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

Qualitative and Quantitative Detection of Typical Reproductive Hormones in Dairy Cows Based on Terahertz Spectroscopy and Metamaterial Technology

Progesterone (PROG) and estrone (E1) are typical reproductive hormones in dairy cows. Assessing the levels of these hormones in vivo can aid in estrus identification. In the present work, the feasibility of the qualitative and quantitative detection of PROG and E1 using terahertz time-domain spectroscopy (THz-TDS) and metamaterial technology was preliminarily investigated. First, the time domain spectra, frequency domain spectra, and absorption coefficients of PROG and E1 samples were collected and analyzed. A vibration analysis was conducted using density functional theory (DFT). Subsequently, a double-ring (DR) metamaterial structure was designed and simulated using the frequency domain solution algorithm in CST Studio Suite (CST) software. This aimed to ensure that the double resonance peaks of DR were similar to the absorption peaks of PROG and E1. Finally, the response of DR to different concentrations of PROG/E1 was analyzed and quantitatively modeled. The results show that a qualitative analysis can be conducted by comparing the corresponding DR resonance peak changes in PROG and E1 samples at various concentrations. The best R2 for the PROG quantitative model was 0.9872, while for E1, it was 0.9828. This indicates that terahertz spectral-metamaterial technology for the qualitative and quantitative detection of the typical reproductive hormones PROG and E1 in dairy cows is feasible and worthy of in-depth exploration. This study provides a reference for the identification of dairy cow estrus.

Early prediction of oestrus for herd fertility management in cattle and buffaloes - a review

Oestrus is defined as a period when a female animal exhibits characteristic sexual behaviour in the presence of a mature male. Oestrous manifestation in dairy animals is due to the oestrogen (E2) effect on the central nervous system (CNS). It is a critical issue to be considered on a priority basis. Inefficient oestrous detection reduces the fertility status of the herd. The primary and most reliable indicator of oestrus is standing to be mounted by a bull or another female herd mate, signalling receptivity and the pre-ovulatory state in dairy cattle. Oestrous detection is primarily a management challenge requiring skill and vigilance. To improve the efficiency of oestrous detection in dairy cattle, visual observation is one of the best methods if done three times a day; however, heat detection aids, if combined, give better results. However, techniques like using teaser bulls, tail painting, chin ball markers, ultrasound (USG) examination, hormonal analysis and examination of cervicovaginal mucus (CVM) improve oestrous detection efficiency. Moreover, the changes in production systems have reduced the expression of oestrous behaviour among cows, due to higher oestrogen (E2) metabolism. Therefore, automated systems, such as pedometers, accelerometers and acoustic sensors like infrared thermography (IRT) and image processing, have significantly enhanced reproductive performance by facilitating oestrous detection and optimizing insemination schedules. From this review, we would conclude that oestrous detection alone contributes considerably to the reproductive status of the herd; therefore, applying different methods of oestrous detection reduces the incidence of missed oestrus and improves the fertility status of the herd.

Association between genomic daughter pregnancy rate and expected milk production on the resumption of estrus behavior in Holstein cattle

The objective of this observational study was to evaluate estrous expression at the first estrus occurring between 7 to 30 d in milk (DIM), as detected by an automated activity monitor (AAM), and its association with genomic daughter pregnancy rate (GDPR) and genomic expected milk production (GEM) in lactating dairy cows. A total of 4,119 lactations from 2,602 Holstein cows were included. Cows were enrolled as first lactation (n = 1,168), second lactation (n = 1,525) and third and greater lactation (n = 1,426). Hair samples were collected from the tail switch, and cows were genotyped using an SNP platform (Clarifide, Zoetis, São Paulo, SP, Brazil). Postpartum cows were examined daily by the farm personnel from calving until 10 DIM. Calving was classified as assisted (forced calf extraction) and unassisted (normal calving). Retained fetal membranes (RFM), hyperketonemia (KET), and left displaced abomasum (LDA) were also recorded. Mean GDPR (± SD) was -0.29 ± 1.4, and the intensity and duration of the first estrus event was 15.9 ± 13.1 x-factor (intensity unit measurement) and 11.1 ± 3.8 h, respectively. Cows that had greater GDPR had greater intensity and longer duration of estrus at the alert, independent of parity. Overall resumption of estrous expression, between 7 to 30 DIM, was 41.2% (1,695/4,119), where 58.8% (2,424/4,119) did not have an estrus event, 31.0% (1,274/4,119) of cows had one event of estrus, and 10.2% (421/4,119) of cows had 2 or more events of estrus early postpartum. Mean DIM (± SD) at first estrus event, detected by the AAM, was 19.4 ± 4.4 d. Days in milk at first event for cows with one event was 20.7 ± 1.6 d and 15.9 ± 3.1 d for cows with 2 or more events of estrus. First lactation cows were more likely to have an estrus event early postpartum when compared with second and third and greater lactation cows (45.2 ± 1.4% [530/1168] vs. 41.6 ± 1.3% [636/1525] vs. 37.2 ± 1.3% [529/1426], respectively). There was an interaction of parity and GDPR on the proportion of cows demonstrating an early postpartum estrus. There was no difference in the proportion of cows with an early postpartum estrus between those with assisted or unassisted calving, RFM, or LDA. However, cows that had KET were less likely to have an alert early postpartum when compared with cows that did not have KET. Mean genomic expected milk production (± SD) was 256.8 ± 600.1 kg. There was no interaction between GEM and parity on estrous expression (i.e., intensity and duration). There was no interaction between GEM and GDPR on the proportion of estrus early postpartum.

Reproduction in the era of genomics and automation

Much progress has been made in the reproductive efficiency of lactating dairy cows across the USA in the past 20years. The standardisation of evaluation of reproductive efficiency, particularly with greater focus on metrics with lesser momentum and less lag-time such as 21-day pregnancy rates (21-day PR), and the recognition that subpar reproductive efficiency negatively impacted profitability were major drivers for the changes that resulted in such progress. Once it became evident that the genetic selection of cattle for milk yield regardless of fertility traits was associated with reduced fertility, geneticists raced to identify fertility traits that could be incorporated in genetic selection programs with the hopes of improving fertility of lactating cows. Concurrently, reproductive physiologists developed ovulation synchronisation protocols such that after sequential treatment with exogenous hormones, cows could be inseminated at fixed time and without detection of oestrus and still achieve acceptable pregnancy per service. These genetic and reproductive management innovations, concurrently with improved husbandry and nutrition of periparturient cows, quickly started to move reproductive efficiency of lactating dairy cows to an upward trend that continues today. Automation has been adopted in Israel and European countries for decades, but only recently have these automated systems been more widely adopted in the USA. The selection of dairy cattle based on genetic indexes that result in positive fertility traits (e.g. daughter pregnancy rate) is positively associated with follicular growth, resumption of ovarian cycles postpartum, body condition score and insulin-like growth factor 1 concentration postpartum, and intensity of oestrus. Collectively, these positive physiological characteristics result in improved reproductive performance. Through the use of automated monitoring devices (AMD), it is possible to identify cows that resume cyclicity sooner after calving and have more intense oestrus postpartum, which are generally cows that have a more successful periparturient period. Recent experiments have demonstrated that it may be possible to adopt targeted reproductive management, utilising ovulation synchronisation protocols for cows that do not have intense oestrus postpartum and relying more heavily on insemination at AMD-detected oestrus for cows that display an intense oestrus postpartum. This strategy is likely to result in tailored hormonal therapy that will be better accepted by the public, will increase the reliance on oestrus for insemination, will improve comfort and reduce labour by reducing the number of injections cows receive in a lactation, and will allow for faster decisions about cows that should not be eligible for insemination.

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.

Impact of postpartum time period associated body condition score on reproductive performance in dairy cows

This study was designed to identify the time period at which the body condition score (BCS) most critically affects the reproductive performance after calving in dairy cows. Data were collected from 4865 lactation records (1821 from primiparous and 3044 from multiparous cows) on 28 dairy farms, including the BCS (at calving, and month 1 and the first artificial insemination [AI] after calving), the presence of peri- or postpartum disorders, reproductive data, and the weather. The data on the extent of BCS loss from calving to the first AI was divided into two periods, namely, between calving and month 1 after calving (period 1), and between month 1 and the first AI after calving (period 2). Cows with BCS of 3.0, 3.25 and ≥ 3.5 at the first AI after calving were more likely (P < 0.05-0.01) to be pregnant at 30 (odds ratio [OR]: 1.36, 1.64, and 1.90) and 45 (OR: 1.39, 1.75, and 1.99) days post-AI and more likely (P < 0.05-0.01) to be pregnant within 180 days after calving (hazard ratio [HR]: 1.18, 1.43, and 1.58) than cows with BCS of ≤2.75. In addition, cows who suffered a BCS loss of ≥0.5 units during period 1 tended to be less likely (HR: 0.79, P < 0.1) to be pregnant within 180 days after calving than cows without a BCS loss. Cows with BCS of 3.0, 3.25, and ≥3.5 at calving were less likely (P < 0.1-0.05) to lose their pregnancy (OR: 0.37, 0.33, and 0.16) than cows with BCS of ≤2.75. These results indicate that the linearly higher BCS (3.0, 3.25, and ≥3.5) at the first AI is positively associated with the probability of pregnancy per AI after the first AI and the likelihood of pregnancy within 180 days after calving, whereas a higher BCS loss of ≥0.5 units during period 1 was adversely associated with the likelihood of pregnancy within 180 days after calving.

Timing of Artificial Insemination Using Sexed or Conventional Semen Based on Automated Activity Monitoring of Estrus in Holstein Heifers

Investigations on the optimum timing of artificial insemination (AI) following automated activity monitoring (AAM) depending on different types of semen in heifers are limited and in part show controversial results. Therefore, the objective of this observational study was to determine the association between the timing of AI using different characteristics of estrus (i.e., the onset, peak, and end of estrus) and pregnancy per AI (P/AI) in Holstein heifers. Heifers were fitted with a neck-mounted AAM system and inseminated with frozen conventional and sexed semen. The pregnancy per AI (n = 4159) from 2858 heifers from six commercial dairy farms in Germany inseminated upon the alert of an AAM system was evaluated. Estrous intensity was classified based on peak activity into low (35 to 89 index value) and high (90 to 100 index value). We detected a quadratic association between the interval from the onset of estrus to AI and P/AI (p = 0.02). The greatest P/AI was observed for heifers inseminated from 9 to 32 h after the onset of estrus. The intervals from the peak of activity to AI and the end of estrus to AI were not associated with P/AI (p ≥ 0.05). Heifers inseminated with frozen conventional semen (50.1%) had a greater P/AI compared with heifers inseminated with frozen sexed semen (43.3%; p = 0.03). There were no interactions between the intervals from the onset, peak, or end of estrus to AI or the type of semen and the P/AI (p ≥ 0.05). The pregnancy per AI was not associated with estrous intensity (50.5% for low intensity vs. 53.0% for high intensity; p = 0.37). In conclusion, inseminating heifers between 9 and 32 h after the onset of estrus, as detected by the AAM, optimized the P/AI regardless of semen type.

Targeted reproductive management for lactating Holstein cows: Reducing the reliance on exogenous reproductive hormones

Adoption of automated monitoring devices (AMD) affords the opportunity to tailor reproductive management according to the cow's needs. We hypothesized that a targeted reproductive management (TRM) would reduce the use of reproductive hormones while increasing the percentage of cows pregnant 305 d in milk (DIM). Holstein cows from 2 herds (n = 1,930) were fitted with an AMD at 251.0 ± 0.4 d of gestation. Early-postpartum estrus characteristics (EPEC; intense estrus = heat index ≥70; 0 = minimum, 100 = maximum) of multiparous cows were evaluated at 40 (herd 1) or 41 (herd 2) DIM and EPEC of primiparous cows were evaluated at 54 (herd 1) or 55 (herd 2) DIM. Control cows received the first artificial insemination at fixed time (TAI; primiparous, herd 1 = 82 and herd 2 = 83 DIM; multiparous, herd 1 = 68 and herd 2 = 69 DIM) following the Double-Ovsynch (DOV) protocol. Cows enrolled in the TRM treatment were managed as follows: (1) cows with at least one intense estrus were inseminated upon AMD detected estrus for 42 d and, if not inseminated, were enrolled in the DOV protocol; and (2) cows without an intense estrus were enrolled in the DOV protocol at the same time as cows in the control treatment. Control cows were re-inseminated based on visual or patch aided detection of estrus, whereas TRM cows were re-inseminated as described for control cows with the aid of the AMD. Cows received a GnRH injection 27 ± 3 d after insemination and, if diagnosed as nonpregnant, completed the 5-d Cosynch protocol and received TAI 35 ± 3 d after insemination. Among cows in the TRM treatment, 55.8 and 42.9% of primiparous and multiparous cows, respectively, received the first insemination in spontaneous estrus. The interaction between treatment and parity affected pregnancy 67 d after the first AI (primiparous: control = 37.6%, TRM = 27.4%; multiparous: control = 41.0%, TRM = 44.7%). The TRM treatment increased re-insemination in estrus (control = 48.3%, TRM = 70.5%). Pregnancy 67 d after re-inseminations tended to be affected by the interaction between treatment and EPEC (no intense estrus: control = 25.3%, TRM = 32.0%; intense estrus: control = 32.9%, TRM = 32.2%). The interaction between treatment and EPEC affected pregnancy by 305 DIM (no intense estrus: control = 80.8%, TRM = 88.2%; intense estrus: control = 87.1%, TRM = 86.1%). Treatment did not affect the number of reproductive hormone treatments among cows that had not had an intense estrus (control = 10.5 ± 0.3, TRM = 9.1 ± 0.2 treatments/cow), but cows in the TRM treatment that had an intense estrus received fewer reproductive hormone treatments than cows in the control treatment (2.0 ± 0.1 vs. 9.6 ± 0.2 treatments/cow). Selecting multiparous cows for first AI in estrus based on EPEC reduced the use of reproductive hormones without impairing the likelihood of pregnancy to first AI. The use of AMD for re-insemination expedited the establishment of pregnancy among cows that did not display an intense estrus early postpartum.

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).

A Recognition Method of Ewe Estrus Crawling Behavior Based on Multi-Target Detection Layer Neural Network

There are some problems with estrus detection in ewes in large-scale meat sheep farming: mainly, the manual detection method is labor-intensive and the contact sensor detection method causes stress reactions in ewes. To solve the abovementioned problems, we proposed a multi-objective detection layer neural network-based method for ewe estrus crawling behavior recognition. The approach we proposed has four main parts. Firstly, to address the problem of mismatch between our constructed ewe estrus dataset and the YOLO v3 anchor box size, we propose to obtain a new anchor box size by clustering the ewe estrus dataset using the K-means++ algorithm. Secondly, to address the problem of low model recognition precision caused by small imaging of distant ewes in the dataset, we added a 104 × 104 target detection layer, making the total target detection layer reach four layers, strengthening the model's ability to learn shallow information and improving the model's ability to detect small targets. Then, we added residual units to the residual structure of the model, so that the deep feature information of the model is not easily lost and further fused with the shallow feature information to speed up the training of the model. Finally, we maintain the aspect ratio of the images in the data-loading module of the model to reduce the distortion of the image information and increase the precision of the model. The experimental results show that our proposed model has 98.56% recognition precision, while recall was 98.04%, F1 value was 98%, mAP was 99.78%, FPS was 41 f/s, and model size was 276 M, which can meet the accurate and real-time recognition of ewe estrus behavior in large-scale meat sheep farming.

Revisiting the Timing of Insemination at Spontaneous Estrus in Dairy Cattle

Cows show spontaneous estrus over 8-20 h but become refractory to the bull about 10-12 h before ovulation. This indicates that ovulation occurs 10-12 h after the end of estrus behavior, yet spermatozoa from the bull ejaculate need to undergo maturation and capacitation for 6 to 8 h in the female reproductive tract before they are capable of fertilization. Traditionally, the onset of estrus has been considered the best timing for artificial insemination (AI) in cattle, that is, 6 to 24 h from the first signs of estrus. However, recent findings suggest this interval should be reduced to 16 to 6 h before ovulation, bringing it closer to the end of estrus. In this review, the end of estrus rather than its onset is proposed as the best guide for AI timing in dairy cattle, and physiological indicators of late estrus are discussed such as relaxation of the intravaginal part of the uterus, a lower cervical mucus viscosity and a softer pre-ovulatory follicular consistency as simple cues indicating a cow is ready for service.

socialh: An R package for determining the social hierarchy of animals using data from individual electronic bins

Cattle have a complex social organization, with negative (agonistic) and positive (affiliative) interactions that affect access to environmental resources. Thus, the social behaviour has a major impact on animal production, and it is an important factor to improve the farm animal welfare. The use of data from electronic bins to determine social competition has already been validated; however, the studies used non-free software or did not make the code available. With data from electronic bins is possible to identify when one animal takes the place of another animal, i.e. a replacement occurs, at the feeders or drinkers. However, there is no package for the R environment to detect competitive replacements from electronic bins data. Our general approach consisted in creating a user-friendly R package for social behaviour analysis. The workflow of the socialh package comprises several steps that can be used sequentially or separately, allowing data input from electronic systems, or obtained from the animals’ observation. We provide an overview of all functions of the socialh package and demonstrate how this package can be applied using data from electronic feed bins of beef cattle. The socialh package provides support for researchers to determine the social hierarchy of gregarious animals through the synthesis of agonistic interactions (or replacement) in a friendly, versatile, and open-access system, thus contributing to scientific research.

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.

Occurrence and greater intensity of estrus in recipient lactating dairy cows improve pregnancy per embryo transfer

The aim of this study was to determine the association between occurrence and intensity of estrous expression with pregnancy success in recipient lactating dairy cows subjected to embryo transfer (ET). Two observational studies were conducted. Holstein cows were synchronized using the same timed ET protocol, based on estradiol and progesterone in both experiments. At 9 d after the end of the timed ET protocol only animals that had ovulated were implanted with a 7-d embryo [experiment 1 (Exp. 1); n = 1,401 ET events from 1,045 cows, and experiment 2 (Exp. 2); n = 1,147 ET events from 657 cows]. Embryos were produced in vivo (Exp. 1 and Exp. 2) and in vitro (only Exp. 2), then transferred to recipient cows as fresh or frozen-thawed. Pregnancy was confirmed at 29 and 58 d after the end of timed ET protocol. In Exp. 1, animals had their estrous expression monitored through a tail chalk applied on the tail head of the cows and evaluated daily for chalk removal (no estrus: 100% of chalk remaining; estrus: <50% of chalk remaining). In Exp. 2, cows were continuously monitored by a leg-mounted automated activity monitor. Estrous expression was quantified using the relative increase in physical activity at estrus in relation to the days before estrus. Estrous expression was classified as no estrus [<100% relative increase in activity (RI)], weak intensity (100-299% RI), and strong intensity (≥300% RI). Data were analyzed by analysis of variance using mixed linear regression models (GLIMMIX) in SAS (SAS Institute Inc.). A total of 65.2% (914/1,401) and 89.2% (1,019/1,142) of cows from Exp. 1 and Exp. 2, respectively, displayed estrus at the end of the ovulation synchronization protocol. In Exp. 1, cows expressing estrus before to ET had greater pregnancy per ET than those that did not [41.0 ± 2.3% (381/914) vs. 31.5 ± 2.9% (151/487), respectively]. Similarly, in Exp. 2, cows classified in the strong intensity group had greater pregnancy per ET compared with cows in the weak intensity and no estrus groups [41.3 ± 2.2% (213/571) vs. 32.7 ± 2.7% (115/353) vs. 11.3 ± 3.5% (26/218), respectively]. There was no effect of ET type on pregnancy per ET in Exp. 1. However, in Exp. 2, cows that received an in vivo-produced embryo, either fresh or frozen, had greater pregnancy per ET compared with cows that received in vitro-produced embryo. Cows receiving embryos in the early blastocyst and blastocyst stage had greater fertility compared with cows receiving embryos in the morula stage. There was an interaction between the occurrence of estrus and the stage of embryo development on pregnancy per ET, cows which displayed estrus and received a morula or early blastocyst had greater pregnancy per ET than cows that did not display estrus. In conclusion, the occurrence and the intensity of estrous expression improved pregnancy per ET in recipient lactating dairy cows and thus could be used as a tool to assist in the decision making of reproduction strategies in dairy farms.