Targeted reproductive management for lactating Holstein cows: Reducing the reliance on exogenous reproductive hormones

Graduate Student Literature Review: Implications of transition cow health for reproductive function and targeted reproductive management

Negative associations of health disorders with reproductive performance, often measured with pregnancy risk per artificial insemination (AI) or the risk of pregnancy loss, have been demonstrated extensively. Most studies investigated common clinical diseases but did not include subclinical disorders comprehensively. They often evaluated cows subjected to hormonal synchronization protocols for timed AI, limiting the ability to understand how disease may affect spontaneous reproductive function, which is essential for targeted management programs with selective hormonal intervention. It is plausible that metabolic and inflammatory disorders have short- and long-term detrimental effects on different features of reproductive function that result in or contribute to reduced fertility. These may include: 1) reestablishment of endocrine function to promote follicular growth and first ovulation postpartum, 2) corpus luteum (CL) function, 3) estrus expression, and 4) uterine environment, fertilization, and embryonic development. In this narrative literature review, we discuss insights and knowledge gaps linking health disorders with these processes of reproductive function. A growing set of observational studies with adequate internal validity suggest that these outcomes may be affected by metabolic and inflammatory disorders that are common in the early postpartum period. A better characterization of these risk factors in multi-site studies with greater external validity is warranted to develop decision-support tools to identify subgroups of cows that are more or less likely to be successful in targeted reproductive management programs.

Effect of a targeted reproductive management program based on automated detection of estrus during the voluntary waiting period on reproductive performance of lactating dairy cows

The objective of this experiment was to evaluate the effect on reproductive performance of a targeted reproductive management (TRM) program for first postpartum insemination (AI) that prioritized AI at detected estrus (AIE) by providing different intervals for estrus detection based on records of automated estrus alerts (AEA) during the voluntary waiting period (VWP). A secondary objective was to evaluate the association between occurrence of AEA during the VWP and reproductive performance. Lactating Holstein cows (n = 1,260) fitted with neck behavior monitoring sensors for detection of estrus were randomly assigned to a program that used all-timed AI (TAI) for first service (ALL-TAI; n = 632) or a TRM program that prioritized AIE and used TAI only for cows not detected in estrus (TP-AIE; n = 628). Cows in the ALL-TAI treatment received TAI at 76 ± 3 days in milk (DIM) after a Double-Ovsynch protocol. Cows in the TP-AIE treatment were eligible for AIE for 30 ± 3 or 16 ± 3 d after a 49 d VWP if at least one (n = 346) or no (n = 233) AEA were recorded from 15 to 49 DIM. Cows not AIE received TAI after an Ovsynch protocol with progesterone supplementation at 90 ± 3 or 76 ± 3 DIM if the cow had or did not have AEA during the VWP, respectively. Data were analyzed by logistic and Cox's proportional hazard regression. In the TP-AIE treatment, 69.3 % of cows received AIE and more cows with (83.3 %) than without (45.0 %) AEA during the VWP received AIE. Cows in the TP-AIE (69.0 ± 0.7 d) treatment had fewer days from calving to first AI than cows in the ALL-TAI (75.7 ± 0.8 d) treatment. The proportion of cows pregnant by 150 DIM (ALL-TAI = 59.1 % and TP-AIE = 56.0 %) and the hazard ratio (HR) for time to pregnancy (1.0 [95 % confidence interval: 0.9, 1.2]) did not differ between treatments and median days to pregnancy were 102 and 107 for the ALL-TAI and TP-AIE treatments, respectively. Overall, the ALL-TAI (42.3 %) treatment had more first service pregnancies per AI (P/AI) than the TP-AIE (29.0 %) treatment. Cows with AEA during the VWP had greater P/AI (42.5 % vs. 28.9 %), proportion of cows pregnant by 150 DIM (67.4 % vs. 47.0 %), and HR for time to pregnancy (1.6 [1.4, 1.9]) than cows without AEA during the VWP. We conclude that a TRM program that prioritized AIE based on AEA during the VWP led to a similar pregnancy rate and proportion of cows pregnant by mid-lactation than a program that used all-TAI with extended VWP despite fewer P/AI to first service. Also, expression of estrus during the VWP was associated with improved reproductive performance. Thus, AEA during the VWP could be used as a predictor of reproductive potential for TRM of lactating dairy cows.

Efficacy of an automated technology at detecting early postpartum estrus events: Can we detect resumption of cyclicity?

The objective of this observational study was to evaluate the efficacy of a neck-mounted automated activity monitor (AAM) at detecting early postpartum resumed ovarian cyclicity. A total of 192 lactating cows (primiparous = 73 and multiparous = 119) were enrolled in this study. Cows were continuously monitored by a neck-mounted AAM early postpartum (7 to 30 d in milk; DIM). Calving was classified as assisted (forced extraction of a calf) or unassisted (normal calving). Retained fetal membrane, metritis, hyperketonemia, clinical mastitis, and milk production were recorded. Cows were classified as healthy (i.e., no disease events) or sick (i.e., any disease event). Estrus events were alerted by the AAM using a proprietary algorithm set by the AAM company. Blood samples, from the coccygeal vein, were collected at 15, 18, 21, 24, 28, and 30 DIM for progesterone (P4) analysis. Resumption of cyclicity was considered when P4 concentration was ≥1 ng/mL on any collection day. Cows were considered anovular when P4 concentration was <1 ng/mL on all collection days. Cows were classified as true positive: P4 ≥ 1 ng/mL and at least one estrus alert; false positive: P4 < 1 ng/mL and at least one estrus alert; true negative: P4 < 1 ng/mL and no estrus alerts; and false negative: P4 ≥ 1 ng/mL and no estrus alerts. Statistical analyses were performed by frequency distribution and mixed effects logistic regression procedures on SAS (SAS Institute Inc.). The specificity, sensitivity, accuracy, and positive predictive value of the sensor to detect cows that had resumed cyclicity were 84.0%, 34.1%, 52.1%, and 79.2%, respectively. Out of the 192 cows, 35.9% (69/192) were anovulatory and 37.5% (72/192) had no estrus events between 7 to 30 DIM. Healthy cows were more likely to resume cyclicity in early lactation compared with cows that were sick (78.3 ± 1.9 vs. 32.8 ± 3.1%, respectively) independent of parity. In conclusion, the sensor had a high specificity for detecting anovular cows, but it had lower sensitivity, and thus was not effective at detecting cyclic cows, perhaps due to silent ovulation early postpartum.

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.

Reproduction in the era of genomics and automation

Much progress has been made in the reproductive efficiency of lactating dairy cows across the USA in the past 20years. The standardisation of evaluation of reproductive efficiency, particularly with greater focus on metrics with lesser momentum and less lag-time such as 21-day pregnancy rates (21-day PR), and the recognition that subpar reproductive efficiency negatively impacted profitability were major drivers for the changes that resulted in such progress. Once it became evident that the genetic selection of cattle for milk yield regardless of fertility traits was associated with reduced fertility, geneticists raced to identify fertility traits that could be incorporated in genetic selection programs with the hopes of improving fertility of lactating cows. Concurrently, reproductive physiologists developed ovulation synchronisation protocols such that after sequential treatment with exogenous hormones, cows could be inseminated at fixed time and without detection of oestrus and still achieve acceptable pregnancy per service. These genetic and reproductive management innovations, concurrently with improved husbandry and nutrition of periparturient cows, quickly started to move reproductive efficiency of lactating dairy cows to an upward trend that continues today. Automation has been adopted in Israel and European countries for decades, but only recently have these automated systems been more widely adopted in the USA. The selection of dairy cattle based on genetic indexes that result in positive fertility traits (e.g. daughter pregnancy rate) is positively associated with follicular growth, resumption of ovarian cycles postpartum, body condition score and insulin-like growth factor 1 concentration postpartum, and intensity of oestrus. Collectively, these positive physiological characteristics result in improved reproductive performance. Through the use of automated monitoring devices (AMD), it is possible to identify cows that resume cyclicity sooner after calving and have more intense oestrus postpartum, which are generally cows that have a more successful periparturient period. Recent experiments have demonstrated that it may be possible to adopt targeted reproductive management, utilising ovulation synchronisation protocols for cows that do not have intense oestrus postpartum and relying more heavily on insemination at AMD-detected oestrus for cows that display an intense oestrus postpartum. This strategy is likely to result in tailored hormonal therapy that will be better accepted by the public, will increase the reliance on oestrus for insemination, will improve comfort and reduce labour by reducing the number of injections cows receive in a lactation, and will allow for faster decisions about cows that should not be eligible for insemination.