 d after first service.

A lateral flow-based portable platform for determination of reproductive status of cattle.

Our objective was to develop and validate a tool integrating a disposable fluorescence-based lateral flow immunoassay (LFIA) coupled with a portable imaging device for estimating circulating plasma concentrations of progesterone (P4). First, we developed and optimized a competitive LFIA test strip to measure P4 in bovine plasma. The LFIA design included a sample pad, a conjugate pad that stores R-phycoerythrin-anti-P4 conjugates, a glass-fiber spacer pad, a nitrocellulose membrane with printed test and control lines, and a cellulose-fiber absorbent pad. To perform a test, 20 µL of plasma and 50 µL of running buffer were added on the sample pad. After 3 min, 45 µL of running buffer was added to initiate sample flow. After allowing 15 min to stabilize the colorimetric signal, strips were introduced in an LFIA portable reader wirelessly linked to a laptop to determine P4 concentration based on test-to-control-line signal (T/C ratio). In a series of experiments (n = 6), the ability of the LFIA to differentiate plasma samples with ≥1 or <1 ng/mL of P4 was evaluated. For each experiment, a calibration curve was constructed using plasma with known concentrations of P4 (0.1 to 3.7 ng/mL; n = 5). The resulting linear equation was then used to determine a T/C ratio cutoff to differentiate samples with ≥1 or <1 ng/mL of P4. In addition, to evaluate the ability of the platform to assign samples to P4 concentration groups without a calibration curve for individual batches, we performed a receiver operating characteristic analysis to identify a single cutoff value for T/C ratio that could potentially be used for all batches. Overall, calibration curves showed a linear relationship between T/C ratio and P4 levels (mean coefficient of determination = 0.74; range 0.42 to 0.99). Next, plasma samples from lactating dairy cows (n = 58) were tested in triplicate to determine the ability of the LFIA system to differentiate plasma samples with ≥1 or <1 ng/mL of P4 using a RIA for P4 as reference test. Overall, the LFIA assay correctly classified 90% of the samples, with 97% sensitivity, 83% specificity, 85% positive predictive value, and 96% negative predictive value. Agreement between the tests was substantial (kappa = 0.79; 95% confidence interval 0.64 to 0.95). When using a single cutoff value for T/C ratio selected by receiver operating characteristic analysis, sensitivity and specificity to determine CL presence were 97 (95% confidence interval 82 to 99) and 79% (95% confidence interval 60 to 92), respectively. These data suggest that the developed portable LFIA system can accurately differentiate plasma samples with ≥1 or <1 ng/mL of P4.

A reproductive management program aimed at increasing reinsemination of nonpregnant dairy cows at detected estrus resulted in similar reproductive performance to a program that favored timed artificial insemination.

The objective of this experiment was to compare time to pregnancy and proportion of cows not pregnant 210 d after first service for cows managed for second and subsequent artificial insemination (AI) services with a reproductive management program that promoted reinsemination at detected estrus (AIE) or a program that promoted timed AI (TAI). After first service, lactating Holstein cows were blocked by parity and randomly assigned to d 32 Resynch (D32R; n = 464) or AIE Resynch (AIER; n = 512). To determine the effect of management strategies on time to pregnancy and cows not pregnant by the end of a 210 d at-risk period after first AI service, cows remained in AIER and D32R until pregnancy or herd exit. Cows in D32R received a GnRH treatment 32 ± 3 d after AI (first treatment intervention; FTI). Nonpregnancy diagnosis was conducted 7 d later by transrectal ultrasonography when nonpregnant cows with a corpus luteum (CL) ≥15 mm completed the Resynch protocol (PGF2α, 56 h later GnRH, and 16 to 18 h later TAI) and cows without a CL (NoCL cows) were enrolled in a PreG-Ovsynch protocol (GnRH, 7 d later GnRH, 7 d later PGF2α, 56 h later GnRH, and 16 to 18 h TAI) to receive TAI. For the AIER treatment, nonpregnant cows with a CL ≥15 mm observed by transrectal ultrasonography 32 ± 3 d after AI (i.e., FTI) received PGF2α to induce estrus. Cows not AIE within 7 d were enrolled in Resynch (GnRH, 7 d later PGF2α, 56 h later GnRH, and 16 to 18 h TAI). Cows in the NoCL group in AIER were enrolled in PreG-Ovsynch. Detection of estrus was performed based on visual observation of behavioral signs of estrus and tail-paint removal. Binomial data were analyzed with logistic regression and time to event data with Cox's proportional regression. After the FTI, a greater proportion of cows were AIE in AIER than D32R (36.0 vs. 11.9%) and more cows were AIE within 7 d of the FTI for AIER (25.0%) than D32R (4.8%). Overall pregnancy per AI at 68 ± 3 d after AI did not differ (AIER = 35.5% vs. D32R = 34.7%). The hazard of pregnancy up to 210 d after first AI for all cows enrolled (hazard ratio = 1.04, 95% CI 0.90 to 1.19) and for cows that received treatments only (D32R = 308, AIER = 349; hazard ratio = 1.00, 95% CI 0.85 to 1.19) did not differ. We conclude that a program aimed at increasing the proportion of cows reinseminated at detected estrus by treatment with PGF2α at 32 ± 3 d after AI may be an alternative strategy for dairy farms that prefer or need to inseminate more cows at detected estrus rather than by TAI.

An automated controlled-release device for intravaginal hormone delivery.

Our objective was to develop and validate an electronically controlled hormone-delivery device for reproductive control of cattle. After development and in vitro testing of a prototype device for intravaginal (IVG) hormone release, we aimed to demonstrate the feasibility of inducing luteal regression by automated treatment with PGF2α. The IVG device comprises an outer 3D-printed plastic housing, fluid reservoirs connected to delivery pumps and tubing, a programmable circuit board, and a retention mechanism. For in vitro testing, 4 pumps were programmed to release different target volumes (0.1, 0.2, 0.5, 1.0, and 2.0 mL) in 4 replicates (n = 80). A Bland-Altman plot was constructed to assess the magnitude of disagreement between expected and delivered volumes. Observations fell within acceptable limits of agreement (1.96 standard deviations) >95% of the time, indicating overall good agreement (mean difference = -0.005 mL). To assess in vivo performance of the IVG device, lactating Holstein cows with at least 1 corpus luteum ≥15 mm in diameter were randomly allocated to 1 of 3 treatments: (1) IM-PGF (n = 6): two 25-mg intramuscular doses of PGF2α 24 h apart; (2) DEV-PGF (n = 6): four 25-mg doses of PGF2α released automatically by the IVG device at 10- or 12-h intervals; and (3) DEV-CTL (n = 4): insertion of an empty IVG device (placebo control). Blood samples were collected at 0, 12, 24, 36, 48, and 72 h after treatment. Data were analyzed by ANOVA with repeated measures. All devices (10/10) remained in situ until removed at 48 h. Progesterone (P4) concentrations from 0 to 72 h were affected by treatment, time, and their interaction. Concentrations of P4 did not differ at time 0 but differed from 24 to 72 h: cows in IM-PGF and DEV-PGF had lesser P4 than cows in DEV-CTL. Conversely, P4 did not differ for IM-PGF and DEV-PGF during the experiment. We conclude that the current IVG hormone-releasing device prototype can be programmed to automatically release PGF2α for successful induction of luteal regression in lactating dairy cows.

Intravaginal instillation of prostaglandin F2α was as effective as intramuscular injection for induction of luteal regression in lactating dairy cows.

Our objectives were to test the efficacy of intravaginal (IVG) administration of PGF2α to induce corpus luteum (CL) regression, compare circulating progesterone (P4) profiles in cows receiving IVG versus intramuscular (IM) treatment with PGF2α, and evaluate reproductive outcomes. Lactating Holstein cows were synchronized using a Double-Ovsynch protocol [GnRH, 7 d later PGF2α, 3 d later GnRH, 7 d later GnRH, 7 d later PGF2α, 1 d later PGF2α, 32 h later GnRH, 16 to 20 h timed artificial insemination (TAI)] to receive TAI at 67 ± 3 d in milk. Seven days after the first GnRH treatment (time 0), cows with at least 1 visible CL ≥15 mm were blocked by parity and randomly assigned to a treatment that consisted of IM injection (IM-PGF; n = 201) or IVG instillation (IVG-PGF; n = 201) of PGF2α. Cows in IM-PGF received a single 25-mg dose of PGF2α (dinoprost tromethamine) intramuscularly. Cows in IVG-PGF received two 25-mg doses of PGF2α 12 h apart delivered through a catheter in the cranial portion of the vagina. Blood samples were collected at 0, 12, 48, and 72 h after treatment. Ovulation to the first GnRH of Double-Ovsynch was determined through transrectal ultrasonography. Only cows with P4 ≥1 ng/mL (functional CL) at time 0 (IM-PGF = 169; IVG-PGF = 179) were included in the analyses. Binary and quantitative data were analyzed by logistic regression and ANOVA with repeated measures, respectively. Results are presented as least squares means. Concentrations of P4 and the proportion of cows with a new CL at time 0 did not differ. Overall, the proportion of cows with CL regression using 1 ng of P4/mL (IM-PGF = 89.0%; IVG-PGF = 86.7%) or 0.5 ng of P4/mL (IM-PGF = 82.2%; IVG-PGF = 82.1%) as the cutoff did not differ. Concentrations of P4 were affected by treatment, time, and treatment × time interaction. Cows in IVG-PGF had greater mean P4 at 12 h than cows in IM-PGF. Mean P4 did not differ at 48 or 72 h after treatment. The proportion of cows with estrus recorded within 3 d of treatment (IM-PGF = 45.4%; IVG-PGF = 48.9%), ovulation risk after treatment (IM-PGF = 88.5%; IVG-PGF = 85.1%), and pregnancies per artificial insemination after TAI (IM-PGF = 51.5%; IVG-PGF = 57.8%) did not differ. We concluded that 2 IVG doses of 25 mg of PGF2α 12 h apart were as effective as a single 25-mg IM dose of PGF2α for inducing luteal regression in lactating dairy cattle.

Profitability of dairy cows submitted to the first service with the Presynch-Ovsynch or Double-Ovsynch protocol and different duration of the voluntary waiting period.

The objective of this study was to compare cash flow and parameters of economic performance for dairy cows submitted to the first service using a combination of insemination at detected estrus and timed AI (TAI) in cows synchronized with the Presynch-Ovsynch (PSOv) protocol versus all TAI after synchronization of ovulation with the Double-Ovsynch (DO) protocol with different durations of the voluntary waiting period. A secondary objective was to calculate the variation in cash flow under different input pricing scenarios through stochastic Monte Carlo simulation. Lactating Holstein cows from a commercial dairy farm were randomly assigned to 3 first-service management programs. Cows in the PSOv treatment (n = 450) received first service through a combination of insemination at detected estrus after a voluntary waiting period of 50 d in milk (DIM; i.e., after second PGF2α treatment of the protocol) and TAI at 72 ± 3 DIM. Cows in the DO60 (n = 458) and DO88 (n = 462) treatments received first service by TAI at 60 ± 3 and 88 ± 3 DIM, respectively. Individual cow cash flow was calculated for the calving interval after enrollment and for an 18-mo period after calving. Cash flow was the aggregation of daily income over feed cost, replacement cost, calf value, recombinant bovine somatotropin treatment cost, reproductive cost, and other operating expenses. All analyses were conducted separately for primiparous and multiparous cows. Continuous, binomial, and time to event outcomes were analyzed using ANOVA, logistic regression, and Cox's proportional hazard regression in SAS (SAS Institute Inc., Cary, NC). Treatments affected the dynamics of pregnancy creation, which affected time to pregnancy during lactation. As a result, we observed differences in the proportion of nonpregnant cows at the end of lactation, herd exit dynamics, lactation length, calving interval, and proportion of cows that calved again. Some of these effects varied by parity, affecting the direction and magnitude of treatment differences within parity group. For primiparous cows, maximum cash flow differences per slot for the 18-mo period were in the range of $26 (PSOv > DO60) to $29 (DO88 > DO60) but did not differ statistically. For multiparous cows, maximum cash flow differences per slot for the 18-mo period were in the range of $122 (PSOv > DO88) to $155 (DO60 > DO88) but did not differ statistically. Despite the substantial differences in cash flow (in particular for multiparous cows) caused by the effect of treatments on reproductive performance, herd exit dynamics, and calving interval, large variability in overall cash flow among individual cows and compensation between multiple outcomes resulted in lack of statistical differences in cash flow. Outcomes from the stochastic analysis indicated that similar trends for differences between treatments would be observed under varying scenarios for economic input values. In conclusion, we did not detect statistically significant cash flow differences between the PSOv, DO60, and DO88 treatments, but numerical trends and stochastic simulation indicated that the DO88 and PSOv treatments were more economically favorable than the DO60 treatment for primiparous cows. For multiparous cows, the DO60 and PSOv treatments were more economically favorable than the DO88 treatment.

Interactions of human chorionic gonadotropin with genotype and parity on fertility responses of lactating dairy cows.

Fertility-promoting effects of treatment of lactating dairy cattle with human chorionic gonadotropin (hCG) after artificial insemination (AI) have been variable. Here, we tested whether fertility response to hCG in lactating Holstein cows interacts with genotype and parity. Primiparous (n = 538) and multiparous (n = 613) cows were treated with hCG (3,300 IU) or vehicle 5 d after AI. Pregnancy was diagnosed on d 32 and 60 after AI. A subset of cows (n = 593-701) was genotyped for 4 single nucleotide polymorphisms (SNP) previously associated with fertility. Treatment with hCG increased progesterone concentration on d 12 after AI regardless of genotype or parity. Pregnancy per AI was improved by hCG in primiparous cows but not in multiparous cows. Moreover, hCG treatment interacted with a SNP in coenzyme Q9 (COQ9) to affect fertility. Fertility of cows treated with vehicle was greatest for the AA allele, whereas fertility was lowest for the same genotype among cows treated with hCG. Pregnancy per AI was also affected by genotype for heat shock protein A1-like (HSPA1L) and progesterone receptor (PGR), but no interactions were observed with treatment. Genotype for a SNP in prostate androgen-regulated mucin-like protein 1 (PARM1) was not associated with fertility. Overall, results show that variation in response to hCG treatment on fertility depends on parity and interacts with a SNP in COQ9.

Reproductive performance of replacement dairy heifers submitted to first service with programs that favor insemination at detected estrus, timed artificial insemination, or a combination of both.

Our objective was to compare the insemination dynamics and time to pregnancy for up to 100 d after the beginning of the artificial insemination period (AIP) for heifers managed with first artificial insemination (AI) service programs that relied primarily on insemination at detected estrus (AIE) after PGF2α treatments, timed artificial insemination (TAI), or a combination of both. Holstein heifers were randomly assigned to receive first AI service with sex-selected semen after 368 ± 10 d of age with (1) AIE after synchronization of estrus with up to 3 PGF2α treatments every 14 d starting on the first day of the AIP (PGF+AIE; n = 317). Heifers not AIE up to 9 d after the third PGF2α received a 5-d Cosynch protocol with progesterone supplementation [GnRH + controlled internal drug release insert (CIDR)-5 d-CIDR removal and PGF2α-3 d-GnRH and TAI] before TAI. Heifers detected in estrus from CIDR removal and PGF2α until the day before TAI received AIE with no GnRH treatment; (2) 2 PGF2α treatments 14 d apart with the second treatment at the beginning of the AIP (PGF+TAI; n = 334). Heifers received AIE for up to 9 d after the second PGF2α treatment. Heifers not AIE received TAI after the 5-d Cosynch protocol and (3) TAI after the 5-d Cosynch protocol (ALL-TAI; n = 315). Heifers failing to conceive to a previous AI received a subsequent AI with conventional semen at detected estrus or TAI after the 5-d Cosynch protocol. Binomial outcomes were analyzed by logistic regression, whereas time to AI and pregnancy were analyzed with Cox's regression. The hazard of first AI up to 45 d of the AIP was greater for ALL-TAI than for PGF+AIE [hazard ratio (HR) = 1.72; 95% confidence interval (CI) =1.45 to 2.03] and PGF+TAI (HR = 1.51; 95% CI = 1.28 to 1.77), but similar for PGF+AIE and PGF+TAI (HR = 1.14; 95% CI = 0.97 to 1.33). A greater proportion of heifers received AIE in PGF+AIE (98.7%) than in PGF+TAI (78.5%). Overall, first service pregnancy per AI did not differ (PGF+AIE = 42.0%; PGF+TAI = 47.3%, ALL-TAI = 43.8%). Time to pregnancy was reduced for ALL-TAI compared with PGF+AIE (HR = 1.20, 95% CI = 1.02 to 1.42), but was similar to that of PGF+TAI (HR = 1.13, 95% CI = 0.96 to 1.33). Time to pregnancy did not differ for PGF+AIE and PGF+TAI (HR = 1.07, 95% CI = 0.91 to 1.25). Median days to pregnancy were 27, 23, and 21 for heifers in PGF+AIE, PGF+TAI, and ALL-TAI, respectively. We concluded that an ALL-TAI program for first service reduced time to pregnancy, albeit a relatively small reduction, when compared with a program that relied primarily on AIE after induction of estrus with PGF2α treatments. The program that combined synchronization of estrus and TAI (PGF+TAI) resulted in similar time to pregnancy than the predominant TAI and predominant AIE programs.