Distribution of staphylococcal and mammaliicoccal species from compost-bedded pack or sand-bedded freestall dairy farms.

The purpose of this prospective observational study was to determine whether dairy cattle housing types were associated with staphylococcal and mammaliicoccal populations found on teat skin, bedding, and in bulk tank milk. Twenty herds (n = 10 sand-bedded freestall herds; n = 10 compost-bedded pack herds) were enrolled. Each herd was visited twice for sample collection, and at each visit, 5 niches were sampled, including bulk tank milk, composite teat skin swab samples collected before premilking teat preparation, composite teat skin swab samples collected after premilking teat preparation, unused fresh bedding, and used bedding. All samples were plated on Mannitol salt agar and Columbia blood agar and staphylococcal-like colonies were selected for further evaluation. Bacterial colonies were speciated using MALDI-TOF mass spectrometry. All species were grouped into 4 categories included host-adapted, opportunistic, environmental, and unclassified. Absolute numbers and proportions of each genus and species were calculated. Proportional data were compared between groups using Fisher's exact test. Data representing 471 staphylococcal-like organisms were analyzed. Overall, 27 different staphylococcal and mammaliicoccal species were identified. Staphylococcus chromogenes was the only species identified from all 20 farms. A total of 20 different staphylococcal-like species were identified from bulk tank milk samples with the most prevalent species being S. chromogenes, followed by Staphylococcus aureus and Mammaliicoccus sciuri. Overall, more staphylococcal and mammaliicoccal isolates were identified among used bedding than unused bedding. The increased numbers of isolates within used bedding were primarily from used sand bedding samples, with 79% (76/96) of used bedding isolates being identified from sand bedding and only 20.8% (20/96) from used compost-bedded pack samples. When comparing categories found among sample types, more unclassified species were found in used sand bedding than in used compost-bedded pack samples. This finding is possibly related to the composting temperatures resulting in reduced growth or destruction of bacterial species. The prevalence of S. aureus was high in bulk tank milk for all herds, regardless of herd type, which may represent the influence of unmeasured management factors. Overall, staphylococcal and mammaliicoccal species were highly prevalent among samples from both farm types.

Effects of acute lying and sleep deprivation on metabolic and inflammatory responses of lactating dairy cows.

Dairy cows that are restricted from lying down have a reduced ability to sleep. In other species, sleep loss is a key risk factor for disease, mediated by changes in metabolic and inflammatory responses. The cumulative effect of lying and sleep deprivation on cow health is unknown. The objective was to determine the effects of lying and sleep deprivation on metabolic and inflammatory responses of dairy cows. Data were collected from 8 multiparous and 4 primiparous lactating cows (199 ± 44 d in milk, 77 ± 30 d pregnant; mean ± standard deviation) enrolled in a study using a crossover design. Each cow was exposed to 2 treatments meant to induce sleep loss: (1) human disturbance (imposed by researchers making noise or physical contact when the cow's posture suggested sleep) and (2) lying deprivation (imposed by a wooden grid placed on the pen floor). Cows experienced a 24-h baseline period (d -1) followed by a 24-h treatment period (d 0), with a 12-d washout period between treatments. Baseline and treatment periods were imposed from 2100 to 2059 h. Cows were housed in individual pens during the acclimation period (d -3 and -2), d -1, and d 0. Nonesterified fatty acid and glucose concentrations were measured at 0300, 0900, 1500, and 2059 h on d -1 and 0. Proinflammatory cytokine mRNA [tumor necrosis factor (TNF), interleukin-1B (IL1B), and interleukin-6 (IL6)] abundance in whole-blood leukocytes, both nonstimulated and stimulated with lipopolysaccharide, were assessed at 2059 h on d -1 (end of baseline) and d 0 (end of treatment). Nonesterified fatty acids and glucose varied by time of day but were not affected by treatment or day. The abundances of TNF and IL1B from both stimulated and nonstimulated cells were higher following 24 h of lying deprivation (d 0) compared with baseline (d -1). Abundance of IL6 was increased in nonstimulated cells after lying deprivation compared with baseline. In contrast, human disturbance for 24 h did not alter TNF, IL1B, or IL6 abundance relative to baseline levels. These results suggest that a short period of lying deprivation generally increases inflammatory responses but not metabolic responses.

An evaluation of a novel device for measuring eating, rumination, and inactive behaviors in lactating Holstein dairy cattle.

Validation of precision dairy-monitoring technologies establishes technology behavioral-monitoring efficacy for research and commercial application. Technology metrics should be associated with behaviors of known physiological importance. The objective of this research project was to evaluate the Nedap SmartTag Neck (Nedap Livestock Management, Groenlo, the Netherlands) for dairy cow behavior measuring accuracy. The behaviors measured were eating, ruminating, and inactivity. Thirty-six lactating Holstein dairy cows were randomly selected from the University of Kentucky's Coldstream Dairy Research Herd and fitted with a Nedap SmartTag Neck. Cows were observed by a single observer for a total of 4 h per cow, including 2 h after the morning milking (0800 h) and 2 h after the evening milking (2000 h), from May to December 2017. The observer recorded the time behaviors occurred using a synchronized watch (CASIO, CASIO America, Inc., Dover, NJ, USA). The hour, minute, and second of the day each behavior occurred were compared with corresponding technology measurements. Pearson correlation coefficients (r; CORR procedure; SAS Institute Inc., Cary, NC, USA), concordance correlation coefficients (CCC; epiR package; R Foundation for Statistical Computing, Vienna, Austria), and Bland-Altman plots (epiR package; R Foundation for Statistical Computing) were used to determine association between visual observations and technology-recorded behaviors. Visually recorded eating, ruminating, and inactive time were moderately to strongly correlated with technology data (CCC ≥ 0.88) and Bland-Altman plots showed no bias, indicating a high level of agreement. In conclusion, the Nedap SmartTag Neck accurately monitored eating, ruminating, and inactivity behaviors and is expected to be effective in monitoring these behaviors in lactating dairy cattle in research or commercial farm settings.

Desiccation Tolerance: Avoiding Cellular Damage During Drying and Rehydration.

Desiccation of plants is often lethal but is tolerated by the majority of seeds and by vegetative tissues of only a small number of land plants. Desiccation tolerance is an ancient trait, lost from vegetative tissues following the appearance of tracheids but reappearing in several lineages when selection pressures favored its evolution. Cells of all desiccation-tolerant plants and seeds must possess a core set of mechanisms to protect them from desiccation- and rehydration-induced damage. This review explores how desiccation generates cell damage and how tolerant cells assuage the complex array of mechanical, structural, metabolic, and chemical stresses and survive.Likewise, the stress of rehydration requires appropriate mitigating cellular responses. We also explore what comparative genomics, both structural and responsive, have added to our understanding of cellular protection mechanisms induced by desiccation, and how vegetative desiccation tolerance circumvents destructive, stress-induced cell senescence.

On-farm use of disease alerts generated by precision dairy technology.

Wearable precision dairy monitoring (PDM) technologies currently used to detect estrus may provide insight into disease detection. However, the incorporation of PDM into farm management and its perceived usefulness for dairy producers have not been explored. As the targeted end users of these products, information is needed on how producers use generated disease alerts as well as barriers to adoption and usefulness. The objective of this research was to assess the perceived usefulness producers attributed to alerts from a daily generated alert list designed to identify sick or injured cows or cows that experienced a major management change. Data from 1,171 cows on 4 commercial farms in Kentucky were collected from October 2015 to October 2016. Each cow was equipped with 2 PDM technologies: a leg tag (measuring activity in steps/d and lying time in h/d) and a neck collar (measuring eating time in h/d). Alerts were generated based on an individual cow's decrease of ≥30% in activity, lying, and eating time compared with each cow's 10-d moving mean. Producers sorted alerts into 3 overall categories: (1) the cow alert was perceived to be true and the cow was visually checked, (2) the cow alert was perceived to be true, but the cow was not visually checked, and (3) the cow alert behavior change was doubted by the producer and the cow was not visually checked. Further subdivisions were also provided to explain the choice for an overall category. Over the year, 24,012 cow alerts were generated (eating time, n = 9,543; lying time, n = 9,777; activity, n = 1,590; or a combination of behaviors, n = 3,102). Only 8% of the alerts were doubted by the producer. Although 55% of alerts were perceived to be true, producers visually assessed cows based on only 21% of the alerts with a large variation between farms (2 to 45% of the alerts visually assessed). Producers were more likely to completely ignore alerts over time. Producers were more likely to perceive cow alerts to be true and visually check cows when ≤20 alerts occurred per day, cows were fresh or in early lactation, alerts occurred during the work week, or when cow alerts were for eating time, activity, or a combination of multiple behaviors. Behavioral disease alerts must be improved and correspond to an actionable change for producers to use them. Incorporating herd management software, creating and managing alerts by lactation stage, and focusing on behaviors producers already find useful could improve future alert utilization.

Comparing dairy farm milk yield and components, somatic cell score, and reproductive performance among United States regions using summer to winter ratios.

Heat stress abatement is a challenge for dairy producers in the United States, especially in the southern states. Thus, managing heat stress is critical to maintain dairy cow performance in the summer. The ability to employ a metric to measure heat stress and evaluate abatement strategies may benefit dairy producers by providing meaningful feedback on the effectiveness of current and future management strategies with the goal of improving heat stress management. Therefore, this study aimed to explore the use of the summer to winter performance ratio metric to quantify and compare farm performance variables among regions of the United States. Monthly performance data recorded by the Dairy Herd Improvement Association from 2007 to 2016, for all US Dairy Herd Improvement Association herds processing records through Dairy Records Management Systems (Raleigh, NC), were obtained. Season dates were based on the astronomical definition of the Northern Hemisphere with summer as June 21 to September 21 and winter as December 21 to March 19. States were grouped into regions based on climate zone classification. Performance records included a total of 16,573 herds [Northeast (n = 7,955), Midwest (n = 6,555), Northern Plains (n = 305), Southeast (n = 1,370), and Southern Plains (n = 388) regions]. Herd test day performance variables energy-corrected milk, somatic cell score, milk fat and protein percentage, conception rate, heat detection rate, and pregnancy rate in summer and winter were used to calculate summer to winter ratios for each region. The MIXED procedure of SAS 9.4 (SAS Institute Inc., Cary, NC) was used to compare test day performance variables. The effects of year, mean days in milk, mean 150-d milk, mean herd size, and number of milkings per day were included as covariates in the models. Dairy cattle performance in all climate regions was negatively affected by summer heat stress, but some regions greater than others. A difference was also observed among regions when comparing summer to winter ratios for each performance parameter. This indicates that summer performance varies by climate region identified by the summer to winter ratio and demonstrates usefulness of the metric to monitor degree of heat stress based on performance.

Technical note: An evaluation of technology-recorded rumination and feeding behaviors in dairy heifers.

Precision dairy monitoring technologies have become increasingly popular for recording rumination and feeding behaviors in dairy cattle. The objective of this study was to validate the rumination and feeding time functions of the CowManager SensOor (Agis, Harmelen, the Netherlands) against visual observation in dairy heifers. The study took place over a 44-d period beginning June 1, 2016. Holstein heifers equipped with CowManager SensOor tags attached according to manufacturer specifications (n = 49) were split into 2 groups based on age, diet, and housing type. Group 1 heifers (n = 24) were calves (mean ± SD) 2.0 ± 2.7 mo in age, fed hay and calf starter, and housed on a straw-bedded pack. Group 2 heifers (n = 25) were 17.0 ± 1.3 mo in age, fed a TMR, confirmed pregnant, and housed in freestalls. Visual observation shifts occurred at 1500, 1700, 1900, and 2100 h. Each heifer was observed for 2 hour-long periods, with both observation periods occurring on the same day. Visual observations were collected using a synchronized watch, and "start" and "stop" times were recorded for each rumination and feeding event. For correlations, data from CowManager SensOor tags and observations were averaged, so a single 1-h observation was provided per animal, reducing the potential for confounding repeated measures being collected for each animal. Concordance correlations (CCC; epiR package; R Foundation for Statistical Computing, Vienna, Austria) and Pearson correlations (r; CORR procedure; SAS Institute Inc., Cary, NC) were used to calculate association between visual observations and technology-recorded behaviors. Visually observed rumination time was correlated with the CowManager SensOor (r = 0.63, CCC = 0.55). Visually observed feeding time was also correlated with the CowManager SensOor (r = 0.88, CCC = 0.72). The difference between technology-recorded data and visual observation was treated as the dependent variable in a mixed linear model (MIXED procedure of SAS). Time of day, age in months, and group were treated as fixed effects. Individual heifers were treated as random and repeated effects. The effects of time of day, age, and group on rumination and feeding times were not significant. The CowManager SensOor was more effective at recording feeding behavior than rumination behavior in dairy heifers. The CowManager SensOor can be used to provide relatively accurate measures of feeding time in heifers, but its rumination time function should be used with caution.

Automated estrous detection using multiple commercial precision dairy monitoring technologies in synchronized dairy cows.

Estrus in dairy cattle varies in duration and intensity, highlighting the need for accurate and continuous monitoring to determine optimal breeding time. The objective of this study was to evaluate precision dairy monitoring technologies (PDMT) for detecting estrus. Estrus was synchronized in lactating Holstein cows (n = 109) using a modified G7G-Ovsynch protocol (last GnRH injection withheld to permit expression of estrus) beginning at 45 to 85 d in milk. Resumption of ovarian cyclicity at enrollment was verified by transrectal ultrasonography for presence of a corpus luteum. Cows were observed visually during 30 min (4 times per day) for behavioral estrus on d -1 to 2 (d 0 = day of estrus). Periods peri-estrus were defined by the temporal blood plasma progesterone patterns on d -5, -4, -3, -2, -1, 0, 2, 4, 6, and 8. Estrous detection by PDMT, an estrous behavior scoring system, and by visual observation of standing estrus were compared with the reference (gold) standard. Only 56% of cows that ovulated were observed standing by visual observation. Sensitivity and specificity for estrous detection were not different among all PDMT. Devices in this study measuring activity in steps, neck movement, high activity of head movement, or a proprietary motion index increased on the day of estrus 69 to 170% from the baseline before estrus. The change in rumination time on the day of estrus decreased for both neck and ear-based technologies (-2 to -16%). Temperature of the reticulorumen, vagina, and ear skin were not different on the day of estrus than day peri-estrus. Daily lying times decreased on average to 24.6% on the day of estrus for IceQube (IceRobotics Ltd., Edinburgh, Scotland). In contrast, lying time increased 15.5 and 33.1% for AfiAct Pedometer Plus (Afimilk, Kibbutz Afikim, Israel) and Track a Cow (ENGS Systems Innovative Dairy Solutions, Rosh Pina, Israel), respectively. All PDMT tested were capable of detecting estrus at least as effectively as visual observation. Four of the 6 PDMT that reported estrous alerts correctly detected 15 to 35% more cows than visual observation 4 times per day. Use of temporal progesterone patterns correctly identified more cows than visual observation alone. Dairy producers considering PDMT should focus on (1) the reference (gold) standard used to test efficacy of a device's alerts and (2) the device that will have the fewest false readings in their operations.

Use of a stochastic simulation model to estimate the cost per case of digital dermatitis, sole ulcer, and white line disease by parity group and incidence timing.

A farm-level stochastic simulation model was modified to estimate the cost per case of 3 foot disorders (digital dermatitis, sole ulcer, and white line disease) by parity group and incidence timing. Disorder expenditures considered within the model included therapeutics, outside labor, and on-farm labor. Disorder losses considered within the model included discarded milk, reduced milk production, extended days open, an increased risk of culling, an increased risk of death (natural or euthanized), and disease recurrence. All estimates of expenditures and losses were defined using data from previously published research in stochastic distributions. Stochastic simulation was used to account for variation within the farm model; 1,000 iterations were run. Sensitivity of foot disorder costs to selected market prices (milk price, feed price, replacement heifer price, and slaughter price) and herd-specific performance variables (pregnancy rate) were analyzed. Using our model assumptions, the cost per disorder case over all combinations of parity group and incidence timing, regardless of incidence likelihood, was lowest for digital dermatitis ($64 ± 24; mean ± standard deviation), followed by white line disease ($152 ± 26) and sole ulcer ($178 ± 29). Disorder costs were greater in multiparous versus primiparous cows and were always highest at the beginning of lactation. The greatest contributing cost categories were decreased milk production, an increased risk of culling, and disease recurrence. The contribution of cost categories to the total cost of disorder varied by disorder type, parity group, and incidence timing. For all disorders, the cost per case increased as milk price or replacement heifer price increased and decreased as feed price, pregnancy rate, or slaughter price increased. Understanding how foot disorder costs change according to cow-specific conditions (i.e., disorder type, parity group, and days in milk at incidence) and herd-specific conditions (i.e., market prices and performance variables) can help improve on-farm decisions about treatment and prevention of foot disorders.

Estimating the value of infectious or noninfectious foot disorder prevention strategies within dairy farms, as influenced by foot disorder incidence rates and prevention effectiveness.

A farm-level stochastic simulation model was adapted to estimate the value of implementing foot disorder prevention on a dairy farm by calculating the return on investment associated with implementation of foot disorder prevention. Two potential strategies for foot disorder prevention were tested: strategy 1 was prevention focused on reducing infectious foot disorders (i.e., digital dermatitis) in the model, and strategy 2 was prevention focused on reducing noninfectious foot disorders (i.e., sole ulcer and white line disease) in the model. For each strategy, we evaluated the effect of foot disorder incidence on the value of prevention by setting the incidence of foot disorders at 3 levels. For strategy 1, the level of digital dermatitis incidence without prevention in all parities was 20, 40, or 60%, and the incidence level of the noninfectious foot disorders in the model were held constant. For strategy 2, levels of sole ulcer and white line disease incidence without prevention in parity ≥3 cows were 5, 15, or 25%, and the incidence level of the infectious foot disorders included in the model were held constant; the incidence levels of noninfectious foot disorders in younger cows were adjusted to be lower. Overall, 6 scenarios were run, 1 for each prevention strategy × foot disorder incidence rate combination. To evaluate how the effectiveness of each prevention strategy would influence the investment value, the effectiveness of prevention could vary from a prevention risk ratio (RR) of 0.0 (100% reduction in disorder incidence) to 1.0 (0% reduction in disorder incidence). When implementing strategy 1, the return on prevention investment per cow-year (mean ± standard deviation) when prevention effectiveness was low (prevention RR = 0.91 to 1.0) and the digital dermatitis incidence rate was originally 20, 40, or 60% was $0.6 ± 0.4, $1.2 ± 0.9, and $1.8 ± 1.3, respectively. In comparison, the return on prevention investment per cow-year when prevention effectiveness was high (prevention RR = 0.00 to 0.09) and the digital dermatitis incidence rate was originally 20, 40, or 60% was $12.2 ± 3.0, $24.4 ± 6.0, and $36.5 ± 9.0, respectively. When implementing strategy 2, the return on prevention investment per cow-year when prevention effectiveness was low and noninfectious foot disorder incidence rates were originally 5, 15, or 25% in parity ≥3 cows was $0.6 ± 0.4, $1.9 ± 1.1, and $3.2 ± 1.9, respectively. In comparison, the return on prevention investment per cow-year when prevention effectiveness was high and noninfectious foot disorder incidence rates were originally 5, 15, or 25% in parity ≥3 cows was $12.4 ± 1.5, $37.3 ± 4.6, and $62.2 ± 7.6, respectively. The return on investment for foot disorder prevention would depend on the cost of the prevention strategy and the other benefits associated with the selected prevention strategy. This model could be used as a decision support tool to help identify the amount that could be paid to implement a selected prevention strategy.

Short communication: A comparison of cow cleanliness, fly population, and fly avoidance behaviors among docked, switch-trimmed, and switch-intact dairy cows in 3 commercial dairy herds.

Tail docking is a controversial practice in the dairy industry. Proponents claim that tail docking keeps the udder cleaner, and therefore improves milk quality and decreases somatic cell count. Opponents of tail docking cite that it causes unnecessary pain, backed by multiple studies that demonstrate no positive benefits of tail docking and that tail docking increases aggravation from flies. The objective of this study was to evaluate and compare cow cleanliness, fly population, and fly-avoidance behaviors among cows with docked, switch-trimmed, and switch-intact tails. A total of 206 cows from 3 Kentucky dairy herds were included in the longitudinal observational study. Each farm included previously docked cows, switch-intact cows, and cows whose switches were trimmed at the initial farm visit. Researchers visited each farm every 2 wk for 8 wk to record cow cleanliness, teat cleanliness, fly population, and fly-avoidance behavior scores. No significant differences were found in cow cleanliness scores, teat cleanliness scores, fly population scores, skin twitching, or foot stomping counts among docked, switch-trimmed, or switch-intact cows. Although the fly population scores did not differ, the amount of tail swings among docked, switch-intact, and switch-trimmed cows were significantly different. The odds of exhibiting tail swinging were 2.63 times greater for docked cows than for switch-trimmed cows and 1.92 times greater than for switch-intact cows. Overall, switch trimming resulted in similar outcomes to tail docking, although neither showed improvements over intact tails.

Meloxicam administration either prior to or after parturition: Effects on behavior, health, and production in dairy cows.

Parturition is often a stressful period, when the incidence of disease is high after calving, which has been associated with an uncontrolled inflammatory response. Therefore, the objective of this study was to test the effect of the administration of a nonsteroidal anti-inflammatory drug (meloxicam) on the behavior, health, and production of peripartum cows. Meloxicam was dosed at 1 mg/kg of body weight, and an empty gel capsule served as a placebo. Both were administered orally with a balling gun. Dairy cows and heifers were randomly assigned to 1 of 3 treatment groups: (1) meloxicam administration before calving, with a placebo administered after calving (MEL-PRE, n = 60), (2) placebo administered before calving, and meloxicam administered after calving (MEL-POST, n = 69), and (3) a placebo administered before calving and after calving (CTL, n = 65). To identify imminent calving events, a vaginal thermometer was inserted approximately 2 wk before the expected calving date and a drop in temperature was used to identify cows close to calving. Calving events were monitored via video cameras, and the amount of time that elapsed between the appearance of the amniotic sac at the vulva until delivery of the calf was used to determine calving difficulty score. Eutocic calving events were defined as cows that calved in ≤70 min, and dystocia was defined as cows that took longer than 70 min to calve. Milk yield and components were measured for the first 15 wk of lactation and accelerometers were used to record activity and lying behaviors. The effects of treatment, breed, parity, calving difficulty, and, when applicable, a repeated measure, along with interaction terms, were analyzed in mixed models. Regardless of the time of administration, dystocic cattle that received meloxicam were less active than dystocic CTL. Dystocic animals displayed more lying bouts on the day of calving and then displayed fewer lying bouts and were less active during the days following calving. No effect of treatment was noted on any health outcomes. Eutocic MEL-PRE animals produced 6.8 kg/d more milk than eutocic CTL. Regardless of calving difficulty, MEL-PRE animals produced more milk fat, protein, and lactose (kg/d) than CTL. In conclusion, meloxicam administration before calving appears promising in increasing milk yield in eutocic cows.

A survey of United States dairy hoof care professionals on costs associated with treatment of foot disorders.

The objective of this study was to collect information regarding hoof care professionals' billing practices and to gather their opinions about foot disorders and the value of their prevention. Responses were gathered from veterinarians (n = 18) and hoof trimmers (n = 116) through both online and paper survey platforms. Because of the limited number of respondents, veterinarian responses were not further analyzed. Of the 6 foot disorders included in the survey, the treatment cost per case was greatest for toe ulcers (mean ± standard deviation; $20.2 ± 8.5), sole ulcers ($19.7 ± 8.6), white line disease ($19.5 ± 8.1), and thin soles ($18.1 ± 8.1), and least for infectious disorders (foot rot and digital dermatitis; $8.0 ± 7.6 and $7.5 ± 9.6, respectively). Of the disorders, digital dermatitis represented most of the foot disorder cases treated by respondents over the past year (43.9 ± 20.4%), whereas toe ulcers and thin soles represented the least (5.3 ± 4.1 and 5.3 ± 5.7%, respectively). Respondents that served mostly large herds (>500 lactating cows) reported a lower prevalence of digital dermatitis (31.6 ± 4.2 vs. 44.4 ± 3.4 and 46.7 ± 3.2% in small and medium herds, respectively) and a higher prevalence of sole ulcers (23.1 ± 3.0 vs. 13.4 ± 2.4 and 13.3 ± 2.3% in small and medium herds, respectively). Region of the United States (Northeast, Midwest, or other) also influenced foot disorder prevalence; respondents from the Northeast reported more sole ulcers than respondents from other regions (22.1 ± 2.3 vs. 12.4 ± 3.3%). When respondents were asked which disorder was associated with the greatest total cost per case to the producer (treatment and labor costs plus the reduction in milk yield, reduced reproductive performance, and so on), hoof trimmers ranked digital dermatitis as having the greatest total cost per case and thin soles as having the least total cost per case. Finally, respondents indicated that the most important benefits of reducing foot disorders were enhanced animal welfare and increased milk production, whereas the least important benefit was reduced veterinary and hoof trimmer fees. Results from this survey can be used to improve the accuracy of foot disorder cost estimates and contribute to better decision-making regarding both foot disorder treatment and prevention.

Animal board invited review: Dairy cow lameness expenditures, losses and total cost.

Lameness is one of the most costly dairy cow diseases, yet adoption of lameness prevention strategies remains low. Low lameness prevention adoption might be attributable to a lack of understanding regarding total lameness costs. In this review, we evaluated the contribution of different expenditures and losses to total lameness costs. Evaluated expenditures included labor for treatment, therapeutic supplies, lameness detection and lameness control and prevention. Evaluated losses included non-saleable milk, reduced milk production, reduced reproductive performance, increased animal death, increased animal culling, disease interrelationships, lameness recurrence and reduced animal welfare. The previous literature on total lameness cost estimates was also summarized. The reviewed studies indicated that previous estimates of total lameness costs are variable and inconsistent in the expenditures and losses they include. Many of the identified expenditure and loss categories require further research to accurately include in total lameness cost estimates. Future research should focus on identifying costs associated with specific lameness conditions, differing lameness severity levels, and differing stages of lactation at onset of lameness to provide better total lameness cost estimates that can be useful for decision making at both the herd and individual cow level.

A 100-Year Review: Lactating dairy cattle housing management.

Since the mid-1800s, farmers have been housing livestock. What began as a part-time solution for cold winters, stormy days, or injured animals has evolved into the main or only area in which cows spend their adult lives. With this change, farmers, academic researchers, and industry innovators have shaped the farm landscape, literally. Over the last 100 years, changes have been made for productivity, health, milk quality, reproduction, animal well-being, and farm profitability. We review a snapshot of those changes and look ahead to the future of lactating dairy cattle housing. All housing systems are moving toward improved cow comfort. Stalls in tiestall and freestall systems are now designed to accommodate cows based on body size and, in some cases, stage of lactation. Farmers may choose to build a compost bedded or traditional bedded-pack barn to maximize cattle rest or accommodate various breeds or sizes of cows. Looking to the future, external pressure and public perception may push farmers to consider other alternatives to total confinement. Future housing plans may include access to pasture or exercise lots, allowing cows to express their preferences for being outside or inside. Housing that allows natural expression of behavior while maintaining cow cleanliness and health may improve the lives of cows and farmers.

Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle.

The objective of this study was to use automated activity, lying, and rumination monitors to characterize prepartum behavior and predict calving in dairy cattle. Data were collected from 20 primiparous and 33 multiparous Holstein dairy cattle from September 2011 to May 2013 at the University of Kentucky Coldstream Dairy. The HR Tag (SCR Engineers Ltd., Netanya, Israel) automatically collected neck activity and rumination data in 2-h increments. The IceQube (IceRobotics Ltd., South Queensferry, United Kingdom) automatically collected number of steps, lying time, standing time, number of transitions from standing to lying (lying bouts), and total motion, summed in 15-min increments. IceQube data were summed in 2-h increments to match HR Tag data. All behavioral data were collected for 14 d before the predicted calving date. Retrospective data analysis was performed using mixed linear models to examine behavioral changes by day in the 14 d before calving. Bihourly behavioral differences from baseline values over the 14 d before calving were also evaluated using mixed linear models. Changes in daily rumination time, total motion, lying time, and lying bouts occurred in the 14 d before calving. In the bihourly analysis, extreme values for all behaviors occurred in the final 24 h, indicating that the monitored behaviors may be useful in calving prediction. To determine whether technologies were useful at predicting calving, random forest, linear discriminant analysis, and neural network machine-learning techniques were constructed and implemented using R version 3.1.0 (R Foundation for Statistical Computing, Vienna, Austria). These methods were used on variables from each technology and all combined variables from both technologies. A neural network analysis that combined variables from both technologies at the daily level yielded 100.0% sensitivity and 86.8% specificity. A neural network analysis that combined variables from both technologies in bihourly increments was used to identify 2-h periods in the 8 h before calving with 82.8% sensitivity and 80.4% specificity. Changes in behavior and machine-learning alerts indicate that commercially marketed behavioral monitors may have calving prediction potential.

Influence of breed, milk yield, and temperature-humidity index on dairy cow lying time, neck activity, reticulorumen temperature, and rumination behavior.

The objective of this study was to compare weekly mean lying time (LT), neck activity (NA), reticulorumen temperature (RT), and rumination time (RU) among 3 breed groups, milk yield (MY), and temperature-humidity index (THI). Cows (n = 36; 12 Holstein, 12 crossbred, and 12 Jersey) were blocked by parity group (primiparous or multiparous), days in milk, and MY. Lying time, NA, RT, RU, and MY were recorded and averaged by day and then by week for each cow. For study inclusion, each cow was required to have 10 wk of LT, NA, RT, and RU data. Maximum THI were recorded and averaged daily. Mean (±SE) days in milk, LT, MY, RT, RU, NA, and maximum THI were 159.0 ± 6.0 d, 11.1 ± 0.1 h/d, 28.7 ± 0.5 kg/d, 38.8 ± 0.0°C, 6.4 ± 0.1 h/d, 323.8 ± 3.8 activity units, and 56.5 ± 0.6, respectively. The MIXED Procedure of SAS (SAS Institute Inc., Cary, NC) was used to evaluate fixed effects of breed, MY, parity, THI, and their interactions on LT, NA, RT, and RU with cow nested within breed as subject. All main effects remained in each model regardless of significance level. Stepwise backward elimination was used to remove nonsignificant interactions. The interactions of breed × parity group and maximum THI × parity group were associated with RT. Increasing THI coincided with increasing RT. Least squares means LT for multiparous cows was significantly greater than LT for primiparous cows (11.4 ± 0.3 and 10.5 ± 0.5 h/d, respectively). Least squares means NA for primiparous cows was greater than for multiparous cows of all breeds (372.1 ± 10.9 and 303.4 ± 7.8, respectively). The CORR Procedure of SAS was used to evaluate relationships among RT, RU, LT, NA, and MY. Rumination time was positively correlated with MY (r = 0.30) and negatively correlated with LT (r = -0.14). Reticulorumen temperature was negatively correlated with MY (r = -0.11). Rumination time was positively correlated with NA (r = 0.18) and negatively correlated with LT (r = -0.14). Lying time and NA were negatively correlated (r = -0.43). Neck activity was positively correlated with MY (r = 0.14). Lying time was negatively correlated with MY (r = -0.25). Milk yield was associated with RU, which may be related to cows with greater MY also having a greater feed intake. Lying time increased and NA decreased with increasing parity, which may be effects of social hierarchy, where primiparous cows are more susceptible to being pushed away from the feed bunk and freestalls. Milk yield was positively associated with RU. Greater milk production requires greater feed intake, which may result in longer RU than for low-yielding cows. Lying time decreased as milk yield increased. The behavioral and physiological differences observed in this study provide new insight into the effects that breed, parity, MY, and THI have on cows.

Technical note: Evaluation of an ear-attached real-time location monitoring system.

Position tracking of cows within the barn environment allows for determining behavioral patterns and activities. Such data might be used for detection of estrus and disease. A newly marketed real-time location monitoring system (Smartbow, Smartbow GmbH, Weibern, Austria) was tested in this study. Cow location was continuously monitored with the Smartbow tags mounted on the cow's ear, which sends low-frequency signals to receivers further transmitting the information to a server. Through incoming data, the server triangulates the location of the cow within the barn environment in real time. The validation of the system was carried out in 4 steps. The first 2 steps served as static testing steps (tags and 1 cow positioned at 30 reference points), and steps 3 and 4 were dynamic steps with cows moving in the barn environment. For 48 h, locations of 15 cows were confirmed each hour by laser measurements performed by a team (step 3) or 1 observer (step 4). Interobserver variability was 0.83 m (range: 0.05 to 2.87 m), and intraobserver variability had a range of 0.02 to 0.31 m. In the 4 validation steps, the mean distance between observer laser measurements and Smartbow was between 1.22 and 1.80 m. Step 4, with 334 observations, resulted in a mean distance difference of 1.22 m (standard error = 1.32 m). Data can be used for development of algorithms to detect sick cows with changed behavioral patterns. Data may also be used to monitor cow responses to physical environment, potentially improving facility design. Time budgets in proximity to important barn features (i.e., feed bunk and water trough) and distances traveled can be calculated and used to identify cows in need of caretaker's attention and identify the cow's exact location in the barn.

Estimating US dairy clinical disease costs with a stochastic simulation model.

A farm-level stochastic model was used to estimate costs of 7 common clinical diseases in the United States: mastitis, lameness, metritis, retained placenta, left-displaced abomasum, ketosis, and hypocalcemia. The total disease costs were divided into 7 categories: veterinary and treatment, producer labor, milk loss, discarded milk, culling cost, extended days open, and on-farm death. A Monte Carlo simulation with 5,000 iterations was applied to the model to account for inherent system variation. Four types of market prices (milk, feed, slaughter, and replacement cow) and 3 herd-performance factors (rolling herd average, product of heat detection rate and conception rate, and age at first calving) were modeled stochastically. Sensitivity analyses were conducted to study the relationship between total disease costs and selected stochastic factors. In general, the disease costs in multiparous cows were greater than in primiparous cows. Left-displaced abomasum had the greatest estimated total costs in all parities ($432.48 in primiparous cows and $639.51 in multiparous cows). Cost category contributions varied for different diseases and parities. Milk production loss and treatment cost were the 2 greatest cost categories. The effect of market prices were consistent in all diseases and parities; higher milk and replacement prices increased total costs, whereas greater feed and slaughter prices decreased disease costs.

Retention payoff-based cost per day open regression equations: Application in a user-friendly decision support tool for investment analysis of automated estrus detection technologies.

Assessing the economic implications of investing in automated estrus detection (AED) technologies can be overwhelming for dairy producers. The objectives of this study were to develop new regression equations for estimating the cost per day open (DO) and to apply the results to create a user-friendly, partial budget, decision support tool for investment analysis of AED technologies. In the resulting decision support tool, the end user can adjust herd-specific inputs regarding general management, current reproductive management strategies, and the proposed AED system. Outputs include expected DO, reproductive cull rate, net present value, and payback period for the proposed AED system. Utility of the decision support tool was demonstrated with an example dairy herd created using data from DairyMetrics (Dairy Records Management Systems, Raleigh, NC), Food and Agricultural Policy Research Institute (Columbia, MO), and published literature. Resulting herd size, rolling herd average milk production, milk price, and feed cost were 323 cows, 10,758kg, $0.41/kg, and $0.20/kg of dry matter, respectively. Automated estrus detection technologies with 2 levels of initial system cost (low: $5,000 vs. high: $10,000), tag price (low: $50 vs. high: $100), and estrus detection rate (low: 60% vs. high: 80%) were compared over a 7-yr investment period. Four scenarios were considered in a demonstration of the investment analysis tool: (1) a herd using 100% visual observation for estrus detection before adopting 100% AED, (2) a herd using 100% visual observation before adopting 75% AED and 25% visual observation, (3) a herd using 100% timed artificial insemination (TAI) before adopting 100% AED, and (4) a herd using 100% TAI before adopting 75% AED and 25% TAI. Net present value in scenarios 1 and 2 was always positive, indicating a positive investment situation. Net present value in scenarios 3 and 4 was always positive in combinations using a $50 tag price, and in scenario 4, the $5,000, $100, and 80% combination. Overall, the payback period ranged from 1.6 yr to greater than 10 yr. Investment analysis demonstration results were highly dependent on assumptions, especially AED system initial investment and labor costs. Dairy producers can use herd-specific inputs with the cost per day open regression equations and the decision support tool to estimate individual herd results.