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In this study accumulation has been determined of several per- and polyfluoroalkyl substances (PFAS) from ingested water to steady state serum concentration for adult beef cattle and sheep raised on a hobby farm impacted by PFAS contamination. PFAS concentrations in stock water were stable for more than a year, they were non-measurable in grass but present at very low levels in soil which equated to just 1% of the intake from water. Prior to quantifying PFAS in cattle serum there had been no breeding for 18 months. Although there were high concentrations of several PFAS in the water, only perfluorooctane sulphonate (PFOS) and perfluorohexane sulphonate (PFHxS) were in cattle serum in appreciable amounts; perfluoroheptane sulphonate (PFHpS), perfluorononanoic acid (PFNA) and perfluorodecanoic acid (PFDA) were much lower. Transfer factors (TFs) for cattle were calculated by dividing steady state serum concentration (ng PFAS/mL) by water concentration (µg PFAS/L). Average and upper estimate TF values for cattle were calculated; the former were 140 (total PFOS, i.e. tPFOS), 130 (PFHpS), 65 (PFHxS), 170 (PFNA), and 120 (PFDA). Previous investigation campaigns at the farm provided relative steady state serum PFAS concentrations for sheep and cattle that allowed adjustment of the cattle TFs. The resulting average estimate TFs for ewes (non-pregnant and not lactating) were 20 (tPFOS) and 30 (PFHxS), other PFAS were not measurable in sheep serum. Discussion on using these TFs in human health risk assessments is provided. With certain assumptions/caveats the TFs allow estimations of PFAS steady state serum concentrations for use in preliminary human health risk assessments (HHRAs) when only PFAS in stock water is known.

Disease and stress can disrupt the circadian rhythm of activity in animals. Sensor technologies can automatically detect variations in daily activity, but it remains difficult to detect exactly when the circadian rhythm disruption starts. Here we report a mathematical Fourier-Based Approximation with Thresholding (FBAT) method designed to detect changes in the circadian activity rhythm of cows whatever the cause of change (typically disease, stress, oestrus). We used data from an indoor positioning system that provides the time per hour spent by each cow resting, in alleys, or eating. We calculated the hourly activity level of each cow by attributing a weight to each activity. We considered 36-h time series and used Fourier transform to model the variations in activity during the first and last 24 h of these 36-h series. We then compared the Euclidian distance between the two models against a given threshold above which we considered that rhythm had changed. We tested the method on four datasets (giving a cumulative total of ~120000 cow*days) that included disease episodes (acidosis, lameness, mastitis or other infectious diseases), reproductive events (oestrus or calving) and external stimuli that can stress animals (e.g. relocation). The method obtained over 80% recall of normal days and detected 95% of abnormal rhythms due to health or reproductive events. FBAT could be implemented in precision livestock farming system monitoring tools to alert caretakers to individual animals needing specific care. The FBAT method also has the potential to detect anomalies in humans to guide healthcare intervention or in wild animals to detect disturbances. We anticipate that chronobiological studies could apply FBAT to help relate circadian rhythm anomalies to specific events., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Several studies have demonstrated that the intensity of estrous expression is associated with ovulation, ovarian and uterine function, and fertility, and is dependent on social hierarchy and the housing system used. Data from recent studies involving spontaneous and induced estrus have shown that a greater relative increase and longer estrus (captured by different automated activity monitors; AAM) are both associated with improved pregnancy per artificial insemination (AI; around 10 to 14% increase) and decreased pregnancy losses. Intensity and duration of estrus were surprisingly weakly associated with preovulatory follicle diameter and concentrations of plasma estradiol at estrus, whereas ovulation failure was associated with low estrus intensity. Studies have also shown that the display of estrous behavior near AI was associated with the modification of expression of genes related to the immune system, adhesion molecules, and prostaglandin synthesis in the endometrium. Transcripts in leukocytes and in the conceptus tissue associated with maternal recognition of pregnancy as well as conceptus elongation were all associated with differences in the intensity of estrous expression. Most recently, studies from the United States and Canada have demonstrated that reproductive programs emphasizing detection of estrus using AAM can be successful and comparable to intensive timed AI protocol-based programs that incorporate GnRH and PGF 2α treatments. Further, one study concluded that the administration of GnRH at AI for spontaneous estrus events greatly improved pregnancy per AI, but only for cows with reduced intensity of estrous expression, showing the potential to use AAM data as a tool in targeted reproductive programs. Quantitative information from estrus events could be used to improve estrus detection and develop decision-making strategies at the farm level. Future studies in this field should aim to better understand ovarian, conceptus, and endometrial mechanisms associated with either the expression or the intensity of estrus, and to refine the identification of phenotypes related to estrus (relative increase, absolute increase, baseline levels, duration, and repeatability within cow) to improve data usage, estrus detection, and possibly genetic selection., (Copyright © 2021 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Until recently, animal behavior has been studied through close and extensive observation of individual animals and has relied on subjective assessments. Wearable technologies that allow the automation of dairy cow behavior recording currently dominate the precision dairy technology market. Wearable accelerometers provide new opportunities in animal ethology using quantitative measures of dairy cow behavior. Recent research developments indicate that quantitative measures of behavior may provide new objective on-farm measures to assist producers in predicting, diagnosing, and managing disease or injury on farms and allowing producers to monitor cow comfort and estrus behavior. These recent research developments and a large increase in the availability of wearable accelerometers have led to growing interest of both researchers and producers in this technology. This review aimed to summarize the studies that have validated lying behavior derived from accelerometers and to describe the factors that should be considered when using leg-attached accelerometers and neck-worn collars to describe lying behavior (e.g., lying time and lying bouts) in dairy cows for research purposes. Specifically, we describe accelerometer technology, including the instrument properties and methods for recording motion; the raw data output from accelerometers; and methods developed for the transformation of raw data into meaningful and interpretable information. We highlight differences in validation study outcomes for researchers to consider when developing their own experimental methodology for the use of accelerometers to record lying behaviors in dairy cows. Finally, we discuss several factors that may influence the data recorded by accelerometers and highlight gaps in the literature. We conclude that researchers using accelerometers to record lying behaviors in dairy cattle should (1) select an accelerometer device that, based on device attachment and sampling rate, is appropriate to record the behavior of interest; (2) account for cow-, farm-, and management-related factors that could affect the lying behaviors recorded; (3) determine the appropriate editing criteria for the accurate interpretation of their data; (4) support their chosen method of recording, editing, and interpreting the data by referencing an appropriately designed and accurate validation study published in the literature; and (5) report, in detail, their methodology to ensure others can decipher how the data were captured and understand potential limitations of their methodology. We recommend that standardized protocols be developed for collecting, analyzing, and reporting lying behavior data recorded using wearable accelerometers for dairy cattle., (© 2020, The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

In this study, the utility of a commercial intravaginal thermometer was evaluated as an automated method for the prediction of calving in a total of 257 healthy pregnant Holstein-Friesian female cattle. The accuracy and the sensitivity of predicting calving within 48 hr before calving were also evaluated. The intravaginal temperature changes from 72 hr before and up to calving were significantly (p ≤ .001) affected by parity, season (summer vs. autumn), the time of day (8 a.m. or 8 p.m.) and the 6-hr time intervals (38.19°C: first interval 0 to 6 hr before calving vs. 38.78°C: twelfth interval 66 to 72 hr before calving), while the gender (p = .943), and the weight of the calf (p = .610), twinning (p = .300), gestation length (p = .186), foetal presentation (p = .123), dystocia (p = .197) and retention of foetal membranes (p = .253) did not affect it significantly. The sensitivity of the SMS of expecting calving within 48 hr and the positive predictive value were 62.4% and 75%, respectively, while the sensitivity and the positive predictive value for the SMS of expulsion reached 100%. It can be concluded that the investigated thermometer is not able to predict calving within 48 hr accurately; however, imminent calving can be accurately alerted., (© 2020 The Authors. Reproduction in Domestic Animals published by Wiley-VCH GmbH.)

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., (The Authors. Published by Elsevier Inc. and Fass Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

The objective of this study was to evaluate estrous detection using a physical activity and rumination monitoring system in a seasonal calving organic grazing (GRAZ) and a low-input conventional (ZEROGRAZ) dairy herd. The study was conducted from June 2014 to August 2017. During each breeding season, physical activity and rumination were monitored electronically using an activity and rumination monitoring system (HR-LD tags; SCR Engineers Ltd., Netanya, Israel). Signals resulting from the activity and rumination monitoring system for individual cows were used to determine consistency of the values using this system with the breeding date of cows. Breeding dates were determined using Estrotect TM patches. The study included 1,463 breeding dates from 531 cows. Within the GRAZ herd, during the summer breeding season the monitoring system was less sensitive for estrous detection (33.8 %) than during the winter breeding season (79.8 %).The activity and rumination monitoring system had a sensitivity of 56.7 %, specificity of 99.3 % and positive predictive value of 59.8 % for the GRAZ herd, and sensitivity of 70.1 %, specificity of 99.2 % and positive predictive value of 66.3 % for the ZEROGRAZ herd. For cows that were determined to be pregnant and subsequently calved as a result of the mating, the sensitivity for estrous detection was slightly greater for the GRAZ (60.7 %) and ZEROGRAZ (72.5 %) herds. The activity and rumination monitoring system evaluated in this study has potential for estrous detection in grazing herds during the winter breeding season and in small-input dairy herds during both, winter and summer breeding seasons., (Copyright © 2020 The Author(s). Published by Elsevier B.V. All rights reserved.)

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.

The growth in wirelessly enabled sensor network technologies has enabled the low cost deployment of sensor platforms with applications in a range of sectors and communities. In the agricultural domain such sensors have been the foundation for the creation of decision support tools that enhance farm operational efficiency. This Research Reflection illustrates how these advances are assisting dairy farmers to optimise performance and illustrates where emerging sensor technology can offer additional benefits. One of the early applications for sensor technology at an individual animal level was the accurate identification of cattle entering into heat (oestrus) to increase the rate of successful pregnancies and thus optimise milk yield per animal. This was achieved through the use of activity monitoring collars and leg tags. Additional information relating to the behaviour of the cattle, namely the time spent eating and ruminating, was subsequently derived from collars giving further insights of economic value into the wellbeing of the animal, thus an enhanced range of welfare related services have been provisioned. The integration of the information from neck-mounted collars with the compositional analysis data of milk measured at a robotic milking station facilitates the early diagnosis of specific illnesses such as mastitis. The combination of different data streams also serves to eliminate the generation of false alarms, improving the decision making capability. The principle of integrating more data streams from deployed on-farm systems, for example, with feed composition data measured at the point of delivery using instrumented feeding wagons, supports the optimisation of feeding strategies and identification of the most productive animals. Optimised feeding strategies reduce operational costs and minimise waste whilst ensuring high welfare standards. These IoT-inspired solutions, made possible through Internet-enabled cloud data exchange, have the potential to make a major impact within farming practices. This paper gives illustrative examples and considers where new sensor technology from the automotive industry may also have a role.

The objectives of the 2 studies reported herein were to validate the accuracy of an automated monitoring device (AMD) to detect side lying, resting, activity, rumination, eating, walking, and panting in nonlactating and lactating dairy cows. Additionally, we aimed to determine whether the total time per cow-state recorded by the AMD within a 30-min interval corresponds to the total time per cow-state recorded simultaneously by visual observation. Study personnel (n = 2) observed pregnant nonlactating Holstein cows (n = 10) for 30 min in the morning and 30 min in the afternoon for 6 consecutive days and recorded continuously each cow-state. In study 2, study personnel (n = 2) observed lactating Holstein cows (n = 10) for 30 min in the morning and 30 min in the afternoon for 6 consecutive days. In both studies, cow-state was recorded every second, and within 1 min, the most prevalent cow-state was considered to be the behavior presented by the cow during that interval. Using the observer as the gold standard, test characteristics were calculated for the minute-by-minute interval analyses. For the 30-min interval analyses, the concordance correlation coefficient (p c ) and the coefficient of determination (R 2 ) between the total minutes for each cow-state recorded by the observer and the AMD were calculated. In study 1, for the minute-by-minute interval analyses, test characteristics were high for rumination (≥90.1%) and eating (≥73.8%), moderate for resting (≥62.9%), but negligible for medium activity (≥17%). For the 30-min interval analyses, the correlations between the total time of visual observations compared with the total time recorded by AMD for rumination (R 2 = 0.97, p c = 0.98) and eating (R 2 = 0.91, p c = 0.94) were very high, for resting (R 2 = 0.77, p c = 0.79) was high, and for medium activity (R 2 = 0.41, p c = 0.41) was low. In study 2, for the minute-by-minute interval analyses, test characteristics were high for rumination (≥79.4%), eating (≥74.2%), and resting (≥73.0%), but they were low for panting (≥31.3%) and negligible for medium activity (≥22.2%). For the 30-min interval analyses, the correlations were similar to study 1 (rumination: R 2 = 0.85, p c = 0.91; eating: R 2 = 0.95, p c = 0.97; resting: R 2 = 0.84, p c = 0.90; medium activity: R 2 = 0.44, p c = 0.57; and panting: R 2 = 0.21, p c = 0.42). In summary, the AMD used in this study provided accurate data regarding resting, rumination, and eating of pregnant nonlactating and lactating Holstein cows., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Economic losses due to dystocia or neonatal death as well as animal welfare and ethical concerns demand a reliable prediction of parturition with the aim to improve survival of the dam and her offspring. In this review, estimation of gestational age by ultrasound and prediction of parturition based on clinical signs, behaviour changes and changes in body temperature, composition of mammary gland secretions as well as hormonal changes are discussed in comparison between horses and cattle. Based on the physiological changes associated with the end of gestation and onset of labor, several systems and methods to predict parturition in horses and cattle have been developed. Physiological differences related to breed, maternal age and parity, pathological changes due to intrauterine growth retardation, placental problems or general illness of the dam but also housing and management systems bias a reliable prediction of parturition. This may be acceptable in cattle as long as birth alarm systems give satisfying results at herd level. The situation is different in the horse where the economic and emotional value of an individual mare and her foal usually reaches different dimensions than in cows. The possibilities for prediction of parturition can thus be discussed together, the consequences, however, may differ considerably between the two species., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Feed intake and time spent eating at the feed bunk are important predictors of dairy cows' productivity and animal welfare, and deviations from normal eating behavior may indicate subclinical or clinical disease. In the current study, we developed a random forests algorithm to predict dairy cows' daily eating time (of a total mixed ration from a common feed bunk) using data from a 3-dimensional accelerometer and a radiofrequency identification (RFID) prototype device (logger) mounted on a neck collar. Models were trained on continuous focal animal observations from a total of 24 video recordings of 18 dairy cows at the Danish Cattle Research Centre (Foulum, Tjele, Denmark). Each session lasted from 21 to 48 h. The models included both the present time signal and observations several seconds back in time (lag window). These time-lagged signals were included with the purpose of capturing changes over time. Because of the high costs of installing an RFID antenna in the feed bunk, we also investigated a model based solely on 3-dimensional accelerometer data. Furthermore, to address the trade-off between prediction accuracy and reduced model complexity and its implications for battery longevity, we investigated the importance of including observations back in time using lag window sizes between 8 and 128 s. Performance was evaluated by internal leave-one-cow-out cross-validation. The results indicated that we obtained accurate predictions of daily eating time. For the most complex model (a lag window size of 128 s), the median of the balanced accuracy was 0.95 (interquartile interval: 0.93 to 0.96), and the median daily eating time deviation was 7 min 37 s (interquartile interval: -6 to 15 min). The median of the average daily eating time during sessions was 3 h 41 min with an interquartile interval of 2 h 56 min to 4 h 16 min. Exclusion of RFID data resulted in a considerable decrease in prediction accuracy, mainly due to a decreased sensitivity of locating the cow at the feed bunk (median balanced accuracy of 0.87 at a lag window size of 128 s). In contrast, prediction accuracy only slightly decreased with decreasing lag window size (median balanced accuracy of 0.94 at a lag window size of 8 s). We suggest a lag window size of 64 s for further development of the prototype logger. The methodology presented in this paper may be relevant for future automatic recordings of eating behavior in commercial dairy herds., (Copyright © 2020 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Worldwide, there is a trend towards increased herd sizes, and the animal-to-stockman ratio is increasing within the beef and dairy sectors; thus, the time available to monitoring individual animals is reducing. The behaviour of cows is known to change in the hours prior to parturition, for example, less time ruminating and eating and increased activity level and tail-raise events. These behaviours can be monitored non-invasively using animal-mounted sensors. Thus, behavioural traits are ideal variables for the prediction of calving. This study explored the potential of two sensor technologies for their capabilities in predicting when calf expulsion should be expected. Two trials were conducted at separate locations: (i) beef cows (n = 144) and (ii) dairy cows (n = 110). Two sensors were deployed on each cow: (1) Afimilk Silent Herdsman (SHM) collars monitoring time spent ruminating (RUM), eating (EAT) and the relative activity level (ACT) of the cow, and (2) tail-mounted Axivity accelerometers to detect tail-raise events (TAIL). The exact time the calf was expelled from the cow was determined by viewing closed-circuit television camera footage. Machine learning random forest algorithms were developed to predict when calf expulsion should be expected using single-sensor variables and by integrating multiple-sensor data-streams. The performance of the models was tested using the Matthew's correlation coefficient (MCC), the area under the curve, and the sensitivity and specificity of predictions. The TAIL model was slightly better at predicting calving within a 5-h window for beef cows (MCC = 0.31) than for dairy cows (MCC = 0.29). The TAIL + RUM + EAT models were equally as good at predicting calving within a 5-h window for beef and dairy cows (MCC = 0.32 for both models). Combining data-streams from SHM and tail sensors did not substantially improve model performance over tail sensors alone; therefore, hour-by-hour algorithms for the prediction of time of calf expulsion were developed using tail sensor data. Optimal classification occurred at 2 h prior to calving for both beef (MCC = 0.29) and dairy cows (MCC = 0.25). This study showed that tail sensors alone are adequate for the prediction of parturition and that the optimal time for prediction is 2 h before expulsion of the calf.

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.

The objective of the present study was to validate the accuracy of algorithms, implemented in the currently available RumiWatch Converter (RWC) version V0.7.4.5 of the RumiWatch System (RWS), for the classification of behavioural characteristics from jaw and head movements which are monitored by a noseband halter comprising a pressure sensor and a triaxial accelerometer. The accurate classification of behavioural characteristics in different time resolutions is critical for the usage of the RWS for scientific and practical purposes as chewing behaviour provides essential indicators for the assessment of diet adequacy in dairy cows. To validate the RWC V0.7.4.5 classification accuracy for behavioural characteristics of rumination, eating, drinking, other activity and ruminating chews per bolus by direct observation as reference method, 14 dairy cows participated in the trial. Concordance between the consolidated 1-min and 1-h classification results was assessed. The RWC V0.7.4.5 classified only rumination and ruminating chews per bolus precisely, whereas an algorithm optimisation for the classification of eating, drinking and other activity is required. Additionally, classification results from the 1-min and 1-h time summaries were not in agreement with each other except for rumination.

The objective of the study was to develop a grazing algorithm for an ear tag-based accelerometer system (Smartbow GmbH, Weibern, Austria) and to validate the grazing algorithm with data from a noseband sensor. The ear tag has an acceleration sensor, a radio chip, and temperature sensor for calibration and it can monitor rumination and detect estrus and localization. To validate the ear tag, a noseband sensor (RumiWatch, Itin and Hoch GmbH, Liestal, Switzerland) was used. The noseband sensor detects pressure and acceleration patterns, and, with a software program specific to the noseband, pressure and acceleration patterns are used to classify data into eating, ruminating, drinking, and other activities. The study was conducted at the University of Minnesota West Central Research and Outreach Center (Morris, MN) and at Teagasc Animal and Grassland Research and Innovation Centre (Moorepark, Fermoy, Co. Cork, Ireland). During May and June 2017, observational data from Minnesota and Ireland were used to develop the grazing algorithm. During September 2018, data were collected by the ear tag and noseband sensor from 12 crossbred cows in Minnesota for a total of 248 h and from 9 Holstein-Friesian cows in Ireland for a total of 248 h. A 2-sided t-test was used to compare the percentage of grazing and nongrazing time recorded by the ear tag and the noseband sensor. Pearson correlations and concordance correlation coefficients (CCC) were used to evaluate associations between the ear tag and noseband sensor. The percentage of total grazing time recorded by the ear tag and by the noseband sensor was 37.0% [95% confidence interval (CI): 32.1 to 42.0] and 40.5% (95% CI: 35.5 to 45.6), respectively, in Minnesota, and 35.4% (95% CI: 30.6 to 40.2) and 36.9% (95% CI: 32.1 to 41.8), respectively, in Ireland. The ear tag and noseband sensor agreed strongly for monitoring grazing in Minnesota (r = 0.96; 95% CI: 0.94 to 0.97, CCC = 0.95) and in Ireland (r = 0.92; 95% CI: 0.90 to 0.94, CCC = 0.92). The results suggest that there is potential for the ear tag to be used on pasture-based dairy farms to support management decision-making., (The Authors. Published by FASS Inc. and Elsevier Inc. on behalf of the American Dairy Science Association®. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).)

The objectives of the 2 studies conducted were to validate the accuracy of an automated monitoring device (AMD; HR-LDn tags, SCR Engineers Ltd., Netanya, Israel) for different types of behaviors or cow-states (side lying, resting, medium activity, high activity, rumination, grazing, walking, and panting) in beef heifers and to determine if the total time per cow-state recorded by the AMD corresponds to the total time per cow-state recorded by instantaneous observations. Cow-state is recorded every second and, within 1 min, the most prevalent cow-state is considered to be the behavior presented by the animal during that interval. Study personnel (n = 2) observed heifers (n = 10) for 20 min from 0800 to 1140 h and 10 min from 1500 to 1640 h during 4 consecutive days and recorded continuously each cow-state at started and ended. Thus, study personnel were able to determine within a 1-min interval, which cow-state was most prevalent and represented the heifer's behavior. Because the proprietary machine learning algorithm prioritizes certain behaviors over others based on their contribution to the understanding of generalized bovine behavior patterns, we also determined the most prevalent behavior observed in 5-min intervals. Test characteristics (sensitivity, specificity, accuracy, and negative and positive predicted values) were calculated using the observer as the gold standard. In study 2, heifer behavior was scanned by observers (n = 2) every 5 min from 0800 to 1100 h and 1500 to 1800 h for 3 consecutive days. Total minutes per cow-state according to the observer were compared with the total minutes per cow-state according to the AMD during the same period to determine the correlation coefficient. In study 1, test characteristics were high (low ≤ 40%, moderate = 41 to 74%, high ≥ 75%) for rumination (≥ 89.7%), grazing (≥ 76.5%), and side lying (≥ 81.8%), and moderate for resting (≥ 48.8%). In study 2, the correlation coefficient for rumination (R2 = 0.92) and grazing (R2 = 0.90) were high and the correlation coefficient for resting (R2 = 0.66) and walking (R2 = 0.33) were moderate. We conclude that the AMD used in this study showed high accuracy when measuring rumination and grazing, but it was subpar when measuring resting and walking. The algorithms employed by the AMD used need to be improved for determination of walking and resting behaviors of beef cattle., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Society of Animal Science. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

The commercially available collar device MooMonitor+ was evaluated with regards to accuracy and application potential for measuring grazing behavior. These automated measurements are crucial as cows feed intake behavior at pasture is an important parameter of animal performance, health and welfare as well as being an indicator of feed availability. Compared to laborious and time-consuming visual observation, the continuous and automated measurement of grazing behavior may support and improve the grazing management of dairy cows on pasture. Therefore, there were two experiments as well as a literature analysis conducted to evaluate the MooMonitor+ under grazing conditions. The first experiment compared the automated measurement of the sensor against visual observation. In a second experiment, the MooMonitor+ was compared to a noseband sensor (RumiWatch), which also allows continuous measurement of grazing behavior. The first experiment on n = 12 cows revealed that the automated sensor MooMonitor+ and visual observation were highly correlated as indicated by the Spearman's rank correlation coefficient (rs) = 0.94 and concordance correlation coefficient (CCC) = 0.97 for grazing time. An rs-value of 0.97 and CCC = 0.98 was observed for rumination time. In a second experiment with n = 12 cows over 24-h periods, a high correlation between the MooMonitor+ and the RumiWatch was observed for grazing time as indicated by an rs-value of 0.91 and a CCC-value of 0.97. Similarly, a high correlation was observed for rumination time with an rs-value of 0.96 and a CCC-value of 0.99. While a higher level of agreement between the MooMonitor+ and both visual observation and RumiWatch was observed for rumination time compared to grazing time, the overall results showed a high level of accuracy of the collar device in measuring grazing and rumination times. Therefore, the collar device can be applied to monitor cow behavior at pasture on farms. With regards to the application potential of the collar device, it may not only be used on commercial farms but can also be applied to research questions when a data resolution of 15 min is sufficient. Thus, at farm level, the farmer can get an accurate and continuous measurement of grazing behavior of each individual cow and may then use those data for decision-making to optimize the animal management.

Analysing behaviours can provide insight into the health and overall well-being of dairy cows. Automatic monitoring systems using e.g., accelerometers are becoming increasingly important to accurately quantify cows' behaviours as the herd size increases. The aim of this study is to automatically classify cows' behaviours by comparing leg- and neck-mounted accelerometers, and to study the effect of the sampling rate and the number of accelerometer axes logged on the classification performances. Lying, standing, and feeding behaviours of 16 different lactating dairy cows were logged for 6h with 3D-accelerometers. The behaviours were simultaneously recorded using visual observation and video recordings as a reference. Different features were extracted from the raw data and machine learning algorithms were used for the classification. The classification models using combined data of the neck- and the leg-mounted accelerometers have classified the three behaviours with high precision (80-99%) and sensitivity (87-99%). For the leg-mounted accelerometer, lying behaviour was classified with high precision (99%) and sensitivity (98%). Feeding was classified more accurately by the neck-mounted versus the leg-mounted accelerometer (precision 92% versus 80%; sensitivity 97% versus 88%). Standing was the most difficult behaviour to classify when only one accelerometer was used. In addition, the classification performances were not highly influenced when only X, X and Z, or Z and Y axes were used for the classification instead of three axes, especially for the neck-mounted accelerometer. Moreover, the accuracy of the models decreased with about 20% when the sampling rate was decreased from 1Hz to 0.05Hz., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

The objective of this study was to validate the CowManager SensOor ear-tag accelerometer (Agis Automatisering BV, Harmelen, the Netherlands) against visual observations of feeding, rumination, resting, and active behaviors of tiestall-housed dairy cows. Prior validation of the sensor has been published for freestall and grazing dairy herds. However, the behavioral differences that exist among these and a tiestall system necessitate additional validation. Lactating Holstein cows (n = 10) at different lactation stages and parities were included in the study. Cows were monitored both visually and with the sensor for 10 h/d for 4 consecutive days (10 cows × 10 h × 4 d = 400 h of observation total). A single trained observer classified each minute of visual observation into 1 of 13 behaviors and then summarized them into the 4 behavioral categories of eating, rumination, not active, or active. The sensor registered ear movements continuously and, based on a proprietary model, converted them into the behavioral categories. Multivariate mixed models were run to obtain covariance estimates, from which correlation coefficients were computed to assess agreement between visual observation and sensor data. The models included the percentage of time spent performing each behavior per day as the dependent variable and technology (visual observation versus sensor) and day as fixed effects. The models also included the random effects of technology and the repeated effects of technology and day. The correlation strength between visual observation and sensor data varied from poor to almost perfect by behavioral category (eating: r = 0.27; rumination: r = 0.69; eating-rumination: r = 0.83; not active: r = 0.95; and active: r = 0.89). The results suggest that the sensor can be used to accurately monitor active and not-active behaviors of tiestall-housed dairy cows. The results also suggest that although the sensor shows promise for identifying feeding behaviors in general, the independent classification of rumination and eating requires additional sensitivity., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

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., (© 2019 Japanese Society of Animal Science.)

The objective of this study was to determine whether data from an electronic drinking system that tracks individual animal attendance with ear tag transponders could be used to detect social competition between dairy cows at the drinker. Specifically, we sought to identify the interval between one cow leaving the drinker and another cow taking her place that most accurately identified competitive replacements (when physical contact initiated by one cow causes the other to entirely remove her head from the drinker and the initiator subsequently places her head in the same drinker). The optimal interval to accurately identify replacements at the drinker was determined using a receiver operating characteristic curve and by evaluating the sensitivity (Se), specificity (Sp), and confusion matrix values (true positives, true negatives, false positives, false negatives) of different thresholds identified by 3 metrics: the Youden index, the point closest to (0,1) on the curve, and the point where Se roughly equals Sp. Lactating Holstein dairy cows (n = 20) were monitored for 4 consecutive 24-h periods by video recording and with an electronic drinking system. Two periods were used to identify the optimal interval (the baseline set), and the other 2 periods were used to validate the interval (the validation set). The occurrence of a replacement identified by video was paired with the interval between drinking events of 2 cows at the same electronic drinker to identify the interval that best predicted replacement events. Based on the low prevalence of replacement events compared with nonreplacement events, the interval with the lowest number of false positives was considered optimal. Of the 3 potential metrics, the point on the receiver operating characteristic curve where Se and Sp were roughly equal yielded the fewest false positives. The optimal interval to identify replacements at the drinker was ≤29 s, with 82% Se and 83% Sp in the baseline set. This interval was found to have 85% Se and 89% Sp when applied to the validation set. These results indicate that social competition between dairy cows at the drinker can be accurately measured with an electronic drinking system., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Precision dairy technology is important because of the possibility to continuously and accurately measure behavior, biometrics, and productivity on commercial and research dairy farms by an automated method with minimal human intervention. The behavior-monitoring collar (BMC) used in this study is a commercially available precision dairy technology (MooMonitor+, Dairymaster, Co. Kerry, Ireland), designed to measure rumination, heat detection, feeding, and resting behavior of dairy cows. The study objective was to compare cow behavior measured by the BMC with visual observations. Twenty-four lactating, group-housed, Holstein dairy cows (mean ± standard deviation; days in milk: 196 ± 101; parity: 2.0 ± 1.1; milk yield: 40.0 ± 9.8 kg/d) were randomly selected for observation at the University of Kentucky's research dairy farm, Lexington). Behavior-monitoring collars were assigned to cows as per farm protocol. Each cow was observed for 240 min within 1 d (0700 to 0900 h, and 1900 to 2100 h). Recordings of rumination, feeding, and resting time (min) by the BMC were compared with visual observation using Pearson correlation, concordance correlation coefficient (CCC), linear regression, and Bland-Altman plots for validation of precision and accuracy. Data from the BMC were considered precise if the correlation coefficient and coefficient of determination were high (>0.70), and mean bias from the Bland-Altman plots included zero with the 95% interval of agreement. The BMC was considered accurate if the slope from the linear regressions did not differ significantly from 1, and the CCC (ρ c ) were at least moderate (>0.90). We found very high Pearson correlation coefficients (0.99, 0.93, and 0.94) and coefficients of determination (0.97, 0.85, and 0.88) for rumination, feeding, and resting, respectively. Bland-Altman plots were acceptable; the plots did not show any bias. The Bland-Altman mean differences ± standard deviation (BMC - observation) were -7.57 ± 6.31, 15.81 ± 11.84, and -13.03 ± 9.37 min, respectively. The Bland-Altman plot's 95% interval of agreement encompassed 100% of the observations of resting time, and all but one cow's observations for both rumination and feeding time. The slope of the linear regression, however, was different than 1 for all behaviors, and rumination was the only behavior with moderate CCC. In summary, this study validates the high precision of rumination, resting, and feeding behaviors measured by a BMC in lactating dairy cows., (Copyright © 2019 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Lying behaviour is important for the welfare of the cow. Therefore, reliable electronic devices may improve the management of the cows, and the devices can be used as a tool in research. However, accelerometer-based devices measure acceleration, and an algorithm is therefore necessary for the calculation of lying behaviour. Thus, validation of such devices is imperative prior to use. The objective of this study was to validate the use of the AfiTagII device for measurements of the lying time and frequency of lying bouts of Danish Holstein (DH) and Danish Jersey (DJ) cows in a loose-house system on two different bedding materials. The validation included correlations and linear regression analyses of data collected by the AfiTagII compared with data collected both by direct observations and recordings from a previously validated device (IceQube). In total, 40 cows were observed directly with primiparous and multiparous DJ and DH cows, equally represented. Furthermore, 21 cows were monitored with both AfiTagII and IceQube devices, and data from both devices were collected simultaneously. The devices were attached to the hind leg of the cow. The estimates of the lying time from the AfiTagII device was highly correlated with the recordings from direct observations (r=0.98), and there was a linear relation between these with an intercept equal to 0 and a slope close to 1. The estimates of the lying time from the AfiTagII device was also highly correlated with the IceQube recordings (r=0.94). However, the intercept deviated from 0. The frequency of lying bouts recorded by the AfiTAgII compared to direct observations showed a positive predictive value of 0.96 for lactating cows on the slatted floor and of 0.85 for the dry cows on the deep bedding. The correlations between frequency of lying bouts recorded with the two devices were high (r=0.94), but the intercept deviated from 0. In conclusion, the AfiTagII has a high accuracy for the measurements of lying behaviour in both DH and DJ cows kept on different bedding materials.

Introduction: In the present study the ante partum ruminating behavior and the later birth course were examined. Between May 2016 and April 2017 all cows, on a North German farm with 430 Holstein Friesian cows, were equipped with a transponder recording the ruminating behavior at least one week before the calculated date of birth. In total, 28 births could be monitored. Regardless of the course of birth, a decrease in ruminal activity was observed in all animals in the days before birth. The most significant decrease was on the last day before the birth. A significant difference occurred only at certain times. The present study confirmed an association between the early decline in ruminal activity and any imminent -dystocia. Further and in-depth studies are needed to create an appropriate algorithm.

In a system in which cows are grouped and given differential access to feeding bins with different rations, and where these groups change over time, it is important to find out how a change in the ration (and hence feeding bin) affects the cow's feeding behaviour. Monitoring the locomotion of cows can be used to predict oestrus and improve health (lameness diagnosis), but activity monitors can also be used to estimate both activity and numbers of feeding visits by cows. Ice tag activity monitors were attached to the right hind legs of ten cows. Walking, standing, lying data and health records were used to record changes before and after a change in each cow's feeding bin. Results comparing activity before and after feeding bin change revealed significant increases in motion index, number of steps taken per minute and number of lying bouts per minute (all P &lt; 0.001). Comparing the behaviours of cows subsequently followed during the dry period showed significant differences in motion indices and number of steps taken per minute (P &lt; 0.001) in the dry period. The results indicate that cows are affected by feeding bin change and group change, which can lead to an increase in behaviour associated with the stress response, especially in heifers.

This study aimed to evaluate the effectiveness of estrous detection technique based on continuous measurements of vaginal temperature (VT) and conductivity (VC) with supervised machine learning in cattle. The VT and VC of 17 cows in tie-stalls were measured using our developed wearable vaginal sensor from Day 11 (Day 0 = ovulation day) to Day 11 of the subsequent estrous cycle at 15-min interval. After the maximum VT and VC were extracted hourly, their changes were expressed as residual VT (rVT = actual VT - mean VT for the same time on the previous 3 days) and as VC ratio (VCr = actual VC/mean VC for the same time on Day 11-13), respectively, and were used for analysis. Trans-rectal ultrasonography was performed to monitor ovarian structure changes. The plasma concentrations of reproductive hormones (progesterone: P 4 , estradiol-17β: E 2 , and LH) were measured in the experimental period. Standing estrus was confirmed by testing with herd mates at 3-h interval. The rVT decreased transiently, which coincided with decreasing P 4 a few days before estrus, and a sharp increase was associated with LH surge during estrus. The VCr increased as estrus approached, corresponding with decreasing P 4 and increasing E 2 and LH. After noise reduction, features, possible to follow-up estrus-associated changes in rVT and VCr, were extracted and used for developing estrous detection models; 9 models were developed with 3 feature sets (features extracted from rVT alone, VCr alone, and combination of rVT and VCr) and 3 machine learning algorithms (decision tree: DT, support vector machine: SVM, and artificial neural network: ANN). Cross-validation showed that models using the features from the combination of rVT and VCr showed better performance in terms of sensitivity and precision than those using features from VCr alone, and precision than those of using features from rVT alone. Within the models using the features from the combination of rVT and VCr, sensitivity and precision of the model generated by ANN were numerically, but not statistically, higher than those generated by DT and SVM. Of 17 estruses, 16 were detected, with one false positive, when the best model was used. Furthermore, both mean and variance of the interval from the beginning of the estrous detection alert to ovulation (27.3 ± 6.7 h, mean ± SD of 16 estruses) were not significantly different to those from the onset of standing estrus to ovulation (30.8 ± 5.8 h, n = 17), indicating that the estrus can be detected real-time by the present technique. Hence, the estrous detection technique based on continuous measurements of VT and VC with supervised machine learning has a potential for efficient and accurate estrous detection in cattle., (Copyright © 2019 Elsevier Inc. All rights reserved.)

The pressure-based noseband sensor system (RWS: RumiWatch System; ITIN + HOCH GmbH Feeding Technology, Liestal, Switzerland) has recently been validated for the measurement of rumination time in mature cows. We aimed in this study at developing a similar pressure-based system for monitoring rumination in young dairy calves. To this end, a vegetable oil-filled silicon tube with a built-in pressure sensor (outer diameter 5.7 mm, length 38 cm) was attached to the noseband of a calf halter. In contrast to the RWS developed for mature cows, the accelerometer, the battery, the data logger, and the SD card of the RWS were integrated into 1 box to reduce the weight of the RWS to 0.35 kg. The box was attached to the halter so that it was located behind the right ear of the calf. Ten pre-weaned German Holstein calves (49-106 kg BW and 33-63 days of age) were equipped with the RWS. Calves were milk-fed thrice a day and offered hay and commercial starter for ad libitum intake. In parallel, animals were monitored by a video camera connected to a video recorder for 12 h. Two independent observers assessed the video records to obtain a reliable gold standard for the evaluation of the newly developed RWS. Data obtained by either RWS or visual video observation were processed as min rumination per h, yielding a total of 120 pairs of values (12 pairs per animal) for regression analysis. Assessment of 2 independent observers were highly correlated (r = 0.99). Results indicated relatively low random error between results obtained from the RWS (on y-axis) and video observations (on x-axis) (R2 = 0.82). However, the intercept of the regression line (y = 7.70 + 0.64 x) was significantly different from zero (P < 0.01) and the 95% confidence interval of the slope (0.79-0.94) did not include the value of 1. This translates to a significant systemic error resulting in overestimation of rumination time which is attributable to nutritive and nonnutritive oral activities that almost exclusively lasted for up to 10 min. Exclusion of false positive rumination signals lasting less than 10 or 5 consecutive min from the analysis reduced the random and systemic errors of the model (R2 = 0.86 and 0.93, respectively). We conclude that the newly developed RWS can be used to provide accurate measurement of rumination time in young calves. However, an extra programmed algorithm in the evaluation software is recommended to make the system more user-friendly for measurements on calves.

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 ± 13.7 d (primiparous, 292 ± 14.1 d; multiparous, 289 ± 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., (Copyright © 2018 Elsevier Inc. All rights reserved.)

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., (Copyright © 2017 Elsevier Inc. All rights reserved.)

The objectives of this observational study were to assess the ability of automated activity monitoring (AAM) to detect estrus for first insemination, the accuracy of detection, and the optimum interval from the estrus alert from the AAM system to insemination. Four commercial farms using 1 of 2 commercial AAM systems were studied over 1 yr. Cows were inseminated between 55 and 80 d in milk (DIM) based on AAM only, then by a combination of AAM and timed artificial insemination (AI). Blood progesterone was measured in 1,014 cows at wk 5, 7, and 9 postpartum; purulent vaginal discharge (PVD) was assessed at wk 5; and lameness and BCS at wk 7. Overall, AAM detected 83% of cows in estrus by 80 DIM. Cows that had 3 serum progesterone <1 ng/mL, had PVD, or were both lame and had BCS ≤2.5 has lesser odds of being detected in estrus by 80 DIM (62, 68, and 53%, respectively). Blood samples were collected on the day of 445 AI based on AAM and 323 timed AI. The proportion of cows not in estrus (progesterone >1 ng/mL) on the day of AI was similar between AAM (4 ± 1.8%) and timed AI (3 ± 1.2%). Managers elected, based on subjective criteria, not to inseminate 17% of cows for which an AAM estrus alert was issued, of which 43% were not in estrus. Activity data were extracted from AAM software for 1,399 AI. Onset of estrus was calculated using the same or similar data processing criteria as the AAM system. Producers recorded the time of AI. The interval from onset of estrus to AI was categorized as 0 to 8, 8 to 16, or 16 to 24 h. We found no effect of AAM system on the probability of pregnancy per AI, but noted an interaction of interval with parity. For multiparous cows, the probability of pregnancy per AI was 31%, which did not differ with the interval to AI. For primiparous cows, the odds of pregnancy were greater if AI occurred 0 to 8 h (49%) than 8 to 16 (36%) or 16 to 24 h (31%) after the estrus alert from the AAM. Automated activity monitoring can detect estrus for first AI in just over the length of 1 estrous cycle for over 80% of cows, but the remainder would likely require intervention for timely insemination. For multiparous cows, performing AI based on AAM once per day would not affect pregnancy per AI, but for primiparous cows AI within 8 h of the onset of estrus may be advantageous., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

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., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Identifying cows in estrus remains a challenge on dairy cattle farms, and tools and technologies have been developed and used to complement or replace visual detection of estrus. Automated activity monitoring (AAM) systems and timed artificial insemination (TAI) are technologies available to dairy farmers, but many factors can influence their relative performance. The objective of the present study was to compare reproductive performance of cows managed with an AAM system combined with TAI, or with a TAI program (Double Ovsynch) for insemination before 88 DIM. From April 2014 to April 2015, 998 cows from 2 herds were randomly assigned either to be inseminated at 85 ± 3 DIM exclusively using the Double Ovsynch protocol for TAI, or to be inseminated based on estrus detection by AAM without hormonal intervention between 50 and 75 DIM; if no alarm was detected by 75 DIM, cows were inseminated following the single Ovsynch protocol (AAM + Ovsynch). The herds used different AAM systems. Parity, hyperketonemia at wk 1 and 2 postpartum (PP), purulent vaginal discharge at wk 5 PP, body condition score at wk 7 PP, and anovulation to wk 9 PP were recorded. These health indicators did not significantly differ between treatments, but did between herds. The effect of treatment on pregnancy at first insemination and by 88 DIM were assessed using logistic regression models. Time to pregnancy was assessed using survival analysis. Results are reported from intention-to-treat analyses. Treatment did not affect pregnancy at first insemination or pregnancy by 88 DIM, but we found significant interactions between treatment and herd for both outcomes. In herd 2, marginal mean pregnancy at first AI was greater with Double Ovsynch (38%) than AAM + Ovsynch (31%), but no difference was observed in herd 1 (Double Ovsynch = 31%; AAM + Ovsynch = 34%). By 88 DIM, a smaller proportion of cows in herd 1 were pregnant in Double Ovsynch (31%) than AAM + Ovsynch (49%), but there was no difference in herd 2 (Double Ovsynch = 38%; AAM + Ovsynch = 38%). We observed a treatment by herd interaction for median (95% confidence interval) time to pregnancy, which were, in herd 1, 110 (106 to 129) and 98 (88 to 113) d, and, in herd 2, 126 (113 to 139) and 116 (105 to 131) d for the Double Ovsynch and AAM + Ovsynch treatments, respectively. The relative performance of AAM-based reproductive management compared with TAI only is likely influenced by herd-specific variables, in particular related to insemination rate when estrus detection is employed., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Reticuloruminal pH has been linked to subclinical disease in dairy cattle, leading to considerable interest in identifying pH observations below a given threshold. The relatively recent availability of continuously monitored data from pH boluses gives new opportunities for characterizing the normal patterns of pH over time and distinguishing these from abnormal patterns using more sensitive and specific methods than simple thresholds. We fitted a series of statistical models to continuously monitored data from 93 animals on 13 farms to characterize normal variation within and between animals. We used a subset of the data to relate deviations from the normal pattern to the productivity of 24 dairy cows from a single herd. Our findings show substantial variation in pH characteristics between animals, although animals within the same farm tended to show more consistent patterns. There was strong evidence for a predictable diurnal variation in all animals, and up to 70% of the observed variation in pH could be explained using a simple statistical model. For the 24 animals with available production information, there was also a strong association between productivity (as measured by both milk yield and dry matter intake) and deviations from the expected diurnal pattern of pH 2 d before the productivity observation. In contrast, there was no association between productivity and the occurrence of observations below a threshold pH. We conclude that statistical models can be used to account for a substantial proportion of the observed variability in pH and that future work with continuously monitored pH data should focus on deviations from a predictable pattern rather than the frequency of observations below an arbitrary pH threshold., (Copyright © 2018 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

The objective was to validate the iVET ® birth monitoring system and to determine if it reduced fetal death in primiparous dairy heifers over a 1-y interval. There were 359 pregnant heifers enrolled; 167 heifers in the iVET ® group were monitored electronically and the remaining 192 (controls) were monitored visually for onset of Stage 2 labor, according to routine farm management. In addition, as a reference, all heifers were observed throughout the study by two independent investigators. Calves born dead or that died within 24 h after birth were defined as stillborn. The interval from appearance of the chorioallantoic sac to recognition of onset of calving in the control group averaged 21 min longer than the iVET ® signal (p = 0.0001) and rate of fetal death was numerically lower in the iVET ® group (8.9%) than in the control group (10.4%, p = 0.65). Interestingly, dystocia occurred more often in the iVET ® group (58.3%) than in the control group (40.9%, p = 0.001). The iVET ® system detected onset of Stage 2 labor earlier than conventional monitoring by farm staff. However, the device was lacking in several aspects and should be improved before its use in primiparous heifers can be recommended., (Copyright © 2017 Elsevier Inc. All rights reserved.)

The effect of training background of persons performing artificial insemination (AI) (herd-owner inseminators (OWNER), AI technicians (AI-T), and fertility consultants (FC)) on pregnancy rate and their ability to detect cows not in oestrus were studied. A total of 1584 re-AI occasions on 754 dairy farms were included. Milk samples for progesterone (P4) analysis in all cases were collected, as were data on the herd, previous breeding attempts, oestrous signs, uterine tone, slipperiness of cervix, and co-operation of the cow. Further breeding attempts and next calving or culling date were sought from registers. The cases were distributed into three categories based on P4 concentrations; <6 nmol/l (no luteal activity, could be in oestrus), 6-10 nmol/l (some luteal activity), and >10 nmol/l (high luteal activity, not in oestrus). Of cows offered for re-AI 7.7% had P4 concentration >10 nmol/l, with no difference between OWNER farms and farms using AI service. OWNERs chose for AI more cows having intermediate P4 than farms using AI service (9.8% vs. 5.9%, p < 0.05). AI-Ts recommended no AI significantly less often than FCs (1.6% vs. 4.9%, p < 0.01). Both groups were equally right: 71% and 68% of cows that were recommended to have no AI had high P4 concentration. Due to courageous and correct rejection of cows with high P4, FCs inseminated proportionally more cows in low P4 and less cows in intermediate P4 than OWNERs (p < 0.05). Of cows finally inseminated, 36.7% became pregnant, with no difference between OWNER farms and farms using AI service. Fertility consultants had higher pregnancy rates than AI-Ts (39.6% vs. 32.6%, p < 0.05). Toneless uterus and sticky cervix at AI significantly correlated with AI occurring at the wrong time (p < 0.001). Behaviour of the cow at AI did not predict P4 concentration. In conclusion, 7.7% of cows offered for re-AI had high P4 concentration. Training of AI personnel increased their ability to detect and reject these cows., (Copyright © 2017 Elsevier Inc. All rights reserved.)

In the present study, the ventral tail base surface temperature (ST) was monitored using a wearable wireless sensor for estrus detection in cattle. Relationships among ST, behavioral estrus expression, ovulation, and changes in hormone profiles during the estrous cycle were examined. Holstein Friesian or Japanese Black female cattle were used in summer (August-September), autumn (October-November) and winter (January-February; three animals per season). On Day 11 of the estrous cycle (Day 0=the day of ovulation), the sensor was attached to the surface of the ventral tail base and ST was measured every 2min until Day 11 of the next estrous cycle. Hourly maximum ST values were used for analysis. To exclude circadian rhythm and seasonal effects, ST changes were expressed as residual temperatures (RT=actual ST - mean ST for the same hour on the previous 3days). Obvious circadian rhythms of the ST were observed and daily changes in the ST significantly differed among seasons. There was no significant seasonal difference, however, in the RT. The mean RT increased significantly ∼24 compared with ∼48h before ovulation. The mean maximum RT was 1.27±0.30°C, which was observed 5.6±2.4h after the onset of estrus, 2.4±1.3h before LH peak, and 26.9±1.2h before ovulation. The ST of the ventral tail base could be monitored throughout the estrous cycle and could detect a substantial change around the time of expression of behavioral estrus. Calculation and analysis of the RT could be useful for automatic estrous detection., (Copyright © 2017 Elsevier B.V. All rights reserved.)

A new apparatus, named Bang-Bang (BB), which can facilitate the in situ nylon bag measurement of ruminal degradation in beef cattle, is described. The BB is an apparatus specially designed as a replacement of the traditional steel chain or flexible plastic tubes for binding the bags used in the in situ nylon bag (ISNB) method. The BB apparatus consists of cylindrical pedestals, sealing clips, nylon string, and nylon bags. A series of in situ rumen incubations using 3 ruminally cannulated beef steers were conducted to compare the BB with the ISNB method by measuring rumen DM degradation kinetics of 12 different feedstuffs commonly offered to beef cattle in China. Greater ( < 0.01) DM disappearance and less ( < 0.01) relative SD (%) were observed with the BB than with the ISNB method. In addition, effective degradability of DM was highly correlated between the 2 methods (ISNB = (1.01 ± 0.04 × BB) - 4.03 ± 2.12; = 0.99, < 0.001, = 12). These results indicate that the BB apparatus can be effectively used to determine the rumen degradation properties of feedstuffs with advantages over the traditional ISNB technique.

The aim of the research described here was to compare different methods of body temperature (BT) measurements in dairy cows. It was hypothesised that reticular temperature (RET) values reflect the physiological status of the animals in an equivalent way to rectal (RT) and vaginal (VT) measurements. RT, VT and RET temperatures of twelve lactating Holstein-Friesian cows were measured over five consecutive days in June and October 2013. While RT and VT were manually measured three times a day, RET was automatically recorded at 10 min intervals using a bolus in the reticulum. For comparison with RT and VT, different RET values were used: single values at the respective recording times (RET-SIN), and mean (RET-MEAN) and median (RET-MED) values of 2 h prior to RT and VT measurements. Overall, body temperatures averaged 38·1 ± 0·6, 38·2 ± 0·4, 38·7 ± 0·9, 38·5 ± 0·7 and 38·7 ± 0·5 °C for RT, VT, RET-SIN, RET-MEAN and RET-MED, respectively. RT and VT were lower than all RET measurements, while RET-SIN and RET-MED were higher than RET-MEAN (P < 0·001). RET-MEAN and RET-MED values were higher in the morning, whereas RT and VT were greatest in the evening (P < 0·001). Overall, records of RT and VT were strongly correlated (r = 0·75; P < 0·001). In contrast to RET-SIN and RET-MEAN, RET-MED was higher correlated to RT and VT. In June, coefficients were higher between all methods than in October. Relation of barn T to RT and VT was stronger when compared to RET measurements. RET-SIN was higher correlated to barn T than RET-MEAN or RET-MED. Correlation between VT and barn T was strongest (r = 0·48; P < 0·001). In summary, RET-MED showed highest correlation with VT and RT. However, single RET measurements (influenced by water or feed intake) can lead to extreme variations and differences to single VT and RT values.

The objective of this study was to compare the reproductive performance of cows inseminated based on automated activity monitoring with hormone intervention (AAM) to cows from the same herds inseminated using only an intensive timed artificial insemination (TAI) program. Cows (n=523) from 3 commercial dairy herds participated in this study. To be considered eligible for participation, cows must have been classified with a body condition score of at least 2.50, but no more than 3.50, passed a reproductive tract examination, and experienced no incidences of clinical, recorded metabolic diseases in the current lactation. Within each herd, cows were balanced for parity and predicted milk yield, then randomly assigned to 1 of 2 treatments: TAI or AAM. Cows assigned to the TAI group were subjected to an ovulation synchronization protocol consisting of presynchronization, Ovsynch, and Resynch for up to 3 inseminations. Cows assigned to the AAM treatment were fitted with a leg-mounted accelerometer (AfiAct Pedometer Plus, Afimilk, Kibbutz Afikim, Israel) at least 10 d before the end of the herd voluntary waiting period (VWP). Cows in the AAM treatment were inseminated at times indicated by the automated alert system for up to 90 d after the VWP. If an open cow experienced no AAM alert for a 39±7-d period (beginning at the end of the VWP), hormone intervention in the form of a single injection of either PGF2α or GnRH (no TAI) was permitted as directed by the herd veterinarian. Subsequent to hormone intervention, cows were inseminated when alerted in estrus by the AAM system. Pregnancy was diagnosed by ultrasound 33 to 46 d after insemination. Pregnancy loss was determined via a second ultrasound after 60 d pregnant. Timed artificial insemination cows experienced a median 11.0 d shorter time to first service. Automated activity-monitored cows experienced a median 17.5-d shorter service interval. No treatment difference in probability of pregnancy to first AI, probability of pregnancy to repeat AI, pregnancy loss, time to pregnancy, or proportion of pregnant cows at 90 d past the VWP existed. Based on these results, inseminating cows using AAM with hormone intervention can achieve a level of reproductive performance comparable to TAI. Considering the strict cow selection criteria used in this study, interpretation of results for on-farm implementation should be performed cautiously; the results cannot be directly extrapolated to whole herds of cows., (Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Unlabelled: Systematic metabolic monitoring began in German-speaking countries in the late 1960s, early 1970s, due to an increase in metabolic disorders as a cause of infertility and mastitis and aimed at their prevention through early diagnosis. Development of a unified monitoring standard: Initiated by Rossow, Gürtler, Ehrentraut, Seidel and Furcht a standard "metabolic monitoring in cattle production" was developed in the 1970s. It included farm analysis, clinical and biochemical controls, prophylaxis and follow-up controls. Key points were: periodic screenings of heavily loaded, healthy indicator animals 2-4 days post partum (p.  p.), 2-8 weeks p.  p. and 1-2 weeks ante partum, maximal 10 animals/group, pooled samples are useful, optimal are individual samples, use of informative sample substrate and parameters, precise handling of specimens, expert assessment and follow-up. Metabolic controls during 1982-1989 in approximately 242  000 cows revealed means of 32.9% ketoses, 20.0% metabolic acidosis, 21.9% metabolic alkalosis, 34.2% nitrogen-metabolism disorders, 17.3% sodium deficiency and 23.7% liver disorders. Development of a metabolic profile after 1989: Reference values at higher milk yield, early diagnosis of diseases of the fat mobilization syndrome and improved early diagnosis by new indicators, including creatine kinase (CK), alkaline phosphatase (AP) with isoenzymes, acute phase proteins, cytokines, antioxidants, carnitine and lipoprotein fractions, were established. Optimized blood and urine screenings have important advantages over milk analysis. They are an important method of health and performance stabilization by exact analysis of causes and derived prevention., Clinical Relevance: The fertility related parameters free fatty acids, β-hydroxybutyrate, urea, inorganic phosphate, CK, AP, sodium, potassium, selenium, copper, β-carotene and net acid-base excretion proved to be a standard spectrum for screenings. These should be tested once a year/herd, if necessary as an inexpensive pool sample for approximately 50 €.

Early identification of sick cattle increases treatment success and decreases mortality. Continuous automated records of behavior can be used to identify sick cattle early in the disease process. The objective was to evaluate accuracy of an ear-attached accelerometer (SensOor) that quantified ear movements and estimated feeding and rumination time through a proprietary algorithm. Accelerometers were attached to the ear tag of 18 steers with an initial mean BW of 326 ± 46 kg. The manufacturer's proprietary software was used to determine time spent "feeding," "ruminating," "active," and "resting." Direct visual observation was used to validate the accelerometer. Sensitivity, specificity, and predictive values were calculated for rumination and feeding separately. Repeated measures were accounted for using mixed model logistic regression. Single minutes of either feeding or rumination in a run of other behavior minutes were changed to the preceding behavior. Accuracy and precision of hourly recorded feeding and rumination times were assessed using the concordance correlation coefficient adjusted for repeated measurements. Sensitivity and specificity were 95 and 76% for feeding and 49 and 96% for rumination, respectively. Concordance correlation between observations and the sensor were 0.79 (95% CI: 0.61 to 0.85) and 0.44 (95% CI: 0.23 to 0.60) for feeding and rumination, respectively. There was large variability among steers, with concordance correlations ranging from 0.09 to 0.98 for rumination time and from 0.58 to 0.96 for feeding time. We conclude that the accelerometer is a promising monitoring system for feeding behavior.

Two activity monitoring systems-Heatime (SCR Engineers Ltd., Netanya, Israel) and IceTag (IceRobotics Ltd., Edinburgh, UK)-were compared on their ability to detect and quantify estrus expression. Holstein heifers (n=57) were fitted with Heatime (HT) and IceTag (IT) sensors from 12 mo of age until confirmation of pregnancy. Upon detection of high activity by HT, ovaries were scanned by ultrasound, a blood sample was collected for analysis of plasma estradiol, and signs of estrus (clear vaginal mucus, uterine muscle tone, visual mounting activity, standing to be mounted, or rump showing signs of repeated acceptance of mounts) were recorded. Because only estrus episodes detected by HT (n=111) were further evaluated, only the positive predictive value was measured. Heifers were housed in groups of 24 in a freestall pen. Data were analyzed using Proc CORR and GLM of SAS (SAS Institute Inc., Cary, NC). The positive predictive value was 84.7% (94/111) for HT and 98.7% (74/75) for IT. Estrus duration was recorded by HT as 14.3±4.1h [mean ± standard deviation (SD)] and by IT as 15.0±4.0h; duration measurements were correlated (r=0.60). The mean duration difference was 0.74±3.52h. Recordings of onset and end of estrus by IT were 3.5±4.3h and 2.9±4.9h earlier than those by HT. The overlap in duration was 9h. Measurements of estrus intensity were correlated (r=0.63). Peak activity was 77.3±19.5 index value (approximately 7.7 SD from basal activity) on HT. The relative increase in activity measured by IT was 360±170% baseline value. Measurements of intensity and duration from HT were correlated (r=0.64) but those from IT were not (r=0.13). Plasma estradiol concentration (11.2±4.6pg/mL) was not correlated with preovulatory follicle diameter or with duration or intensity of estrus. Diameter of preovulatory follicle (15.7±2.6mm) had no correlation with duration of estrus and was only weakly correlated with intensity measured by either system. Baseline steps/hour was negatively correlated with intensity from both sensors (r=-0.37 and -0.70 for HT and IT). Estrus episodes accompanied by 2 or 3 of the monitored signs of estrus had greater intensity and duration on HT but not on IT. Preovulatory follicle diameter and plasma estradiol concentration did not influence occurrence of estrus signs. Results indicate that both systems identified estrus precisely, with correlated characterization and similar timing. In contrast, relationships with plasma estradiol concentration and signs of estrus require further investigation., (Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

This study was performed to investigate the effect of the transition from barn feeding to pasture on the pattern of reticuloruminal pH values in 8 multiparous dairy cows. A indwelling wireless data transmitting system for pH measurement was given to 8 multiparous cows orally. Reticuloruminal pH values were measured every 600 s over a period of 42 days. After 7 days of barn feeding (period 1), all of the animals were pastured with increasing grazing times from 2 to 7 h/day over 7 days (period 2). From day 15 to day 21 (period 3), the cows spent 7 h/day on pasture. Beginning on day 22, the animals had 20 h/day access to pasture (day and night grazing). To study reticuloruminal adaptation to pasture feeding, the phase of day and night grazing was subdivided into another 3 weekly periods (periods 4-6). Despite a mild transition period from barn feeding to pasture, significant effects on reticuloruminal pH values were observed. During barn feeding, the mean reticuloruminal pH value for all of the cows was 6.44 ± 0.14, and the pH values decreased significantly (p < 0.001) during period 2 and 3 to 6.24 ± 0.17 and 6.21 ± 0.19 respectively. During periods 4, 5 and 6, the reticuloruminal pH values increased again (pH 6.25 ± 0.22; pH 6.31 ± 0.17; pH 6.37 ± 0.16). Our results showed that the animals had significantly lowered reticuloruminal pH during the periods of feed transition from barn to pasture feeding. Despite these significant changes, the decrease was not harmful, as indicated by data of feed intake and milk production., (Journal of Animal Physiology and Animal Nutrition © 2014 Blackwell Verlag GmbH.)

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., (Copyright © 2015 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

This study was conducted to test the hypothesis that the specificity of infrared thermography (IRT) in detecting cows about to ovulate could be improved using different body parts that are less likely to be contaminated by fecal matter. In addition, the combined activity and rumination data captured by accelerometers were evaluated to provide a more accurate indication of ovulation than the activity and rumination data alone. Thermal images of 30 cows were captured for different body areas (eye, ear, muzzle, and vulva) twice daily after AM and PM milking sessions during the entire experimental period. Milk progesterone data and insemination records were used to determine the date of ovulation. Cows were fitted with SCR heat and rumination long-distance tags (SCR HR LD) for 1 month. Activity- and rumination-based estrus alerts were initially identified using default threshold values set by the manufacturer; however, a range of thresholds was also created and tested for both activity and rumination to determine the potential for higher levels of accuracy of ovulation detection. Visual assessment of mounting indicators resulted in 75% sensitivity (Se), 100% specificity (Sp), and 100% positive predictive value (PPV). Overall, IRT showed poor performance for detecting cows about to ovulate. Vulval temperature resulted in the greatest (80%) Sp but the poorest (21%) Se compared with the IRT temperatures of other body areas. The SCR HR LD tags default threshold value resulted in 78% Se, 57% Sp, and 70% PPV. Lowering the activity threshold from the default value improved the sensitivity but created a large number of false positives, which resulted in a decrease in specificity. Lowering the activity threshold to 20 resulted in a detection performance of 80% Se, 94% Sp, and 67% PPV, whereas the rumination levels achieved 35% Se, 69% Sp, and 14% PPV. The area under the curve for the activity level, rumination level, and the combined measures of activity and rumination levels were 0.82, 0.54, and 0.75, respectively. Alerts generated by SCR HR LD tags based on a lower activity threshold level had high sensitivity and may be able to detect a high proportion of cows in ovulatory periods in pasture-based system; however, the specificities and positive predictive value were lower than the visual assessment of mounting indicators., (Copyright © 2015 Elsevier Inc. All rights reserved.)

The aim of this study was to evaluate the accuracy and precision of indwelled wireless sensors relative to intrareticuloruminal differences in dairy cows transitioned from a forage to a high-concentrate diet. A feeding trial was performed with 8 rumen-cannulated Holstein cows. The cows were stepwise switched from 0 to 60% concentrate in the diet and fed 5 wk. Samples from the free ruminal liquid (FRL) from the ventral rumen and from the particle-associated ruminal liquid (PARL) in the rumen mat were manually taken at 0, 4, and 8 h after the morning feeding on d 0, 7, 14, and 34 of the experiment through the ruminal cannula to measure pH in FRL and PARL using a pH electrode. Additionally indwelling reticular wireless pH sensors were used to measure reticular pH every 10 min throughout the experiment. Precision and accuracy properties as a measure of reproducibility of the methods were statistically evaluated. Data showed significant differences among pH readings of indwelling sensors and pH measurements taken by means of a conventional electrode in both FRL and PARL (P<0.05). These differences became more evident when 60% concentrate diet was fed. Across all experimental days, the pH of the FRL was greatest and the pH reported by indwelling sensors intermediate, whereas the pH of PARL was lowest. The concordance correlation coefficient (CCC) analysis revealed a high agreement between indwelling sensors and FRL (CCC=0.709) but a low agreement with the pH of PARL (CCC=0.495). In conclusion, the study indicated that wireless sensors can satisfactorily reflect the pH of FRL but poorly reflect that of PARL.

Interest in the monitoring of heart rate variability (HRV) has increased recently, as it gives more detailed and immediate information about the level of stress than traditional behavioral or hypothalamus-pituitary-adrenal measures. In this study, we evaluated heart rate (HR) and parasympathetic HRV parameters to monitor cardiac stress responses to palpation per rectum (PPR) in lactating (LACT; n = 11) and nonlactating (NLACT; n = 12) dairy cows. Heart rate and HRV were recorded from 40 min before PPR until 120 min after it was completed. Heart rate, the root mean square of successive differences (RMSSD), and the high-frequency component (HF) of HRV were analyzed by examining 5-min time windows. To compare cardiac responses to PPR between groups, changes in HR and HRV parameters were calculated as area under the curve (AUC) for LACT and NLACT cows. An immediate increase in HR was detected during PPR in both LACT (+21.4 ± 2.4 beats/min) and NLACT cows (+20.6 ± 2.3 beats/min); however, no differences were found between groups on the basis of parameters of AUC. The increase in HR in both groups along with a parallel decrease in RMSSD (LACT cows: -5.2 ± 0.4 ms; NLACT cows: -5.1 ± 0.4 ms) and HF [LACT cows: -10.1 ± 0.8 nu (where nu = normalized units); NLACT cows: -16.9 ± 1.2 nu] during PPR indicate an increase in the sympathetic, and a decrease in the parasympathetic tone of the autonomic nervous system. The increase in RMSSD (LACT cows: +7.3 ± 0.7 ms; NL cows: +17.8 ± 2.2 ms) and in HF (LACT cows: +24.3 ± 2.6 nu; NLACT cows: +32.7 ± 3.5 nu) immediately after PPR indicated a rapid increase in parasympathetic activity, which decreased under the baseline values 10 min following PPR. The amplitude and the maximum RMSSD and HF values were greater in NLACT cows than in LACT animals, suggesting a higher short-term cardiac responsiveness of NLACT cows. However, the magnitude and the duration of the stress response were greater in LACT cows, as indicated by the analysis of AUC parameters (area under the HRV response curve and time to return to baseline). Cow response to the PPR was more prominent in parasympathetic HRV measures than in HR. Based on our results, the effect of PPR on the cows' cardiac stress responses may have an impact on animal welfare on dairy farms, and investigating the effect of lactation on the cardiac stress reactions could prove useful in modeling bovine stress sensitivity. Further research is needed to find out whether the differences due to lactation are physiological or management related., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Considerable technological advances have been made in the automated detection of estrus in dairy cattle, but few studies have evaluated their relative performance on the same animals or assessed cow-related factors that affect their performance. Our objective was to assess the performance and reliability of three devices commercially available in France for cow estrus detection. The devices were a pedometer (PM; Afitag) and two activity meters (AM1; Heatime-RuminAct, and AM2; HeatPhone). Two algorithms were tested for AM2. We fitted 63 lactating Holstein cows with the three detectors from calving to 90 days after calving. The onset and pattern of cyclicity were monitored from 7 to 90 days postpartum measuring progesterone concentration in milk twice weekly. A total of 211 ovulations were identified. Cyclicity was classified as normal in 60% of cows (38/63). Calculated over the operating period of all the devices (179 periods of estrus), the sensitivities and positive predictive values were, respectively, 71% and 71% for PM, 62% and 84% for AM1, 61% and 67% for the first algorithm of AM2, and 62% and 87% for the second algorithm of AM2. Both activity meters had a lower sensitivity but a higher positive predictive value than the PM (P < 0.05). For all devices, the performance in estrus detection was much poorer at the first postpartum ovulation than at subsequent ovulations (P < 0.05). Lactation rank and milk production affected some devices (P < 0.05). These devices could be used to reinforce visual observations, especially after 50 days postpartum, the minimum recommended delay to insemination. However, their full benefit remains to be verified in different farming systems and taking into account the specific objectives of the dairy farmer., (Copyright © 2014 Elsevier Inc. All rights reserved.)

The study aimed to examine the influence of supplemented conjugated linoleic acids (CLA) to periparturient cows receiving different concentrate proportions antepartum on rumen pH (RpH) and rumen temperature (RT). Twenty pregnant German Holstein cows were equipped with rumen probes for continuous RpH and RT measurement in a frequency of 15 min to investigate effects of dietary concentrate and CLA around parturition and the impact of parturition itself on RpH and RT. Cows had ad libitum access to partial mixed rations, 3 weeks prior to calving until day 7 post-partum. Antepartum, cows received 100 g/day control fat (CON) or CLA supplement, either in low (20%; CON-20, CLA-20) or high concentrate diet (60%; CON-60, CLA-60). Post-partum, concentrate proportion was adjusted to 50% while fat supplementation continued. Compared with adapted feeding, high concentrate proportions antepartum tended to increase DMI and reduced RpH. Groups CON-60 and CLA-60 spent more than 4 h per day below RpH 5.6 during late pregnancy, indicating the presence of subacute rumen acidosis (SARA). The RT remained unaffected antepartum. Before calving, cows spent less time below RpH 5.6 and SARA could be detected in each group post-partum. Mean RpH increased slightly antepartum, whereas few hours before parturition a sharp decrease in RpH could be observed, accompanied with increased RT. Overall, it seems that CLA supplementation influences RpH and RT. Bearing in mind that rumen parameters fluctuate during day and herd level must be known, rumen probes for continuous RpH and RT measurement could be a useful management tool for animal health surveillance and may also help to predict parturition.