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ABSTRACT: The objective was to compare differences in reproductive performance for dairy cows grouped based on the combination of data for predictors available during the prepartum period and before the end of the voluntary waiting period (VWP), automated estrus alerts (AEA) during the VWP, and the combination of both factors. In a cohort study, data for AEA and potential predictors of the percentage of cows that receive AI at detected estrus (AIE), pregnancies per AI (P/AI) for first service, and the percentage of cows pregnant by 150 DIM (P150) were collected from −21 to 49 DIM for lactating Holstein cows (n = 886). The association between each reproductive outcome with calving season (cool, warm), calving-related events (yes, no), genomic daughter pregnancy rate (gDPR; high, medium, low), days in the close-up pen (ideal, not ideal), health disorder events (yes, no), rumination time (high or low CV prepartum and high or low increase rate postpartum), and milk yield (MY) by 49 DIM (high, medium, low) were evaluated in univariable and multivariable logistic regression models. Individual predictors (health disorders, gDPR, and MY) associated with the 3 reproductive outcomes in all models were used to group cows based on risk factors (RF; yes, n = 535 or no, n = 351) for poor reproductive performance. Specifically, cows were included in the RF group if any of the following conditions were met: the cow was in the high MY group, had low gDPR, or had at least 1 health disorder recorded. Cows were grouped into estrus groups during the VWP based on records of AEA (estrus VWP [E-VWP], n = 476 or no estrus VWP [NE-VWP], n = 410). Finally, based on the combination of levels of AEA and RF, cows were grouped into an estrus and no RF (E-NoRF, n = 217), no estrus and RF (NE-RF, n = 276), no estrus and no RF (NE-NoRF, n = 134), and estrus and RF (E-RF, n = 259) groups. Cows received AIE up to 31 d after the end of the VWP, and if they did not receive AIE, they received timed AI after an Ovsynch plus progesterone protocol. Logistic and Cox proportional hazard regression compared differences in reproductive outcomes for different grouping strategies. The NoRF (AIE: 76.9%; P/AI: 53.1%; P150: 84.5%) and E-VWP (AIE: 86.8%; P/AI: 44.8%; P150: 82.3%) groups had more cows AIE and higher P/AI and P150 than the RF (AIE: 64.5%; P/AI: 34.9%; P150: 72.9%) and NE-VWP (AIE: 50.0%; P/AI: 38.9%; P150: 72.1%) groups, respectively. When both factors were combined, the largest and most consistent differences were between the E-NoRF (AIE: 91.3%; P/AI: 58.7%; P150: 88.5%) and NE-RF groups (AIE: 47.3%; P/AI: 35.8%; P150: 69.5%). Compared with the whole population of cows or cows grouped based on a single factor, the E-NoRF and NE-RF groups had the largest and most consistent differences with the whole cow cohort. The E-NoRF and NE-RF groups also had statistically significant differences of a large magnitude when compared with the remaining cow cohort after removal of the respective group. We conclude that combining data for AEA during the VWP with other predictors of reproductive performance could be used to identify groups of cows with larger differences in expected reproductive performance than if AEA and the predictors are used alone.

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

The objective of this study was to compare the reproductive performance of cows inseminated based on automated activity monitoring with hormone intervention (AAM) to cows from the same herds inseminated using only an intensive timed artificial insemination (TAI) program. Cows (n = 523) from 3 commercial dairy herds participated in this study. To be considered eligible for participation, cows must have been classified with a body condition score of at least 2.50, but no more than 3.50, passed a reproductive tract examination, and experienced no incidences of clinical, recorded metabolic diseases in the current lactation. Within each herd, cows were balanced for parity and predicted milk yield, then randomly assigned to 1 of 2 treatments: TAI or AAM. Cows assigned to the TAI group were subjected to an ovulation synchronization protocol consisting of presynchronization, Ovsynch, and Resynch for up to 3 inseminations. Cows assigned to the AAM treatment were fitted with a leg-mounted accelerometer (AfiAct Pedometer Plus, Afimilk, Kibbutz Afikim, Israel) at least 10 d before the end of the herd voluntary waiting period (VWP). Cows in the AAM treatment were inseminated at times indicated by the automated alert system for up to 90 d after the VWP. If an open cow experienced no AAM alert for a 39 ± 7-d period (beginning at the end of the VWP), hormone intervention in the form of a single injection of either PGF2a or GnRH (no TAI) was permitted as directed by the herd veterinarian. Subsequent to hormone intervention, cows were inseminated when alerted in estrus by the AAM system. Pregnancy was diagnosed by ultrasound 33 to 46 d after insemination. Pregnancy loss was determined via a second ultrasound after 60 d pregnant. Timed artificial insemination cows experienced a median 11.0 d shorter time to first service. Automated activity-monitored cows experienced a median 17.5-d shorter service interval. No treatment difference in probability of pregnancy to first AI, probability of pregnancy to repeat AI, pregnancy loss, time to pregnancy, or proportion of pregnant cows at 90 d past the VWP existed. Based on these results, inseminating cows using AAM with hormone intervention can achieve a level of reproductive performance comparable to TAI. Considering the strict cow selection criteria used in this study, interpretation of results for on-farm implementation should be performed cautiously; the results cannot be directly extrapolated to whole herds of cows. [ABSTRACT FROM AUTHOR]

This study included 2 objectives. The first objective was to describe estrus-related changes in parameters automatically recorded by the CowManager SensOor (Agis Automatisering, Harmelen, the Netherlands), DVM bolus (DVM Systems LLC, Greeley, CO), HR Tag (SCR Engineers Ltd., Netanya, Israel), IceQube (IceRobotics Ltd., Edinburgh, UK), and Track a Cow (Animart Inc., Beaver Dam, WI). This objective was accomplished using 35 cows in 3 groups between January and June 2013 at the University of Kentucky Coldstream Dairy. We used a modified Ovsynch with G7G protocol to partially synchronize ovulation, ending after the last PGF2α injection (d 0) to allow estrus expression. Visual observation for standing estrus was conducted for four 30-min periods at 0330, 1000, 1430, and 2200 h on d 2, 3, 4, and 5. Eighteen of the 35 cows stood to be mounted at least once during the observation period. These cows were used to compare differences between the 6 h before and after the first standing event (estrus) and the 2 wk preceding that period (nonestrus) for all technology parameters. Differences between estrus and nonestrus were observed for CowManager SensOor minutes feeding per hour, minutes of high ear activity per hour, and minutes ruminating per hour; twice daily DVM bolus reticulorumen temperature; HR Tag neck activity per 2 h and minutes ruminating per 2 h; IceQube lying bouts per hour, minutes lying per hour, and number of steps per hour; and Track a Cow leg activity per hour and minutes lying per hour. No difference between estrus and nonestrus was observed for CowManager SensOor ear surface temperature per hour. The second objective of this study was to explore the estrus detection potential of machine-learning techniques using automatically collected data. Three machine-learning techniques (random forest, linear discriminant analysis, and neural network) were applied to automatically collected parameter data from the 18 cows observed in standing estrus. Machine learning accuracy for all technologies ranged from 91.0 to 100.0%. When we compared visual observation with progesterone profiles of all 32 cows, we found 65.6% accuracy. Based on these results, machine-learning techniques have potential to be applied to automatically collected technology data for estrus detection. [ABSTRACT FROM AUTHOR]

This study was conducted to test the hypothesis that the specificity of infrared thermography (IRT) in detecting cows about to ovulate could be improved using different body parts that are less likely to be contaminated by fecal matter. In addition, the combined activity and rumination data captured by accelerometers were evaluated to provide a more accurate indication of ovulation than the activity and rumination data alone. Thermal images of 30 cows were captured for different body areas (eye, ear, muzzle, and vulva) twice daily after AM and PM milking sessions during the entire experimental period. Milk progesterone data and insemination records were used to determine the date of ovulation. Cows were fitted with SCR heat and rumination long-distance tags (SCR HR LD) for 1 month. Activity- and rumination-based estrus alerts were initially identified using default threshold values set by the manufacturer; however, a range of thresholds was also created and tested for both activity and rumination to determine the potential for higher levels of accuracy of ovulation detection. Visual assessment of mounting indicators resulted in 75% sensitivity (Se), 100% specificity (Sp), and 100% positive predictive value (PPV). Overall, IRT showed poor performance for detecting cows about to ovulate. Vulval temperature resulted in the greatest (80%) Sp but the poorest (21%) Se compared with the IRT temperatures of other body areas. The SCR HR LD tags default threshold value resulted in 78% Se, 57% Sp, and 70% PPV. Lowering the activity threshold from the default value improved the sensitivity but created a large number of false positives, which resulted in a decrease in specificity. Lowering the activity threshold to 20 resulted in a detection performance of 80% Se, 94% Sp, and 67% PPV, whereas the rumination levels achieved 35% Se, 69% Sp, and 14% PPV. The area under the curve for the activity level, rumination level, and the combined measures of activity and rumination levels were 0.82, 0.54, and 0.75, respectively. Alerts generated by SCR HR LD tags based on a lower activity threshold level had high sensitivity and may be able to detect a high proportion of cows in ovulatory periods in pasture-based system; however, the specificities and positive predictive value were lower than the visual assessment of mounting indicators. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study tested the hypothesis that a commercially available system for detecting estrus based on cow activity would perform similarly to that of typical, visual assessment of mounting indicators placed on the tail head of the cow. The hypothesis was applied to a large, pasture-grazed, seasonal-calving dairy herd, and the technology was tested as a stand-alone system. One of 2 types of commercially available collar-mounted activity meters was fitted to 635 cows, and the activity data collected during the 37-d artificial breeding period were analyzed. The first collar-mounted activity meter monitored activity only (AO collars), whereas the second meter measured activity and rumination characteristics (AR collars). Only activity data were used in the current study. Activity-based estrus alerts were initially identified using the default activity threshold value recommended by the manufacturer, but a range of activity threshold values was then analyzed to determine their effect on estrus detection performance. Milk progesterone data and insemination records were used to identify gold standard positive (n = 835) and negative (n = 22,660) estrus dates, to which activity alerts were compared. Visual assessment of mounting indicators resulted in a manual detection performance of 91.3% sensitivity (SN), 99.8% specificity (SP), and 94.5% positive predictive value (PPV). The AR collars achieved 76.9, 99.4, and 82.4% for SN, SP, and PPV, whereas the AO collars achieved 62.4, 99.3, and 76.6% for SN, SP, and PPV, respectively. The observed performance of the activity systems may be underestimated due to test design and applied assumptions, including determining the date of estrus. Lowering the activity threshold from the default value improved sensitivity but the number of false positive alerts was considered to become unmanageable from a practical perspective as sensitivity reached peak values. Time window analysis, receiver operating characteristic curves, and curves of SN and PPV were found to be useful in the analysis and interpretation of results. They generate relevant performance data that allow for meaningful comparisons between similar studies. Although the 2 activity systems tested did not perform to the high level of manual estrus detection found in this study, the potential exists for these systems to be a valuable tool on farms with lower estrus detection performance or for farmers managing larger herds. [ABSTRACT FROM AUTHOR]