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.

Interval from Oestrus to Ovulation in Dairy Cows-A Key Factor for Insemination Time: A Review

This review describes the oestrus-to-ovulation interval, the possibility of predicting the time of ovulation, and the optimum time for insemination relative to oestrus in dairy cows. The duration of oestrus in dairy cows is approximately 8-20 h, with differences possibly related to the methods of oestrus detection and the frequency of observations. Most cows ovulate approximately 24-33 h after the onset of oestrus and 15-22 h after the end of oestrus. The interval from the preovulatory luteinising hormone (LH) surge to ovulation is approximately 4-30 h. Ovulation occurs when follicle diameter averages 18-20 mm. When it is possible to correctly determine the beginning of oestrus, artificial insemination can be performed utilizing the "a.m.-p.m. rule", and only one insemination may be applied. In cows with too long or too short oestrus-to-ovulation intervals, fertility can be compromised. One important factor that can alter the oestrus-to-ovulation interval is acute or chronic heat stress during the warm season. When there is a risk that insemination may occur too early or too late with respect to the time of ovulation, GnRH administration can be considered.

Postpartum health is associated with detection of estrus by activity monitors and reproductive performance in dairy cows

The objective of this prospective observational study was to investigate associations of postpartum health with estrus detection (ED) by activity monitors and pregnancy outcomes in dairy cows. A total of 1,743 Holstein cows from 2 commercial dairy herds in Ontario, Canada were enrolled 3 wk before expected parturition and examined for health variables until 9 wk postpartum. Body condition score (BCS) and lameness were measured at 3 wk prepartum, and serum concentrations of total Ca, haptoglobin (Hp), and nonesterified fatty acids were measured at 2 and 6 ± 2 d in milk (DIM), and blood β-hydroxybutyrate (BHB) and metritis were assessed at 4, 8, 11, and 15 ± 2 DIM. Cows were examined for purulent vaginal discharge (PVD) and endometritis (ENDO) by endometrial cytology at wk 5, for lameness at wk 3 and 7, for BCS at wk 9 postpartum, and for time to onset of cyclicity by biweekly serum progesterone (P4) measurements. Additional disease data were obtained from farm records. Reproductive management for first AI was primarily based on ED by activity monitors until at least 75 DIM, and cows not detected in estrus were synchronized. Data were analyzed in multivariable logistic or Cox proportional hazards regression models including blood markers, health variables, potential covariates, and herd as a random effect. Estrus was detected in 77% of primiparous and 66% of multiparous cows between 50 or 55 DIM and 75 DIM. In 1,246 cows, the model-predicted probability of ED (percentage point difference) was lower in cows that had retained placenta (-14%), ENDO (-7%), PVD (-8%), delayed cyclicity (no P4 > 1 ng/mL by wk 9; -12%), or ≥0.5-point BCS loss (-14%) compared with cows without each of these risk factors, and it was negatively associated with blood BHB at 15 DIM. Considering only variables measured on farm (not requiring laboratory analysis), the probability of ED was lower (56 vs. 81%) in cows with >1 risk factor compared with cows without risk factors. The predicted probability of pregnancy at first artificial insemination (percentage point difference) was lower in cows that had ENDO (-7%) or PVD (-7%), and negatively associated with serum Hp at 6 ± 2 DIM. In cows detected in estrus by 75 DIM (n = 888), risk factors for reduced pregnancy rate by 250 DIM (adjusted hazard ratio (AHR); 95% confidence intervals) included difficult calving (AHR: 0.67; 0.45 to 1.00), metritis (AHR: 0.79; 0.61 to 1.01), PVD (AHR: 0.79; 0.65 to 0.97), or lameness (AHR: 0.79; 0.62 to 1.01), and it was negatively associated with serum Hp at 6 ± 2 DIM. Monitoring postpartum health may be used to identify cows that are more or less likely to be detected in estrus by activity monitors and to become pregnant in a timely manner. This would support a selective reproductive management program with targeted interventions.

Differences in Milk Proteomic Profiles between Estrous and Non-Estrous Dairy Cows

Efficient reproductive management of dairy cows depends primarily upon accurate estrus identification. However, the currently available estrus detection methods, such as visual observation, are poor. Hence, there is an urgent need to discover novel biomarkers in non-invasive bodily fluids such as milk to reliably detect estrus status. Proteomics is an emerging and promising tool to identify biomarkers. In this study, the proteomics approach was performed on milk sampled from estrus and non-estrus dairy cows to identify potential biomarkers of estrus. Dairy cows were synchronized and timed for artificial insemination, and the cows with insemination leading to conception were considered to be in estrus at the day of insemination (day 0). Milk samples of day 0 (estrus group) and day -3 (non-estrus group) from dairy cows confirming to be pregnant were collected for proteomic analysis using the tandem mass tags (TMT) proteomics approach. A total of 89 differentially expressed proteins were identified, of which 33 were upregulated and 56 were downregulated in the estrus milk compared with the non-estrus milk. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that acetyl coenzyme A carboxylase α (ACACA), apolipoprotein B (APOB), NAD(P)H steroid dehydrogenase-like (NSDHL), perilipin 2 (PLIN2), and paraoxonase 1 (PON1) participated in lipid binding, lipid storage, lipid localization, and lipid metabolic process, as well as fatty acid binding, fatty acid biosynthesis, and fatty acid metabolism, and these processes are well documented to be related to estrus regulation. These milk proteins are proposed as possible biomarkers of estrus in dairy cows. Further validation studies are required in a large population to determine their potential as estrus biomarkers.

Influence of Estrus on Dairy Cow Milk Exosomal miRNAs and Their Role in Hormone Secretion by Granulosa Cells

Estrus is crucial for cow fertility in modern dairy farms, but almost 50% of cows do not show the behavioral signs of estrus due to silent estrus and lack of suitable and high-accuracy methods to detect estrus. MiRNA and exosomes play essential roles in reproductive function and may be developed as novel biomarkers in estrus detection. Thus, we analyzed the miRNA expression patterns in milk exosomes during estrus and the effect of milk exosomes on hormone secretion in cultured bovine granulosa cells in vitro. We found that the number of exosomes and the exosome protein concentration in estrous cow milk were significantly lower than in non-estrous cow milk. Moreover, 133 differentially expressed exosomal miRNAs were identified in estrous cow milk vs. non-estrous cow milk. Functional enrichment analyses indicated that exosomal miRNAs were involved in reproduction and hormone-synthesis-related pathways, such as cholesterol metabolism, FoxO signaling pathway, Hippo signaling pathway, mTOR signaling pathway, steroid hormone biosynthesis, Wnt signaling pathway and GnRH signaling pathway. Consistent with the enrichment signaling pathways, exosomes derived from estrous and non-estrous cow milk both could promote the secretion of estradiol and progesterone in cultured bovine granulosa cells. Furthermore, genes related to hormonal synthesis (CYP19A1, CYP11A1, HSD3B1 and RUNX2) were up-regulated after exosome treatment, while exosomes inhibited the expression of StAR. Moreover, estrous and non-estrous cow-milk-derived exosomes both could increase the expression of bcl2 and decrease the expression of p53, and did not influence the expression of caspase-3. To our knowledge, this is the first study to investigate exosomal miRNA expression patterns during dairy cow estrus and the role of exosomes in hormone secretion by bovine granulosa cells. Our findings provide a theoretical basis for further investigating milk-derived exosomes and exosomal miRNA effects on ovary function and reproduction. Moreover, bovine milk exosomes may have effects on the ovaries of human consumers of pasteurized cow milk. These differential miRNAs might provide candidate biomarkers for the diagnosis of dairy cow estrus and will assist in developing new therapeutic targets for cow infertility.

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).

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 PGF_2α 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 (HR) = 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.

Industry 4.0 and Precision Livestock Farming (PLF): An up to Date Overview across Animal Productions

Precision livestock farming (PLF) has spread to various countries worldwide since its inception in 2003, though it has yet to be widely adopted. Additionally, the advent of Industry 4.0 and the Internet of Things (IoT) have enabled a continued advancement and development of PLF. This modern technological approach to animal farming and production encompasses ethical, economic and logistical aspects. The aim of this review is to provide an overview of PLF and Industry 4.0, to identify current applications of this rather novel approach in different farming systems for food producing animals, and to present up to date knowledge on the subject. Current scientific literature regarding the spread and application of PLF and IoT shows how efficient farm animal management systems are destined to become. Everyday farming practices (feeding and production performance) coupled with continuous and real-time monitoring of animal parameters can have significant impacts on welfare and health assessment, which are current themes of public interest. In the context of feeding a rising global population, the agri-food industry and industry 4.0 technologies may represent key features for successful and sustainable development.

Estimation of the true prevalence of inaccurate artificial inseminations in Irish milk recording dairy cows using a Bayesian latent class analysis

Inaccurate artificial insemination (IAI) refers to an artificial insemination (AI) that is performed when a cow is not in oestrus. IAIs have economic impacts on the dairy industry through of semen wastage or iatrogenic pregnancy loss. However, few studies have quantified the prevalence of IAIs in a population. The primary objective of this prospective study was to estimate the cow-level true prevalence of IAIs in Irish milk recording dairy herds using a latent class model with a Bayesian framework. Milk samples were collected at a milk recording laboratory from 576 dairy cows in 125 herds who had received an AI on the same day they were sampled for routine milk constituent analysis. Milk progesterone (MP4) analysis was conducted on these samples using radioimmunoassay to determine the progesterone concentration. Fertility data (i.e., subsequent calving date) was retrospectively obtained from the Irish National Cattle Breeding Federation for milk sampled cows and an apparent conception (AC) to the sample AI was determined based on an estimated gestational range of 270-290 days. Both tests (MP4 and AC) were used in a latent class model to estimate the true prevalence of IAI. For the MP4 test, a concentration of ≥ 5 ng/mL in whole milk was deemed to be test positive while for the AC test, a cow that did not conceive to the sampled AI was deemed test positive. Prior information for prevalence of IAI was obtained from a literature review while MP4 sensitivity (Se) and specificity (Sp) were obtained from expert opinion. Non-informative priors were used for the Se and Sp of the AC test. Posterior inferences (median and 95 % Bayesian probability intervals; BPI) were obtained using the 'rjags' package in the R statistical software. In the final model, median cow-level true prevalence of IAI was 4.4 % (BPI; 1.7-9.0 %). Median Se and Sp estimates for MP4, were 83.0 % (BPI; 65.0-96.2 %) and were 97.4 % (BPI; 94.6-99.6 %), respectively. Median Se and Sp estimates for AC, were 64.8 % (BPI; 44.5-88.6 %) and 49.8 % (BPI; 45.3-54.1 %), respectively. The present study estimates that the overall cow-level true prevalence of IAI in Irish dairy cows is relatively low. This is the first study to report the cow-level true prevalence of IAI using a Bayesian latent class model.

Influence of Estrus on the Milk Characteristics and Mid-Infrared Spectra of Dairy Cows

Simple Summary

Some studies have confirmed the variation in milk profiles when dairy cows show estrus. However, only a few milk components, such as fat, protein, and lactose, have been investigated so far, and thus any changes in the many other parts of milk’s composition due to estrus are unknown. Milk mid-infrared (MIR) spectra consist of wavenumbers, which provide insight into the chemical composition of milk. The MIR spectrum reflects the global composition of milk, but this information is currently underused. In this study, we considered MIR wavenumbers as traits, and directly studied the spectral information as a way to study the estrus of dairy cows linked to milk composition. This research provides a deeper understanding of the milk MIR spectrum and may lead to new approaches for estrus detection in dairy cows from routine milk analysis, thereby guiding an opportune insemination time.

Abstract

Milk produced by dairy cows is a complex combination of many components. However, at present, changes in only a few milk components (e.g., fat, protein, and lactose) during the estrus cycle in dairy cows have been documented. Mid-infrared (MIR) spectroscopy is a worldwide method routinely used for milk analysis, as MIR spectra reflect the global composition of milk. Therefore, this study aimed to investigate the changes in milk MIR spectra and milk production traits (fat, protein, lactose, urea, total solids (TS), and solid not fat (SnF)) due to estrus. Cows that were successfully inseminated, leading to conception, were included. Cows confirmed to be pregnant were considered to be in estrus at the day of insemination (day 0). A general linear mixed model, which included the random effect of cows, the fixed classification effects of parity number, days in relation to estrus, as well as the interaction between parity number and days in relation to estrus, was applied to investigate the changes in milk production traits and 1060 milk infrared wavenumbers, ranging from 925 to 5011 cm⁻¹, of 371 records from 162 Holstein cows on the days before (day −3, day −2, and day −1) and on the day of estrus (day 0). The days in relation to estrus had a significant effect on fat, protein, urea, TS, and SnF, whose contents increased from day −3 to day 0. Lactose did not seem to be significantly influenced by the occurrence of estrus. The days in relation to estrus had significant effects on the majority of the wavenumbers. Besides, we found that some of the wavenumbers in the water absorption regions were significantly changed on the days before and on the day of estrus. This suggests that these wavenumbers may contain useful information. In conclusion, the changes in the milk composition due to estrus can be observed through the analysis of the milk MIR spectrum. Further analyses are warranted to more deeply explore the potential use of milk MIR spectra in the detection of estrus.

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 PGF_2α (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.

Factors associated with estrous expression and subsequent fertility in lactating dairy cows using automated activity monitoring

The objective of this observational study was to identify factors associated with estrous duration (DU) and intensity measured as the peak of activity (PA) change and subsequent fertility in lactating Holstein cows using a neck-mounted automated activity monitor (Heatime Pro, SCR Engineers Ltd., Netanya, Israel). Ambient temperature and relative humidity were recorded hourly to calculate the temperature-humidity index (THI). A total of 5,933 estrus events from 3,132 cows located on 8 commercial dairy farms in Germany were used for this study. Farms participated in monthly DHIA testing. Pregnancy diagnosis was performed either by transrectal palpation [farm 1: 42 ± 3 d; farm 3: 40 ± 3 d; farms 4 and 8: 38 ± 3 d; farm 5: 43 ± 3 d after artificial insemination (AI)] or transrectal ultrasonography (farms 2, 6, and 7: 30 ± 3 d after AI). Estrous intensity was categorized based on peak activity of estrus into low (35-89 index value), and high (90-100 index value) PA. Overall, 73.5% of estrus events were of high PA. The mean (± standard error of the mean) DU was 14.94 ± 0.06 h. There was a strong correlation between DU and PA (r = 0.67). In the final statistical model, only PA was associated with pregnancy per artificial insemination (P/AI), with 1.35 greater odds of pregnancy for cows with high PA compared with cows with low PA. Increased THI 1 wk before AI was associated with shorter DU, lower PA, and decreased P/AI. A small percentage of cows (4.7%) showed short interestrus intervals (i.e., more than 1 activity peak within 7 d close to the event of estrus), resulting in reduced DU, PA, and P/AI. The change of weighted rumination was associated with DU and PA, as a lower nadir was associated with a greater risk for high PA and long DU. There was no association, however, between the nadir of change of weighted rumination and P/AI. Whereas milk yield and somatic cell count from the DHIA test date before AI were negatively associated with estrous expression, neither milk yield nor somatic cell count was associated with P/AI. Surprisingly, multiparous cows expressed estrus with longer DU (13.15 ± 0.31 h) compared with primiparous cows (12.52 ± 0.32 h), whereas PA did not differ among parities. Pregnancy per AI was greater for primiparous (29.4%) than for multiparous (22.1%) cows. An estrus event with long DU or high PA was more likely later in lactation. Milk fat, milk protein, milk urea nitrogen, and lactose from the DHIA test date closest to AI had no association with estrous expression or P/AI. In conclusion, DU and PA were highly correlated, and cows with high PA were particularly associated with greater odds for pregnancy. A negative association between estrous expression and P/AI was identified for increased THI 1 wk before AI and cows with short interestrus intervals using automated activity monitor.

Timing of artificial insemination using fresh or frozen semen after automated activity monitoring of estrus in lactating dairy cows

The objective of this observational experiment was to determine the association between the time of artificial insemination (AI) and pregnancy per AI (P/AI) in lactating Holstein cows inseminated with either fresh or frozen semen considering different characteristics of an estrus event (i.e., onset, peak, and end) using an automated activity monitoring system. A total of 3,607 AI services based on the alert of an automated activity monitoring system (Heatime; SCR Engineers Ltd., Netanya, Israel) were evaluated from 4 commercial dairy farms in Germany. Pregnancy diagnosis was performed by transrectal palpation 38 ± 3 d after AI or by transrectal ultrasonography 30 ± 3 d after AI. Estrus intensity was categorized based on peak activity of estrus (PAE) into low (35-89 index value) and high (90-100 index value) intensity. The mean (± standard deviation) duration of an estrus event was 14.3 ± 4.6 h. The mean (± standard deviation) interval from onset of estrus (OE; moment where index value was ≥35) to AI was 16.8 ± 8.0 h, from PAE to AI was 11.9 ± 8.1 h, and from end of estrus (EE; moment where index value returned to <35) to AI was 2.5 ± 8.7 h. Primiparous cows had greater P/AI than multiparous cows, whereas first AI postpartum yielded greater P/AI compared with subsequent AI services. Type of semen was not associated with P/AI. Cows with heat stress 1 wk before AI had decreased P/AI. Cows with low estrus intensity (26.0%) were less fertile compared with cows showing high estrus intensity (32.8%). Cows with intermediate 100-d milk yield had decreased P/AI compared with cows with either low or high 100-d milk yield. There was a quadratic effect of the interval from OE to AI on P/AI. At 38 d after AI, P/AI was greatest for cows inseminated from 7 to 24 h after OE, within 18 h after PAE, or from 5 h before EE to 12 h after EE. There was no interaction between the interval from OE to AI and type of semen. There tended to be an interaction between the intervals from PAE to AI and type of semen and from EE to AI and type of semen. Cows inseminated with fresh semen within 5 h before EE had greater P/AI compared with frozen semen, whereas cows inseminated with frozen semen from 13 to 18 h after EE had greater P/AI compared with fresh semen. In conclusion, inseminating cows from 7 to 24 h after OE or 1 to 18 h after PAE yielded greatest P/AI irrespective of type of semen. In addition, high estrus intensity was positively associated with P/AI.

Cow-level prevalence and risk factors for estrus detection inaccuracy in seasonal calving pasture-based dairy cows

High submission rates and pregnancies per AI are essential to ensure compact calving is achieved in seasonal calving pasture-based systems. Estrus detection inaccuracy (EDI) is one area that negatively impacts pregnancies per AI as it increases the inseminations per pregnancy with little probability of conception, while also having the potential to disrupt established pregnancies. The aims of this cross-sectional study were to provide cow-level estimates of EDI prevalence and determine cow-level risk factors for EDI in seasonal calving pasture-based systems. A total of 1071 milk samples were obtained from 984 cows on 19 farms in spring 2018 and analyzed by radioimmunoassay to determine the progesterone concentration at the time of artificial insemination. Based on a validation study on a subset of cows, an inaccurate estrus detection was described as a concentration of progesterone in foremilk of ≥3 ng/ml which corresponded to a composite milk progesterone value of 5 ng/ml. To investigate selected risk factors for EDI, we conducted statistical analyses using two multivariate logistic regression models, stratifying by insemination number (first versus repeat). The overall prevalence of EDI was 4.7% with a prevalence of 3.3% of EDI at first insemination and 14.1% at repeat insemination. Absence of a mounting abrasion (Adjusted odds ratio (AOR) = 3.0) was a significant risk factor for EDI on first insemination while abnormal preceding repeat interval (AOR = 9.5), the absence of an observed standing estrus (AOR = 12.5) and the absence of a mounting abrasion (AOR = 4.1) were significant risk factors for EDI on repeat insemination. The results indicate that cow-level estimated prevalence of EDI in a selection of pasture-based herds was low at first insemination but higher for repeat insemination. It confirms that certain cow-level risk factors existed for EDI, thus providing preliminary evidence for potential future investigation into the targeted use of on-farm progesterone assays in pasture-based herds.

Symposium review: Linking activity-sensor data and physiology to improve dairy cow fertility

Several studies have demonstrated that the intensity of estrous expression is associated with ovulation, ovarian and uterine function, and fertility, and is dependent on social hierarchy and the housing system used. Data from recent studies involving spontaneous and induced estrus have shown that a greater relative increase and longer estrus (captured by different automated activity monitors; AAM) are both associated with improved pregnancy per artificial insemination (AI; around 10 to 14% increase) and decreased pregnancy losses. Intensity and duration of estrus were surprisingly weakly associated with preovulatory follicle diameter and concentrations of plasma estradiol at estrus, whereas ovulation failure was associated with low estrus intensity. Studies have also shown that the display of estrous behavior near AI was associated with the modification of expression of genes related to the immune system, adhesion molecules, and prostaglandin synthesis in the endometrium. Transcripts in leukocytes and in the conceptus tissue associated with maternal recognition of pregnancy as well as conceptus elongation were all associated with differences in the intensity of estrous expression. Most recently, studies from the United States and Canada have demonstrated that reproductive programs emphasizing detection of estrus using AAM can be successful and comparable to intensive timed AI protocol-based programs that incorporate GnRH and PGF_2α treatments. Further, one study concluded that the administration of GnRH at AI for spontaneous estrus events greatly improved pregnancy per AI, but only for cows with reduced intensity of estrous expression, showing the potential to use AAM data as a tool in targeted reproductive programs. Quantitative information from estrus events could be used to improve estrus detection and develop decision-making strategies at the farm level. Future studies in this field should aim to better understand ovarian, conceptus, and endometrial mechanisms associated with either the expression or the intensity of estrus, and to refine the identification of phenotypes related to estrus (relative increase, absolute increase, baseline levels, duration, and repeatability within cow) to improve data usage, estrus detection, and possibly genetic selection.

Monitoring estrous activity in pasture-based dairy cows

Correctly identifying cows in estrus and inseminating them at the optimal time are critical components of reproductive management. Technologies for detecting cows in estrus have developed from tail paint in the 1970's to automated activity monitors and mount detectors in recent decades. The objectives of this study were to identify animal characteristics associated with estrous behaviour, measured using a mount detector (FlashMate; Farmshed Labs) and an accelerometer-based activity monitor (Moomonitor, Dairymaster), to examine the relationship between estrous behaviour measured by both devices, and to examine the characteristics associated with pregnancy per AI. Four hundred and sixty eight lactating dairy cows managed on three research farms were enrolled and data were available from 465 cows and 369 cows with Moomonitor and FlashMate data, respectively. Of 234 cows that provided both Moomonitor and FlashMate data, the mean (±SEM) onset of device activation occurred 1.1 (±0.4) h earlier with the Moomonitor compared with the FlashMate. The mean (±SD) duration of Moomonitor activity and FlashMate activity was 17.2 ± 6.1 h and 8.7 ± 5.8 h, respectively. The duration of Moomonitor activity and FlashMate activity was negatively associated with total milk yield during the first five weeks of lactation. The duration of FlashMate activity, but not the duration of Moomonitor activity was positively associated with days in milk. Pregnancy per AI was positively associated with BCS, days in milk, the duration of FlashMate activity and the interval from the onset of Moomonitor and FlashMate activity to AI. Inseminating cows ≤2 h after FlashMate activation or ≤4 h after Moomonitor activation was associated with reduced odds of pregnancy compared with later timing of AI. Overall, 55% of cows received mounts for ≤8 h, highlighting the need for ≥3 periods of estrous observation daily or the use of estrous detection aids that continuously monitor cows. Finally, the study reiterated the importance of maximising body condition score and days in milk at breeding to increase behavioural expression of estrus and pregnancy per AI.

Performance and optimization of an ear tag automated activity monitor for estrus prediction in dairy heifers

The objectives of this study were to evaluate the performance of the SCR eSense ear tag automated activity monitor (AAM) to detect estrus behavior in Holstein heifers and to determine the optimal time from estrus alert to artificial insemination (AI) using sex-sorted or conventional semen. In total, 281 heifers were fitted with the AAM once eligible for breeding (>13.5 m of age). For the first AI, estrus was synchronized using 500 μg of cloprostenol (PGF), given 14 d apart, and heifers were given estrus detection patches (Estrotect™) after the second PGF. Heifers were inseminated at randomly attributed times after high activity alert from the AAM system or if the estrus patch had ≥ 50% colour change. Most heifers received sex-sorted semen for the first AI and conventional semen for subsequent inseminations. Pregnancy diagnosis was performed at 30 d post AI and heifers had four opportunities to become pregnant. In a subset of heifers (n = 149), ovaries were scanned every 12 h from the time of AI until ovulation (OV). The system recorded a heat index (measure of estrus strength), maximum activity change, maximum rumination change and duration of high activity. The sensitivity was 91.0%, with a false positive and false negative rate of 8.0%, and the positive predictive value to detect true estrus events was 83.5%. Pregnancy per AI to first AI was 67.6% and 97.9% of heifers become pregnant after four inseminations. Most false positive estrus events had a heat index < 45 and a rumination change < -20, while false negative events had a rumination change ≥ -20. Odds of pregnancy was not associated with any estrus characteristics measured by the system. However, pre-ovulatory follicle diameter had a weak correlation (r < 0.25) with all estrus characteristics. The average (range) interval of onset of high activity, peak activity and end of high activity to OV was 28 h (16-46 h), 22 h (10-40 h) and 16 h (0-36 h), respectively. For conventional semen, each hour increase in interval from activity onset or peak activity to AI reduced the predicted probability of pregnancy by 3.8 and 4.2%, respectively. For sex-sorted semen, the relationship between activity onset or peak activity to AI and predicted probability of pregnancy was quadratic, but not significant. Overall, the SCR eSense ear tag AAM performed well and strategies to identify false positive and false negative estrus events, along with optimization of timing of AI, should further improve performance in Holstein heifers.

Symposium review: Real-time continuous decision making using big data on dairy farms

We are developing a real-time, data-integrated, data-driven, continuous decision-making engine, The Dairy Brain, by applying precision farming, big data analytics, and the Internet of Things. This is a transdisciplinary research and extension project that engages multidisciplinary scientists, dairy farmers, and industry professionals. Dairy farms have embraced large and diverse technological innovations such as sensors and robotic systems, and procured vast amounts of constant data streams, but they have not been able to integrate all this information effectively to improve whole-farm decision making. Consequently, the effects of all this new smart dairy farming are not being fully realized. It is imperative to develop a system that can collect, integrate, manage, and analyze on- and off-farm data in real time for practical and relevant actions. We are using the state-of-the-art database management system from the University of Wisconsin-Madison Center for High Throughput Computing to develop our Agricultural Data Hub that connects and analyzes cow and herd data on a permanent basis. This involves cleaning and normalizing the data as well as allowing data retrieval on demand. We illustrate our Dairy Brain concept with 3 practical applications: (1) nutritional grouping that provides a more accurate diet to lactating cows by automatically allocating cows to pens according to their nutritional requirements aggregating and analyzing data streams from management, feed, Dairy Herd Improvement (DHI), and milking parlor records; (2) early risk detection of clinical mastitis (CM) that identifies first-lactation cows under risk of developing CM by analyzing integrated data from genetic, management, and DHI records; and (3) predicting CM onset that recognizes cows at higher risk of contracting CM, by continuously integrating and analyzing data from management and the milking parlor. We demonstrate with these applications that it is possible to develop integrated continuous decision-support tools that could potentially reduce diet costs by $99/cow per yr and that it is possible to provide a new dimension for monitoring health events by identifying cows at higher risk of CM and by detecting 90% of CM cases a few milkings before disease onset. We are securely advancing toward our overarching goal of developing our Dairy Brain. This is an ongoing innovative project that is anticipated to transform how dairy farms operate.