Assessing ovulation detection performance in commercial dairy herds using progesterone concentrations from limited numbers of strategically collected milk samples

It is important to assess ovulation detection performance in commercial dairy herds both to investigate low reproductive performance and to enable herd managers to monitor the effectiveness of their system for detecting ovulations. A method was developed to assess ovulation detection performance that uses limited numbers of strategically collected milk samples, assesses performance over the period when herd managers are making maximal effort to detect ovulations, and when assessing proportions of ovulations detected, accounts for false positive diagnoses of estrus and for cows that have not recommenced postpartum ovulatory cycles. Milk was sampled from cows not diagnosed in estrus early in the breeding program (about d 26 in year-round calving herds and d 22 in seasonal calving herds); milk samples were also collected from cows on the day of insemination. Cows with high milk progesterone concentrations were assumed to have had undetected ovulations and false positive diagnoses of estrus, respectively. The method was successfully implemented in 161 of 167 commercial dairy herds. Positive predictive values (PPV; the proportions of ovulation diagnoses where ovulation was, in fact, imminent) were generally high in both year-round and seasonal calving herds (median values were 0.96 and 0.97, respectively), but 25% of herds had PPV <0.95. Ovulation detection sensitivities (ODS) were low in most year-round calving herds, but many seasonal calving herds had high ODS values; median ODS were 0.73 and 0.94, respectively. However, in 25% of seasonal calving herds, ODS was <0.91. These findings indicate that this method for assessing ovulation detection performance can be successfully implemented in commercial dairy herds with appropriate professional support. The wide range of ODS and the absence of correlation between ODS and PPV suggest that it is possible for managers of many commercial herds in Australia to achieve increased reproductive efficiency through increases in ODS and PPV.     

Female-to-male sex pheromones of low volatility in the Asian elephant,Elephas maximus

In their natural ecosystems, the sexes of Asian elephants,Elephas maximus, live separately. For several weeks prior to ovulation, the urine and cervical mucus of female Asian elephants contain extractable chemical agents of low volatility that elicit a high frequency of flehmen responses from bull elephants as an integral part of mating. Subsequent to flehmen responses, male sexual arousal occurs and, if the female is available, mating results. During the course of our project to determine the agent(s) and describe the responses associated with female to male sexual communication, we have identified an unusual compound. This compound, apparently the sole component of the active fraction, was identified by mass, proton nuclear magnetic resonance, ultraviolet/visible, and infrared spectrometries as indolo-[2,1-b] quinazoline-6,12-dione (tryptanthrine). Exhaustive and repetitive bioassays established that pure authentic (synthetic) tryptanthrine was not the compound responsible for the bioresponse. Rather a coeluting minor component, also of low volatility, elicited the male bioresponse.     

三种激素对奶牛同期发情效果比较及输精时间对受胎率的影响

应用繁殖新技术可以增加奶牛的牛群数量和奶牛产奶量，加快育种进程。同情发情是指在人为控制下使奶牛在一定时间内集中发情，以便有计划地合理组织配种。本试验利用三种外源激素，孕马血清促性腺激素（PMSG）、垂体促卵泡素（FSH）和孕酮栓＋PG（CIDR），分别对青年母牛和经产母牛进行同期发情处理，研究三种外源激素对母牛同期发情的效果；同时研究发情后不同输精时间对受胎率的影响。结果表明采用三种外源激素进行同期发情处理，均获得了较高的同期发情率，用PMSG处理效果较好，三组青年母牛的同期发情率（78％、82％、9％）均分别高于三组经产母牛的发情率（76％、78％、86％）。青年母牛最佳输精时间为发情结束后10-12h，而经产母牛最佳输精时间为发情结束后13-15h。     

Possible induction by estrous cows of pheromone production in penmates

The primary attractive odors of estrous cows apparently induced production of secondary attractive odors in penmates. Presence of both odors was reflected by the bull's olfactory behavior. The inducing effect was strongest on the day of estrus and the day before. The information content of the primary and secondary odors was different since a sexually experienced bull was able to distinguish olfactorily between estrous and nonestrous cows. The secondary odors attracted at least transient bull's interest, resulting in sniffing, urine tasting, and flehmen reactions, but not mounting attempts. The induced production of attractive odors and lack of precise smell discrimination in penmates may lead to erroneous detection of estrus in cows and heifers that show false estrous signs without corresponding changes in their reproductive organs. Similar factors may be involved in triggering mounting among feedlot steers (buller steer syndrome).     

Effects of induced clinical mastitis during preovulation on endocrine and follicular function

The objective of the study was to determine if experimentally induced clinical mastitis before ovulation resulted in alterations of endocrine function, follicular growth, or ovulation. On d 8 (estrus = d 0), cows were challenged (TRT; n = 19) with Streptococcus uberis or were not challenged (control; n = 14). Forty-eight hours after induction of luteal regression on d 12, blood samples were collected to determine estradiol-17β, LH pulse frequency, and occurrence of the LH surge. Ovaries were scanned to monitor follicular growth and ovulation. Cows with clinical mastitis (n = 12) had elevated rectal temperatures, somatic cell counts, and mammary scores. Estrus and ovulation occurred in 4 of 12 clinically infected cows and in all control cows. Cows that were challenged but did not develop clinical mastitis (n = 5) displayed estrus and ovulated. Due to differences in expression of estrus, cows were further subdivided for analyses into 4 groups: control, TRT-EST (infected cows that displayed estrus; n = 4), TRT-NOEST (infected cows that did not display estrus; n = 8), and NOMAS (cows that were inoculated but did not develop mastitis; n = 4). Ovulation rate was 100% for CON, NOMAS, and TRT-EST compared with 0% for TRT-NOEST cows. Size of the ovulatory follicle (“presumed” ovulatory follicle in TRT-NOEST cows) was similar for all groups. Frequency of LH pulses was decreased in TRT-NOEST compared with CON, TRT-EST, and NO-MAS. Estradiol-17β increased over time in CON, NO-MAS, and TRT-EST cows, but did not increase in TRT-NOEST cows. Cows with clinical mastitis may exhibit estrus and ovulate normally or have disruptions in normal physiology including decreased LH pulsatility, absence of an LH surge and estrous behavior, suppressed estradiol-17β, and failure to ovulate.     

Quantification by simulation of the effect of herd management practices and cow fertility on the reproductive and economic performance of Holstein dairy herds

The performance of dairy herds is affected mainly by factors related to cows' characteristics and herd management practices. However, these factors are interrelated, and as such, the estimation of their individual effect on the performance of dairy herds remains difficult. The aim of this study was to estimate the weight of these factors as well the interactions between them on the reproductive and economic performance of dairy farms. A stochastic dynamic model was used to simulate most physiological and management processes occurring on a dairy farm. A herd of 60 Holstein cows, with a milk yield of 8,000 L/cow-year, representative of French Holstein dairy herds, was simulated. A total of 216 scenarios were run by combining 2 levels of postpartum cyclicity resumption (average: 45 d, high: 75 d), 3 levels of 21-d conception rate of the herd (i.e., proportion of cows pregnant 21 d after insemination; low: 25%, average: 45%, high: 70%), 3 levels of probability of pregnancy loss until 120 d (low: 3%, average: 15%, high: 43%), 3 levels of sensitivity of estrus detection by the farmer (low: 20%, average: 50%, high: 90%), 2 alternative managerial goals (constant number of cows or constant volume of milk sold), and 2 types of management for the sale and purchase of animals (closed or open herd). The effect of each factor was estimated by sensitivity analysis. The parameter that had the greatest effect on reproductive performance was the sensitivity of estrus detection: a 10-percentage-point increase between the low and average levels and between the average and high levels reduced the calving interval by 16 and 5.7 d, respectively. However, the factor that had the greatest effect on economic performance was the 21-d conception rate: a 10-percentage-point increase between the low and average levels and between the average and high levels increased the gross margin by €62.2 and €22.3/cow-year, respectively. The pregnancy loss until 120 d had an effect on economic performance: an increase of 1 percentage point of this parameter decreased the gross margin by €2/cow-year. The other factors studied, and their interactions, did not have a major effect (low value of sensitivity indices). Closed herds or farms with a constant number of cows had economic losses of €58/cow-year compared with open herds or to farms with constant volume of milk sold. Altogether, our data suggest that, in a typical French dairy farm, farmers' efforts on estrus detection will be more profitable when associated with improvement of the conception rate of the cows.     

Intensity of estrus following an estradiol-progesterone-based ovulation synchronization protocol influences fertility outcomes

The objective of this study was to examine the association between increased physical activity at the moment of timed artificial insemination (AI), detected by an automated activity monitor (AAM), and fertility outcomes. This paper also investigated factors affecting estrous expression in general. A total of 1,411 AI events from 1,040 lactating Holstein cows were recorded, averaging 1.3 ± 0.6 (±standard deviation) events per cow. Activity (measured as steps/h) was monitored continuously by a leg-mounted AAM located on the rear leg of the cow. Ovulation was synchronized by a timed AI protocol based on estradiol and progesterone. Ovarian ultrasonography was performed in all cows on d ?11 (AI = d 0) and in a subset of cows on d 0 (n = 588) and d 7 (n = 819) to determine the presence of a corpus luteum and follicles. The body condition score (1 to 5 scale) was assessed on d 0 and a blood sample was collected for progesterone measurement on d 7. Using the AAM, an estrus event was determined when the relative increase (RI) in physical activity of the cow exceeded 100% of the baseline activity. The physical activity was classified as strong RI (≥300% RI), moderate RI (100–300% RI), or no estrus (<100% RI). Milk production was measured daily and averaged between d ?11 and 0. Pregnancy was diagnosed at 32 and 60 d post-AI and pregnancy losses were calculated. The mean RI at estrus was 328.3 ± 132.1%. Cows with strong RI had greater pregnancy per AI than those with moderate RI and those that did not express estrus (35.1 vs. 27.3 vs. 6.2%). When including only cows that successfully ovulated after timed AI, those that displayed strong intensity RI still had greater pregnancy per AI than those with moderate intensity RI or those that did not express estrus (45.1 vs. 34.8 vs. 6.2%). Cows expressing strong RI at timed AI had greater ovulation rates compared with moderate RI and cows that did not express estrus (94.9 vs. 88.2 vs. 49.5%). Furthermore, pregnancy losses were reduced in cows with strong RI compared with cows expressing moderate RI (13.9 vs. 21.7%). Cows with a strong RI at estrus were more likely to have a corpus luteum at the beginning of the protocol and had greater concentration of progesterone 7 d post-AI. Multiparous cows expressed lower RI compared with primiparous cows. Cows with lower body condition score tended to have decreased RI at estrus. No correlation between estrous expression and pre-ovulatory follicle diameter was observed. Also, no correlation was observed between milk production at AI and RI. In conclusion, strong intensity RI of estrus events at timed AI was associated with improved ovulation rates and pregnancy per AI, and reduced pregnancy losses. These results provide further evidence that measurements of estrous expression can be used to predict fertility at the time of AI and possibly be used as a tool to assist decision making strategies of reproduction programs.     

Performance of automated activity monitoring systems used in combination with timed artificial insemination compared to timed artificial insemination only in early lactation in dairy cows

Identifying cows in estrus remains a challenge on dairy cattle farms, and tools and technologies have been developed and used to complement or replace visual detection of estrus. Automated activity monitoring (AAM) systems and timed artificial insemination (TAI) are technologies available to dairy farmers, but many factors can influence their relative performance. The objective of the present study was to compare reproductive performance of cows managed with an AAM system combined with TAI, or with a TAI program (Double Ovsynch) for insemination before 88 DIM. From April 2014 to April 2015, 998 cows from 2 herds were randomly assigned either to be inseminated at 85 ± 3 DIM exclusively using the Double Ovsynch protocol for TAI, or to be inseminated based on estrus detection by AAM without hormonal intervention between 50 and 75 DIM; if no alarm was detected by 75 DIM, cows were inseminated following the single Ovsynch protocol (AAM + Ovsynch). The herds used different AAM systems. Parity, hyperketonemia at wk 1 and 2 postpartum (PP), purulent vaginal discharge at wk 5 PP, body condition score at wk 7 PP, and anovulation to wk 9 PP were recorded. These health indicators did not significantly differ between treatments, but did between herds. The effect of treatment on pregnancy at first insemination and by 88 DIM were assessed using logistic regression models. Time to pregnancy was assessed using survival analysis. Results are reported from intention-to-treat analyses. Treatment did not affect pregnancy at first insemination or pregnancy by 88 DIM, but we found significant interactions between treatment and herd for both outcomes. In herd 2, marginal mean pregnancy at first AI was greater with Double Ovsynch (38%) than AAM + Ovsynch (31%), but no difference was observed in herd 1 (Double Ovsynch = 31%; AAM + Ovsynch = 34%). By 88 DIM, a smaller proportion of cows in herd 1 were pregnant in Double Ovsynch (31%) than AAM + Ovsynch (49%), but there was no difference in herd 2 (Double Ovsynch = 38%; AAM + Ovsynch = 38%). We observed a treatment by herd interaction for median (95% confidence interval) time to pregnancy, which were, in herd 1, 110 (106 to 129) and 98 (88 to 113) d, and, in herd 2, 126 (113 to 139) and 116 (105 to 131) d for the Double Ovsynch and AAM + Ovsynch treatments, respectively. The relative performance of AAM-based reproductive management compared with TAI only is likely influenced by herd-specific variables, in particular related to insemination rate when estrus detection is employed.     

Effect of estrous expression on timing and failure of ovulation of Holstein dairy cows using automated activity monitors

The aim of this study was to determine if estrous expression, as measured by an automated activity monitor (AAM), affects timing and failure of ovulation of lactating Holstein dairy cows. Cows were equipped with 2 AAM, 1 neck-mounted (AAM_C) and 1 leg-mounted (AAM_L), by 10 d postpartum and enrolled into the trial when their activity crossed the alert threshold on the AAM_C. A total of 850 episodes of estrus from 293 different cows were used for this study. When cows were enrolled, their ovaries were scanned by transrectal ultrasonography and gait and body condition scored. Ovaries of cows detected in estrus were scanned twice daily for a maximum of 3 d to determine the disappearance of the preovulatory follicle (ovulation) and the interval from estrus to ovulation was calculated. Physical activity data recorded from the AAM were used to determine estrus behavior using 2 traits: (1) peak activity and (2) duration. Peak activity was only available for the AAM_L. Peak activity was defined as the maximum activity during an estrus episode. Duration of estrus was defined as the time the activity of the cow exceeded threshold values set by the AAM software. The AAM_C correctly identified 87.8% of the estrus alerts, with 12.2% false positives. The average (±standard deviation) intervals from activity alert to ovulation were 25.8 ± 10.2 and 24.7 ± 9.3 h for the AAM_C and AAM_L, respectively. Changes in estrous expression were associated with differences in the interval from alert to ovulation. Cows with short intervals to ovulation were found to have less intense estrous expression than cows with medium and long length intervals to ovulation using the AAM_C, whereas using the AAM_L, cows with short intervals to ovulation exhibited less intense estrous expression than cows with medium but the same as those with long intervals to ovulation. Furthermore, irrespective of the AAM, estrus events with less estrous expression had increased odds of having a short interval to ovulation (below the median of 20 h) when compared with those having greater estrous expression (2.6 and 1.9 increased odds for the AAM_C and AAM_L, respectively). Ovulation failure was affected by estrous expression because estrus events with greater peak activity or longer duration had reduced ovulation failure compared with those with less estrous expression (AAM_C peak activity: 1.9 ± 1.4 vs. 9.5 ± 1.7%; AAM_L peak activity: 2.3 ± 1.4 vs. 6.2 ± 1.5%; AAM_C duration: 2.1 ± 1.4 vs. 8.9 ± 1.7%). In addition, cows with more estrous expression had greater pregnancy per artificial insemination than those with less estrous expression with both the AAM_C (42.3 ± 0.4 vs. 31.7 ± 0.4%) and the AAM_L (43.1 ± 0.4 vs. 36.3 ± 0.4%). Pregnancy per artificial insemination results were consistent even when removing cows that failed to ovulate. In conclusion, expression of estrus was highly associated with ovulation timing, ovulation failure, and fertility when using 2 different AAM. Cows with greater estrous expression have longer intervals from activity alert to ovulation, experience less ovulation failure, and have greater pregnancy per artificial insemination.     

An ex ante analysis on the use of activity meters for automated estrus detection: To invest or not to invest?

The technical performance of activity meters for automated detection of estrus in dairy farming has been studied, and such meters are already used in practice. However, information on the economic consequences of using activity meters is lacking. The current study analyzes the economic benefits of a sensor system for detection of estrus and appraises the feasibility of an investment in such a system. A stochastic dynamic simulation model was used to simulate reproductive performance of a dairy herd. The number of cow places in this herd was fixed at 130. The model started with 130 randomly drawn cows (in a Monte Carlo process) and simulated calvings and replacement of these cows in subsequent years. Default herd characteristics were a conception rate of 50%, an 8-wk dry-off period, and an average milk production level of 8,310 kg per cow per 305 d. Model inputs were derived from real farm data and expertise. For the analysis, visual detection by the farmer (“without” situation) was compared with automated detection with activity meters (“with” situation). For visual estrus detection, an estrus detection rate of 50% and a specificity of 100% were assumed. For automated estrus detection, an estrus detection rate of 80% and a specificity of 95% were assumed. The results of the cow simulation model were used to estimate the difference between the annual net cash flows in the “with” and “without” situations (marginal financial effect) and the internal rate of return (IRR) as profitability indicators. The use of activity meters led to improved estrus detection and, therefore, to a decrease in the average calving interval and subsequent increase in annual milk production. For visual estrus detection, the average calving interval was 419 d and average annual milk production was 1,032,278 kg. For activity meters, the average calving interval was 403 d and the average annual milk production was 1,043,398 kg. It was estimated that the initial investment in activity meters would cost €17,728 for a herd of 130 cows, with an additional cost of €90 per year for the replacement of malfunctioning activity meters. Changes in annual net cash flows arising from using an activity meter included extra revenues from increased milk production and number of calves sold, increased costs from more inseminations, calvings, and feed consumption, and reduced costs from fewer culled cows and less labor for estrus detection. These changes in cash flows were caused mainly by changes in the technical results of the simulated dairy herds, which arose from differences in the estrus detection rate and specificity between the “with” and “without” situations. The average marginal financial effect in the “with” and “without” situations was €2,827 for the baseline scenario, with an average IRR of 11%. The IRR is a measure of the return on invested capital. Investment in activity meters was generally profitable. The most influential assumptions on the profitability of this investment were the assumed culling rules and the increase in sensitivity of estrus detection between the “without” and the “with” situation.