Purpose: The progression of posttraumatic osteoarthritis (OA) is likely multifactorial involving biochemical and biomechanical changes secondary to the injury. Both the inflammatory response and postoperative biomechanical changes that may contribute to OA progression may also be magnified after surgical treatment of multiple ligament knee injuries (MLKIs) when compared to anterior cruciate ligament (ACL) injury; however, the incidence of OA has not been directly compared after ACL and MLKI reconstructions. The purposes of this claims database study were to 1) compare the incidence of OA diagnoses between MLKI and ACL-injured patients, and 2) utilize a machine learning approach to identify risk factors for OA diagnosis following MLKI. We hypothesized that the incidence of OA diagnosis within 5 years of injury would be greater after MLKI than isolated ACL injury and that the risk factors of OA diagnosis would include increased age, obesity, female sex, and meniscal treatment.

Purpose: After an anterior cruciate ligament (ACL) injury, up to 50% to 80% of patients will develop radiographic evidence of posttraumatic osteoarthritis (PTOA). The injury and reconstructive surgery initiates a complex mechanobiologic disease process with subsequent alterations knee joint biomechanics that is believed to result in joint degeneration. This relationship is poorly understood and the effects of strenuous conditions, such as those experienced during running and landing, on cartilage breakdown are unknown.

Purpose: A previous meta-anlysis of studies involving healthy animals demonstrated that a moderate daily dose of exercise may be chondroprotective. However, it remains unknown if exercise following joint injury may either accelerate posttraumatic changes or have a chondroprotective effect in animal models of posttraumatic osteoarthritis (PTOA). The purpose of the current systematic review and meta-analysis was to evaluate the role of exercise on knee cartilage changes in animals with either anterior cruciate ligament or medial meniscus transection.

The objective of this study was to assess precision dairy monitoring technology (PDMT) measured variable effects on 3 postpartum diseases: metritis (MET; n = 112), hyperketonemia (KET; n = 46), and hypocalcemia (CAL; n = 90). The PDMT variables included lying time, step count, lying bouts, rumination time, eating time, time around the feedbunk, reticulorumen temperature, milk yield, milk conductivity, milk fat percentage, milk protein percentage, milk fat:protein ratio, milk lactose percentage, and body weight. A uterine discharge examination was conducted to determine MET at 3, 5, 7, 11, 14, 17, 19, and 21 DIM using a MetriCheck (Simcro Tech Ltd, Hamilton, New Zealand) device. A scale of 1 to 3 was used, and cows with scores >= 2 were classified as MET. A cow was classified with KET when any of the blood samples that were collected at 3, 7, 14, and 21 days in milk (DIM) were over the threshold (beta-hydroxybutyrate concentration >= 1.2 mmol/L of blood). A cow was classified with CAL when any of the samples were below the calcium threshold (calcium concentration <= 8.6 mg/dL of blood serum). The relationships between each PDMT variable and disease status were analyzed individually using the GLM procedure of SAS 9.3. The 21 DIM means of variables were compared with disease status (Yes or No) for MET, KET, or CAL. Cows with CAL spent less time ruminating (HR tag, 466 + 95 min/d vs. 507 + 92 min/d; respectively, P = 0.04), experienced longer lying time (IceQube, 9.4 + 1.6 h/d vs. 8.2 + 1.7 h/d, respectively, P < 0.01) and displayed lesser neck activity than cows without CAL (HR tag, 373 +/- 101 units of movement/d vs. 444 +/- 123 units of movement/d, respectively, P = 0.01). Cows with KET spent less time ruminating (HR tag, 447 +/- 107 min/d vs. 494 +/- 85 min/d, P = 0.01; SmartBow, 510 +/- 89 min/d vs. 547 +/- 69 min/d, P = 0.03; Cow-Manager SensOor, 544 +/- 140 min/d vs. 595 +/- 96 min/d, respectively, P = 0.05), fewer steps (AfiAct Pedometer Plus, 3,355 +/- 766 steps/d vs. 3,723 +/- 877 steps/d; respectively, P = 0.01, respectively), lesser neck activity (HR tag, 351 +/- 105 units of movement/d vs. 417 +/- 110 units of movement/d, respectively, P < 0.01), greater lying times (IceQube, 9.4 +/- 1.7 h/d vs. 8.7 +/- 1.6 min/d, respectively, P = 0.02) than cows without KET. Multiparous cows with KET had heavier body weights than multiparous cows without KET (AfiWeigh, 753 +/- 70 kg vs. 715 +/- 78 kg, respectively, P = 0.02), and yielded less milk (AfiMilk MPC milk meter 31 +/- 10 kg/d vs. 37 +/- 8 kg/d, respectively, P = 0.03). Cows with MET took fewer steps (IceQube, 1104 +/- 343 steps/d vs. 1335 +/- 375 steps/d, respectively, P < 0.01) and had greater fat:protein ratio than cows without MET(1.35 +/- 0.05 vs. 1.18 +/- 0.09, respectively, P = 0.02).

Animal welfare can be negatively affected when dairy cattle experience heat stress. Managing heat stress has become more of a challenge than ever before, due to the increasing number of production animals with increased milk yield, and therefore greater metabolic activity. Environmental temperatures have increased by 1.0°C since the 1800s and are expected to continue to increase by another 1.5°C between 2030 and 2052. Heat stress affects production, reproduction, nutrition, health, and welfare. Means exist to monitor and evaluate heat stress in dairy cattle, as well as different ways to abate heat, all with varying levels of effectiveness. This paper is a summary and compilation of information on dairy cattle heat stress over the years.

The southeastern United States experiences an extended hot season with a high environmental temperature and relative humidity. With increasing global temperatures, managing dairy cattle in regions with tropical, subtropical, and Mediterranean climates is becoming an increasing challenge. Heat-stressed cows will decrease feed intake, decrease productivity, and increase respiration rate in an attempt to maintain internal body temperature. Temperature-humidity index (THI) is a unitless value that has been used to measure the magnitude of heat stress on dairy cows. Many researchers have studied the THI threshold at which dairy cattle begin to experience heat stress. When housing cows in a confinement setting, a pasture-based setting, or a combination of the two, the appropriate heat abatement should be implemented to allow cows to perform to their potential and to improve overall animal welfare. This review summarizes heat abatement strategies that have been studied to reduce the negative effects of heat stress.

The objectives of the study were to use a heat stress scoring system to evaluate the severity of heat stress on dairy cows using different heat abatement techniques. The scoring system ranged from 1 to 4, where 1 = no heat stress; 2 = mild heat stress; 3 = severe heat stress; and 4 = moribund. The accuracy of the scoring system was then predicted using 3 machine learning techniques: logistic regression, Gaussian naïve Bayes, and random forest. To predict the accuracy of the scoring system, these techniques used factors including temperature-humidity index, respiration rate, lying time, lying bouts, total steps, drooling, open-mouth breathing, panting, location in shade or sprinklers, somatic cell score, reticulorumen temperature, hygiene body condition score, milk yield, and milk fat and protein percent. Three different treatments, namely, portable shade structure, portable polyvinyl chloride pipe sprinkler system, or control with no heat abatement, were considered, where each treatment was replicated 3 times with 3 second-trimester lactating cows. Results indicate that random forest outperformed the other 2 methods, with respect to both accuracy and precision, in predicting the sprinkler group's score. Both logistic regression and random forest were consistent in predicting scores for control, shade, and combined groups. The mean probability of predicting non-heat-stressed cows was highest for cows in the sprinkler group. Finally, the logistic regression method worked best for predicting heat-stressed cows in control, shade, and combined. The insights gained from these results could aid dairy producers to detect heat stress before it becomes severe, which could decrease the negative effects of heat stress, such as milk loss.

Precision dairy monitoring involves the use of technologies to measure physiological, behavioral, and production indicators on individual animals to detect events of interest. Estrus, disease, and calving detection are common applications, although estrus detection is the most tested and used. Many precision dairy monitoring technologies (PDMT) are commercially available and are being used in research and on farms. As a result, a common question from researchers and producers alike is, "what PDMT should I buy?" The answer to this question is inherently complicated because it depends on many factors, some of which researchers have yet to explore. The objective of this paper is to examine the less quantitively researchable aspects of PDMT adoption and use on-farm. This will be done through 3 lists of 5, determined from published theory and my own experience. First, the 5 main factors that influence adoption of an innovation: (1) relative advantage, (2) compatibility, (3) complexity, (4) trialability, and (5) observability. Each of these factors is at play to a different extent in the 5 adopter categories: (1) innovators, (2) early adopters, (3) early majority, (4) late majority, and (5) laggards. From my experience and research, the top PDMT are those that improve (1) farm efficiency; (2) farm economics; (3) decisionmaking; (4) animal welfare; and (5) producer happiness. Implementing PDMT on a farm is an enormous and potentially expensive decision. As this part of the industry continues to progress, the potential for different PDMT is endless. Sound research and producer feedback are imperative to ensuring that PDMT continue to improve and become more widely adopted.

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.

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.

Pegbovigrastim is a modified form of the naturally-occurring bovine granulocyte colony-stimulating factor. At the time of the study, pegbovigrastim was commercially available under veterinary prescription as Imrestor (Elanco Animal Health, Greenfield, Indiana). According to the product label, when the product is applied 7 days before calving and again at the day of calving, clinical mastitis incidence decreases by 28% in the first 30 days of lactation in dairy cows and heifers. The objective of this study was to further demonstrate the effectiveness of Imrestor at reducing clinical mastitis incidence, and to demonstrate the effectiveness at reducing metritis incidence in the first 30 days of lactation on a commercial dairy operation.

This information was presented at the 2017 Operations Managers Conference, organized by PRO-DAIRY in the Department of Animal Science In the College of Agriculture and Life Sciences at Cornell University. Softcover copies of the entire conference proceedings may be purchased at http://ansci.cals.cornell.edu/extension-outreach/adult-extension/dairy-management/order-proceedings-resources.

36 Significance of cow cooling practices and bulk tank milk quality parameters in southeastern United States dairy farms. Z. Mason*1, D. T. Nolan2, P. D. Krawczel3, G. M. Pighetti3, C. S. Petersson-Wolfe4, A. E. Stone1,2, J. M. Bewley2, and S. H. Ward5, 1Mississippi State University, Starkville, MS, 2University of Kentucky, Lexington, KY, 3University of Tennessee, Knoxville, TN, 4Virginia Polytechnic Institute and State University, Blacksburg, VA, 5North Carolina State University, Raleigh, NC.

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.