Motivations and attitudes of Brazilian dairy farmers regarding the use of automated behaviour recording and analysis systems.

In this Research Communication we investigate the motivations of Brazilian dairy farmers to adopt automated behaviour recording and analysis systems (ABRS) and their attitudes towards the alerts that are issued. Thirty-eight farmers participated in the study distributed into two groups, ABRS users (USERS, n = 16) and non-users (NON-USERS, n = 22). In the USERS group 16 farmers accepted being interviewed, answering a semi-structured interview conducted by telephone, and the answers were transcribed and codified. In the NON-USERS group, 22 farmers answered an online questionnaire. Descriptive analysis was applied to coded answers. Most farmers were young individuals under 40 years of age, with undergraduate or graduate degrees and having recently started their productive activities, after a family succession process. Herd size varied with an overall average of approximately 100 cows. Oestrus detection and cow's health monitoring were the main reasons given to invest in this technology, and cost was the most important factor that prevented farmers from purchasing ABRS. All farmers in USERS affirmed that they observed the target cows after receiving a health or an oestrus alert. Farmers believed that they were able to intervene in the evolution of the animals' health status, as the alerts gave a window of three to four days before the onset of clinical signs of diseases, anticipating the start of the treatment.The alerts issued by the monitoring systems helped farmers to reduce the number of cows to be observed and to identify pre-clinically sick and oestrous animals more easily. Difficulties in illness detection and lack of definite protocols impaired the decision making process and early treatment, albeit farmers believed ABRS improved the farm's routine and reproductive rates.

Evaluation of transpalpebral ultrasonographic measurement of optic nerve sheath diameter for indirect assessment of intracranial pressure in anesthetized and standing healthy adult horses.

OBJECTIVE: To determine whether an association exists between direct intracranial pressure (ICP) measurement and ultrasonographic measurement of optic nerve sheath diameter (ONSD) in anesthetized and standing horses. DESIGN: Cross-sectional study performed on a convenience sample of healthy adult horses. SETTING: University teaching hospital. ANIMALS: Eight adult horses donated to the University. Enrolled horses were free of abnormalities on physical examination, CBC, neurological evaluation, and ophthalmological examination. MEASUREMENTS AND MAIN RESULTS: Horses were anesthetized in lateral recumbency for placement of an ICP transducer. Three head positions (neutral, elevated, and lowered) were used to alter ICP. ICP and ONSD in 2 directions (D1 and D2) were recorded at 5 and 10 minutes after position change to elevated and lowered. ICP and ONSD measurements were repeated in standing sedated horses 24-36 hours after recovery from anesthesia. Linear regressions were performed with ICP as the dependent variable and ONSD as the independent variable by head position and times. Linear regressions were also performed for change from neutral under anesthesia, with ONSD as the independent variable and ICP as the dependent variable, by head position and times. Significance was set at P < 0.05. There was a moderate association between ICP and ONSD in horses with head lowered at 5 and 10 minutes (R2 values = 63%-78%) and weak association in head elevated at 10 minutes (R2 values = 56%-63%). There was a weak association between change from neutral ICP and change from neutral ONSD in the elevated anesthetized position at 10 minutes for summed D1 + D2 (R2  = 33%). CONCLUSIONS: Consistent associations between direct ICP and ONSD in anesthetized or standing horses were not observed. This inconsistency limits the clinically utility of transpalpebral ultrasonographic ONSD measurement for ICP monitoring in horses.

Welfare Quality® for dairy cows: towards a sensor-based assessment.

This Research Reflection addresses the possibilities for Welfare Quality® to evolve from an assessment method based on data gathered on punctual visits to the farm to an assessment method based on sensor data. This approach could provide continuous and objective data, while being less costly and time consuming. Precision Livestock Farming (PLF) technologies enabling the monitorisation of Welfare Quality® measures are reviewed and discussed. For those measures that cannot be assessed by current technologies, some options to be developed are proposed. Picturing future dairy farms, the need for multipurpose and non-invasive PLF technologies is stated, in order to avoid an excessive artificialisation of the production system. Social concerns regarding digitalisation are also discussed.

Monitoring great ape heart health through innovative electrocardiogram technology: Training methodologies and welfare implications.

Assessing and treating cardiovascular disease (or heart disease) is a growing concern for institutions housing great apes, as it is a major cause of mortality in all four taxa managed in human care. As part of a proactive monitoring plan, zoological managers and veterinarians often elect to perform electrocardiograms (ECGs) on their great ape populations. ECGs noninvasively evaluate cardiac electrical activity, and are thereby capable of providing information regarding heart function. This electrical signature is transcribed as a visual display of waveforms, referred to as telemetry strips, and can detect irregularities in heart rhythm, also known as arrhythmia. While traditional 6- or 12-lead ECGs are recommended periodically as part of a thorough heart performance evaluation, here we discuss the KardiaMobile (KM) device as an additional primate welfare tool. KM is a small, Food and Drug Administration-cleared, clinical-grade mobile ECG monitor that requires only 30 s of pressure to flag heart rate or arrhythmic abnormalities. We detail the training process and applicability to great apes in human care.

Research methodology for in vivo measurements of resting energy expenditure, daily body temperature, metabolic heat and non-viral tissue-specific gene therapy in baboons.

A large number of studies have shown that the baboon is one of the most commonly used non-human primate (NHP) research model for the study of immunometabolic complex traits such as type 2 diabetes (T2D), insulin resistance (IR), adipose tissue dysfunction (ATD), dyslipidemia, obesity (OB) and cardiovascular disease (CVD). This paper reports on innovative technologies and advanced research strategies for energetics and translational medicine with this NHP model. This includes the following: measuring resting energy expenditure (REE) with the mobile indirect calorimeter Breezing®; monitoring daily body temperature using subcutaneously implanted data loggers; quantifying metabolic heat with veterinary infrared thermography (IRT) imaging, and non-viral non-invasive, tissue-specific ultrasound-targeted microbubble destruction (UTMD) gene-based therapy. These methods are of broad utility; for example, they may facilitate the engineering of ectopic overexpression of brown adipose tissue (BAT) mUCP-1 via UTMD-gene therapy into baboon SKM to achieve weight loss, hypophagia and immunometabolic improvement. These methods will be valuable to basic and translational research, and human clinical trials, in the areas of metabolism, cardiovascular health, and immunometabolic and infectious diseases.

Estimating the net return of a remote calving alarm system in a dairy farm.

The aim of the study was to evaluate the net return of the implementation of a remote calving monitoring system for obstetrical and neonatal assistance on the herd economy in a dairy farm model. A total of 680 parturitions over a 7-yr period were evaluated. Age at first calving was restricted from 23 to 27 mo for primiparous cows to be included. Among groups of cows that were ready to calve in a 15-d interval, primiparous and multiparous were randomly assigned to the experimental group and monitored through a calving alarm system, whereas the others accounted for controls. Final parturition groups were as follows: control primiparous (CPP, n = 218), control multiparous (CM, n = 345), monitored primiparous (MPP, n = 56), and monitored multiparous (MM, n = 61). Monitored groups received prompt calving assistance and first neonatal care, whereas the presence of farm personnel was discontinuous for controls. A biological model was built considering significant differences in calf loss, early culling, milk production, and days open between groups. Then, a partial budget model was used to estimate costs and net return on a simulated herd of 100 lactating cows. Incidence of calf death was greater in control groups (11.06% and 10.73% in CPP and CM, respectively) compared with monitored cows (0.00% and 1.69% in MPP and MM, respectively). Multiparous cows with calf loss had increased relative risk (relative risk = 3.487) for early culling compared with multiparous counterparts with no neonatal loss. Daily milk production in the first 2 mo was 3.79 kg greater in multiparous cows with no dead calf, compared with their counterparts. A significant difference in median days open was found in MPP and CPP (118 and 148 d, respectively). In the final economic model, different simulations were analyzed. They were created assuming different prices or hypothesizing calving monitoring only in primiparous animals. The model estimated different, but always positive, net return. In conclusion, implementing a calving alarm system led to a net return from €37 to 90 per cow per year (€1 = US$1.15 at the time of the study). However, the device alone is not sufficient: it must be supported by qualified calving monitoring and assistance. Optimized personnel presence in the calving area at the right time leads to prompt calving and neonatal calf assistance and colostrum feeding within the first hours of life, thus reducing calf death and days open, and increasing milk production.

Individual dairy cow management: achievements, obstacles and prospects.

This review deals with the prospects and achievements of individual dairy cow management (IDCM) and the obstacles and difficulties encountered in attempts to successfully apply IDCM into routine dairy management. All aspects of dairy farm management, health, reproduction, nutrition and welfare are discussed in relation to IDCM. In addition, new IDCM R&D goals in these management fields are suggested, with practical steps to achieve them. The development of management technologies is spurred by the availability of off-the-shelf sensors and expanded recording capacity, data storage, and computing capabilities, as well as by demands for sustainable dairy production and improved animal wellbeing at a time of increasing herd size and milk production per cow. Management technologies are sought that would enable the full expression of genetic and physiological potential of each cow in the herd, to achieve the dairy operation's economic goals whilst optimizing the animal's wellbeing. Results and conclusions from the literature, as well as practical experience supported by published and unpublished data are analyzed and discussed. The object of these efforts is to identify knowledge and management routine gaps in the practical dairy operation, in order to point out directions and improvements for successful implementation of IDCM in the dairy cows' health, reproduction, nutrition and wellbeing.

Accelerometer activity tracking in horses and the effect of pasture management on time budget.

BACKGROUND: Accelerometry is an accepted means of quantifying human physical activity. Quantitative physical activity tracking could be beneficial for studies into equine health and disease prevention, for example in relation to obesity management. OBJECTIVES: Validate accelerometer use in grazing horses, determine between-day repeatability, and assess the effects of pasture size on time budget (i.e. duration in each activity category). STUDY DESIGN: Proof of concept. METHODS: Accelerometers (ActiGraph) were positioned at the poll. Horses underwent 5 min of observed activity in three categories: standing, grazing and ambulating. Receiver-operating characteristic curve analysis, used on ten second data epochs, calculated cut points between the activities. A 20-day study was then undertaken on 6 horses at pasture. Time in each category (per day) was deduced; a Mann Whitney U test was performed to compare standard vs. small paddock and day vs. night turn out. RESULTS: Cut-off values with the optimum sensitivity (94.7-97.7%) and specificity (94.7-96.8%) were found to be <127.6 counts for standing, 127.6-702.7 counts for grazing and >702.7 counts for ambulating. Repeatability was analysed descriptively: Median (IQR) of the between-day difference in minutes standing, grazing and ambulating were 46.9 (21.3-87.9), 77.3 (40.2-124.5) and 15.6 (6.8-40.2) respectively. Median times standing and ambulating were significantly different between standard and small paddocks: standing: 8.7 vs. 10.3 h (P<0.001); ambulating: 55.7 vs. 39.6 min (P = 0.002). There was no significant difference in the median time spent grazing. There were significant differences between day and night: standing: 32.95% vs. 50.97% (P = 0.001), grazing: 60.81% vs. 46.77% (P<0.001) and ambulating: 4.57% vs. 2.40% (P<0.001). MAIN LIMITATIONS: Small sample size and lack of cross-validation of cut-off points on independent, 'unseen' data. CONCLUSIONS: Accelerometry can differentiate standing, grazing and ambulating in horses. Our proof-of-concept study demonstrates modifying pasture size influences activity budgets; opening avenues into studying obesity management.

Dairy cattle behavior classifications based on decision tree learning using 3-axis neck-mounted accelerometers.

Demand has been increasing recently for an automated monitoring system of animal behavior as a tool for the management of livestock animals. This study investigated the association between the behavior of dairy cattle and the acceleration data collected using three-axis neck-mounted accelerometers, as well as the feasibility of improving the precision of behavior classifications through machine learning. In total 38 Holstein dairy cows were used, and kept in four different farms. A logger was mounted to each collar to obtain acceleration data for calculating the activity level and variations. At the same time the behavior of the cattle was observed visually. Characteristic acceleration waves were recorded for eating, rumination, and lying, respectively; and the activity level and variations were significantly different among these behaviors (p < 0.01). Decision tree learning was performed on the data set from Farm A and validated its precision; which proved to be 99.2% in cross-validation, and 100% in test data sets from Farms B to D. This study showed that highly precise classifications for eating, rumination, and lying is possible by using decision tree learning to calculate the activity level and variations of cattle based on the data obtained by three-axis accelerometers mounted to a collar.

Assessment of the temperature cut-off point by a commercial intravaginal device to predict parturition in Piedmontese beef cows.

Dystocic parturitions have an adverse impact on animal productivity and therefore the profitability of the farm. In this regard, accurate prediction of calving is essential since it allows for efficient and prompt assistance of the dam and the calf. Numerous approaches to predict parturition have been studied, among these, measurement of intravaginal temperature (IVT) is the most effective method at the field level. Thus, objectives of this experiment were, 1) to find an IVT cut-off to predict calving within 24 h, and 2) to clarify the use of IVT as an automated method of calving detection in housed beef cows. A commercial intravaginal electronic device (Medria Vel'Phone®) with a sensor that measures the IVT every 12 h was used. Piedmontese cows (n = 211; 27 primiparous and 184 multiparous) were included in this study. One-way analysis of variance was used to assess the temperature differences at 0, 12, 24, 36, 48 and 60 h before parturition. Receiving operator characteristic curves were built to determine the temperature cut-off which predicts calving within 24 h with the highest summation of sensitivity (Se) and specificity (Sp). Binomial logistic regression models were computed to identify factors that may affect the IVT before calving. Mean gestation length was 291.5 ±â€¯13.7 d (primiparous, 292 ±â€¯14.1 d; multiparous, 289 ±â€¯9.2 d). A decrease (P < 0.001) in the average IVT was found from 60 h before calving until the expulsion of the IVT device. A significant (P < 0.05) reduction in the IVT was noticeable from 24 h before until parturition. The IVT drop to predict parturition 24 h before calving was 0.21 °C (area under the curve [AUC] = 0.72; Se = 66%, Sp = 76%). Furthermore, the IVT cut-off value to predict parturition within 24 h was 38.2 °C (AUC = 0.89; Se = 86%, Sp = 91%). None of the evaluated fixed effects (parity, dystocia, season or length of gestation) affected (P ˃ 0.05) the IVT variation from 60 h before and up to calving. To conclude, the IVT average seems to be a better parameter than the drop in temperature to predict parturition within 24 h. In this regard, a cut-off of 38.2 °C showed a high Se and Sp for predicting calving. This study demonstrates the usefulness of a commercially available device to predict calving to improve management in stabled beef farms.

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.

Farm-specific economic value of automatic lameness detection systems in dairy cattle: From concepts to operational simulations.

Although prototypes of automatic lameness detection systems for dairy cattle exist, information about their economic value is lacking. In this paper, a conceptual and operational framework for simulating the farm-specific economic value of automatic lameness detection systems was developed and tested on 4 system types: walkover pressure plates, walkover pressure mats, camera systems, and accelerometers. The conceptual framework maps essential factors that determine economic value (e.g., lameness prevalence, incidence and duration, lameness costs, detection performance, and their relationships). The operational simulation model links treatment costs and avoided losses with detection results and farm-specific information, such as herd size and lameness status. Results show that detection performance, herd size, discount rate, and system lifespan have a large influence on economic value. In addition, lameness prevalence influences the economic value, stressing the importance of an adequate prior estimation of the on-farm prevalence. The simulations provide first estimates for the upper limits for purchase prices of automatic detection systems. The framework allowed for identification of knowledge gaps obstructing more accurate economic value estimation. These include insights in cost reductions due to early detection and treatment, and links between specific lameness causes and their related losses. Because this model provides insight in the trade-offs between automatic detection systems' performance and investment price, it is a valuable tool to guide future research and developments.

Towards practical application of sensors for monitoring animal health; design and validation of a model to detect ketosis.

The objective of this study was to design and validate a mathematical model to detect post-calving ketosis. The validation was conducted in four commercial dairy farms in Israel, on a total of 706 multiparous Holstein dairy cows: 203 cows clinically diagnosed with ketosis and 503 healthy cows. A logistic binary regression model was developed, where the dependent variable is categorical (healthy/diseased) and a set of explanatory variables were measured with existing commercial sensors: rumination duration, activity and milk yield of each individual cow. In a first validation step (within-farm), the model was calibrated on the database of each farm separately. Two thirds of the sick cows and an equal number of healthy cows were randomly selected for model validation. The remaining one third of the cows, which did not participate in the model validation, were used for model calibration. In order to overcome the random selection effect, this procedure was repeated 100 times. In a second (between-farms) validation step, the model was calibrated on one farm and validated on another farm. Within-farm accuracy, ranging from 74 to 79%, was higher than between-farm accuracy, ranging from 49 to 72%, in all farms. The within-farm sensitivities ranged from 78 to 90%, and specificities ranged from 71 to 74%. The between-farms sensitivities ranged from 65 to 95%. The developed model can be improved in future research, by employing other variables that can be added; or by exploring other models to achieve greater sensitivity and specificity.

Towards practical application of sensors for monitoring animal health: the effect of post-calving health problems on rumination duration, activity and milk yield.

Three sources of sensory data: cow's individual rumination duration, activity and milk yield were evaluated as possible indicators for clinical diagnosis, focusing on post-calving health problems such as ketosis and metritis. Data were collected from a computerised dairy-management system on a commercial dairy farm with Israeli Holstein cows. In the analysis, 300 healthy and 403 sick multiparous cows were studied during the first 3 weeks after calving. A mixed model with repeated measurements was used to compare healthy cows with sick cows. In the period from 5 d before diagnosis and treatment to 2 d after it, rumination duration and activity were lower in the sick cows compared to healthy cows. The milk yield of sick cows was lower than that of the healthy cows during a period lasting from 5 d before until 5 d after the day of diagnosis and treatment. Differences in the milk yield of sick cows compared with healthy cows became greater from 5 to 1 d before diagnosis and treatment. The greatest significant differences occurred 3 d before diagnosis for rumination duration and 1 d before diagnosis for activity and milk yield. These results indicate that a model can be developed to automatically detect post-calving health problems including ketosis and metritis, based on rumination duration, activity and milk yield.

The use of infrared thermography and accelerometers for remote monitoring of dairy cow health and welfare.

Increasing reliance on automated systems on-farm has led to a need for remote monitoring of health and welfare. We aimed to validate 2 methods that could be integrated into automated systems currently in use: infrared thermography (IRT) to measure respiration rate (RR), and accelerometers to measure the flinch, step, kick (FSK) response and assessing stress and discomfort. We monitored 22 multiparous, nonlactating, Friesian and Friesian × Jersey cows (average 5.1 yr of age) during a baseline period (2 min), a restraint in a crush (2 min), and then a recovery period after exposure to a startle (2 min). We measured RR with continuous IRT imaging of airflow through the nostrils and by counting flank movements from video and live recordings. We recorded heart rate (HR) and HR variability using HR monitors, and we recorded FSK from continuous video analysis of leg movements and indirectly using accelerometers attached to both hind legs. The FSK response was scored between 1 and 4 based on the height and direction of each leg movement. We observed no change in RR, HR variability, or FSK in response to the startle; however, HR increased briefly by 10 bpm. Bland-Altman plots indicated good agreement between the different methods of measuring RR, with average differences of -0.01 ± 0.87, 0.83 ± 0.57, and 0.37 ± 1.02 breaths/min for video versus live, IRT versus live and IRT versus video, respectively. Acceleration was also highly correlated with FSK scores of ≤3 (R2 = 0.96) and ≤2 (R2 = 0.89) and moderately correlated with FSK scores of 1 (R2 = 0.66) over the 4-min sampling period. The results show that accelerometers can provide an indirect measure of the FSK response, and IRT can be used reliably to measure RR. With further development, both technologies could be integrated into existing systems for remote monitoring of dairy cows' health and welfare on-farm.

Use of rumination and activity monitoring for the identification of dairy cows with health disorders: Part II. Mastitis.

The objectives of this study were to evaluate (1) the performance of an automated health-monitoring system (AHMS) to identify cows with mastitis based on an alert system (health index score, HIS) that combines rumination time and physical activity; (2) the number of days between the first HIS alert and clinical diagnosis (CD) of mastitis by farm personnel; and (3) the daily rumination time, physical activity, and HIS patterns around CD. Holstein cows (n=1,121; 451 nulliparous and 670 multiparous) were fitted with a neck-mounted electronic rumination and activity monitoring tag (HR Tags, SCR Dairy, Netanya, Israel.) from at least -21 to 80 d in milk (DIM). Raw data collected in 2-h periods were summarized per 24 h as daily rumination and activity. An HIS (0 to 100 arbitrary units) was calculated daily for individual cows with an algorithm that used rumination and activity. A positive HIS outcome was defined as an HIS of <86 units during at least 1 d from -5 to 2 d after CD. Blood concentrations of nonesterified fatty acids, ß-hydroxybutyrate, total calcium, and haptoglobin were also determined in a subgroup of cows (n=459) at -11±3, -4±3, 0, 3±1, 7±1, 14±1, and 28±1 DIM. The sensitivity of the HIS was 58% [95% confidence interval (CI): 49, 67] for all cases of clinical mastitis (n=123), and 55% (95% CI: 46, 64; n=114) and 89% (95% CI: 68, 100; n=9) for cases of mastitis alone or concurrent with other health disorders, respectively. Among clinical cases, sensitivity was 80.7% (95% CI: 67, 97) for cases caused by Escherichia coli (n=31) and ranged from 45 to 48% for cases caused by gram-positive bacteria (n=39; Streptococcus agalactiae, Streptococcus dysgalactiae, Streptococcus uberis, Streptococcus spp., Staphylococcus spp., and Trueperella pyogenes), Staphylococcus aureus (n=11), or cases with no bacterial growth (n=25). Days between the first HIS <86 and CD were -0.6 (95% CI: -1.1, -0.2) for all cases of mastitis. Cows diagnosed with mastitis had alterations of their rumination, activity, HIS patterns, and reduced milk production around CD depending on the type of mastitis case. Cows with mastitis also had some alterations of their calcium and haptoglobin concentrations around calving. The AHMS used in this study was effective for identifying cows with clinical cases of mastitis caused by E. coli and cows with another disease occurring during an event of mastitis, but it was less effective in identifying cows with mastitis not caused by E. coli.

Use of rumination and activity monitoring for the identification of dairy cows with health disorders: Part I. Metabolic and digestive disorders.

The objectives of this study were to evaluate (1) the performance of an automated health-monitoring system (AHMS) to identify cows with metabolic and digestive disorders-including displaced abomasum, ketosis, and indigestion-based on an alert system (health index score, HIS) that combines rumination time and physical activity; (2) the number of days between the first HIS alert and clinical diagnosis (CD) of the disorders by farm personnel; and (3) the daily rumination time, physical activity, and HIS patterns around CD. Holstein cattle (n=1,121; 451 nulliparous and 670 multiparous) were fitted with a neck-mounted electronic rumination and activity monitoring tag (HR Tags, SCR Dairy, Netanya, Israel) from at least -21 to 80 d in milk (DIM). Raw data collected in 2-h periods were summarized per 24 h as daily rumination and activity. A HIS (0 to 100 arbitrary units) was calculated daily for individual cows with an algorithm that used rumination and activity. A positive HIS outcome was defined as a HIS of <86 during at least 1 d from -5 to 2 d after CD. Blood concentrations of nonesterified fatty acids, ß-hydroxybutyrate, total calcium, and haptoglobin were determined in a subgroup of cows (n=459) at -11±3, -4±3, 0, 3±1, 7±1, 14±1, and 28±1 DIM. The sensitivity of the HIS was 98% [95% confidence interval (CI): 93, 100] for displaced abomasum (n=41); 91% (95% CI: 83, 99) for ketosis (n=54); 89% (95% CI: 68, 100) for indigestion (n=9); and 93% (95% CI: 89, 98) for all metabolic and digestive disorders combined (n=104). Days (mean and 95% CI) from the first positive HIS <86 and CD were -3 (-3.7, -2.3), -1.6 (-2.3, -1.0), -0.5 (-1.5, 0.5), and -2.1 (-2.5, -1.6) for displaced abomasum, ketosis, indigestion, and all metabolic and digestive disorders, respectively. The patterns of rumination, activity, and HIS for cows flagged by the AHMS were characterized by lower levels than for cows without a health disorder and cows not flagged by the AHMS from -5 to 5 d after CD, depending on the disorder and parameter. Differences between cows without health disorders and those flagged by the AHMS for blood markers of metabolic and health status confirmed the observations of the CD and AHMS alerts. The overall sensitivity and timing of the AHMS alerts for cows with metabolic and digestive disorders indicated that AHMS that combine rumination and activity could be a useful tool for identifying cows with metabolic and digestive disorders.

Assessment of Fluid Balance and the Approach to Fluid Therapy in the Perioperative Patient.

Perioperative patients can be highly dynamic and have various metabolic, physiologic, and organ system derangements that necessitate smart monitoring strategies and careful fluid therapy. The interplay between changing patient status, therapeutic interventions, and patient response makes effective monitoring crucial to successful treatment. Monitoring the perioperative patient and an approach to fluid therapy are discussed in this text.

Reproductive management practices and performance of Canadian dairy herds using automated activity-monitoring systems.

The objectives of this study were to describe the characteristics and motivations of producers who had implemented automated activity-monitoring (AAM) systems and to compare herd reproductive performance before and after the implementation of an AAM system and between herds with AAM and herds managing reproduction based on timed artificial insemination (TAI) or based on other programs. Freestall dairy herds located in Ontario and the western provinces of Canada and enrolled in Dairy Herd Improvement were surveyed through a mail questionnaire between April and July 2010. The data describe the characteristics and reproductive management practices of herds using AAM systems. A total of 505 questionnaires (29%) were returned. On average, 21-d pregnancy risk, conception risk, and 21-d insemination risk did not differ between herds managing reproduction based on an AAM system (18, 39, and 50%, respectively) or a TAI-based program (17, 38, and 49%, respectively). Herds that implemented an AAM system had a significant increase in annual pregnancy risk, from 15 to 17%, and insemination risk increased from 42 to 50%, whereas conception risk was unchanged (37 and 35%) following adoption of the system. The majority of respondents with AAM systems first used the system to manage reproduction in lactating cows. Most herds with AAM were performing artificial insemination twice per day, most commonly with an interval from the estrus alarm to artificial insemination of 7 to 12 h. The most commonly reported reason to adopt an AAM system was a desire to improve reproductive performance. These results support the findings from randomized trials that AAM-based programs can yield comparable reproductive performance to TAI-based programs.