Effect of gonadotropin-releasing hormone administered at the time of artificial insemination for cows detected in estrus by conventional estrus detection or an automated activity-monitoring system

The objectives of this study were to determine the effects of GnRH at the time of artificial insemination (AI) on ovulation, progesterone 7 d post-AI, and pregnancy in cows detected in estrus using traditional methods (tail chalk removal and mount acceptance visualization) or an automated activity-monitoring (AAM) system. We hypothesized that administration of GnRH at the time of AI would increase ovulation rate, plasma progesterone post-AI, and pregnancy per AI (P/AI) in cows detected in estrus. In experiment 1, Holstein cows (n = 398) were blocked by parity and randomly assigned to receive an injection of GnRH at the time of estrus detection/AI (GnRH, n = 197) or to remain untreated (control, n = 201) on 4 farms. The GnRH was administered as 100 µg of gonadorelin acetate. Ovarian structures and plasma progesterone were assessed in a subset of cows (GnRH, n = 52; control, n = 55) in experiment 1 at the time of AI and 7 d later. In experiment 2, a group of 409 cows in an AAM farm were enrolled as described for experiment 1 (GnRH, n = 207; control, n = 202). Data were categorized for parity (primiparous vs. multiparous), season (cool vs. warm), number of services (first vs. > first), DIM (>150 DIM vs. ≤150 DIM), and for AAM cows in experiment 2 for activity level (high: 90–100 index vs. low: 35–89 index). Pregnancy diagnosis was performed between 32 and 45 d post-AI (P1) and 60 to 115 d post-AI (P2). In experiment 1, there was no difference in plasma progesterone at day of estrus detection (control = 0.09 ng/mL vs. GnRH = 0.16 ng/mL), 7 d later (control = 2.03 ng/mL vs. GnRH = 2.18 ng/mL), and ovulation rate (GnRH = 83.2% vs. control = 77.9%) between treatments. There were no effects of GnRH in experiment 1 for P/AI at P1 (control = 43.3% vs. GnRH = 38.6%), P2 (control = 38.4% vs. GnRH = 34.5%), and for pregnancy loss (control = 9.8% vs. GnRH = 8.2%). In experiment 2, there were no effects of GnRH for P/AI at P1 (control = 39.6% vs. GnRH = 40.1%), P2 (control = 35.0% vs. GnRH = 37.4%), and for pregnancy loss (control = 9.5% vs. GnRH = 6.2%). There was a tendency for a parity effect on P/AI for P1, but not P2 or for pregnancy loss. High-activity cows had greater P/AI in P1 (low activity = 27.9% vs. high activity = 44.1%), P2 (low activity = 21.8% vs. high activity = 41.2%), and lower pregnancy loss (low activity = 20.7% vs. high activity = 5.1%), but there were no interactions between treatment and activity level. The current study did not support the use of GnRH at estrus detection to improve ovulatory response, progesterone 1 wk post-AI, and P/AI. More research is needed to investigate the relationship between GnRH at the time of AI and activity level in herds using AAM systems.     

Technical note: A novel approach to the detection of estrus in dairy cows using ultra-wideband technology

Detection of estrus is a key determinant of profitability of dairy herds, but estrus is increasingly difficult to observe in the modern dairy cow with shorter duration and less-intense estrus. Concurrent with the unfavorable correlation between milk yield and fertility, estrus-detection rates have declined to less than 50%. We tested ultra-wideband (UWB) radio technology (Thales Research & Technology Ltd., Reading, UK) for proof of concept that estrus could be detected in dairy cows (two 1-wk-long trials; n = 16 cows, 8 in each test). The 3-dimensional positions of 12 cows with synchronized estrous cycles and 4 pregnant control cows were monitored continuously using UWB mobile units operating within a network of 8 base units for a period of 7 d. In the study, 10 cows exhibited estrus as confirmed by visual observation, activity monitoring, and milk progesterone concentrations. Automated software was developed for analysis of UWB data to detect cows in estrus and report the onset of estrus in real time. The UWB technology accurately detected 9 out of 10 cows in estrus. In addition, UWB technology accurately confirmed all 6 cows not in estrus. In conclusion, UWB technology can accurately detect estrus and hence we have demonstrated proof of concept for a novel technology that has significant potential to improve estrus-detection rates.     

Technical note: Evaluation of an ear-attached movement sensor to record cow feeding behavior and activity

The ability to monitor dairy cow feeding behavior and activity could improve dairy herd management. A 3-dimensional accelerometer (SensOor; Agis Automatisering BV, Harmelen, the Netherlands) has been developed that can be attached to ear identification tags. Based on the principle that behavior can be identified by ear movements, a proprietary model classifies sensor data as “ruminating,” “eating,” “resting,” or “active.” The objective of the study was to evaluate this sensor on accuracy and precision. First, a pilot evaluation of agreement between 2 independent observers, recording behavior from 3 cows for a period of approximately 9 h each, was performed. Second, to evaluate the sensor, the behavior of 15 cows was monitored both visually (VIS) and with the sensor (SENS), with approximately 20 h of registration per cow, evenly distributed over a 24-h period, excluding milking. Cows were chosen from groups of animals in different lactation stages and parities. Each minute of SENS and VIS data was classified into 1 of 9 categories (8 behaviors and 1 transition behavior) and summarized into 4 behavioral groups, namely ruminating, eating, resting, or active, which were analyzed by calculating kappa (κ) values. For the pilot evaluation, a high level of agreement between observers was obtained, with κ values of ≥0.96 for all behavioral categories, indicating that visual observation provides a good standard. For the second trial, relationships between SENS and VIS were studied by κ values on a minute basis and Pearson correlation and concordance correlation coefficient analysis on behavior expressed as percentage of total registration time. Times spent ruminating, eating, resting, and active were 42.6, 15.9, 31.6, and 9.9% (SENS) respectively, and 42.1, 13.0, 30.0, and 14.9% (VIS), respectively. Overall κ for the comparison of SENS and VIS was substantial (0.78), with κ values of 0.85, 0.77, 0.86, and 0.47 for “ruminating,” “eating,” “resting,” and “active,” respectively. Pearson correlation and concordance correlation coefficients between SENS and VIS for “ruminating,” “eating,” “resting,” and “active” were 0.93, 0.88, 0.98, and 0.73, and 0.93, 0.75, 0.97, and 0.35, respectively. In conclusion, the results provide strong evidence that the present ear sensor technology can be used to monitor ruminating and resting behavior of freestall-housed dairy cattle. Our results also suggest that this technology shows promise for monitoring eating behavior, whereas more work is needed to determine its suitability to monitor activity of dairy cattle.     

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.     

Short communication: Sensitivity of estrus alerts and relationship with timing of the luteinizing hormone surge

Both the sensitivity of an estrus detection system and the consistency of alarms relative to ovulation determine its value for a farmer. The objective of this study was to compare an activity-based system and a milk progesterone–based system for their ability to detect estrus reliably, and to investigate how their alerts are linked to the time of the LH surge preceding ovulation. The study was conducted on an experimental research farm in Flanders, Belgium. The activity alerts were generated by a commercial activity meter (ActoFIT, DeLaval, Tumba, Sweden), and milk progesterone was measured using a commercial ELISA kit. Sensitivity and positive predictive value of both systems were calculated based on 35 estrus periods over 43 d. Blood samples were taken for determination of the LH surge, and the intervals between timing of the alerts and the LH surge were investigated based on their range and standard deviation (SD). Activity alerts had a sensitivity of 80% and a positive predictive value of 65.9%. Alerts were detected from 39 h before until 8 h after the LH surge (range: 47 h, SD: 16 h). Alerts based on milk progesterone were obtained from a recently developed monitoring algorithm using a mathematical model and synergistic control. All estruses were correctly identified by this algorithm, and the LH surge followed, on average, 62 h later. Using the mathematical model, model-based indicators for the estimation of ovulation time can be calculated. Depending on which model-based indicator was used, ranges of 33 to 35 h and SD of about 11 h were obtained. Because detection of the LH surge was very labor intensive, only a limited number of potential estrus periods could be studied.     

Assessment of an accelerometer system for detection of estrus and treatment with gonadotropin-releasing hormone at the time of insemination in lactating dairy cows

Two experiments were conducted to evaluate an accelerometer system (Heatime; SCR Engineers Ltd., Netanya, Israel) to manage reproduction in lactating dairy cows. In experiment 1, lactating Holstein cows (n = 112) were fitted with an accelerometer system and were treated with GnRH followed 7 d later by PGF_2α to synchronize estrus. A total of 89 cows that had a follicle >10 mm in diameter and a functional corpus luteum at the PGF_2α injection that regressed by 48 h after induction of luteolysis were included in the analysis. Overall, 71% of cows were detected in estrus by the accelerometer system and 95% of cows showing estrus ovulated within 7 d after induction of luteolysis. Of the cows not detected in estrus by the accelerometer system, 35% ovulated within 7 d after induction of luteolysis. Duration of estrus activity (mean ± SD) was 16.1 ± 4.7 h and was neither affected by parity nor milk production. Intervals (means ± SD) from induction of luteolysis, onset of activity, peak raw activity, and peak weighted activity to ovulation was 82.2 ± 9.5, 28.7 ± 8.1, 20.4 ± 7.8, and 16.4 ± 7.4 h, respectively, and the interval from AI to ovulation was 7.9 ± 8.7 h, but ranged from −12 to 26 h. In experiment 2, cows were assigned randomly to receive an intramuscular injection of GnRH at artificial insemination (AI) after detection of estrus by the accelerometer system or receive no treatment (control). Nine hundred seventy-nine AI services from 461 cows were analyzed. Treatment with GnRH at AI did not affect fertility at 35 or 65 d after AI, and no interaction was detected between treatment and season or treatment and AI number. Overall, two-thirds of the cows that were considered properly synchronized were inseminated based on the accelerometer system and ovulated after AI. The remaining cows either were not inseminated because they were not detected in estrus or would not have had a chance to conceive to AI because they failed to ovulate after estrus. Furthermore, mean time of AI in relation to ovulation determined by the accelerometer system was acceptable for most of the cows that displayed estrus; however, variability in the duration of estrus and timing of AI in relation to ovulation could lead to poor fertility in some cows. For lactating dairy cows detected in estrus by the accelerometer system, treatment with GnRH at the time of AI without reference to the onset of estrus did not increase fertility.     

Reproductive performance of lactating dairy cows managed for first service using timed artificial insemination with or without detection of estrus using an activity-monitoring system

Lactating dairy cows (n = 1,025) on a commercial dairy farm were randomly assigned at 10 ± 3 d in milk (DIM) to 1 of 3 treatments for submitting cows to first artificial insemination (AI) and were fitted with activity-monitoring tags (Heatime; SCR Engineers Ltd., Netanya, Israel) at 24 ± 3 DIM. Cows (n = 339) in treatment 1 were inseminated based on increased activity from the end of the voluntary waiting period (50 DIM) until submission to an Ovsynch protocol; cows without increased activity from 21 to 65 DIM began an Ovsynch protocol at 65 ± 3 DIM, whereas cows without increased activity from 21 to 50 DIM but not from 51 to 79 DIM began an Ovsynch protocol at 79 ± 3 DIM. Cows (n = 340) in treatment 2 were inseminated based on activity after the second PGF_2α injection of a Presynch-Ovsynch protocol at 50 DIM, and cows without increased activity began an Ovsynch protocol at 65 ± 3 DIM. Cows (n = 346) in treatment 3 were monitored for activity after the second PGF_2α injection of a Presynch-Ovsynch protocol, but all cows received timed AI (TAI) at 75 ± 3 DIM after completing the Presynch-Ovsynch protocol. The activity-monitoring system detected increased activity in 56, 69, and 70% of cows in treatments 1, 2, and 3, respectively. Treatment-2 cows had the fewest average days to first AI (62.5), treatment-3 cows had the most average days to first AI (74.9), and treatment-1 cows had intermediate average days to first AI (67.4). Treatment-1 and -2 cows in which inseminations occurred as a combination between increased activity and TAI had fewer overall pregnancies per AI (P/AI) 35 d after AI (32% for both treatments) compared with treatment-3 cows, all of which received TAI after completing the Presynch-Ovsynch protocol (40%). Based on survival analysis, although the rate at which cows were inseminated differed among treatments, treatment did not affect the proportion of cows pregnant by 300 DIM. Thus, use of an activity-monitoring system to inseminate cows based on activity reduced days to first AI, whereas cows receiving 100% TAI after completing a Presynch-Ovsynch protocol had more P/AI. The trade-off between AI service rate and P/AI in the rate at which cows became pregnant was supported by an economic analysis in which the net present value ($/cow per year) differed by only $4 to $8 among treatments. We conclude that a variety of strategies using a combination of AI based on increased activity using an activity-monitoring system and synchronization of ovulation and TAI can be used to submit cows for first AI.     

Inseminations at estrus induced by presynchronization before application of synchronized estrus and ovulation

A controlled field study examined conception rates after 2 timed artificial insemination (TAI) breeding protocols conducted on 2 commercial dairy farms. Estrous cycles in postpartum lactating cows were presynchronized with 2 injections of PGF(2alpha) given 14 d apart (Pre-synch) and then, after 12 d, the standard Ovsynch protocol (injection of GnRH 7 d before and 48 h after an injection of PGF(2alpha), with one TAI at 12 to 16 h after the second GnRH injection) or Heatsynch protocol [injection of GnRH 7 d before an injection of PGF(2alpha), followed 24 h later by 1 mg of estradiol cypionate (ECP) and one TAI 48 h after ECP] was applied. Experimental design allowed artificial insemination to occur anytime after the second Presynch injection and during the designed breeding week when estrus was detected. Of the 1846 first services performed, only 1503 (rate of compliance = 81.4%) were performed according to protocol. Numbers of cows inseminated, logistic-regression adjusted conception rates, and days in milk (DIM) were for inseminations made: 1) during 14 d after first Presynch injection (n = 145; 22.6%; 54 +/- 0.4 DIM); 2) during 12 d after second Presynch injection (n = 727; 33%; 59 +/- 0.2 DIM); 3) during 7 d after the first GnRH injection of Ovsynch or Heatsynch (n = 96; 32.1%; 74 +/- 0.5 DIM); 4) after estrus as part of Heatsynch (n = 212; 44.6%; 76 +/- 0.3 DIM); 4) after TAI as part of Heatsynch (n = 154; 21.1%; 76 +/- 0.4 DIM); 5) after estrus as part of Ovsynch (n = 43; 48.7%; 77 +/- 0.7 DIM); and 6) after TAI as part of Ovsynch (n = 271; 24.4%; 77 +/- 0.3 DIM). Conception rates when AI occurred after one Presynch injection were less than when AI occurred after 2 Presynch injections. Conception rates for those inseminated after either Presynch injection did not differ from those inseminated after combined Heatsynch + Ovsynch. Cows in the Ovsynch and Heatsynch protocols inseminated after estrus during the breeding week had greater conception rates than those receiving the TAI, but overall conception rates did not differ between protocols. Among cows inseminated after detected estrus, conception was greater for cows in the Heatsynch + Ovsynch protocol (77 +/- 0.4 DIM) than for those inseminated after either Presynch injection (54 +/- 0.4 or 59 +/- 0.2 DIM). We concluded that conception rates after Heatsynch and Ovsynch were similar under these experimental conditions, and that delaying first AI improved fertility for cows inseminated after detected estrus.     

Technical note: Evaluation of an ear-attached real-time location monitoring system

Position tracking of cows within the barn environment allows for determining behavioral patterns and activities. Such data might be used for detection of estrus and disease. A newly marketed real-time location monitoring system (Smartbow, Smartbow GmbH, Weibern, Austria) was tested in this study. Cow location was continuously monitored with the Smartbow tags mounted on the cow's ear, which sends low-frequency signals to receivers further transmitting the information to a server. Through incoming data, the server triangulates the location of the cow within the barn environment in real time. The validation of the system was carried out in 4 steps. The first 2 steps served as static testing steps (tags and 1 cow positioned at 30 reference points), and steps 3 and 4 were dynamic steps with cows moving in the barn environment. For 48 h, locations of 15 cows were confirmed each hour by laser measurements performed by a team (step 3) or 1 observer (step 4). Interobserver variability was 0.83 m (range: 0.05 to 2.87 m), and intraobserver variability had a range of 0.02 to 0.31 m. In the 4 validation steps, the mean distance between observer laser measurements and Smartbow was between 1.22 and 1.80 m. Step 4, with 334 observations, resulted in a mean distance difference of 1.22 m (standard error = 1.32 m). Data can be used for development of algorithms to detect sick cows with changed behavioral patterns. Data may also be used to monitor cow responses to physical environment, potentially improving facility design. Time budgets in proximity to important barn features (i.e., feed bunk and water trough) and distances traveled can be calculated and used to identify cows in need of caretaker's attention and identify the cow's exact location in the barn.     

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 9.24 h in heifers and 8.12 h 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.     

Follicular size and response to Ovsynch versus detection of estrus in anovular and ovular lactating dairy cows

In a commercial dairy herd, 316 lactating Holsteins were studied to determine the percentage of anovular cows, to examine follicular sizes in anovular cows, and to compare synchronized ovulation (Ovsynch) versus detection of estrus on fertility of ovular and anovular cows. Ultrasonography examinations at 47 to 53 d and at 54 to 60 d postpartum were used to measure follicles and to classify cows as ovular or anovular. Anovular cows were identified as those with no detectable luteal tissue by ultrasonography and by low progesterone in blood samples collected weekly. Anovular cows included 28% of 122 primiparous cows and 15% of 194 multiparous cows. Of 64 anovular cows, 20% had follicles > or = 25 mm that might be considered cystic (4% of total cows), 58% had 15- to 24-mm follicles, and 22% had 9- to 14-mm follicles. Cows identified as ovular and anovular were randomly assigned within cyclic status to one of two artificial insemination (AI) strategies: 1) AI after detected estrus during 21 d, or 2) timed AI after a 10-d Ovsynch protocol. Weekly ultrasonography continued for 21 d to detect ovulations. For the Ovsynch sub-groups, 97% of ovular and 94% of anovular cows ovulated after the second GnRH injection. Within 21 d, spontaneous ovulations for the detection of estrus sub-groups were 42% of anovular cows vs. 89% of ovular cows. Conception rates were greater for ovular cows regardless of treatment, but conception rates between respective Ovsynch and estrus detection groups for ovular (32%, 35%) or anovular (9%, 11%) cows were similar. Although 20% of lactating cows were not cyclic by about 60 d postpartum, nearly all ovulated following Ovsynch. However, anovular cows had lower conception than ovular cows whether inseminated after detected estrous or after Ovsynch.     

Daily lying behavior of lactating Holstein cows during an estrus synchronization protocol and its associations with fertility

Objectives were to quantify lying behavior (LB) during an estradiol and progesterone-based synchronization protocol, to assess risk factors for ovulation, pregnancy per AI (P/AI), and degree of behavioral change at estrus, and to investigate the associations between estrus LB and walking activity. Holstein cows (43.6 ± 11.0 kg of milk/d) were fitted with leg-mounted accelerometers. Total lying time/d (L_time), bout frequency (bout_N), average lying bout duration, and relative increase in walking activity (ACT%) were evaluated for 1,411 timed artificial insemination events. The day with lowest L_time or bout_N among d ?2, ?1, and 0 (day of timed artificial insemination) determined the day of behavioral estrus. The variables L_time% and bout_N% represent relative ratios between lowest L_time and baseline (d ?7), L_time, and lowest bout_N, and baseline (d ?7) bout_N, respectively [e.g., (lowest L_time/baseline L_time) × 100]. Correlation coefficients between L_time% and bout_N% and ACT% were ?0.38 and ?0.31, respectively. Estrus LB change was considered large if <75% of baseline and small if ≥75% of baseline for both L_time% and bout_N%; average lying bout duration did not change with estrus. Lowest L_time% and bout_N% corresponded to, respectively, 65 ± 21% (mean ± standard deviation; 447 ± 157 min/d) and 65 ± 24% (8.5 ± 4.0 bouts/d) of baseline. The change in L_time% at estrus was smaller when cows had milk yield above average; the change in bout_N% was smaller among multiparous cows and for estrus occurring in the colder season. Likelihood of ovulation was greater when there was larger change in L_time% [odds ratio = 4.9; ovulation rate = 93 (large change) and 76% (small change)], as well as when a corpus luteum was present at start of protocol (odds ratio = 3.6; in the model with L_time%). Likelihood of pregnancy at d 32 was 1.6 times greater for estrus with large change in LB [L_time% or bout_N%; P/AI = 34% (large change in L_time%) and 26% (small change in L_time%)]. Among estrus events with ACT% ≥300% (high intensity), classification by small or large L_time% did not influence P/AI at 32 d. The magnitude of LB change at estrus and its association with fertility suggest potential application toward improved use of activity monitors (e.g., increased estrus detection, fertility prediction). The contribution of LB to accuracy of estrus detection when physical activity is known remains to be addressed. The relationship between intensity of estrus expression and fertility requires further investigations of its physiological rationale and on-farm applications.     

Short communication: Conception rates following detection of estrus and timed AI in dairy cows synchronized using GnRH and PGF2alpha

The objective of this study was to compare conception rates of cows exhibiting spontaneous estrus and receiving artificial insemination (AI) before completion of a timed AI protocol with cows that did not display estrus spontaneously, but were inseminated after 1 of 3 GnRH-PGF_2α protocols. Cows (n = 432) in 2 herds were administered GnRH on d -7 and were tail-chalked daily. Cows detected in estrus before d 0 were inseminated immediately. Cows not detected in estrus by d 0 were administered PGF_2α and were tail-chalked daily until 48 h after PGF_2α. Cows detected in estrus from d −7 to 48 h after PGF_2α were inseminated and designated as treatment A (n = 46). Cows not detected in estrus and not inseminated by 48 h after PGF_2α were assigned randomly to receive either GnRH 48 h after PGF_2α and timed AI 16 h later (treatment B; n = 132), or GnRH and timed AI 64 h after PGF_2α (treatment C; n = 127), or timed AI 64 h after PGF_2α (treatment D; n = 127). Pregnancy was diagnosed 38 to 45 d after AI by palpation per rectum of uterine contents. Nearly 11% of all cattle exhibited spontaneous estrus and received immediate AI. Herd did not influence the percentage of cows detected in estrus and inseminated. Conception rates did not differ among treatments. Conception rates differed between herds, but no interaction of herd × treatment was detected. No differences were detected between herds for days in milk, milk production, AI service number, or parity.     

Estimates of intra- and interclass correlation coefficients for rump touches and the number of steps during estrus in postpartum cows

Estrus traits have economic value in dairy production systems and could be incorporated into genetic selection indices. In an effort to further understand selection responses, 2 studies were performed to estimate the intra- and interclass correlation coefficients for estrus traits. Holstein-Friesian cows (n = 1,197; study 1) across 5 pasture-based grazing dairy herds were fitted with a capacitive touch sensing (CTS) device on the rump (FlashMate, Farmshed Labs Limited, Hamilton, New Zealand). The daily number of rump touches were subjected to a peak detection program to objectively identify periods of increased rump touches above baseline (indicative of estrus). The number of times touched and the sum of the touch duration were used to compare farms and estimate the intraclass correlation (repeatability). For study 2, postpartum Holstein (n = 85) and Guernsey (n = 5) cows in a confinement-style dairy were used. Cows were fitted with an IceQube accelerometer (IceRobotics Ltd., Edinburgh, United Kingdom) to measure steps taken per hour and a CTS device was applied to both rumps. The interclass correlation for the number of rump touches and number of steps taken during estrus was calculated. Data collected from 5 herds (study 1) demonstrated a 2- to 3-fold difference between herds in the number of rump touches and total touch time during estrus. The intraclass correlation (repeatability; estimates of maximum heritability) for rump touches during estrus was 0.22. For study 2, the number of steps and the number of rump touches during estrus increased in a synchronous manner. The intraclass correlation (repeatability) for number of steps during estrus was 0.26. The interclass correlation (r) for the number of rump touches and the number of steps was 0.46 (R² = 0.21). Based on the R², at least 20% of the variation in the number of steps during estrus was explained by the number of touches to the rump of the cow. Selecting cows for the number of steps taken during estrus could increase the number of rump touches (mounts, chin rests, and so on, received from other cows) if a genetic correlation exists for the phenotypic correlation that we observed.     

Methodological guidelines: Cow milk mid-infrared spectra to predict reference enteric methane data collected by an automated head-chamber system

Various methodological protocols were tested on milk samples from cows fed diets affecting both methanogenesis and milk synthesis to identify the best approach for the prediction of GreenFeed system (GF) measured methane (CH₄) emissions by milk mid-infrared (MIR) spectroscopy. The models developed were also tested on a data set from cows fed chemical inhibitors of CH₄ emission [3-nitrooxypropanol (3NOP)] that just marginally affect milk composition. A total of 129 primiparous and multiparous Holstein cows fed diets with different methanogenic potential were considered. Individual milk yield (MY) and dry matter intake were recorded daily, whereas fat- and protein-corrected milk (FPCM) was recorded twice a week. The MIR spectra from 2 consecutive milkings were collected twice a week. Twenty CH₄ spot measurements with GF were taken as the basic measurement unit (BMU) of CH₄. The equations were built using partial least squares regression by splitting the database into calibration and validation data sets (excluding 3NOP samples). Models were developed for milk MIR spectra by milking and on day spectra obtained by averaging spectra from 2 consecutive milkings. Models based on day spectra were calibrated by using CH₄ reference data for a measurement duration of 1, 2, 3, or 4 BMU. Models built from the average of the day spectra collected during the corresponding CH₄ measurement periods were developed. Corrections of spectra by days in milk (DIM) and the inclusion of parity, MY, and FPCM as explanatory variables were tested as tools to improve model performance. Models built on day milk MIR spectra gave slightly better performances that those developed using spectra from a single milking. Long duration of CH₄ measurement by GF performed better than short duration: the coefficient of determination of validation (R²V) for CH₄ emissions expressed in grams per day were 0.60 vs. 0.52 for 4 and 1 BMU, respectively. When CH₄ emissions were expressed as grams per kilogram of dry of matter intake, grams per kilogram of MY, or grams per kilogram of FPCM, performance with a long duration also improved. Coupling GF reference data with the average of milk MIR spectra collected throughout the corresponding CH₄ measurement period gave better predictions than using day spectra (R²V = 0.70 vs. 0.60 for CH₄ as g/d on 4 BMU). Correcting the day spectra by DIM improved R²V compared with the equivalent DIM-uncorrected models (R²V = 0.67 vs. 0.60 for CH₄ as g/d on 4 BMU). Adding other phenotypic information as explanatory variables did not further improve the performance of models built on single day DIM-corrected spectra, whereas including MY (or FPCM) improved the performance of models built on the average of spectra (uncorrected by DIM) recorded during the CH₄ measurement period (R²V = 0.73 vs. 0.70 for CH₄ as g/d on 4 BMU). When validating the models on the 3NOP data set, predictions were poor without (R²V = 0.13 for CH₄ as g/d on 1 BMU) or with (R²V = 0.31 for CH₄ as g/d on 1 BMU) integration of 3NOP data in the models. Thus, specific models would be required for CH₄ prediction when cows receive chemical inhibitors of CH₄ emissions not affecting milk composition.     

Evaluation of protocols to synchronize estrus and ovulation in seasonal calving pasture-based dairy production systems

Lactating dairy cows (n = 1,538) were enrolled in a randomized complete block design study to evaluate protocols to synchronize estrus and ovulation. Within each herd (n = 8), cows were divided into 3 calving groups: early, mid, and late, based on days in milk (DIM) at mating start date (MSD). Early calving cows (n = 1,244) were ≥42 DIM at MSD, mid-calving cows (n = 179) were 21 to 41 DIM at MSD, and late-calving cows (n = 115) were 0 to 20 DIM at MSD. Cows in the early, mid-, and late-calving groups were synchronized to facilitate estrus or timed AI (TAI) at MSD (planned breeding 1; PB1), 21 d (PB2), and 42 d (PB3) after MSD, respectively. For each PB, cows in the relevant calving group were stratified by parity and calving date and randomly assigned to 1 of 4 experimental groups: (1) d −10 GnRH (10 μg of i.m. buserelin) and controlled internal drug release insert (CIDR; 1.38 g of progesterone); d −3 PGF_2α (25 mg of i.m. dinoprost); and d −2 CIDR out and AI at observed estrus (CIDR_OBS); (2) same as CIDR_OBS, but GnRH 36 h after CIDR out and TAI 18 h later (CIDR_TAI); (3) same as CIDR_TAI, but no CIDR (Ovsynch); or (4) untreated controls (CTRL). The CIDR_OBS, CIDR_TAI, and Ovsynch had shorter mean intervals from calving to first service compared with the CTRL (69.2, 63.4, and 63.7 vs. 73.7 d, respectively). Both CIDR_OBS (predicted probability; PP of pregnancy = 0.59) and CIDR_TAI (PP of pregnancy = 0.54) had increased odds of conceiving at first service compared with Ovsynch [PP of pregnancy = 0.45; odds ratio (OR) = 1.81 and OR = 1.46, respectively], and Ovsynch had decreased likelihood of conceiving at first service (OR = 0.70) compared with CTRL (PP of pregnancy = 0.53). Both CIDR_TAI hazard ratio; HR [95% confidence interval = 1.21 (1.04, 1.41)] and Ovsynch [HR (95% confidence interval) = 1.23 (1.05, 1.44)] were associated with an increased likelihood of earlier conception compared with the CTRL. A greater proportion of cows on the CIDR_TAI treatment successfully established pregnancy in the first 42 d of the breeding season compared with the CTRL (0.75 vs. 0.67 PP of 42-d pregnancy, respectively). Protocols to synchronize estrus and ovulation were effective at achieving earlier first service and conception in pasture-based seasonal calving dairy herds. However, animals that conceived following insemination at observed estrus had a decreased likelihood of embryo loss to first service compared with animals bred with TAI (PP of embryo loss after first service = 0.05 vs. 0.09; OR = 0.52).     

Association of estrous expression detected by an automated activity monitoring system within 40 days in milk and reproductive performance of lactating Holstein cows

The objective of this observational study was to evaluate the association of estrous expression within 40 days in milk (DIM) using a neck-mounted automated activity monitor (Heatime Pro; SCR Engineers Ltd.) with reproductive performance in lactating Holstein cows. A total of 2,077 cows (614 primiparous cows and 1,463 multiparous cows) from 5 commercial dairy farms were included in the statistical analyses. Activity data from the first 7 d after calving were excluded. An estrus event was defined as an activity change index ≥35 for more than 2 h. Cows were classified according to the number of estrus events from d 7 until d 40 postpartum into 3 categories: (1) no estrus event (Estrus0); (2) one estrus event (Estrus1), and (3) 2 or more estrus events (Estrus2). Generalized linear mixed models were used to analyze continuous and categorical data. Shared frailty models were used for time to event data. Overall, 52.7% of cows had no estrus event detected by an automated activity monitor system from d 7 until d 40 postpartum. Herd level prevalence of Estrus0 ranged from 37.5 to 58.4%. Estrous expression from d 7 until d 40 postpartum affected estrous duration and estrous intensity at first artificial insemination (AI). Cows in Estrus0 had the shortest duration (13.2 ± 0.33 h) compared with cows in Estrus1 (13.8 ± 0.36 h) and Estrus2 (14.8 ± 0.41 h). Cows in Estrus2 had a longer estrous duration at first postpartum AI compared with cows in Estrus1. Among Estrus0 cows, 46.2% had an estrus event with high intensity at first postpartum AI. Among cows in Estrus1 and Estrus2, 50.8 and 53.8% had an estrus event with high intensity at first postpartum AI, respectively. There was a significant difference between Estrus2 and Estrus0 and a tendency between Estrus0 and Estrus1. There was no difference between Estrus1 and Estrus2. For Estrus0, Estrus1, and Estrus2 cows, pregnancy per AI was 29.4, 30.9, and 37.8%, respectively. There was a significant difference between Estrus0 and Estrus2 and Estrus1 and Estrus2. There was no difference between Estrus0 and Estrus1. Estrous expression from d 7 until d 40 postpartum affected time to first AI and time to pregnancy. Compared with Estrus0 cows, cows in Estrus1 [hazard risk (HR) = 1.74] and Estrus2 (HR = 1.77) had an increased hazard of being inseminated within 100 DIM. There was no difference between Estrus1 and Estrus2. Median DIM to first AI were 70, 59, and 58 for cows in Estrus0, Estrus1, and Estrus2, respectively. Compared with Estrus0 cows, cows in Estrus1 (HR = 1.28) and Estrus2 (HR = 1.33) had an increased hazard of becoming pregnant within 200 DIM. There was no difference between Estrus1 and Estrus2. Median DIM to pregnancy were 127, 112, and 103 for Estrus0 cows, Estrus1 and Estrus2, respectively. In conclusion, cows with no estrous expression from 7 to 40 DIM had reduced estrous expression at first AI and inferior reproductive performance compared with cows that displayed estrous activity.     

Evaluation of an automated repetitive sequence-based PCR system for subtyping Enterobacter sakazakii

Enterobacter sakazakii is regarded as a ubiquitous organism that can be isolated from a wide range of foods and environments. Infection in at-risk infants has been epidemiologically linked to the consumption of contaminated powdered infant formula. Preventing the dissemination of this pathogen in a powdered infant formula manufacturing facility is an important step in ensuring consumer confidence in a given brand together with the protection of the health status of a vulnerable population. In this study we report the application of a repetitive sequence-based PCR typing method to subtype a previously well-characterized collection of E. sakazakii isolates of diverse origin. While both methods successfully discriminated between the collection of isolates, repetitive sequence-based PCR identified 65 types, whereas pulsed-field gel electrophoresis identified 110 types showing > or =95% similarity. The method was quick and easy to perform, and our data demonstrated the utility and value of this approach to monitor in-process contamination, which could potentially contribute to a reduction in the transmission of E. sakazakii.     

Resynchronization of ovulation protocols for dairy cows including or not including gonadotropin-releasing hormone to induce a new follicular wave: Effects on re-insemination pattern,ovarian responses,and pregnancy outcomes

Our objectives were to evaluate the pattern of re-insemination, ovarian responses, and pregnancy per artificial insemination (P/AI) of cows submitted to different resynchronization of ovulation protocols. The base protocol started at 25 ± 3 d after artificial insemination (AI) and was as follows: GnRH, 7 and 8 d later PGF_2α, GnRH 32 h after second PGF_2α, and fixed timed AI (TAI) 16 to 18 h after GnRH. At 18 ± 3 d after AI, cows were randomly assigned to the G25 (n = 1,100) or NoG25 (n = 1,098) treatments. The protocol for G25 and NoG25 was the same, except that cows in NoG25 did not receive GnRH 25 ± 3 d after AI. At nonpregnancy diagnosis (NPD), 32 ± 3 d after AI, cows from G25 and NoG25 with a corpus luteum (CL) ≥15 mm in diameter and a follicle ≥10 mm completed the protocol (G25 CL = 272, NoG25 CL = 194), whereas cows from both treatments that did not meet these criteria received a modified Ovsynch protocol with P4 supplementation [controlled internal drug release insert plus GnRH, controlled internal drug release insert removal, and PGF_2α 7 and 8 d later, GnRH 32 h after second PGF_2α, and TAI 16 to 18 h after GnRH (G25 NoCL = 53, NoG25 NoCL = 78)]. Serum concentrations of progesterone (P4) were determined and ovarian ultrasonography was performed thrice weekly from 18 ± 3 d after AI until 1 d after TAI (G25 = 46, NoG25 = 44 cows). A greater percentage of NoG25 cows were re-inseminated at detected estrus (NoG25 = 53.5%, G25 = 44.6%), whereas more cows had a CL at NPD in G25 than NoG25 (83.7 and 71.3%). At 32 d after AI, P/AI was similar for G25 and NoG25 for inseminations at detected estrus (38.4 and 42.9%), TAI services for cows with no CL (40.4 and 36.7%), and for all services combined (39.6 and 39.0%). However, P/AI were greater for cows with a CL in G25 than NoG25 (40.6 and 32.8%) that received TAI. More cows ovulated spontaneously or in response to GnRH for the G25 than the NoG25 treatment (70 and 36%) but a similar proportion had an active follicle at NPD (G25 = 91% and NoG25 = 96%). The largest follicle diameter at NPD (G25 = 15.0 ± 0.4 mm, NoG25 = 16.5 ± 0.6 mm) and days since it reached ≥10 mm (G25 = 4.0 ± 0.3 d, NoG25 = 5.8 ± 0.6 d) were greater for the NoG25 than G25 treatment. For cows with a CL at NPD, CL regression after NPD, ovulation after TAI, and ovulatory follicle diameter did not differ. In conclusion, removing the first GnRH of a modified Resynch-25 protocol for cows with a CL at NPD and a modified Ovsynch protocol with P4 supplementation for cows without a CL at NPD resulted in a greater percentage of cows re-inseminated at detected estrus and a similar proportion of cows pregnant in spite of reduced P/AI for cows with a CL at NPD.     

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

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