Estrus In Dairy Cattle Research Articles

Time of insemination in relation to pregnancy rates in beef cattle after oestrus detection with automated activity monitoring system

BackgroundThe timing of artificial insemination is critical to achieve acceptable results in cattle production systems. Over the past 60 years the length and expression of oestrus in dairy cattle has altered. Recent studies have indicated the optimal timing for insemination after the commencement of oestrus may now be earlier than traditional recommendations in beef cattle, as is the case in dairy cattle. The aim of the current study was to evaluate the effect of time from onset of oestrus [as determined by an automated activity monitoring system (AAMS)] to artificial insemination (AI) on pregnancy outcome in Norwegian beef cattle. Five commercial beef suckler herds participated in a cohort study by providing data on the time of AAMS alarm and time of AI. Blood sampling on the day of AI was performed and serum progesterone concentration measured. Pregnancy detection was performed by transrectal ultrasonography and aging of the fetus performed when necessary. A mixed logistic regression model was fitted to study the effect of time from AAMS alarm to AI on pregnancy outcome. Time categories used in the model were < 12 h, 12–24 h, and > 24 h.ResultsAI periods (n = 229) with serum progesterone concentration < 1 ng/mL were available for analysis. Overall pregnancy risk per AI for the whole study period was 65.5%, with an inter-herd variation from 10 to 91%. Median time elapsed from AAMS alarm to AI was 17.75 h. Herd affected pregnancy outcome (P = 0.001), while breed and parity status (heifer/cow) did not. The time category closer to AAMS alarm 0–12 h showed a numerically lower pregnancy risk as compared to the baseline group which had AI 12–24 h after onset of oestrus.ConclusionThis study found no evidence to support a change in the recommended timing of AI in beef suckler cows.

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.

Estrus Detection in a Dairy Herd Using an Electronic Nose by Direct Sampling on the Perineal Region.

Estrus detection is very important for the profitability of dairy herds. Different automatic systems for estrus detection have been developed over the last decades. Our study aimed to assess the ability of the electronic nose (EN) MENT-EGAS prototype to detect estrus, based on odor release from the perineal headspace in dairy cattle by direct sampling. The study was performed in an Italian dairy farm using 35 multiparous Holstein-Friesian cows. The cows were divided into three groups: group I included 10 lactating 5-month pregnant cows, group II included 19 lactating cycling cows, and group III included 6 cows that were artificially inseminated 18 days before the trial. Odors from the perineal headspace were collected using the MENT-EGAS prototype. In group I, odors were collected once a day for 5 consecutive days. In group II, odors were collected twice daily from day 18 until day 1 of the reproductive cycle. In group III, odors were also collected twice daily from the presumable day 18 of gestation until day 22. Principal component analyses (PCA) of the perineal headspace samples were performed. PCA in group I revealed no significant discrimination. PCA in group II revealed clear discrimination between proestrus and estrus, and between estrus and metestrus but no significant discrimination was obtained between proestrus and metestrus. PCA in group III revealed that in four cows the results were similar to group I and in two cows the results were similar to group II. On day 40 of the presumable pregnancy, the ultrasound examination revealed that only the four cows were pregnant and the other two cows were regularly cycling. On the basis of our findings, we conclude that it is possible to accurately detect estrus in dairy cattle from directly collected odor samples using the MENT-EGAS prototype. This represents the first study of estrus detection using an EN detection by direct sampling. EN technologies, such as MENT-EGAS, could be applied in the future in dairy cattle farms as a precise, non-invasive method for estrus detection.

Resynchronizing the first eligible estrus in dairy cattle after a prior insemination and fertility of the prior insemination after gonadotropin-releasing hormone and progesterone treatments

The hypothesis of this study tested whether application of designed treatments to synchronize estrus in nonpregnant previously inseminated lactating dairy cows increased the proportion of nonpregnant cows in estrus before early pregnancy diagnosis on Day 32 after the previous insemination (Day 0) and increase fertility of the pretreatment insemination. A progesterone insert (CIDR) and GnRH were applied to cows after insemination to resynchronize the returning estrus of cows that failed to conceive on Day 0. The combination of GnRH (Day 14) and a CIDR insert (d 17 through 24) in experiment 1 (n = 347 cows) did not increase (P = 0.13) the proportion of nonpregnant cows returning to estrus before pregnancy diagnosis, but increased (P < 0.01) the synchrony of their return by 24.4% points, and delayed (P < 0.01) that return by 2.3 ± 0.3 d compared with controls. Ovulation risk after GnRH treatment on Day 14 was only 10%. For cows that failed to return to estrus before Day 32, progesterone concentration on Days 14 and 17 were less than that in cows that returned to estrus by Day 32 and in pregnant cows. Cows that returned to estrus had larger follicles and fewer numbers of CL on Day 21 than pregnant cows and cows that failed to return to estrus, but concentrations of pregnancy-associated glycoproteins on Day 28 indicated that cows failing to return to estrus were likely pregnant but suffered embryo death. In experiment 2 (n = 881), use of GnRH alone (Day 7), a CIDR insert alone (Days 14 through 21), or in combination, failed to increase the proportion of nonpregnant cows in estrus before pregnancy diagnosis on Day 32 compared with controls. Cows receiving the CIDR insert had increased (P < 0.01) synchrony of estrus by 24–34% points compared with cows that did not receive a CIDR insert. More cows receiving GnRH had 2 or more CL on Days 14 and 21 compared with controls. Ovulation risk after GnRH on Day 7 was greater than 66%. In both experiments combined, treatments with GnRH or GnRH + CIDR insert increased (P = 0.015) pretreatment pregnancy per AI by 7.1% points, but did not affect pregnancy loss. Although administering GnRH with or without a CIDR insert synchronized returns to estrus, treatments failed to increase the proportion of nonpregnant cows reinseminated before pregnancy diagnosis, but increased pretreatment pregnancy risk.

Presence of multiple corpora lutea affects the luteolytic response to prostaglandin F2α in lactating dairy cows.

Since the 1970s, luteolytic doses used for synchronizing estrus in dairy cattle have remained unchanged. This study aimed to evaluate the dose-response effect of prostaglandin F2α (PGF2α), which is used for synchronizing estrus, and subsequent fertility in cows with two or more corpora lutea (CL). The study population consisted of 1,683 cows with a single CL (1CL), 501 cows with multiple CL receiving a single dose of PGF2α (2CL1), and 252 cows with multiple CL receiving a 1.5 × PGF2α dose (2CL1.5). Cows with a single CL (n = 1,245) showed estrus significantly (P < 0.01) earlier (3.01 ± 1.23 days; mean ± SD) than cows with multiple CL (n = 287; 3.33 ± 1.69 days). Using 1CL cows as reference, the odds ratio (OR) for the estrus response in 2CL1 cows was 0.13 (P < 0.0001), whereas the ORs for estrus response and pregnancy of 2CL1.5 cows were 1.8 (P = 0.0001) and 1.7 (P = 0.001), respectively. Based on the results for only the 2CL1 cows, the OR for the estrus response was 0.7 (P = 0.01) for cows producing ≥ 45 kg of milk at treatment, compared to the remaining cows producing < 45 kg of milk. Our results showed that the presence of multiple CL reduced the estrus response to that induced by a single PGF2α dose and milk production was inversely associated with this response, whereas an increased PGF2α dose improved the estrus response. Therefore, an increase in the standard PGF2α dose is recommended.

Calving and estrus detection in dairy cattle using a combination of indoor localization and accelerometer sensors

Accelerometers (neck- and leg-mounted) and ultra-wide band (UWB) indoor localization sensors were combined for the detection of calving and estrus in dairy cattle. In total, 13 pregnant cows and 12 cows with successful insemination were used in this study. Data were collected two weeks before and two weeks after delivery for calving. Similarly, data were collected two weeks before and two weeks after artificial insemination (AI) for estrus. Different cow variables were extracted from the raw data (e.g., lying time, number of steps, ruminating time, travelled distance) and used to build and test the detection models. Logistic regression models were developed for each individual sensor as well as for each combination of sensors (two or three) for both calving and estrus. Moreover, the detection performance within different time intervals (24 h, 12 h, 8 h, 4 h, and 2 h) before calving and AI was investigated.In general, for both calving and estrus, the performance of the detection within 2–4 h was lower than for 8 h-24 h. However, the use of a combination of sensors increased the performance for all investigated detection time intervals. For calving, similar results were obtained for the detection within 24 h, 12 h, and 8 h. When one sensor was used for calving detection within 24–8 h, the localization sensor performed best (Precision (Pr) 73–77%, Sensitivity (Se) 57–58%, Area under curve (AUC) 90–91%), followed by the leg-mounted accelerometer (Pr 67–77%, Se 54–55%, AUC = 88–90%) and the neck-mounted accelerometer (Pr 50–53%, Se 47–48%, AUC = 86–88%). As for calving, the results of estrus were similar for the time intervals 24 h-8 h. In this case, similar results were obtained when using any of the three sensors separately as when combining a neck- and a leg-mounted accelerometers (Pr 86–89%, Se 73–77%). For both calving and estrus, the performance improved when localization was combined with either the neck- or leg-mounted accelerometer, especially for the sensitivity (73–91%). Finally, for the detection with one sensor within a time interval of 4 h or 2 h, the Pr and Se decreased to 55–65% and 42–62% for estrus and to 40–63% and 33–40% for calving. However, the combination of localization with either leg or neck-mounted accelerometer as well as the combination of the three sensors improved the Pr and Se compared to one sensor (Pr 72–87%, Se 63–85%). This study demonstrates the potential of combining different sensors in order to develop a multi-functional monitoring system for dairy cattle.

Automated estrous detection using multiple commercial precision dairy monitoring technologies in synchronized dairy cows

Estrus in dairy cattle varies in duration and intensity, highlighting the need for accurate and continuous monitoring to determine optimal breeding time. The objective of this study was to evaluate precision dairy monitoring technologies (PDMT) for detecting estrus. Estrus was synchronized in lactating Holstein cows (n = 109) using a modified G7G-Ovsynch protocol (last GnRH injection withheld to permit expression of estrus) beginning at 45 to 85 d in milk. Resumption of ovarian cyclicity at enrollment was verified by transrectal ultrasonography for presence of a corpus luteum. Cows were observed visually during 30 min (4 times per day) for behavioral estrus on d -1 to 2 (d 0 = day of estrus). Periods peri-estrus were defined by the temporal blood plasma progesterone patterns on d -5, -4, -3, -2, -1, 0, 2, 4, 6, and 8. Estrous detection by PDMT, an estrous behavior scoring system, and by visual observation of standing estrus were compared with the reference (gold) standard. Only 56% of cows that ovulated were observed standing by visual observation. Sensitivity and specificity for estrous detection were not different among all PDMT. Devices in this study measuring activity in steps, neck movement, high activity of head movement, or a proprietary motion index increased on the day of estrus 69 to 170% from the baseline before estrus. The change in rumination time on the day of estrus decreased for both neck and ear-based technologies (-2 to -16%). Temperature of the reticulorumen, vagina, and ear skin were not different on the day of estrus than day peri-estrus. Daily lying times decreased on average to 24.6% on the day of estrus for IceQube (IceRobotics Ltd., Edinburgh, Scotland). In contrast, lying time increased 15.5 and 33.1% for AfiAct Pedometer Plus (Afimilk, Kibbutz Afikim, Israel) and Track a Cow (ENGS Systems Innovative Dairy Solutions, Rosh Pina, Israel), respectively. All PDMT tested were capable of detecting estrus at least as effectively as visual observation. Four of the 6 PDMT that reported estrous alerts correctly detected 15 to 35% more cows than visual observation 4 times per day. Use of temporal progesterone patterns correctly identified more cows than visual observation alone. Dairy producers considering PDMT should focus on (1) the reference (gold) standard used to test efficacy of a device's alerts and (2) the device that will have the fewest false readings in their operations.

Vocalization as an indicator of estrus climax in Holstein heifers during natural estrus and superovulation

The reliable detection of estrus is an important scientific and practical challenge in dairy cattle farming. Female vocalization may indicate reproductive status, and preliminary evidence suggests that this information can be used to detect estrus in dairy cattle. The aim of this study was to associate the changes in the vocalization rate of dairy heifers with behavioral estrus indicators as well as test the influence of the type of estrus (natural estrus vs. superovulation-induced estrus). We analyzed 6 predefined estrus-related behavior patterns (standing to be mounted, head-side mounting, active mounting, chin resting, being mounted while not standing, and active sniffing in the anogenital region) and vocalization rates in the peri-estrus period (day of estrus ± 1 d) of 12 German Holstein heifers using audio-visual recordings. Each heifer was observed under natural estrus and a consecutive superovulation induced by FSH and cloprostenol. Estrus was determined by behavioral patterns and confirmed by clinical examination (vaginoscopy and ultrasound imaging of the ovaries) as well as by the concentration of peripheral progesterone. Estrus behavior and vocalization rates were analyzed in 3-h intervals (an average of 19 intervals for each heifer), and an estrus score was calculated based on the 6 behaviors. The interval with the highest estrus score (I0) was considered the estrus climax. We demonstrated similar time courses for the estrus score and vocalization rate independent of estrus type. However, in natural estrus, the maximum vocalization rate (±SE) occurred in the interval before estrus climax (I-1; 42.58 ± 21.89) and was significantly higher than that in any other interval except estrus climax (I0; 27.58 ± 9.76). During natural estrus, the vocalization rate was significantly higher within the interval before estrus climax (I-1; 42.58 ± 21.89 vs. 11.58 ± 5.51) than under superovulation. The results underscore the potential use of vocalization rate as a suitable indicator of estrus climax in automated estrus detection devices. Further studies and technical development are required to record and process individual vocalization rates.

Comparison of three devices for the automated detection of estrus in dairy cows

Considerable technological advances have been made in the automated detection of estrus in dairy cattle, but few studies have evaluated their relative performance on the same animals or assessed cow-related factors that affect their performance. Our objective was to assess the performance and reliability of three devices commercially available in France for cow estrus detection. The devices were a pedometer (PM; Afitag) and two activity meters (AM1; Heatime-RuminAct, and AM2; HeatPhone). Two algorithms were tested for AM2. We fitted 63 lactating Holstein cows with the three detectors from calving to 90 days after calving. The onset and pattern of cyclicity were monitored from 7 to 90 days postpartum measuring progesterone concentration in milk twice weekly. A total of 211 ovulations were identified. Cyclicity was classified as normal in 60% of cows (38/63). Calculated over the operating period of all the devices (179 periods of estrus), the sensitivities and positive predictive values were, respectively, 71% and 71% for PM, 62% and 84% for AM1, 61% and 67% for the first algorithm of AM2, and 62% and 87% for the second algorithm of AM2. Both activity meters had a lower sensitivity but a higher positive predictive value than the PM (P < 0.05). For all devices, the performance in estrus detection was much poorer at the first postpartum ovulation than at subsequent ovulations (P < 0.05). Lactation rank and milk production affected some devices (P < 0.05). These devices could be used to reinforce visual observations, especially after 50 days postpartum, the minimum recommended delay to insemination. However, their full benefit remains to be verified in different farming systems and taking into account the specific objectives of the dairy farmer.

Expression and detection of estrus in dairy cows: the role of new technologies

Despite the widespread adoption of hormonal synchronization protocols that allow for timed artificial insemination (AI), detection of estrus plays an important role in the reproductive management program on most dairies in the United States. Increased physical activity is a secondary sign of estrus in dairy cattle, and a new generation of electronic systems that continuously monitor physical activity to predict timing of AI have been developed and marketed to the dairy industry. A variety of management and physiologic challenges inhibit detection of behavioral estrus on farms, but the prevalence of anouvular cows near the end of the voluntary waiting period is particularly problematic. Only 70% of lactating Holstein cows were detected in estrus when using an activity monitoring system, with the remaining 20% of cows classified as anovular and 10% ovulating without showing signs of activity. Mean time of AI in relation to ovulation based on the activity monitoring system was acceptable for most of the cows with increased activity, however, variability in the duration of estrus and timing of AI in relation to ovulation could result in poor pregnancy outcomes in some cows. Use of a Presynch–Ovsynch protocol for submission of cows for first AI has been widely adopted by dairies in the United States, and a combined approach in which AI based on activity is followed by submission of cows not detected with activity to timed AI after synchronization of ovulation may be an effective strategy for submission of cows to first AI. Based on a field trial on a large commercial dairy in the United States, the activity monitoring system detected 70% of cows with increased activity after the second PGF2α injection of a Presynch–Ovsynch protocol, however, cows inseminated to increased activity had fewer pregnancies per AI (P/AI) compared with cows with increased activity after the second PGF2α injection that received timed AI after completing the Presynch–Ovsynch protocol. Based on an economic model comparing reproductive management programs with varying levels of AI to estrus v. timed AI, the rate of estrus detection and the P/AI to inseminations based on AI to detected estrus v. timed AI affected the decision to inseminate based on activity v. timed AI. In conclusion, an activity monitoring system detected increased activity in about 70% of lactating Holstein cows on a large commercial dairy in the United States, however, synchronization of ovulation and timed AI was beneficial to inseminate cows not detected with increased activity by the activity monitoring system.

Towards an Automated Detection of Oestrus in Dairy Cattle

Heat detection is a key factor in the profitability of dairy herds. However, this detection demands a significant part of the breeder's working time and is made difficult by the short duration and the discrete behavioural changes associated with oestrus in modern dairy cows. Progress has been made in monitoring cow with electronics, biosensors and computer. As a result, automated heat detection systems have been developed. Currently available tools are automated detectors of standing heat, activity-metres and automated in-line systems measuring milk progesterone. Camera-software systems and monitoring of body temperature are being developed and may also be used as heat detection tools. The heat detection rate of most systems is above 80% with a specificity of detection generally higher than 90%. The accuracy, however, may vary considerably depending on the tool and model developed. The initial investment of several thousands of euros required for these automated systems becomes a source of profit in large herds, provided the recorded data are properly managed.

Effect of time from estrus to AI on pregnancy rates in estrous synchronized beef heifers

For the last 65 years, the industry standard for time of artificial insemination (AI) relative to onset of estrus has been established as 12 h after observed estrus. More recent experiments indicated the optimum time for AI was 4–12 h after onset of estrus in dairy cattle, but little recent information is available on the effect of time of AI relative to the onset of estrus in beef cattle. Therefore, we conducted a retrospective study on the impact of time from onset of estrus to artificial insemination using records from estrous synchronized crossbred Angus heifers ( n = 1205). Data were compiled from experiments on estrous synchronization systems and AI where estrous detection was performed electronically with HeatWatch ®. Only data from heifers detected in estrus was used. Heifers averaged 440 days of age, and weighed 368 kg at insemination. Heifers were assigned to one of the following 4 h blocks of time based on time from onset of estrus (hour 0) to insemination: 0–4 ( n = 79), 4–8 ( n = 163), 8–12 ( n = 211), 12–16 ( n = 163), 16–20 ( n = 193), 20–24 ( n = 149), >24 h ( n = 247). Pregnancy rates to AI were not affected by AI technician or season of breeding ( P > 0.80). However, time of insemination relative to onset of estrus affected ( P < 0.01) pregnancy rates to AI. Heifers inseminated between 4 and 24 h after onset of estrus had greater AI pregnancy rates ( P < 0.05; 63.7%) compared to the 0–4 h (48.1%) and >24 h (55.9%) groups. The time period for achieving acceptable pregnancy rates to AI in beef heifers appears to be approximately 20 h in length. Estrous synchronization programs resulting in a majority of heifers in estrus between 4 and 24 h before AI should yield the greatest pregnancy rates.

On the use of physical activity monitoring for estrus detection in dairy 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.

Bioimplantable impedance and temperature monitor low power micro-system suitable for estrus detection

Based on cyclic physiologic animal bioimpedance and body temperature a new method is being evaluated to predict estrus in dairy cattle with the aim of improving artificial insemination efficiency. Preceding in-vivo tests are being performed with a dedicated system based on the impedance converter AD5933, and a two-electrode configuration bioimplantable capsule. Acquired results will allow the optimal design of an implantable and autonomous low-power system. The implantable microsystem is being designed and simulated employing low-power techniques in a 0.35μm CMOS standard process.

Comparison of a camera-software system and typical farm management for detecting oestrus in dairy cattle at pasture

AIM: To compare the sensitivity, specificity, predictive values and accuracy of detection of oestrus using a novel oestrus detection-strip (ODS) and a camera-software device (CSD) with typical farm management practices of visual observation and use of tail paint in dairy cattle at pasture. METHODS: Dairy cows (n=480) in a seasonal-calving herd managed at pasture under typical commercial conditions in New Zealand were stratified by age, body condition score and days in milk, then randomly allocated to one of two groups prior to the planned start of mating (PSM). Tail paint was applied to all cows and oestrus detected by visual observation of oestrous behaviour and removal of paint, by farm staff. One group (n=240) was fitted with ODS and also monitored for signs of oestrus using a CSD, while the Control group (n=240) was monitored using tail paint and visual observations only. Cows detected in oestrus were artificially inseminated (AI), and pregnancy status determined using rectal palpation and ultrasonography, 51–52 days after the end of a 55-day AI period. Results of pregnancy diagnosis were used to confirm the occurrence of oestrus, and the sensitivity, specificity, predictive value and accuracy of detection of oestrus compared between oestrus detection methods. RESULTS: The sensitivity and accuracy of oestrus detection in the Control group, using visual observation and tail paint, were low. Compared with the Control group, detection of oestrus using the ODS and CSD resulted in greater sensitivity (85% vs 78%; p=0.006), specificity (99.6% vs 98.0%; p<0.001), positive predictive value(PPV;88% vs 51%;p<0.001) and overall accuracy (99.0% vs 98.0%; p<0.001). Negative predictive value (NPV) did not differ significantly between groups (99.4% vs 99.3%; p=0.28). Pregnancy rate to first service was higher in the CSD group than in the Control group (72% vs 39%; p<0.05). Use of the CSD significantly increased the cumulative proportion of cows pregnant to AI over the breeding period (p=0.044). CONCLUSION AND CLINICAL RELEVANCE: The ODS and CSD was satisfactory for detection of oestrus in seasonal-calving dairy herds grazing on pasture and could improve the sensitivity and accuracy of detection of oestrus in herds where these are low.

Effect of Different Prostaglandin Treatment Protocols on Luteolysis and Ovulation in Dairy Cows

During the past 25 years, several methods were developed to synchronize estrus in dairy cattle. Synchronization with prostaglandin F2α (PGF2α) is successful when cows are bred at a detected estrus because estrus detection rates and artificial insemination (Al) are more efficient than daily detection of estrus (Stevenson and Pursley, 1994). The success of estrous induction with PGF2α depends on the presence of a functional corpus luteum (CL). In case of a palpable CL, Archbald et al (1994) found that the percentage of milking cows observed in estrus within seven days after treatment (25 mg of PGF2α) was 55% (61/111). This management tool still does not control the time of AI, because estrus detection continues to be necessary, which is evidenced by the lower pregnancy rate after timed AI compared with AI after detected estrus. This might be partially explained by the variation in time of ovulation over periods of five days with respect to time of AI (Stevenson et al, 1987). Various attempts have been made to overcome this variability in response to PGF2α treatments. The administration of other hormones in conjunction with PGF2α, such as progesterone, estradiol benzoate, human chorionic gonadotropin (hCG) and gonadotropin releasing hormone (GnRH) (Deletang 1975; De Rensis and Peter 1999; Pursley et al, 1996), have been attempted. There was a better degree of synchronization but the pregnancy rate was similar to that of untreated cows. Similarly, two prostaglandin injections at an eight-hour interval was more effective on the incidence of luteolysis than a single injection (Archbald et al, 1993). There are several studies dealing with the effect of prostaglandin on the area changes of corpus luteum and follicles in heifers (Kastellic and Ginther 1991), on corpus luteum size and plasma progesterone concentrations in cattle (Assey et al, 1993), and the effect of different doses of PGF2α on fertility (Lagar 1977), however the effect of different doses of PGF2α on the area changes of the corpus luteum, the largest follicle and the progesterone (P4) concentration has not been examined. The objective of our study was to compare the effect of different doses of PGF2α (first trial-0 mg, 25 mg, 35 mg; second trial-2 x PGF2α treatment at an eight-hour interval) on the corpus luteum, the largest follicle and the progesterone concentration in dairy cattle. The time to ovulation after AI in PGF2α-treated and untreated cows (third trial) was also determined.

Detection and Characterization of Estrus in Dairy Cattle with an Electronic Heatmount Detector and an Electronic Activity Tag

The length and onset of estrus was studied in 71 lactating dairy cows using an electronic heatmount sensor (HeatWatch; DDx Inc., Boulder, CO, DeForest, WI) and an electronic activity tag (Heat Seeker, Boumatic, Madison, WI). Three methods were used to determine estrus: 1) the electronic heatmount system, 2) an increased activity ratio algorithm determined by the Heat Seeker, and 3) an increased activity count algorithm calculated for each estrous period. Mounting and physical activity variables were characterized, and the effects of synchrony, parity, and weather on these variables were determined with data from two different trials. Cows in trial 1 were not synchronized, while cows in trial 2 were synchronized. The results of the study were consistent as follows: mean numbers of mounts were 6.70±0.7 and 5.42±0.80 for trials 1 and 2, respectively; each mount lasted 3.20±0.19s (trial 1) and 3.36±0.42s (trial 2). Total mounting activity averaged 5.83±0.78h per estrous period in trial 1 and 5.57±1.02h in trial 2. Estrus identified by the increased activity count algorithm corresponded more closely to standing mount activity (determined by the HeatWatch System) than did the increased activity ratio algorithm. Synchrony, parity, and weather did not have a direct effect on physical activity. Hot weather decreased the duration of standing mount activity significantly, but did not affect the number or duration of individual mounts. All three methods of estrus detection employed improved the efficiency of detection over visual observation.

The use of electronic pedometers in determining activity to predict oestrus in dairy cattle

It is well documented that cows exhibit an increase in physical activity around the time of oestrus (Kiddy, 1977). Pedometers are receiving a great deal of attention to automate and record the cow activity data. Some previous field studies indicate that pedometers are superior to casual observations (Kiddy, 1977 and Redden et al, 1993). However, others show limited success due to a large number of false positive identifications (Moore and Spahr, 1991). The aim of the experiment was to investigate the possibility of using twice daily pedometer readings in determining activity to predict oestrus in dairy cattle under UK management.

The use of electronic pedometers in determining activity to predict oestrus in dairy cattle

It is well documented that cows exhibit an increase in physical activity around the time of oestrus (Kiddy, 1977). Pedometers are receiving a great deal of attention to automate and record the cow activity data. Some previous field studies indicate that pedometers are superior to casual observations (Kiddy, 1977 and Redden et al, 1993). However, others show limited success due to a large number of false positive identifications (Moore and Spahr, 1991). The aim of the experiment was to investigate the possibility of using twice daily pedometer readings in determining activity to predict oestrus in dairy cattle under UK management.

Automated Electronic Activity Measurement for Detection of Estrus in Dairy Cattle

Two trials were conducted to characterize the performance of an automated electronic activity tag system as an aid for detection of estrus in dairy cattle. In trial 1, activity tags were attached to the rear leg of 24 cows at approximately 35 d postpartum and remained attached until pregnancy. Data collection included 66 periods of predicted estrus (cyclic periods of 18 to 24 d prior to the date of pregnancy). A summary of the data recorded in the activity tags was transmitted telemetrically to a personal computer at each milking via stationary antenna in the milking parlor. An electronic flag used an increased activity ratio to determine the ratio of activity in a test period of the previous 12h to activity in the same 12h during the 2 d previous to the test period, thus indicating estrus. Activity patterns were characterized from data recorded on the tags at 2-h intervals. The tag detected 74% of predicted periods of estrus versus 58% reported by herders. An increased activity ratio for at least 4 consecutive h reduced false-positive designations. In trial 2, activity tags were attached to front and rear legs on five cows, and activity patterns from the two sites were compared for 2 mo. Patterns of activity were similar from tags attached at either site, and the sites were not different in their discrimination between periods in which estrus did or did not occur. The activity tag system was an effective practical tool to detect estrus.