Progesterone profiles in postpartum dairy cows with inflammatory disorders

The objective of this prospective cohort study was to determine if progesterone (P4) profiles differed between dairy cows with or without inflammatory disorders early postpartum. A total of 708 cows from 2 commercial herds were enrolled 3 wk before parturition and examined for clinical health disorders (difficult calving, retained placenta, metritis, displaced abomasum, mastitis, or lameness) until 5 wk postpartum. Serum haptoglobin (Hp) was measured in blood at 2 and 6 DIM (range ±2 d); metritis was assessed at 4, 8, 11, and 15 DIM; and purulent vaginal discharge and endometritis (≥6% PMN in endometrial cytology sampled by cytobrush) were assessed at 35 ± 3 DIM. As Hp ≥0.8 g/L or endometritis were associated with ovarian dysfunction in previous studies, cows with serum Hp ≥0.8 g/L at either time point and endometritis, regardless of clinical disease, were classified as the cohort with inflammatory disorders (INFLAM; n = 139). Clinically healthy cows without difficult calving, with singleton birth, with Hp <0.8 g/L at both sampling times, without endometritis or purulent vaginal discharge, and BCS ≥3.00 (1 to 5 scale) were classified as healthy (n = 133). Cows with only one of the 2 conditions (high Hp or endometritis) were excluded. Cohorts had serum P4 measured twice weekly from 35 to 70 DIM (±3 d), and the first detected luteal phase (LP) during the sampling period was defined as the period from onset of luteal activity (P4 increase to ≥1 ng/mL) until decline of P4 to <1 ng/mL. The odds of prolonged LP (≥21 d long), average LP length, peak P4, and time to P4 decline (hazard rate) were analyzed using multivariable mixed logistic, linear, or Cox proportional hazard regression models including INFLAM status, parity, sampling day (when applicable), and herd as a random effect considering the covariates of season, milk yield at first DHIA test, and DIM at onset of cyclicity or LP length (when applicable). Cows with INFLAM had greater odds of prolonged LP (LSM ± SEM; 67% vs. 37% ± 7%), greater average LP length (17 vs. 15 ± 2 d), lesser P4 at d 4 (4.6 vs. 5.5 ± 0.3 ng/mL) and d 7 (6.0 vs. 7.7 ± 0.3 ng/mL) of the LP, and lesser peak P4 (6.9 vs. 8.2 ± 0.3 ng/mL) during the LP than healthy cows. Status of INFLAM was associated with time to P4 decline in multiparous but not primiparous cows; the LP of INFLAM multiparous cows was less likely to have luteolysis (P4 decline) by d 14 (adjusted hazard ratio [AHR] and 95% CI: 0.54; 0.31 to 0.94) or by d 21 (AHR: 0.32; 0.12 to 0.84) than in healthy multiparous cows. In conclusion, postpartum cows with markers of systemic inflammation at wk 1 and uterine inflammation at wk 5 had altered luteal function (prolonged LP and lower P4 concentrations) before first breeding, which is a possible pathway linking postpartum health disorders and reduced fertility.

Graduate Student Literature Review: Implications of transition cow health for reproductive function and targeted reproductive management

Negative associations of health disorders with reproductive performance, often measured with pregnancy risk per artificial insemination (AI) or the risk of pregnancy loss, have been demonstrated extensively. Most studies investigated common clinical diseases but did not include subclinical disorders comprehensively. They often evaluated cows subjected to hormonal synchronization protocols for timed AI, limiting the ability to understand how disease may affect spontaneous reproductive function, which is essential for targeted management programs with selective hormonal intervention. It is plausible that metabolic and inflammatory disorders have short- and long-term detrimental effects on different features of reproductive function that result in or contribute to reduced fertility. These may include: 1) reestablishment of endocrine function to promote follicular growth and first ovulation postpartum, 2) corpus luteum (CL) function, 3) estrus expression, and 4) uterine environment, fertilization, and embryonic development. In this narrative literature review, we discuss insights and knowledge gaps linking health disorders with these processes of reproductive function. A growing set of observational studies with adequate internal validity suggest that these outcomes may be affected by metabolic and inflammatory disorders that are common in the early postpartum period. A better characterization of these risk factors in multi-site studies with greater external validity is warranted to develop decision-support tools to identify subgroups of cows that are more or less likely to be successful in targeted reproductive management programs.

Is prolonged luteal phase a problem in lactating Holstein cows?

In this study, the main objective was to assess if long luteal phases could have other causes than pregnancy losses. We enrolled Holstein dairy cows ≥50 d in milk (DIM) from a commercial herd in Brazil from October 2016 to August 2017. All cows received an estradiol-based synchronization protocol, and, on the day of insemination (d 0), were randomly assigned either an artificial insemination (AI) or a placebo insemination (PBO) in a 3:1 ratio. An ultrasound was used to assess the presence of a CL on d17, 24, and 31, which, combined to the information from patches for the detection of estrus, was used to determine the length of the luteal phase following AI or PBO. Pregnancy was assessed by ultrasound on d 31 and cows that were pregnant were excluded from the analyses. The length of the estrous cycles was categorized as short (<17 d), normal (17-23 d), long (24-30 d), and very long (≥31 d). We compared the proportion of cows in each category between the AI and PBO groups using a cumulative ordinal mixed model. We define prolonged luteal phase as estrous cycles ≥24 d and tested its association with potential risk factors (parity, season, DIM, uterine size and position score, milk production, body condition score, and the presence of a corpus luteum (CL) at enrollment to the synchronization protocol) using mixed logistic regression models. Results are presented as odds ratio (OR) and 95% credible intervals (BCI). Data from 876 inseminations (AI: n = 616, PBO: n = 260) was collected. Overall, 12% of estrous cycles were short, 31% were normal, 19% were long, and 38% were very long. There was no difference in the odds of being in longer estrous cycle categories for the AI compared with the PBO group (OR = 0.92, 95% BCI = 0.76-1.10). Season and presence of a CL at enrollment were associated with prolonged luteal phase. In the AI group, there was a possible effect of early pregnancy losses on the lifespan of the CL, but not the PBO group, which led us to conclude that long and very long estrous cycles were not all caused by the embryonic loss. In fact, the high prevalence of cows with an extended CL lifespan in the present study suggests this could be an under- or miss-reported characteristic of high-producing lactating Holstein cows. This finding may have important repercussions in the understanding of the CL function physiology of lactating Holstein cows.

Associations of inflammatory and reproductive tract disorders postpartum with pregnancy and early pregnancy loss in dairy cows

Our objective was to describe associations of postpartum health with pregnancy and pregnancy loss (P-LOSS) from d 19 to 40 after first postpartum artificial insemination (AI) in lactating Holstein cows. In 2 commercial dairy herds in Ontario, Canada, 468 Holstein cows were enrolled 21 ± 3 d before expected parturition when body condition score (BCS) and lameness were assessed. Serum total Ca, haptoglobin (Hp), and nonesterified fatty acids (NEFA) were measured at 2 and 6 ± 2 d in milk (DIM). Blood β-hydroxybutyrate (BHB) measurement and metritis detection were done at 4, 8, 11, and 15 ± 2 DIM. Cows were examined for endometritis (ENDO; ≥11.5% polymorphonuclear cells in endometrial cytology) and purulent vaginal discharge (PVD) at 35 ± 3 DIM. Lameness was assessed again at 21 and 49 ± 3 DIM and BCS at 63 ± 3 DIM. First postpartum AI occurred primarily (86%) based on detection of estrus by activity monitors, on average (± standard deviation) at 65 ± 9 DIM, and the remaining cows received timed AI at 86 ± 18 DIM. Serum progesterone (P4) was measured on d 8 and 12 after AI, and pregnancy at first AI (P/AI) was estimated by the expression of ISG15 in peripheral blood leukocytes at d 19 after AI and by pregnancy-associated glycoprotein in serum at d 29, 33, and 40 after AI. Each metabolite (Ca, Hp, NEFA, and BHB) was categorized above or below a cut-point identified with receiver operating characteristic curve analysis associated with P/AI confirmed by ultrasound at d 33 from a larger data set. Data were analyzed using multivariable mixed logistic regression models, accounting for parity, health variables, covariates (season at calving and at AI, milk yield at first Dairy Herd Improvement Association test [categorized into terciles], AI method, and DIM), and herd. The proportions of cows classified pregnant at d 19, 29, 33, and 40 after AI were 64%, 54%, 50%, and 45%, respectively. At d 19 after AI, P/AI was less likely in cows diagnosed with ENDO (52% vs. 69%) or PVD (54% vs. 67%). At d 29, P/AI was less likely in cows with Hp ≥1.54 g/L at 2 DIM (38% vs. 55%) or PVD (35% vs. 56%). Both metritis and ENDO were associated with decreased P/AI at d 40 after AI. Cows diagnosed with metritis had greater risk of P-LOSS from d 19 to 29 (43% vs. 22%) or from d 33 to 40 (37% vs. 7%) than cows without metritis. From d 29 to 33, the risk of P-LOSS was greater in cows with NEFA ≥0.73 mM at 2 DIM (13% vs. 5%) or BCS ≤2.75 at 63 DIM (14% vs. 5%). The concentration of P4 on d 8 after AI was positively associated with P/AI at d 29, 33, and 40, and negatively associated with P-LOSS from d 19 to 29. Postpartum health disorders, particularly reproductive tract disease, can have detrimental effects on early pregnancy establishment and on pregnancy maintenance from d 19 to 40 after AI.

Association of activity and subsequent fertility of dairy cows after spontaneous estrus or timed artificial insemination

The objective of this observational study was to evaluate the association between increased physical activity at first artificial insemination (AI) and subsequent pregnancy per AI (P/AI) in lactating Holstein cows following spontaneous estrus or a timed AI (TAI) protocol. We also wanted to identify factors associated with the intensity of activity increase (PA) captured by automated activity monitors (AAM) and fertility. Two experiments were conducted, in which cows either were inseminated based on the alert of the AAM system (AAM cows) or received TAI following a 7-d Ovsynch protocol (TAI cows) if not inseminated within a farm-specific period after calving. Experiment 1 included 2,698 AI services from AAM cows and 1,042 AI services from TAI cows equipped with the Smarttag Neck (Nedap Livestock Management) from a dairy farm in Slovakia (farm 1). In the second experiment, 6,517 AI services from AAM cows and 1,226 AI services from TAI cows fitted with Heatime (Heatime Pro; SCR Engineers Ltd.) from 8 dairy farms in Germany (farms 2-9) were included. Pregnancy diagnosis was performed on a weekly basis by transrectal ultrasound (farms 1, 3, 7, 8) or by transrectal palpation (farms 2, 4-6, 9). Estrous intensity was represented by the peak value of the change in activity. In experiment 1, PA was categorized into low (x-factor 0-20) and high (x-factor 21-100) PA, and in experiment 2 into low (activity change = 35-89) and high (activity change = 90-100) PA. In TAI cows from both experiments, PA was additionally categorized into cows with no AAM alert. Data were analyzed separately for AAM and TAI cows using multinomial logistic regression models for PA in TAI cows and logistic regression models for PA in AAM cows and P/AI in both groups. In experiment 1, P/AI of AAM cows was greater for AI services performed with conventional frozen semen (57.6%) compared with sexed semen (47.2%), whereas type of semen only tended to be associated with P/AI in TAI cows (54.4% conventional frozen semen vs. 48.9% sexed semen). In experiment 2, P/AI was greater for fresh semen (AAM cows: 44.4% vs. TAI cows: 44.2%) compared with conventional frozen semen (AAM cows: 37.6% vs. TAI cows: 34.6%). In both experiments, pregnancy outcomes were associated with PA. In experiment 1, AAM cows with high PA (55.1%) had greater P/AI than cows with low PA (49.8%). Within TAI cows, cows with no alert (38.8%) had reduced P/AI compared with cows with low (54.2%) or high PA (61.8%). In experiment 2, AAM cows with high PA (45.8%) had greater P/AI compared with cows with low PA (36.4%). Timed AI cows with no alert (27.4%) had decreased P/AI compared with cows with low (41.1%) or high (50.8%) PA. The greatest risk factors for high PA were parity (experiment 1) and season of AI (except for TAI cows from experiment 1). We conclude that high PA at the time of AI is associated with greater odds of pregnancy for both AAM and TAI cows. In both experiments, about 2 thirds of AAM cows (experiment 1: 69.9% and experiment 2: 70.7%) reached high PA, whereas only approximately one-third or less of TAI cows (experiment 1: 37.3% and experiment 2: 23.6%) showed high PA. Although we observed similar results using 2 different AAM systems for the most part, risk factors for high PA might differ between farms and insemination type (i.e., AAM vs. TAI).

Evaluating automated infrared thermography and vulva exposure tracking as components of an estrus detection platform in a commercial dairy herd

The primary objective of this study was to develop an automated infrared thermography platform (Estrus BenchMark) capable of measuring skin temperature and tail movements as a means of identifying cows in estrus. The secondary objective was to evaluate the accuracy of Estrus BenchMark to detect estrus compared to in-line milk progesterone (P₄) analysis (Herd Navigator System) in a commercial dairy herd managed under a robotic milking system. Data were collected on forty-six cows from 45 to 120 d after calving. Cows were flagged in estrus when milk P₄ fell below 5 ng/mL. The Estrus BenchMark true positive estrus alerts (Sensitivity; Se%) were compared to Herd Navigator System estrus alerts at different time-windows (±12 h, ±24 h, ±48 h, and ±72 h) relative to the Estrus BenchMark estrus alerts for all the estrus alerts (AE) and confidence-quality estrus (CQE; >80% quality) alerts identified by Herd Navigator System. The Estrus BenchMark captured skin temperature and tail movements resulting in vulva exposure (left tail movements, LTail; right tail movements, RTail; and pooled tail movements, PTail) for each milking event. Skin temperature tended to increase when the milk P₄ concentration (Least-Squares Means ± SE) dropped for AE (estrus day [d 0]; P₄; 3.51 ± 0.05 ng/mL, Skin temperature; 33.31 ± 2.38 °C) compared with d -7 (P₄; 20.22 ± 0.73 ng/mL; Skin temperature: 32.05 ± 3.77 °C). The increase in skin temperature, however, was significant in cows with CQE > 80% at d 0 (32.75 ± 0.29 °C) compared to d -7 (31.80 ± 0.28 °C). The prevalence of tail movements to expose vulva was greater (P = 0.01) in AE at d 0 (LTail: 62.50%; PTail; 68.75%; and RTail: 56.25%) compared with d -7 (LTail: 18.75%; PTail: 9.37%: and RTail: 9.37%), and d +4 (LTail: 9.37%; PTail: 9.37%; and RTail: 12.5%). Moreover, the higher prevalence of tail movements at d 0 was observed in cows with CQE > 80% (LTail; 65%, PTail; 80%, and RTail; 70%) compared to those with CQE < 80%. The highest Estrus BenchMark Youden index (YJ; 0.45), diagnostic odds ratio (DOR; 9.04), and Efficiency (0.77) were achieved for AE in a ±48 h window and at ±72 h window for CQE (YJ; 0.66, DOR; 25.29, and Efficiency 0.76) relative to Herd Navigator System estrus alerts. The highest Estrus BenchMark resulted in 58% estrus detection rates for AE and 80% for cows with CQE compared to the Herd Navigator System.

Clinical Overview of Luteal Deficiency in Dairy Cattle

Simple Summary

Luteal deficiency is defined as reduced progesterone production by the corpus luteum, either in the amount or duration, or both. The clinical manifestations include primary infertility and pregnancy loss during the late embryonic/early fetal period (30–50 days post-AI). This work provides a clinical overview of the current understanding of luteal deficiency and its association with low fertility in dairy cows.

Abstract

Luteal deficiency is defined as reduced progesterone (P4) steroidogenesis by the corpus luteum (CL), either in the amount or duration, or both. This work provides a clinical overview of the current understanding of luteal deficiency and its association with low fertility in dairy cows. Low plasma P4 concentrations during the luteal phase post-artificial insemination (AI) are associated with lower conception rates. Treatments post-AI with P4, gonadotropin-releasing hormone (GnRH) or human chorionic gonadotropin (hCG) improve fertility in some conditions. Sub-luteal function during the late embryonic period (at pregnancy diagnosis, i.e., 28–34 days post-AI), is just one factor among other factors associated with pregnancy loss. Treatment with P4 in cows with one CL favors pregnancy maintenance, while GnRH treatment does the same in cows carrying twins. The diagnosis of sub-luteal function can be made clinically on the basis of plasma or milk P4 concentrations. Automated in-line milk P4 analysis systems to diagnose luteal activity emerge as a very interesting tool in dairy herds. Monitoring plasma or milk P4 concentrations with the help of Doppler ultrasonography to assess the CL function would allow individualizing the luteal phase support.

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.

Positive genetic merit for fertility traits is associated with superior reproductive performance in pasture-based dairy cows with seasonal calving

New Zealand's Fertility Breeding Value (FertBV) is reported as the percentage of a sire's daughters that calve in the first 42 d of the seasonal calving period and is an estimate of genetic merit for fertility for dairy cattle. Reproductive physiology, milk production, and changes in body weight and body condition score of 2 groups of cows divergent in FertBV (+5.0%: POS; -5.1%: NEG) were characterized during their first 2 lactations. Cows grazed fresh pasture and were managed in a seasonal calving system; they were bred by artificial insemination on observed estrus for the entire breeding period (98 d in lactation 1 and 76 d in lactation 2). During lactation 1, all animals were primiparous and were randomly allocated to 1 of 2 herds, ensuring each herd was balanced for FertBV and expected calving date. During lactation 2, cows that became pregnant during lactation 1 were managed as 1 herd. Cows not inseminated in the first 42 d of the breeding season were examined for the presence of a corpus luteum and treated with an anestrus program. On average, the interval from calving to ovulation was 19 d longer in lactation 1 and 10 d longer in lactation 2 for NEG FertBV cows. The percent of cows submitted for artificial insemination after 21 d (i.e., submission rate) was 38 and 25 percentage points greater in the POS FertBV cows during lactations 1 and 2, respectively. Pregnancy rate from 42 d of breeding was 33 and 30 percentage points greater, respectively. There was no effect of FertBV on vaginal discharge score postcalving; however, POS FertBV cows had a 50% lower risk of having subclinical endometritis (polymorphonuclear leukocytes >7%) 42 d postcalving. Interactions between FertBV and month relative to calving identified that NEG FertBV cows were fatter (greater body condition score) in the month before calving, but thinner between 3 and 5 mo postcalving. There was no effect of FertBV on lactation length, estimated 270-d milk yields, or daily milk, fat, or protein yields, and only small effects on milk fat and protein percentage across the lactations. In summary, the POS FertBV cows had superior uterine health, a shorter calving to ovulation interval, a greater submission rate, and a greater pregnancy rate earlier in the breeding season when compared with the NEG FertBV cohort. Based on these results, these may be useful phenotypes to include in genetic selection indices.

Genetic parameters for reproductive losses estimated from in-line milk progesterone profiles in Swedish dairy cattle

This study assessed the extent of reproductive losses and associated genetic parameters in dairy cattle, using in-line milk progesterone records for 14 Swedish herds collected by DeLaval's Herd Navigator. A total of 330,071 progesterone samples were linked to 10,219 inseminations (AI) from 5,238 lactations in 1,457 Swedish Red and 1,847 Swedish Holstein cows. Pregnancy loss traits were defined as early embryonic loss (1-24 d after AI), late embryonic loss (25-41 d after AI), fetal loss (42 d after AI until calving), and total pregnancy loss (from d 1 after AI until calving). The following classical fertility traits were also analyzed: interval from calving to first service, interval from calving to last service, interval between first and last service, calving interval, and number of inseminations per service period. Least squares means with standard error (LSM ± SE), heritabilities, and genetic correlations were estimated in a mixed linear model. Fixed effects included breed, parity (1, 2, ≥3), estrus cycle number when the AI took place, and a linear regression on 305-d milk yield. Herd by year and season of AI, cow, and permanent environmental effect were considered random effects. Extensive (approximately 45%) early embryonic loss was found, but with no difference between the breeds. Swedish Red was superior to Swedish Holstein in the remaining pregnancy loss traits with, respectively: late embryonic loss of 6.1 ± 1.2% compared with 13.3 ± 1.1%, fetal loss of 7.0 ± 1.2% compared with 12.3 ± 1.2%, and total pregnancy loss of 54.4 ± 1.4% compared with 60.6 ± 1.4%. Swedish Red also had shorter calving to first service and calving to last service than Swedish Holstein. Estimated heritability was 0.03, 0.06, and 0.02 for early embryonic, late embryonic, and total pregnancy loss, respectively. Milk yield was moderately genetically correlated with both early and late embryonic loss (0.52 and 0.39, respectively). The pregnancy loss traits were also correlated with several classical fertility traits (-0.46 to 0.92). In conclusion, Swedish Red cows had lower reproductive loss during late embryonic stage, fetal stage, and in total, and better fertility than Swedish Holstein cows. The heritability estimates for pregnancy loss traits were of the same order of magnitude as previously reported for classical fertility traits. These findings could be valuable in work to determine genetic variation in reproductive loss and its potential usefulness as an alternative fertility trait to be considered in genetic or genomic evaluations.

Review: Sensor techniques in ruminants: more than fitness trackers

In this position paper, I shall summarise the current status of sensor technologies in ruminant livestock farming with emphasis on dairy cattle, outline the case for why I believe that sensor technologies could revolutionise global dairy farming in a positive way, describe the significant barriers that exist if that goal is to be achieved and highlight the benefits to animal wellbeing, profitability and sustainability that could result if the technologies are implemented to a significant extent. I shall not provide a comprehensive review of the sensor technology literature since that has been done before, but I intend to provide a sensible amount of background information and data that will allow the reader to obtain a picture not only of today's sensor usage but, more importantly, the possible future direction of dairy animal-oriented sensor technologies, and I shall substantiate my claims and conclusions with relevant literature.

Genetic parameters of endocrine fertility traits based on in-line milk progesterone profiles in Swedish Red and Holstein dairy cows

Evaluating fertility traits based on endocrine progesterone profiles is becoming a promising option to improve dairy cow fertility. Several studies have been conducted on endocrine fertility traits, mainly in the Holstein breed. In this study, focusing also on the Swedish Red (SR) breed, genetic parameters were estimated for classical and endocrine fertility traits, the latter based on in-line milk progesterone records obtained for 14 Swedish herds using DeLaval Herd Navigator (DeLaval International, Tumba, Sweden). A total of 210,403 observations from 3,437 lactations of 1,107 SR and 1,538 Holstein cows were used. Mixed linear animal models were used for estimation of genetic parameters. Least squares means analysis showed that Holstein cows had a 2.5-d-shorter interval from calving to commencement of luteal activity (C-LA) and longer length of first inter-ovulatory interval (IOI) than SR cows. The highest mean interval for C-LA, IOI, and first luteal phase length (LPL) was observed in the fourth parity. The incidence of short (<18 d), normal, (18-24 d), and long (>24 d) IOI was 29.3, 40.7, and 30%, respectively. Genetic analysis indicated moderate heritability (h²) for C-LA (h² = 0.24), luteal activity during the first 60 d in milk (LA60, h² = 0.15), proportion of samples with luteal activity (PLA, h² = 0.13), and calving to first heat (CFH, h² = 0.18), and low heritability estimates for LPL (h² = 0.08) and IOI (h² = 0.03) in the combined data set for both breeds. Similar heritability estimates were obtained for each breed separately except for IOI and LPL in SR cows, for which heritability was estimated to be zero. Swedish Red cows had 0.01 to 0.06 higher heritability estimates for C-LA, LA60, and PLA than did Holstein cows. Calving interval had moderate heritability among the classical traits for Holstein and the combined data set, but h² was zero for SR. Commencement of luteal activity had a strong genetic correlation with LA60 (mean ± SE; -0.88 ± 0.06), PLA (-0.72 ± 0.11), and CFH (0.90 ± 0.04). Similarly, CFH had a strong genetic correlation with IOI (0.98 ± 0.20). Number of inseminations per series showed a weak genetic correlation with all endocrine traits except IOI. Overall, endocrine traits had higher heritability estimates than classical traits in both breeds, and may have a better potential to explain the actual reproductive status of dairy cows than classical traits. This might favor inclusion of some endocrine fertility traits-especially those related to commencement of luteal activity-as selection criteria and breeding goal traits if recording becomes more common in herds. Further studies on genetic and genomic evaluations for endocrine fertility traits may help to provide firm conclusions. A prerequisite is that the data from automatic devices be made available to recording and breeding organizations in the future and included in a central database.

Technical note: Validation of an automated in-line milk progesterone analysis system to diagnose pregnancy in dairy cattle

The in-line milk analysis system (IMAS) is an automated biosensor technology that samples and quantifies milk progesterone concentrations (P4c) at frequent intervals starting early postpartum until pregnancy. The objective was to validate the use of pregnancy notifications (PregN) generated by an IMAS based on P4c profiles after artificial insemination (AI) to determine pregnancy and nonpregnancy status in dairy cows. Records of 1,821 AI events from 715 Holstein cows that had milk P4c (ng/mL) measured every 2.2 ± 1.9 d (mean ± standard deviation) between 24.5 ± 8.2 and 173.4 ± 49.3 d in milk through a real-time IMAS (Herd Navigator, DeLaval International, Tumba, Sweden) were evaluated. Based on variations in adjusted milk P4c (< vs. ≥ the 5.0 ng/mL threshold), the system determined the sampling frequency, onset and cessation of luteal phases, and pregnancy. If a luteal phase initiated (P4c increased to ≥5.0 ng/mL) after AI and remained uninterrupted, a PregN was generated starting at (mean ± standard deviation) 31.0 ± 4.3 d until 53.4 ± 7.9 d after AI, when sampling stopped, unless a decline in P4c (to <5.0 ng/mL) occurred indicating nonpregnancy and imminent estrus. The assessment of IMAS PregN at 4 weekly intervals was tested, and a confirmed calving occurrence between 262 and 296 d after AI, with no other subsequent AI recorded, was the gold standard for pregnancy. In total, 14.1 (256/1,821), 41.0 (746/1,821), and 50.7% (924/1,821) of AI events were followed by a decline in P4c before 19, 23, and 30 d after AI, respectively. Frequency of the last 3 sampling events preceding P4c decline was greater if P4c decline occurred between 18 and 25 d after AI (1.4 ± 0.5 samples per day) compared with before 17 or beyond 26 d after AI (1.0 ± 0.5 samples per day). At 30 ± 3 (27 to 33) d after AI, PregN occurred in 46.8% (853/1,821) of AI events, of which 15.2% (130/853) had a decline in P4c between 30 and 55 d after AI and 17.1% (146/853) was later confirmed nonpregnant based on the gold standard. A total of 40.7% (742/1,821) of AI events was confirmed pregnant by the gold standard, which was no different than the proportion of PregN at 51 ± 3 (48 to 54) d (40.9%; 744/1,821). At any time point between 27 and 54 d after AI, sensitivity and negative predictive values for PregN were greater than 95.0 and 96.0%, respectively, whereas specificity values were less than 90.0% for PregN before 40 d but greater than 94.0% for PregN beyond 41 d after AI. In conclusion, IMAS is able to diagnose pregnancy based on P4c profiles with high precision and determine early nonpregnancy based on the spontaneous cessation of the luteal phase. However, for accuracy greater than 95.0%, pregnancy declaration based on IMAS notifications alone should occur no earlier than 41 d after AI.