Comparison of three reproductive management strategies for lactating dairy cows using combination of estrus detection or ovulation synchronization and Fixed-Timed Artificial Insemination

The objective of this study was to compare the reproductive performance of lactating dairy cows submitted to first AI after combination of estrus detection and fixed timed AI (FTAI) and FTAI only. Cows were randomly assigned to receive AI at detected estrus between 50 and 70 d in milk (DIM), if not detected in estrus, were enrolled in either Ovsynch (ED-Ov, n = 485) or PRIDsynch (ED-PR, n = 505) protocols; or received FTAI at 80 DIM after Double-Ovsynch protocol (DO, n = 501). Cows were body condition scored (BCS) at calving and at 43 DIM; and evaluated for postpartum disorders within 7 d postpartum; clinical mastitis, lameness and bovine respiratory disease were recorded until first AI. Ovarian cyclicity was monitored at 43 and 50 DIM, and at 70 and 77 DIM. Pregnancy diagnoses (PD) were performed at 32 and 63 d after AI. Overall prevalence of postpartum anovulation was 7.8%. Pregnancy per AI (P/AI) did not differ between reproductive strategies at 32 d PD (ED-Ov = 43.2%; ED-PR = 41.7%; DO= 45.3%). Primiparous cows had greater P/AI than multiparous cows (53.7% vs 36.8%). Cows on farm 1 had lower P/AI compared with their counterparts on farm 2 (42.1% vs 45.4%). Cows with BCS > 2.5 at 43 DIM had greater P/AI compared with cows with BCS ≤ 2.5 (44.5% vs 34.7%). Similar P/AI for cow's receiving AI at detected estrus and FTAI, low prevalence of disease anovulation may have contributed to the similar performance of ED-Ov, ED-PR and DO.

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

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.

Peri-estrus activity and mounting behavior and its application to estrus detection in Hanwoo (Korea Native Cattle)

This study was conducted to investigate the change in activity and mounting behavior in Hanwoo (Korean Native Cattle) during the peri-estrus period and its application to estrus detection. A total of 20 Hanwoo cows were fitted with a neck-collar accelerometer device, which measured the location and acceleration of cow movements and recorded the number of instances of mounting behavior by the altitude data. The data were analyzed in three periods (24-, 6-, and 2-h periods). Blood samples were collected for 5 days after the prostaglandin F2α (PGF2α) injection, and the concentrations of estradiol, progesterone, follicle-stimulating hormone, and luteinizing hormone were determined by enzyme-linked immunosorbent assays. Activity and mounting behavior recorded over 2-h periods significantly increased as estrus approached and were more efficient at detecting estrus than over 24- and 6-h periods (p < 0.05). Endocrine patterns did not differ with the variation of individual cows during the peri-estrus period (p > 0.05). Activity was selected as the best predictor through stepwise discriminant analysis. However, activity alone is not enough to detect estrus. We suggest that a combination of activity and mounting behavior may improve estrus detection efficiency in Hanwoo. Further research is necessary to validate the findings on a larger sample size.

Evaluating automated infrared thermography and vulva exposure tracking as components of an estrus detection platform in a commercial dairy herd

The primary objective of this study was to develop an automated infrared thermography platform (Estrus BenchMark) capable of measuring skin temperature and tail movements as a means of identifying cows in estrus. The secondary objective was to evaluate the accuracy of Estrus BenchMark to detect estrus compared to in-line milk progesterone (P₄) analysis (Herd Navigator System) in a commercial dairy herd managed under a robotic milking system. Data were collected on forty-six cows from 45 to 120 d after calving. Cows were flagged in estrus when milk P₄ fell below 5 ng/mL. The Estrus BenchMark true positive estrus alerts (Sensitivity; Se%) were compared to Herd Navigator System estrus alerts at different time-windows (±12 h, ±24 h, ±48 h, and ±72 h) relative to the Estrus BenchMark estrus alerts for all the estrus alerts (AE) and confidence-quality estrus (CQE; >80% quality) alerts identified by Herd Navigator System. The Estrus BenchMark captured skin temperature and tail movements resulting in vulva exposure (left tail movements, LTail; right tail movements, RTail; and pooled tail movements, PTail) for each milking event. Skin temperature tended to increase when the milk P₄ concentration (Least-Squares Means ± SE) dropped for AE (estrus day [d 0]; P₄; 3.51 ± 0.05 ng/mL, Skin temperature; 33.31 ± 2.38 °C) compared with d -7 (P₄; 20.22 ± 0.73 ng/mL; Skin temperature: 32.05 ± 3.77 °C). The increase in skin temperature, however, was significant in cows with CQE > 80% at d 0 (32.75 ± 0.29 °C) compared to d -7 (31.80 ± 0.28 °C). The prevalence of tail movements to expose vulva was greater (P = 0.01) in AE at d 0 (LTail: 62.50%; PTail; 68.75%; and RTail: 56.25%) compared with d -7 (LTail: 18.75%; PTail: 9.37%: and RTail: 9.37%), and d +4 (LTail: 9.37%; PTail: 9.37%; and RTail: 12.5%). Moreover, the higher prevalence of tail movements at d 0 was observed in cows with CQE > 80% (LTail; 65%, PTail; 80%, and RTail; 70%) compared to those with CQE < 80%. The highest Estrus BenchMark Youden index (YJ; 0.45), diagnostic odds ratio (DOR; 9.04), and Efficiency (0.77) were achieved for AE in a ±48 h window and at ±72 h window for CQE (YJ; 0.66, DOR; 25.29, and Efficiency 0.76) relative to Herd Navigator System estrus alerts. The highest Estrus BenchMark resulted in 58% estrus detection rates for AE and 80% for cows with CQE compared to the Herd Navigator System.

Economics of Rebreeding Nonpregnant Dairy Cows Diagnosed by Transrectal Ultrasonography on Day 25 after Artificial Insemination

Pregnancy rates of Holstein cows showed a substantial decline in the past years, which caused intensive TAI programs for nonpregnant cows to shorten the period between unsuccessful insemination and the next attempt on the same cow. Although many studies examined the improvement in pregnancy rates following TAI, only a few examined the economic impact of such programs. In this study, we look at the feasibility of reproductive programs that included early pregnancy diagnosis performed by transrectal ultrasonography 25 days after artificial insemination (AI) and TAI of nonpregnant cows. This resulted in the following two TAI programs: a modified OvSynch program with a second PGF2α treatment at 24 h interval (GPPG, n = 100) and a modified OvSynch program with an intravaginal progesterone-release device inserted between days 0−7 (PRID + GPPG, n = 100). Cows included in the TAI programs recorded an improvement in the cumulative pregnancy rate (67% vs. 53%; 69% vs. 53%) compared to those in which this strategy was not applied (p < 0.05). An economic analysis was performed using a decision-support tool to estimate the net present value (NPV; USD/cow/year). The analysis revealed a difference in NPV of 89.6 USD/cow/year between the programs (rebreeding the nonpregnant cows following the TAI program vs. AI at detected estrus). In summary, rebreeding the nonpregnant cows after early negative pregnancy diagnosis (25 days after AI) using this strategy can improve the cumulative pregnancy rate and profitability of dairy farms.

Potential effects of hormonal synchronized breeding on genetic evaluations of fertility traits in dairy cattle: A simulation study

About 30% of producers use hormone protocols to synchronize ovulation and perform timed artificial insemination (AI) in Canada. Days from calving to first service (CTFS) and first service to conception (FSTC) become masked phenotypes leading to biased genetic evaluations of cows for these fertility traits. The objectives of this study were to (1) demonstrate and quantify the potential amount of bias in genetic evaluations, and (2) find a procedure that could remove the bias. Simulation was used for both objectives. The proposed solution was to identify cows that have been treated by hormone protocols, make their CTFS and FSTC missing, and perform a multiple trait analysis including traits that have high genetic correlations with CTFS and FSTC, and which are not affected by the hormone protocols themselves. A total of 12 scenarios (S1-S12) were tested, changing the percentage of herds and cows that were randomly selected to be under timed AI. Cows that were given hormone protocols had CTFS of 86 d and FSTC of 0, which were used in genetic evaluation. Four criteria were used to indirectly measure the presence of bias: (1) the correlation between true (TBV) and estimated (EBV) breeding values (accuracy); (2) the differences in the mean EBV of top 25, 50, and 75 sires; (3) changes in correlation between TBV and EBV rankings; and (4) the changes in mean EBV over the simulated generations. All criteria changed unfavorably and proportionally to the increased use of timed AI. The accuracy within each class of animals (cows, dams, or sires) decreased proportionally with increased use of timed AI, varying from 0.32 (S12) to 0.52 (S1) for bull EBV for CTFS. The average EBV of the top sires (best 25, 50, 75, or 100 sires) approached population average EBV values when increasing the number of treated animals. The sire rank correlation between EBV and TBV within simulated scenarios was smaller for scenarios with more synchronized animals, going from 0.38 (S12) to 0.67 (S1). The long-term use of hormonal synchronized cows clearly decreased the mean EBV over generations in the population for CTFS and FSTC. The inclusion of genetically correlated traits in a multiple trait model was effective in removing the bias due to the presence of hormonal synchronized cows. However, given the constraints within the simulation, it is important that further investigation with real data is conducted to determine the true effect of including timed AI records within genetic evaluations of fertility traits in dairy cattle.

Rumen-Reticular Temperature During Estrus and Ovulation Using Automated Activity Monitors in Dairy Cows

This study aimed to determine changes in rumen-reticular temperature (RRT) at estrus or ovulation and determine if these changes are associated with the intensity of estrous expression. Cows were equipped with an automated activity monitor (AAM) and a rumen-reticular bolus thermometer. A total of 190 estrus episodes were used where physical activity data was recorded using the AAM and ovulation was determined via ultrasonography of the ovaries at alert and twice daily, for a maximum of 60 h. Estrous expression was assessed using the maximum activity and duration in which activity remained above the AAM threshold; both characteristics were categorized using the median. Temperature data was collected for the duration of estrus, as well as for the interval of time where ovulation was determined to occur. Three measures of temperature were calculated: (1) positive area under the curve (AUC), (2) maximum positive temperature change (PTC), (3) maximum negative temperature change (NTC) at estrus (AUC_E/ PTC_E/ NTC_E) and around ovulation (AUC_O/PTC_O/ NTC_O). Both AUC and PTC were greater during estrus than around ovulation (2.7 ± 0.2 and 1.1 ± 0.3°C² for AUC_E and AUC_O; 0.55 ± 0.03 and 0.26 ± 0.04°C for PTC_E and PTC_O, respectively). In contrast, NTC was lower around ovulation than estrus (-0.28 ± 0.05 and -0.60 ± 0.06°C for NTC_E and NTC_O). Cows with greater estrous expression had greater AUC and PTC during estrus than around ovulation, but cows with lesser estrous expression had similar AUC and PTC. Increases in AUC [High THI (Temperature Humidity Index): High activity: 4.7 ± 0.5, Low activity: 1.5 ± 0.4; Low THI: High activity: 3.1 ± 0.2, Low activity: 1.4 ± 0.2 °C²] and PTC (High THI: High activity: 0.79 ± 0.08, Low activity: 0.36 ± 0.07; Low THI: High activity: 0.60 ± 0.04, Low activity: 0.47 ± 0.04°C) associated with estrous expression were found to be greater on days with higher THI. Alerts created using standard deviations from the mean were unable to detect estrus or ovulation with acceptable precision. Further research is required to determine how changes in RRT can be used successfully to predict estrus and ovulation.

Symposium review: Linking activity-sensor data and physiology to improve dairy cow fertility

Several studies have demonstrated that the intensity of estrous expression is associated with ovulation, ovarian and uterine function, and fertility, and is dependent on social hierarchy and the housing system used. Data from recent studies involving spontaneous and induced estrus have shown that a greater relative increase and longer estrus (captured by different automated activity monitors; AAM) are both associated with improved pregnancy per artificial insemination (AI; around 10 to 14% increase) and decreased pregnancy losses. Intensity and duration of estrus were surprisingly weakly associated with preovulatory follicle diameter and concentrations of plasma estradiol at estrus, whereas ovulation failure was associated with low estrus intensity. Studies have also shown that the display of estrous behavior near AI was associated with the modification of expression of genes related to the immune system, adhesion molecules, and prostaglandin synthesis in the endometrium. Transcripts in leukocytes and in the conceptus tissue associated with maternal recognition of pregnancy as well as conceptus elongation were all associated with differences in the intensity of estrous expression. Most recently, studies from the United States and Canada have demonstrated that reproductive programs emphasizing detection of estrus using AAM can be successful and comparable to intensive timed AI protocol-based programs that incorporate GnRH and PGF_2α treatments. Further, one study concluded that the administration of GnRH at AI for spontaneous estrus events greatly improved pregnancy per AI, but only for cows with reduced intensity of estrous expression, showing the potential to use AAM data as a tool in targeted reproductive programs. Quantitative information from estrus events could be used to improve estrus detection and develop decision-making strategies at the farm level. Future studies in this field should aim to better understand ovarian, conceptus, and endometrial mechanisms associated with either the expression or the intensity of estrus, and to refine the identification of phenotypes related to estrus (relative increase, absolute increase, baseline levels, duration, and repeatability within cow) to improve data usage, estrus detection, and possibly genetic selection.

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.

A 100-Year Review: Practical female reproductive management

Basic knowledge of mechanisms controlling reproductive processes in mammals was limited in the early 20th century. Discoveries of physiologic processes and mechanisms made early in the last century laid the foundation to develop technologies and programs used today to manage and control reproduction in dairy cattle. Beyond advances made in understanding of gonadotropic support and control of ovarian and uterine functions in basic reproductive biology, advancements made in artificial insemination (AI) and genetics facilitated rapid genetic progress of economically important traits in dairy cattle. Technologies associated with management have each contributed to the evolution of reproductive management, including (1) hormones to induce estrus and ovulation to facilitate AI programs; (2) pregnancy diagnosis via ultrasonography or by measuring conceptus-derived pregnancy-associated glycoproteins; (3) estrus-detection aids first devised for monitoring only physical activity but that now also quantitate feeding, resting, and rumination times, and ear temperature; (4) sex-sorted semen; (5) computers and computerized record software packages; (6) handheld devices for tracking cow location and retrieving cow records; and (7) genomics for increasing genetic progress of reproductive and other economically important traits. Because of genetic progress in milk yield and component traits, the dairy population in the United States has been stable since the mid 1990s, with approximately 9 to 9.5 million cows. Therefore, many of these technologies and changes in management have been developed in the face of increasing herd size (4-fold since 1990), and changes from pastoral or dry-lot dairies to increased housing of cows in confinement buildings with freestalls and feed-line lockups. Management of groups of "like" cows has become equally important as management of the one. Management teams, including owner-managers, herdsmen, AI representatives, milkers, and numerous consultants dealing with health, feeding, and facilities, became essential to develop working protocols, monitor training and day-to-day chores, and evaluate current trends and revenues. Good management teams inspect and follow through with what is routinely expected of workers. As herd size will undoubtedly increase in the future, practical reproductive management must evolve to adapt to the new technologies that may find more herds being milked robotically and applying technologies not yet conceived or introduced.

Potential of connected devices to optimize cattle reproduction

Estrus and calving are two major events of reproduction that benefit from connected devices because of their crucial importance in herd economics and the amount of time required for their detection. The objectives of this review are to: 1) provide an update on performances reached by sensor systems to detect estrus and calving time; 2) discuss current economic issues related to connected devices for the management of cattle reproduction; 3) propose perspectives for these devices. The main physiological parameters monitored separately or in combination by connected devices are the cow activity, body temperature and rumination or eating behavior. The combination of several indicators in one sensor may maximize the performances of estrus and calving detection. An effort remains to be made for the prediction of calvings that will require human assistance (dystocia). The main reasons to invest in connected devices are to optimize herd reproductive performances and reduce labor on farm. The economic benefit was evaluated for estrus detection and depends on the initial herd performances, herd size, labor cost and price of the equipment. Major issues associated with the use of automated sensor systems are the weight of financial investment, the lack of economic analysis and limited skills of the users to manage associated technologies. In the near future, connected devices may allow a precise phenotyping of reproductive and health traits on animals and could help to improve animal welfare and public perception of animal production.

Survey of reproduction management on Canadian dairy farms

The objectives of this study were to (1) quantify current reproduction management practices, and (2) assess the association between these practices and herd reproductive performance on dairy farms in Canada. A bilingual survey was developed, validated, and administered from March to May 2014 to collect general and reproduction management and performance measures [annual 21-d pregnancy rate (PR), 21-d insemination rate (IR), and conception risk (CR)]. Associations between management practices and reproductive performance measures were tested using linear regression models. A total of 832 questionnaires were completed online and by mail, representing a response rate of 9%. On average, farms had 77 lactating cows (median=50) and 13 dry cows (median=10), and Holstein was the most common breed (92% of herds). Lactating cow housing was tiestall on 61% of the farms, freestall on 37%, and bedded pack on 2%. The average voluntary waiting period was 58 d in milk (DIM). The main reproduction management practice per farm was defined as the means employed for >50% of inseminations. Farms reported their main reproduction management practice for first and subsequent inseminations, respectively, as visual estrus detection (51 and 44% of herds), timed AI (21 and 23% of herds), automated activity monitoring (AAM; 10 and 10% of herds), other management practice (bulls; 2 and 2% of herds), and a combination of management practices (16 and 21% of herds). On farms using visual estrus detection, cows were observed for signs of estrus on average 3.5 times per day, for an average total of 36 min/d. The most common use of reproductive hormones was to synchronize ovulation using Ovsynch (58% of the farms). Average PR, IR, and CR were 17.6, 44.1, and 40.5%, respectively. In linear regression analyses adjusted for confounders, pregnancy rate was significantly associated with geographic region, housing (tiestall: PR=15.4%, freestall: PR=17.6%), herd size (<50 lactating cows: PR=16.2%, 50-100 cows: PR=16.5%, >100 cows: PR=17.8%), voluntary waiting period (≤60 DIM: PR=17.6%, >60 DIM: PR=15.9%), and frequency of insemination per day (once daily: PR=16.6%, twice or more daily: PR=18.1%). The main reproduction management practice at first and subsequent inseminations was divergently associated with IR and CR, but not with PR (visual heat detection: PR=17.4%, timed AI: PR=18.4%, AAM: PR=17.1%, combined practices: PR=18.2%).

Invited review: Changes in the dairy industry affecting dairy cattle health and welfare

The dairy industry in the developed world has undergone profound changes over recent decades. In this paper, we present an overview of some of the most important recent changes in the dairy industry that affect health and welfare of dairy cows, as well as the science associated with these changes. Additionally, knowledge gaps are identified where research is needed to guide the dairy industry through changes that are occurring now or that we expect will occur in the future. The number of farms has decreased considerably, whereas herd size has increased. As a result, an increasing number of dairy farms depend on hired (nonfamily) labor. Regular professional communication and establishment of farm-specific protocols are essential to minimize human errors and ensure consistency of practices. Average milk production per cow has increased, partly because of improvements in nutrition and management but also because of genetic selection for milk production. Adoption of new technologies (e.g., automated calf feeders, cow activity monitors, and automated milking systems) is accelerating. However, utilization of the data and action lists that these systems generate for health and welfare of livestock is still largely unrealized, and more training of dairy farmers, their employees, and their advisors is necessary. Concurrently, to remain competitive and to preserve their social license to operate, farmers are increasingly required to adopt increased standards for food safety and biosecurity, become less reliant on the use of antimicrobials and hormones, and provide assurances regarding animal welfare. Partly because of increasing herd size but also in response to animal welfare regulations in some countries, the proportion of dairy herds housed in tiestalls has decreased considerably. Although in some countries access to pasture is regulated, in countries that traditionally practiced seasonal grazing, fewer farmers let their dairy cows graze in the summer. The proportion of organic dairy farms has increased globally and, given the pressure to decrease the use of antimicrobials and hormones, conventional farms may be able to learn from well-managed organic farms. The possibilities of using milk for disease diagnostics and monitoring are considerable, and dairy herd improvement associations will continue to expand the number of tests offered to diagnose diseases and pregnancy. Genetic and genomic selection for increased resistance to disease offers substantial potential but requires collection of additional phenotypic data. There is every expectation that changes in the dairy industry will be further accentuated and additional novel technologies and different management practices will be adopted in the future.