Combining reproductive outcomes predictors and automated estrus alerts recorded during the voluntary waiting period identified subgroups of cows with different reproductive performance potential.

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 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 insemination at detected estrus (AIE) and 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 one health disorder recorded. Cows were grouped into estrus groups during the VWP based on records of AEA (E-VWP, n = 476 or 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 did not receive AIE, 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, 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 group 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.

Development and evaluation of a lateral flow-based portable optical system for determination of the pregnancy status of dairy cows.

Our objectives were to develop and evaluate an integrated system consisting of a lateral-flow immunoassay (LFIA) and an electronic portable imaging device for determination of pregnancy status of cows based on plasma concentrations of pregnancy-specific protein B (PSPB). Experiment 1 was conducted to test the performance of the LFIA for PSPB (PSPB-LFIA) whereas experiment 2 was conducted to evaluate the performance of the integrated system including both the LFIA and imaging device. The PSPB-LFIA strips were made of nitrocellulose membrane with polystreptavidin, anti-mouse antibody, Europium-anti-PSPB conjugates, and biotin-PSPB. After adding buffer and plasma in a 96-well plate, strips were dipped to initiate flow and were read in a fluorescence microscope to estimate PSPB concentrations based on the test-to-control line signal (T/C ratio). The T/C ratio of standards was linearly associated with PSPB (R2 = 0.99 in both experiments) concentrations. To test the ability to identify pregnant cows of the PSPB-LFIA only or the integrated system, plasma samples were collected and transrectal ultrasonography (TUS) was conducted 29 to 35 d post AI in lactating Holstein cows (Experiment 1: n = 83; Experiment 2: n = 205). A cow was considered pregnant (Preg) if concentrations of PSPB in plasma obtained by ELISA were ≥2 ng/mL or if an embryo was visible by TUS. In Experiment 1, the accuracy of the PSPB-LFIA compared with ELISA was 92.7% (91.2% Se; 96.1% Sp; 98.1% PPV; 83.3% NPV) and compared with TUS was 90.4% (100% Se; 78.9% Sp; 84.9% PPV; 100% NPV). The agreement between LFIA and ELISA (kappa = 0.84; 95%CI 0.71-0.96) or LFIA and TUS (kappa = 0.80; 95%CI 0.67-0.93) as methods to classify cows as Preg or Non-Preg was high. In Experiment 2, the accuracy of the PSPB-LFIA compared with ELISA was 96.1% (93.8% Se; 100% Sp; 100% PPV; 90.5% NPV) and compared with TUS was 92.2% (99.0% Se; 84.7% Sp; 87.6% PPV; 98.8% NPV). The agreement between LFIA and ELISA (kappa = 0.92; 95%CI 0.86-0.97) or LFIA and TUS (kappa = 0.84; 95%CI 0.77-0.92) as methods to classify cows as Preg or Non-Preg was high. We conclude that a system integrating a fluorescence-based LFIA and an optical reader was effective for classifying cows as pregnant or not pregnant based on estimations of plasma concentrations of PSPB. This novel system serves as a platform for further development of on-farm pregnancy testing tools based on measurement of biomarkers of pregnancy in bodily fluids of cattle.

Reproductive physiological outcomes of dairy cows with different genomic merit for fertility: biomarkers, uterine health, endocrine status, estrus features, and response to ovarian synchronization.

Our overarching objective was to characterize associations between genomic merit for fertility and the reproductive function of lactating dairy cows in a prospective cohort study. In this manuscript, we present results of the association between genomic merit for fertility and indicators of metabolic status and inflammation, uterine health, endocrine status, response to synchronization, and estrous behavior in dairy cows. Lactating Holstein cows entering their first (n = 82) or second (n = 37) lactation were enrolled at parturition and fitted with an ear-attached sensor for automated detection of estrus. Ear-notch tissue samples were collected from all cows and submitted for genotyping using a commercial genomic test. Based on genomic predicted transmitting ability values for daughter pregnancy rate (gDPR) cows were classified into a high (Hi-Fert; gDPR > 0.6; n = 36), medium (Med-Fert; gDPR -1.3 to 0.6; n = 45), and low (Lo-Fert; gDPR < -1.3; n = 38) group. At 33 to 39 d in milk (DIM), cohorts of cows were enrolled in the Presynch-Ovsynch protocol for synchronization of estrus and ovulation. Body weights, body condition scores (BCS), and uterine health measurements (i.e., vaginal discharge, uterine cytology) were collected from parturition to 60 DIM and milk yield was collected through 90 DIM. Blood samples were collected weekly through 3 wk of lactation for analysis of ß-hydroxybutyrate, nonesterified fatty acids, and haptoglobin plasma concentrations. Body weight, BCS, NEFA, BHB, and Haptoglobin were not associated with fertility groups from 1 to 9 wk after parturition. The proportion of cows classified as having endometritis at 33 to 36 DIM tended to be greater for the Lo-Fert than the Hi-Fert group. The proportion of cows that resumed cyclicity did not differ at any time point evaluated and there were no significant associations between probability or duration and intensity of estrus with fertility group. Cows of superior genetic merit for fertility were more likely to ovulate, have a functional CL, have greater circulating P4, and have larger ovulatory size than cows of inferior fertility potential at key time points during synchronization of estrus and ovulation. Despite observing numerical differences with potential performance consequences for the proportion of cows that responded to synchronization of ovulation and were both cyclic and responded to the Ovsynch portion of the synchronization protocol, we did not observe significant differences between fertility groups. Although not consistent and modest in magnitude, the collective physiological and endocrine differences observed suggested that cows of superior genetic fertility potential might have improved reproductive performance, at least in part, because of modestly improved endocrine status, uterine health, and ability to ovulate.

The ovarian function and endocrine phenotypes of lactating dairy cows during the estrous cycle were associated with genomic-enhanced predictions of fertility potential.

The objectives of this prospective cohort study were to characterize associations among genomic merit for fertility with ovarian and endocrine function and the estrous behavior of dairy cows during an entire, non-hormonally manipulated estrous cycle. Lactating Holstein cows entering their first (n = 82) or second (n = 37) lactation had ear-notch tissue samples collected for genotyping using a commercial genomic test. Based on genomic predicted transmitting ability values for daughter pregnancy rate (gDPR) cows were classified into a high (Hi-Fert; gDPR > 0.6 n = 36), medium (Med-Fert; gDPR -1.3 to 0.6 n = 45), and low fertility (Lo-Fert; gDPR < -1.3 n = 38) group. At 33 to 39 DIM, cohorts of cows were enrolled in the Presynch-Ovsynch protocol for synchronization of ovulation and initiation of a new estrous cycle. Thereafter, the ovarian function and endocrine dynamics were monitored daily until the next ovulation by transrectal ultrasonography and concentrations of progesterone (P4), estradiol, and FSH. Estrous behavior was monitored with an ear-attached automated estrus detection system that recorded physical activity and rumination time. Overall, we observed an association between fertility group and the ovarian and hormonal phenotype of dairy cows during the estrous cycle. Cows in the Hi-Fert group had greater circulating concentrations of P4 than cows in the Lo-Fert group from d 4 to 13 after induction of ovulation and from day -3 to -1 before the onset of luteolysis. The frequency of atypical estrous cycles was 3-fold greater for cows in the Lo-Fert than the Hi-Fert group. We also observed other modest associations between genomic merit for fertility with the follicular dynamics and estrous behavior. There were several associations between milk yield and parity with ovarian, endocrine, and estrous behavior phenotypes as cows with greater milk yield and in the second lactation were more likely to have unfavorable phenotypes. These results demonstrate that differences in reproductive performance between cows of different genomic merit for fertility classified based on gDPR may be partially associated with circulating concentrations of P4, the incidence of atypical phenotypes during the estrous cycles, and to a lesser extent the follicular wave dynamics. The observed physiological and endocrine phenotypes might help explain part of the differences in reproductive performance between cows of superior and inferior genomic merit for fertility.

Metabolic-digestive clinical disorders of lactating dairy cows were associated with alterations of rumination, physical activity, and lying behavior monitored by an ear-attached sensor.

The objective of this observational cohort study was to characterize the pattern of rumination time (RT), physical activity (PA), and lying time (LT) monitored by an automated health monitoring system, based on an ear-attached sensor, immediately before, during, and after clinical diagnosis (CD) of metabolic-digestive disorders. Sensor data were collected from 820 lactating Holstein cows monitored daily from calving up to 21 DIM for detection of health disorders (HD). Cows were grouped retrospectively in the no-clinical health disorder group (NCHD; n = 616) if no HD were diagnosed, or the metabolic-digestive group (METB-DIG; n = 58) if diagnosed with clinical ketosis or indigestion only. Cows with another clinical health disorder within -7 to +7 d of CD of displaced abomasum, clinical ketosis, or indigestion were included in the metabolic-digestive plus one group (METB-DIG+1; n = 25). Daily RT, PA, and LT, and absolute and relative changes within -7 to +7 d of CD were analyzed with linear mixed models with or without repeated measures. Rumination time and PA were smaller, and LT was greater for the METB-DIG and METB-DIG+1 group than for cows in the NCHD group for most days from -7 to +7 d of CD of HD. In general, daily RT, PA, and LT differences were larger between the METB-DIG+1 and NCHD groups than between the METB-DIG and NCHD groups. In most cases, RT and PA decreased to a nadir and LT increased to a peak immediately before or after CD of HD, with a return to levels similar to the NCHD group within 7 d of CD. Absolute values and relative changes from 5 d before CD to the day of the nadir for RT and PA or peak for LT were different for cows in the METB-DIG and METB-DIG+1 group than for the NCHD group. For PA, the METB-DIG+1 group had greater changes than the METB-DIG group. For cows affected by metabolic-digestive disorders, RT, PA, and LT on the day of CD and resolution of clinical signs were different than for cows in the NCHD group, but an increase in RT and PA or a decrease in LT was observed from the day of CD to the day of resolution of clinical signs. We conclude that dairy cows diagnosed with metabolic-digestive disorders including displaced abomasum, clinical ketosis, and indigestion presented substantial alterations in the pattern of RT, PA, and LT captured by an ear-attached sensor. Thus, automated health monitoring systems based on ear-attached sensors might be used as an aid for identifying cows with metabolic-digestive disorders. Moreover, RT, PA, and LT changes after CD might be positive indicators of recovery from metabolic-digestive disorders.

Metritis and clinical mastitis events in lactating dairy cows were associated with altered patterns of rumination, physical activity, and lying behavior monitored by an ear-attached sensor.

Understanding changes in parameters recorded by automated health monitoring systems based on ear-attached sensors on the days immediately before and after diagnosis of metritis and clinical mastitis can help develop dairy cow health monitoring strategies. The objective of this observational cohort study was to characterize rumination time, physical activity, and lying time monitored by an ear-attached sensor before, during, and after clinical diagnosis (CD) of metritis and clinical mastitis. Lactating Holsteins monitored daily for 21 d in milk for detection of health disorders were retrospectively included in the no clinical health disorder group (NCHD; n = 616) if no disorders were diagnosed. Cows were included in the metritis (MET; n = 69) or clinical mastitis (MAST; n = 36) group if diagnosed only with nonsevere metritis (watery, reddish, and fetid uterine discharge with or without pyrexia) or nonsevere clinical mastitis (visibly abnormal milk secretion with or without signs of udder inflammation, with no pyrexia and no systemic signs of disease), respectively. Cows diagnosed with severe metritis (signs of metritis plus systemic signs of disease) or severe clinical mastitis (signs of mastitis plus pyrexia and systemic signs of disease), and cows diagnosed with nonsevere metritis or clinical mastitis plus another disorder within -7 to +7 d of CD of metritis or clinical mastitis diagnosis, were included in the metritis plus (MET+; n = 25) or the clinical mastitis plus (MAST+; n = 15) group, respectively. Cows were fitted with an ear-attached accelerometer to measure rumination time, physical activity, and lying time. Mean daily values, mean value absolute change, and relative change for the mean daily value from 3 or 5 d before CD to the nadir for cows with metritis and clinical mastitis, respectively, were analyzed with linear mixed models with or without repeated measures. Rumination time and physical activity were lesser, and lying time was greater for the MET and MET+ groups than for the NCHD group for most days from -4 to +7 d of CD of metritis. Generally, daily rumination time, physical activity, and lying time differences were greater and more prolonged between the MET+ and NCHD than between the MET and NCHD groups. Similarly, cows in the MAST and MAST+ groups had lesser rumination time and physical activity than cows in the NCHD group for several days before diagnosis. Lying time was greater for the MAST+ than the NCHD group on d -1 and 0 relative to CD. Absolute values and relative changes from 3 d before CD to the day of the nadir for rumination time and physical activity, or peak for lying time, were different for cows in the MET and MET+ groups than for the NCHD group. Similar results were observed for the MAST and MAST+ groups compared with the NCHD group. For cows with metritis, either an increase in rumination time and physical activity or a decrease in lying time was observed from the day of CD to resolution of clinical signs, but no changes were observed for the NCHD. Cows with clinical mastitis and the NCHD group had different rumination times, physical activity, and lying times on the day of CD and resolution of clinical signs, but cows with clinical mastitis had no significant changes from the day of CD to resolution of clinical signs. We conclude that cows affected by metritis and clinical mastitis presented substantial alterations of the patterns of rumination time, physical activity, and lying time captured by an ear-attached sensor. Thus, automated health monitoring systems based on ear-attached sensors might be used as an aid for identifying cows with metritis and clinical mastitis. Moreover, behavioral parameter changes after CD might be good indicators of resolution of clinical signs of metritis but not mastitis.

Effect of a targeted reproductive management program designed to prioritize insemination at detected estrus and optimize time to insemination on the reproductive performance of lactating dairy cows.

The primary objective of this randomized controlled experiment was to evaluate the insemination dynamic and reproductive performance of cows managed with a targeted reproductive management (TRM) program designed to prioritize artificial insemination (AI) at detected estrus (AIE) and optimize timing of AI by grouping cows based on detection of estrus during the voluntary waiting period (VWP). Our secondary objective was to evaluate reproductive outcomes for cows with or without estrus during the VWP. Lactating Holstein cows fitted with an ear-attached sensor for detection of estrus were randomly assigned to a TRM treatment that prioritized AIE based on detection of estrus during the VWP (TP-AIE; n = 488), a non-TRM treatment that prioritized AIE (P-AIE; n = 489), or an all timed AI (TAI) treatment with extended VWP (ALL-TAI; n = 491). In TP-AIE, cows with or without automated estrus alerts (AEA) recorded during the VWP received AIE if detected in estrus for at least 31 ± 3 or 17 ± 3 d after a 49 d VWP, respectively. Cows not AIE with or without AEA during the VWP received TAI after Ovsynch with progesterone supplementation and 2 PGF2α treatments (P4-Ov) at 90 ± 3 or 74 ± 3 d in milk (DIM), respectively. In P-AIE, cows received AIE if detected in estrus for 24 ± 3 d after a 49 d VWP, and if not AIE received TAI at 83 ± 3 DIM after P4-Ov. In ALL-TAI, cows received TAI at 83 ± 3 DIM after a Double-Ovsynch protocol. Data were analyzed by logistic and Cox's proportional hazard regression. The proportion of cows AIE did not differ for TP-AIE (71.0%) and P-AIE (74.6%). Overall P/AI at 39 d after first service was greater for the ALL-TAI (47.6%) than for the P-AIE (40.2%) and TP-AIE (39.5%) treatments. The hazard of pregnancy up to 150 DIM was greater for cows in TP-AIE (hazard ratio = 1.2; 95% confidence interval: 1.1-1.4) and P-AIE (hazard ratio = 1.2; 95% confidence interval: 1.1-1.4) than for cows in the ALL-TAI treatment which resulted in median time to pregnancy of 89, 89, and 107 d. Conversely, the proportion of cows pregnant at 150 DIM did not differ (ALL-TAI 78.5%, P-AIE 76.3%, TP-AIE 76.0%). Except for a few outcomes for which no difference was observed, cows detected in estrus during the VWP had better performance than cows not detected in estrus. Cows with AEA during the VWP were more likely to receive AIE, had greater P/AI, and greater pregnancy rate up to 150 DIM regardless of first service management. We conclude that a TRM program designed to prioritize AIE by grouping cows based on detection of estrus during the VWP was an effective strategy to submit cows for first service resulting in similar or improved performance than a non-TRM program that prioritized AIE or an all-TAI program with extended VWP. Also, AEA recorded during the VWP might be used as a strategy for identifying subgroups of cows with different reproductive performance.

Symposium review: Use of multiple biological, management, and performance data for the design of targeted reproductive management strategies for dairy cows.

As the reproductive efficiency of dairy cattle continues to improve in response to better management and use of technology, novel reproductive management approaches will be required to improve herd performance, profitability, and sustainability. A potential approach currently being explored is targeted reproductive management. This approach consists of identifying cows with different reproductive and performance potential using multiple traditional and novel sources of biological, management, and performance data. Once subgroups of cows that share biological and performance features are identified, reproductive management strategies specifically designed to optimize cow performance, herd profitability, or alternative outcomes of interest are implemented on different subgroups of cows. Tailoring reproductive management to subgroups of cows is expected to generate greater gains in outcomes of interest than if the whole herd is under similar management. Major steps in the development and implementation of targeted reproductive management programs for dairy cattle include identification and validation of robust predictors of reproductive outcomes and cow performance, and the development and on-farm evaluation of reproductive management strategies for optimizing outcomes of interest for subgroups of cows. Predictors of cow performance currently explored for use in targeted management include genomic predictions; behavioral, physiological, and performance parameters monitored by sensor technologies; and individual cow and herd performance records. Once the most valuable predictive sources of variation are identified and their effects quantified, novel analytic methods (e.g., machine learning) for prediction will likely be required. These tools must identify groups of cows for targeted management in real time and with no human input. Despite some encouraging research evidence supporting the development of targeted reproductive management strategies, extensive work is required before widespread implementation by commercial farms.