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This study aimed to evaluate the accuracy of the accelerometer-equipped collar RUMI to detect estrus in dairy cows, establish a recommendation for the optimal timing for artificial insemination (AI) when using this device, and characterize the blood flow of the dominant follicle (F) and the corpus luteum (CL) as ovulation approaches. Forty-seven cycling cows were monitored following synchronization with a modified G6G protocol, allowing for spontaneous ovulation. Ultrasound examinations were conducted every 12 h, starting 48 h after the second PGF2α dose, to monitor uterine and ovarian changes. Blood samples were also collected to determine serum progesterone (P4) levels. Each cow was fitted with a RUMI collar, which continuously monitored behavioral changes to identify the onset, offset, and peak of activity of estrus. One-way ANOVA assessed the relationship between physiological parameters and time before ovulation. Results showed that the RUMI collar demonstrated high specificity (100%), sensitivity (90.90%), and accuracy (93.62%) for estrus detection. The optimal AI window was identified as between 11.4 and 15.5 h after heat onset. Increased blood flow to the F and reduced luteal activity were observed in the 48 h prior to ovulation. Further research is needed to assess the influence of this AI window on conception rates, and if it should be modified considering external factors.

Dairy cows are high value farm animals requiring careful management to achieve the best results. Since the advent of robotic and high throughput milking, the traditional few minutes available for individual human attention daily has disappeared and new automated technologies have been applied to improve monitoring of dairy cow production, nutrition, fertility, health and welfare. Cows milked by robots must meet legal requirements to detect healthy milk. This review focuses on emerging technical approaches in those areas of high cost to the farmer (fertility, metabolic disorders, mastitis, lameness and calving). The availability of low cost tri-axial accelerometers and wireless telemetry has allowed accurate models of behaviour to be developed and sometimes combined with rumination activity detected by acoustic sensors to detect oestrus; other measures (milk and skin temperature, electronic noses, milk yield) have been abandoned. In-line biosensors have been developed to detect markers for ovulation, pregnancy, lactose, mastitis and metabolic changes. Wireless telemetry has been applied to develop boluses for monitoring the rumen pH and temperature to detect metabolic disorders. Udder health requires a multisensing approach due to the varying inflammatory responses collectively described as mastitis. Lameness can be detected by walk over weigh cells, but also by various types of video image analysis and speed measurement. Prediction and detection of calving time is an area of active research mostly focused on behavioural change.

Sixty-seven Holstein-Friesian cows, from 20 days postpartum, were recruited into the study and fitted with both a pedometer (SAE Afikim) and a Heatime neck collar (SCR Engineers) and allocated a heat mount detector (either scratchcard [Dairymac] or KaMaR [KaMaR]) or left with none, relying only on farm staff observation. Common production stressors and other factors were assessed to determine their impact on the ability of each method to accurately detect oestrus and to investigate effects on the frequency of false-positive detections. Only 74 per cent of all potential oestrus periods (episodes of low progesterone) were identified by combining information from all methods. There was no difference between the methods in terms of sensitivity for detecting ‘true oestrus events’ (approximately 60 per cent), with the exception of scratchcards, which were less efficient (36 per cent). Pedometers and KaMaRs had higher numbers of false-positive identifications. No production stressors had any consequence on false-positives. The positive predictive values for neck collars or observation by farm staff were higher than those of other methods, and combining these two methods yielded the best results. Neck collars did not detect any of the nine oestrus events occurring in three cows with a body condition score (BCS) of less than 2, and the efficiency of correctly identifying oestrus was also reduced by high milk yield (odds ratio [OR]=0.34). Pedometer efficiency was reduced by lameness, low BCS or high milk yield (OR=0.42, 0.15 or 0.30, respectively).

A multilocation study examined pregnancy risk (PR) after delaying AI in suckled beef cows from 60 to 75 h when estrus had not been detected by 60 h in response to a 7-d CO-Synch + progesterone insert (CIDR) timed AI (TAI) program (d -7: CIDR insert concurrent with an injection of GnRH; d 0: PGF injection and removal of CIDR insert; and GnRH injection at TAI [60 or 75 h after CIDR removal]). A total of 1,611 suckled beef cows at 15 locations in 9 states (CO, IL, KS, MN, MS, MT, ND, SD, and VA) were enrolled. Before applying the fixed-time AI program, BCS was assessed, and blood samples were collected. Estrus was defined to have occurred when an estrus detection patch was >50% colored (activated). Pregnancy was determined 35 d after AI via transrectal ultrasound. Cows ( = 746) detected in estrus by 60 h (46.3%) after CIDR removal were inseminated and treated with GnRH at AI (Control). Remaining nonestrous cows were allocated within location to 3 treatments on the basis of parity and days postpartum: 1) GnRH injection and AI at 60 h (early-early = EE; = 292), 2) GnRH injection at 60 h and AI at 75 h (early-delayed = ED; = 282), or 3) GnRH injection and AI at 75 h (delayed-delayed = DD; = 291). Control cows had a greater ( < 0.01) PR (64.2%) than other treatments (EE = 41.7%, ED = 52.8%, DD = 50.0%). Use of estrus detection patches to delay AI in cows not in estrus by 60 h after CIDR insert removal (ED and DD treatments) increased ( < 0.05) PR to TAI when compared with cows in the EE treatment. More ( < 0.001) cows that showed estrus by 60 h conceived to AI at 60 h than those not showing estrus (64.2% vs. 48.1%). Approximately half (49.2%) of the cows not in estrus by 60 h had activated patches by 75 h, resulting in a greater ( < 0.05) PR than their nonestrous herd mates in the EE (46.1% vs. 34.5%), ED (64.2% vs. 39.2%), and DD (64.8% vs. 31.5%) treatments, respectively. Overall, cows showing estrus by 75 h (72.7%) had greater ( < 0.001) PR to AI (61.3% vs. 37.9%) than cows not showing estrus. Use of estrus detection patches to allow for a delayed AI in cows not in estrus by 60 h after removal of the CIDR insert improved PR to TAI by optimizing the timing of the AI in those cows.

Detection of estrus in dairy cattle is effectively aided by electronic activity tags or pedometers. Characterization of estrus intensity and duration is also possible from activity data. This study aimed to develop an algorithm to detect and characterize behavioral estrus from hourly recorded activity data and to apply the algorithm to activity data from an experimental herd. The herd comprised of Holstein (n=211), Jersey (n=126), and Red Dane (n=178) cattle, with virgin heifers (n=132) and lactating cows in the first 4 parities; n=895 cow-parities, with a total of 3,674 activity episodes. The algorithm was based on deviations from exponentially smoothed hourly activity counts and was used to identify onset, duration, and intensity of estrus. Learning data included 461 successful inseminations with activity records over a 2-wk period before and after the artificial insemination. Rates of estrus detection and error rate depended on the chosen threshold level. At a threshold giving 74.6% detection rate, daily error rate was 1.3%. When applied to a subset of the complete data where milk progesterone was also available, concordance of days to first activity-detected estrus with the similar trait based on progesterone was also dependent on the chosen threshold so that, with stricter thresholds, the agreement was closer. A single-trait mixed model was used to determine the effects of systematic factors on the estrus activity traits. In general, an activity episode lasted 9.24h in heifers and 8.12h in cows, with the average strength of 1.03 ln units (equivalent to a 2.8-fold increase) in both age groups. Red Danes had significantly fewer days to first episode of high activity than Holsteins and Jerseys (29.4, 33.1, and 33.9 d, respectively). However, Jerseys had significantly shorter duration and less strength of estrus than both Red Danes and Holsteins of comparable age. The random effect of cow affected days to first episode of high activity and strength as well as estrus duration. Days from calving to first episode of high activity correlated negatively with body condition scores in early lactation. The results suggest that data from activity monitors could supply valuable information about fertility traits and could thereby be helpful in management of herd fertility. To establish the complementarities or interdependence between progesterone and activity measurements, further studies with more information from different sources of measuring estrus are needed.

The objective of this study was to evaluate the factors that may affect conception rates (CR) following artificial insemination (AI) or embryo transfer (ET) in lactating Holstein cows. Estrous cycling cows producing 33.1±7.2kg of milk/d received PGF2α injections and were assigned randomly to 1 of 2 groups (AI or ET). Cows detected in estrus (n = 387) between 48 and 96h after the PGF2α injection received AI (n = 227) 12h after detection of estrus or ET (n = 160) 6 to 8 d later (1 fresh embryo, grade 1 or 2, produced from nonlactating cows). Pregnancy was diagnosed at 28 and 42 d after estrus, and embryonic loss occurred when a cow was pregnant on d 28 but not pregnant on d 42. Ovulation, conception, and embryonic loss were analyzed by a logistic model to evaluate the effects of covariates [days in milk (DIM), milk yield, body temperature (BT) at d 7 and 14 post-AI, and serum concentration of progesterone (P4) at d 7 and 14 post-AI] on the probability of success. The first analysis included all cows that were detected in estrus. The CR of AI and ET were different on d 28 (AI, 32.6% vs. ET, 49.4%) and 42 (AI, 29.1% vs. ET, 38.8%) and were negatively influenced by high BT (d 7) and DIM. The second analysis included only cows with a corpus luteum on d 7. Ovulation rate was 84.8% and was only negatively affected by DIM. Conception rates of AI and ET were different on d 28 (AI, 37.9% vs. ET, 59.4%) and 42 (AI, 33.8% vs. ET, 46.6%) and were negatively influenced by high BT (d 7). The third analysis included only ovulating cows that were 7 d postestrus. Conception rates of AI and ET were different on d 28 (AI, 37.5% vs. ET, 63.2%) and 42 (AI, 31.7% vs. ET, 51.7%) and were negatively influenced by high BT (d 7). There was a positive effect of serum concentration of P4 and a negative effect of milk production on the probability of conception for the AI group but not for the ET group. The fourth analysis was embryonic loss (AI, 10.8% vs. ET, 21.5%). The transfer of fresh embryos is an important tool to increase the probability of conception of lactating Holstein cows because it can bypass the negative effects of milk production and low P4 on the early embryo. The superiority of ET vs. AI is more evident in high-producing cows. High BT measured on d 7 had a negative effect on CR and embryonic retention.

Background Prolonged pre-insemination anestrus (i.e. delayed puberty) is a major contributing factor for culling up to 30% of the replacement gilts at large breeding farm units in Vojvodina. It is imperative to determine if these gilts are acyclic (prepubertal) or cyclic, but just fail to exhibit behavioural estrus. Recent investigations demonstrate that treatment with equine chorionic gonadotropin (eCG) can increase the diestrous phase duration in sexually mature gilts. Based on these finding, the aim of the present studies was to determine the reproductive status of delayed puberty gilts following injection with eCG. Methods Two experiments were conducted on a swine breeding farm in Vojvodina. In Exp. 1, 20 prepubertal (acyclic) gilts, and 120 sexually mature (cyclic) gilts were injected with a single injection of 400 IU eCG + 200 IU human chorionic gonadotropin (hCG) or with 1000 IU eCG (cyclic gilts), at d5, d11 or d17 after spontaneous estrus detection, to determine their ovarian reaction and induced estrus manifestation. In Exp. 2, sixty delayed puberty gilts (estrus not detected until 8 month of age, av. 258 days) were culled from breeding herd and slaughtered to determine their reproductive status based on ovarian anatomical features. The second group of gilts (n = 60) was treated with a single 1000 IU eCG injection to determine their reproductive status, based on the interval between eCG injection to estrus detection and duration. The data were analyzed by descriptive statistics, t -test, analysis of variance and Duncan’s test in the software package Statistics 10 th . Results Ovulations were induced in 90% of acyclic (sexually immature) and, on average, 93.3% of cyclic (sexually mature) gilts after the eCG injection. On average, 4 days after the eCG injection, estrus was detected in 85% of the treated acyclic (sexually immature) gilts and in 95% (19/20) of the cyclic (sexually mature) gilts, treated with eCG on day 17 after spontaneous estrus detection. The interval from eCG to induced estrus detection was prolonged (av. 25 days) in 95% (19/20) of the sexually mature gilts treated with eCG on day 5 and in 90% (18/20) of gilts treated on day 11 after spontaneous estrus detection (Exp. 1). Forty anestrous gilts reached cyclic pubertal ovarian activity. Estrus manifestation was detected in 56 gilts (93.3% of the total 60 treated prolonged anestrous gilts, av. 259 days of age), after a single 1000 IU eCG injection. Thirty-four gilts (60.7% of the total gilts in estrus) with prolonged eCG to estrus interval (av. 24.7 days) were considered spontaneously cyclic (sexually mature), but behaviourally anestrous before treatment. The remaining 22 (39.3% of the total gilts in estrus) were considered truly sexually immature (acyclic) before the treatment or were eCG injected in the late luteal or proestrous phase of spontaneous estrous cycle (Exp. 2). Conclusions In 66.7% of the delayed puberty gilts, pre-ovulatory follicles (PoF), corpora hemorrhagica (CH), corpora lutea (CL), or corpora albicantia (CA) were found on the ovaries upon post mortem examination. These gilts were considered as sexually mature before slaughtering. In 60.7% of the delayed puberty gilts, behavioural estrus was detected an average of 24.7 days following eCG injections. These gilts were considered as eCG treated during the luteal phase (diestrus) of the spontaneous estrus cycle. Both findings suggest that delayed puberty gilts actually reached cyclic pubertal ovarian activity (sexual maturity) before culling from the breeding herd.

One of the major contributors to poor fertility of a dairy herd is ineffective detection of estrus. Recently, it has become evident that cow factors contribute largely to low detection rates. Until now, standing behavior has been the symptom used to determine the right moment for insemination. However, standing behavior is not observed in over 50% of the cows in estrus in a number of herds, and no data are available to relate the moment of ovulation to other estrous symptoms. Therefore, in the present study, cows were observed for a number of visual signs of estrus, twice per day for 30min, and the moment of ovulation was estimated by transrectal ultrasonographic scanning of the ovaries. In this study, 100 cows were detected in estrus visually, using a scoring system. Standing behavior was observed in 50% of those cows, although there was more than one cow in estrus at a time in most (85%) cases. There was no correlation between follicular size and ovulation time or estrous behavior score. Levels of 305-d milk yield and parity were also not correlated with estrous behavior scores. However, a significant correlation (0.31) was found between detection score and day of ovulation. A retrospective analysis revealed that cows that ovulated 0 to 24h after artificial insemination (AI) scored almost three times the number of estrous behavior points compared to the ones that ovulated 24 to 48h after AI. Ovulation more than 48h after AI resulted in pregnancy in only 15% of the cows.

Efficient detection of estrus is a permanent challenge for successful reproductive performance in dairy cattle. In this context, comprehensive knowledge of estrus-related behaviors is fundamental to achieve optimal estrus detection rates. This review was designed to identify the characteristics of behavioral estrus as a necessary basis for developing strategies and technologies to improve the reproductive management on dairy farms. The focus is on secondary symptoms of estrus (mounting, activity, aggressive and agonistic behaviors) which seem more indicative than standing behavior. The consequences of management, housing conditions and cow- and environmental-related factors impacting expression and detection of estrus as well as their relative importance are described in order to increase efficiency and accuracy of estrus detection. As traditional estrus detection via visual observation is time-consuming and ineffective, there has been a considerable advancement of detection aids during the last 10 years. By now, a number of fully automated technologies including pressure sensing systems, activity meters, video cameras, recordings of vocalization as well as measurements of body temperature and milk progesterone concentration are available. These systems differ in many aspects regarding sustainability and efficiency as keys to their adoption for farm use. As being most practical for estrus detection a high priority – according to the current research – is given to the detection based on sensor-supported activity monitoring, especially accelerometer systems. Due to differences in individual intensity and duration of estrus multivariate analysis can support herd managers in determining the onset of estrus. Actually, there is increasing interest in investigating the potential of combining data of activity monitoring and information of several other methods, which may lead to the best results concerning sensitivity and specificity of detection. Future improvements will likely require more multivariate detection by data and systems already existing on farms.