Short communication: Decrease in rumination time as an indicator of the onset of calving

Evaluation of sensor-based health monitoring in dairy cows: Exploiting rumination times for health alerts around parturition

The use of sensor-based measures of rumination time as a parameter for early disease detection has received a lot of attention in scientific research. This study aimed to assess the accuracy of health alerts triggered by a sensor-based accelerometer system within 2 different management strategies on a commercial dairy farm. Multiparous Holstein cows were enrolled during the dry-off period and randomly allocated to conventional (CON) or sensor-based (SEN) management groups at calving. All cows were monitored for disorders for a minimum of 10 DIM following standardized operating procedures (SOP). The CON group (n = 199) followed an established monitoring protocol on the farm. The health alerts of this group were not available during the study but were later included in the analysis. The SEN group (n = 197) was only investigated when the sensor system triggered a health alert, and a more intensive monitoring approach was implemented according to the SOP. To analyze the efficiency of the health alerts in detecting disorders, the sensitivity (SE) and specificity (SP) of health alerts were determined for the CON group. In addition, all cows were divided into 3 subgroups based on their health status and the status of the health alerts in order to retrospectively compare the course of rumination time. Most health alerts (87%, n = 217) occurred on DIM 1. For the confirmation of diagnoses, health alerts showed SE and SP levels of 71% and 47% for CON cows. In SEN cows, SE of 71% and 75% and SP of 48% and 43% were found for the detection of ketosis and hypocalcemia, respectively. The rumination time of the subgroups was affected by DIM and the interaction between DIM and the status of health alert and health condition.

Application of behavior data to predictive exploratory models of metritis self-cure and treatment failure in dairy cows

The objective was to evaluate the performance of exploratory models containing routinely available on-farm data, behavior data, and the combination of both to predict metritis self-cure (SC) and treatment failure (TF). Holstein cows (n = 1,061) were fitted with a collar-mounted automated-health monitoring device (AHMD) from -21 ± 3 to 60 ± 3 d relative to calving to monitor rumination time and activity. Cows were examined for diagnosis of metritis at 4 ± 1, 7 ± 1, and 9 ± 1 d in milk (DIM). Cows diagnosed with metritis (n = 132), characterized by watery, fetid, reddish/brownish vaginal discharge (VD), were randomly allocated to 1 of 2 treatments: control (CON; n = 62), no treatment at the time of metritis diagnosis (d 0); or ceftiofur (CEF; n = 70), subcutaneous injection of 6.6 mg/kg of ceftiofur crystalline-free acid on d 0 and 3 relative to diagnosis. Cure was determined 12 d after diagnosis and was considered when VD became mucoid and not fetid. Cows in CON were used to determine SC, and cows in CEF were used to determine TF. Univariable analyses were performed using farm-collected data (parity, calving season, calving-related disorders, body condition score, rectal temperature, and DIM at metritis diagnosis) and behavior data (i.e., daily averages of rumination time, activity generated by AHMD, and derived variables) to assess their association with metritis SC or TF. Variables with P-values ≤0.20 were included in the multivariable logistic regression exploratory models. To predict SC, the area under the curve (AUC) for the exploratory model containing only data routinely available on-farm was 0.75. The final exploratory model to predict SC combining routinely available on-farm data and behavior data increased the AUC to 0.87, with sensitivity (Se) of 89% and specificity (Sp) of 77%. To predict TF, the AUC for the exploratory model containing only data routinely available on-farm was 0.90. The final exploratory model combining routinely available on-farm data and behavior data increased the AUC to 0.93, with Se of 93% and Sp of 87%. Cross-validation analysis revealed that generalizability of the exploratory models was poor, which indicates that the findings are applicable to the conditions of the present exploratory study. In summary, the addition of behavior data contributed to increasing the prediction of SC and TF. Developing and validating accurate prediction models for SC could lead to a reduction in antimicrobial use, whereas accurate prediction of cows that would have TF may allow for better management decisions.

Use of rumination time in health risk assessment of prepartum dairy cows

The objectives of this observational cohort study were to evaluate the associations of rumination time (RT) in the last week of pregnancy with transition cow metabolism, inflammation, health, and subsequent milk production, reproduction, and culling. Pregnant nulliparous (n = 199) and parous (n = 337) cows were enrolled 21 d before the expected calving. RT and physical activity were monitored automatically by sensors from d -21 to 15 relative to calving. Blood samples were collected on d -14, -5, 4, 8, and 12 ± 1 relative to calving. Diagnoses of clinical health problems were performed by researchers from calving to 15 d in milk (DIM). In classification 1, cows were ranked based on average daily RT in the last week of pregnancy and classified into terciles as short RT (SRT), moderate RT (MRT), or long RT (LRT) for association analyses. In classification 2, RT deviation from the parity average was used in a receiver operating characteristic curve to identify the best threshold to predict postpartum clinical disease. Cows were then classified as above the threshold (AT) or below the threshold (BT). Compared with cows with LRT, cows with SRT had greater serum concentrations of NEFA (0.47 vs 0.40 ± 0.01 mmol/L), BHB (0.58 vs 0.52 ± 0.01 mmol/L), and haptoglobin (0.22 vs 0.18 ± 0.008 g/L) throughout the transition period, and reduced concentrations of glucose, cholesterol, albumin, and magnesium in a time-dependent manner. Parous cows with SRT had higher odds of postpartum clinical disease (adjusted odds ratio [AOR]: 3.7; 95% confidence interval [CI]: 2.1-6.4), lower odds of pregnancy by 210 DIM (AOR: 0.34; CI: 0.15-0.75), and lower milk production (46.9 vs 48.6 ± 0.5 kg/d) than parous cows with LRT. Deviation in prepartum RT had good predictive value for clinical disease in parous cows (area under the curve [AUC]: 0.65; CI: 0.60-0.71) but not in nulliparous (AUC: 0.51; CI: 0.42-0.59). Separation of parous cows according to the identified threshold (≤-53 min from the parity average) resulted in differences in serum concentrations of NEFA (AT = 0.31 ± 0.006, BT = 0.38 ± 0.014 mmol/L), BHB (AT = 0.49 ± 0.008, BT = 0.53 ± 0.015 mmol/L), and globulin (AT = 32.3 ± 0.3, BT = 34.8 ± 0.5 g/L) throughout the transition period, as well as in serum cholesterol, urea, magnesium, albumin, and haptoglobin in a time-dependent manner. BT parous cows had higher odds of clinical disease (AOR: 3.7; CI: 2.1-6.4), reduced hazard of pregnancy (AHR: 0.64, CI: 0.47-0.89), greater hazard of culling (AHR: 2.1, CI: 1.2-3.6), and lower milk production (46.3 ± 0.7 vs 48.5 ± 0.3 kg/d). External validation using data from 153 parous cows from a different herd and the established threshold in RT deviation (≤-53 min) resulted in similar predictive value, with the odds of postpartum disease 2.4 times greater in BT than AT (37.5 vs 20.1%). In conclusion, RT in the wk preceding calving was a reasonable predictor of postpartum health and future milk production, reproduction, and culling in parous cows but not in nulliparous cows.

Evaluation of automated monitoring calving prediction in dairy buffaloes a new tool for calving management

Abstract Buffalo is one of the leading milk-producing dairy animals. Its production and reproduction are affected due to some factors including inadequate monitoring around parturition, which cause economic losses like delayed birth process, increased risk of stillbirth, etc. The appropriate calving monitoring is essential for dairy herd management. Therefore, we designed a study its aim was, to predict the calving based on automated machine measured prepartum behaviors in buffaloes. The data were collected from n=40 pregnant buffaloes of 2nd to 5th parity, which was synchronized. The NEDAP neck and leg logger tag was attached to each buffalo at 30 days before calving and automatically collected feeding, rumination, lying, standing, no. of steps, no. of switches from standing to lying (lying bouts) and total motion activity. All behavioral data were reduced to -10 days before the calving date for statistical analysis to use mixed model procedure and ANOVA. Results showed that feeding and rumination time significantly (P<0.05) decreased from -10 to -1 days before calving indicating calving prediction. Moreover, Rumination time was at lowest (P<0.001) value at 2h before the calving such behavioral changes may be useful to predict calving in buffaloes. Similarly, lying bouts and standing time abruptly decreased (P<0.05) from -3 to -1 days before calving, while lying time abruptly increased (P<0.01) from -3 to -1 days before calving (531.57±23.65 to 665.62±18.14, respectively). No. of steps taken and total motion significantly (P<0.05) increased from -10 to -1 days before calving. Feeding time was significantly (P<0.02) lowered in 3rd parity buffaloes compared with 2nd, 4th and 5th parity buffaloes, while standing time of 5th parity buffaloes were lowered (P<0.05) as compared to 2nd to 4th parity buffalos at -1 day of prepartum. However, rumination, lying, no. of steps taken and total motion activity at -1 day of prepartum was independent (P>0.05) of parity in buffaloes. Neural network analysis for combined variables from NEDAP technology at the daily level yielded 100.0% sensitivity and 98% specificity. In conclusion NEDAP technology can be used to measured behavioral changes -10 day before calving as it can serve as a useful guide in the prediction calving date in the buffaloes.

Association between Prepartum Alerts Generated Using a Commercial Monitoring System and Health and Production Outcomes in Multiparous Dairy Cows in Five UK Herds

Identifying cows that are at greater risk for disease prior to calving would be a valuable addition to transition management. Prior to the commercial release of software features in an automated behavioral monitoring system, designed to identify cows in the dry period at greater risk of disease postpartum, a retrospective analysis was carried out in five dairy herds to evaluate whether the software could identify prepartum cows that subsequently received health treatments postpartum and whether prepartum alerts (transition alerts) are associated with a reduction in milk production in the subsequent lactation. Herd management and production records were analyzed for cows receiving treatment in the first 21 d of lactation (days in milk, DIM) for clinical mastitis, reproductive tract disease (metritis, retained fetal membranes), metabolic disease (hypocalcemia, ketosis and displaced abomasum) and for cows exiting the herd by 60 DIM. Data was gathered for 986 cows, 382 (38.7%) of which received a transition alert and 604 (61.3%) that did not. During the first 21 DIM 312 (31.6%) cows went on to receive a disease treatment, of these 51.9% (n = 162/312) were transition alert cows and 48.1% (n = 150/312) non-transition alert cows, while 8.6% (n = 33/382) alert cows exited the herd by 60 DIM compared to 4.8% (n = 29/604) of cows that did not receive an alert. A cow receiving a transition alert (OR = 1.76, 95% confidence interval (CI) = 1.27-2.44) and increasing parity (OR = 2.03, 95% CI = 1.44-2.86) were both associated with increased risk of receiving a disease treatment in the first 21 DIM. The occurrence of a transition alert was negatively associated with both week 4 milk yield (daily average yield in fourth week of lactation) and predicted 305 d yield. Transition alerts correctly predicted 62.5% (95% CI: 59.3-65.5) of treatments with a sensitivity of 42.4% (95% CI: 37.4-45.5) and a specificity of 75.2% (95% CI: 71.5-78.6). Associations were identified between postpartum health and production outcomes and prepartum behavioral measures from an automated activity monitoring system.

Ruminating, Eating, and Locomotion Behavior Registered by Innovative Technologies around Calving in Dairy Cows

The hypothesis for this study was that there are correlations between ruminating, eating, and locomotion behavior parameters registered by the RumiWatch sensors (RWS) before and after calving. The aim was to identify correlations between registered indicators, namely, rumination, eating, and locomotion behavior around the calving period. Some 54 multiparous cows were chosen from the entire herd without previous calving or other health problems. The RWS system recorded a variety of parameters such as rumination time, eating time, drinking time, drinking gulps, bolus, chews per minute, chews per bolus, activity up and down time, temp average, temp minimum, temp maximum, activity change, other chews, ruminate chews, and eating chews. The RWS sensors were placed on the cattle one month before expected calving based on service data and removed ten days after calving. Data were registered 10 days before and 10 days after calving. We found that using the RumiWatch system, rumination time was not the predictor of calving outlined in the literature; rather, drinking time, downtime, and rumen chews gave the most clearcut correlation with the calving period. We suggest that using RumiWatch to combine rumination time, eating time, drinking, activity, and down time characteristics from ten days before calving, it would be possible to construct a sensitive calving alarm; however, considerably more data are needed, not least from primiparous cows not examined here.

Effect of Prepartum Magnesium Butyrate Supplementation on Rumination Time, Milk Yield and Composition, and Blood Parameters in Dairy Cows

Background: Magnesium butyrate (MgB) supplementation of dairy cows during the three-week close-up period was tested for its effects on blood energy analytes, rumination time, inflammation, and lactation performance. Methods: Daily milk yield was recorded and weekly milk samples collected for the first 70 days of lactation from MgB supplemented (MgB, n = 34), and unsupplemented (Control, n = 31) multiparous Holstein-Friesian cows. During a period from week 3 to week 10 postpartum, blood samples were taken and analyzed for various parameters, and ruminant activity was measured. Results: The MgB group yielded 25.2% more milk than the Control during week 1, and had increased milk fat and protein concentrations over a longer duration. Somatic cell counts (SCC) were decreased in the MgB group independent of days in milk. No differences were observed between groups in terms of plasma non-esterified fatty acids, β-hydroxybutyrate, glucose, or blood iCa levels. The MgB group had lower haptoglobin (Hp) levels during lactation relative to the Control group. Time spent ruminating increased after calving with MgB due to a shorter post calving rumination delay relative to the Control group. Conclusions: Prepartum MgB supplementation improved lactation performance without affecting blood energy analytes. The basis by which MgB also improved rumination activity remains to be determined, as DMI could not be assessed. As MgB lowered SCC and Hp concentrations, it is speculated that MgB may help minimize postpartum inflammatory processes.

Impact of sensor data pre-processing strategies and selection of machine learning algorithm on the prediction of metritis events in dairy cattle

With all the sensor data currently generated at high frequency in dairy farms, there is potential for earlier diagnosis of postpartum diseases compared with traditional monitoring methodologies. Our objectives were 1) to compare the impact of sensor data pre-processing on classifier performance by using multiple time windows before a given metritis event, while considering other cow-level factors and farm-scheduled activities; 2) to compare the performance of random forest (RF), k-nearest neighbors (k-NN), and support vector machine (SVM) classifiers at different decision thresholds using different number of past observations (time-lags) for the detection of behavioral patterns associated with changes in metritis scores; and 3) to compare classifier performance between each one of the five behaviors registered every hour by an ear-tag 3-axis accelerometer (CowManager, Agis Autimatisering, Harmelen, Netherlands). A total of 239 metritis events were created by comparing metritis scores between two consecutive clinical evaluations from cows that were retrospectively selected from a dataset containing sensor data and health information during the first 21 days postpartum from June 2014 to May 2017. Hourly sensor data classified by the accelerometer as either ruminating, eating, not active (including both standing or lying), and two different levels of activity (active and high activity) behaviors corresponding to the 3 days before each metritis event were aggregated every 24-, 12-, 6-, and 3-hour time windows. Multiple time-lags were also used to determine the optimal number of past observations needed for optimal classification. Similarly, different decision thresholds were compared in terms of model performance. Depending on the classifier, algorithm hyperparameters were optimized using grid search (RF, k-NN, SVM) and random search (RF). All behaviors changed throughout the study period and showed distinct daily patterns. From the three algorithms, RF had the highest F₁ score followed by k-NN and SVM. Furthermore, sensor data aggregated every 6- or 12-h time windows had the best model performance at multiple time-lags. We concluded that the data from the first 3 days post-partum should be discarded when studying metritis, and either one of the five behaviors measured with CowManager could be used when predicting metritis when sensor data were aggregated every 6- or 12-hour time windows, and using time-lags corresponding to 2-3 days before a given event, depending on the time window used. This study shows how to maximize sensor data in their potential for disease prediction, enhancing the performance of algorithms used in machine learning.

Early detection of mastitis in cows using the system based on 3D motions detectors

Mastitis is one of the major health problems in dairy herds leading to a reduction in the leading to a reduction in the quality of milk and economic losses. The research aimed to present the system, which uses electronic 3D motion detectors to detect the early symptoms of mastitis. The system would allow more effective prevention of this illness. The experiment was carried out on 118 cows (64 Holstein Friesian and 54 Brown Swiss). The animals were kept in free-stall barn with access to pasture. The occurrence of mastitis cases was noticed in veterinary register. Microbiological culture was taken from milk in order to confirm the development of infection. Data from motion detectors were defined as time spent by animals on feed intake, ruminating, physical activity and rest, and were expanded by adding information about feeding group, breed type and lactation number. During analyses, two approaches were used to process the same dataset: artificial neural networks (ANN) and logistic regression. The obtained ANN and the logistic regression models proved to be satisfactory from the perspective of applied criteria of goodness of fit (area under curve-exceed 0.8). Quality parameters (accuracy, sensitivity and specifity) of logistic regression are relatively high (larger than 0.73), whereas the ranks of significance of the studied variables varied across datasets. These proposed models can be useful for automating the detection of mastitis once integrated into the farm's IT system.

How to Predict Parturition in Cattle? A Literature Review of Automatic Devices and Technologies for Remote Monitoring and Calving Prediction

Cattle farming is facing an increase in number of animals that farmers must care for, together with decreasing time for observation of the single animal. Remote monitoring systems are needed in order to optimize workload and animal welfare. Where the presence of personnel is constant, for example in dairy farms with great number of lactating cows or with three milking/day, calving monitoring systems which send alerts during the prodromal stage of labor (stage I) could be beneficial. On the contrary, where the presence of farm personnel is not guaranteed, for example in smaller farms, systems which alert at the beginning of labor (stage II) could be preferred. In this case, time spent observing periparturient animals is reduced. The reliability of each calving alarm should also be considered: automatic sensors for body temperature and activity are characterized by a time interval of 6-12 h between the alarm and calving. Promising results have been shown by devices which could be placed within the vaginal canal, thus identifying the beginning of fetal expulsion and optimizing the timing of calving assistance. However, some cases of non-optimal local tolerability and cow welfare issues are reported. Future research should be aimed to improve Sensitivity (Se), Specificity (Sp) and Positive Predictive Value (PPV) of calving alert devices in order to decrease the number of false positive alarms and focusing on easy-to-apply, re-usable and well tolerated products.

Assessment of Sensitivity and Profitability of an Intravaginal Sensor for Remote Calving Prediction in Dairy Cattle

One critical point of dairy farm management is calving and neonatal first care. Timely calving assistance is associated with the reduction of calf mortality and postpartum uterine disease, and with improved fertility in dairy cattle. This study aimed to evaluate the performance and profitability of an intravaginal sensor for the prediction of stage II of labor in dairy farms, thus allowing proper calving assistance. Seventy-three late-gestating Italian Holstein cows were submitted to the insertion of an intravaginal device, equipped with light and temperature sensors, connected with a Central Unit for the commutation of a radio-signal into a cell phone alert. The remote calving alarm correctly identified the beginning of the expulsive phase of labor in 86.3% of the monitored cows. The mean interval from alarm to complete expulsion of the fetus was 71.56 ± 52.98 min, with a greater range in cows with dystocia (p = 0.012). The sensor worked correctly in both cold and warm weather conditions, and during day- or night-time. The intravaginal probe was well tolerated, as any cow showed lesions to the vaginal mucosa after calving. Using sex-sorted semen in heifers and beef bull semen in cows at their last lactation, the economic estimation performed through PrecisionTree™ software led to an income improvement of 119 € and 123 €/monitored delivery in primiparous and pluriparous cows, respectively. Remote calving alarm devices are key components of "precision farming" management and proven to improve animal welfare, to reduce calf losses and to increase farm incomes.

Using rumination time to manage health and reproduction in dairy cattle: a review

Early detection of disease is the key to successful management of the dairy cattle which leads to timely treatment and prevention of costs associated with prolonged treatment and reduced milk yield. Electronic systems that allow for monitoring of physiological parameters like rumination, are now commercially available. This review paper discusses different aspects of rumination time that could be used to monitor the health and reproduction of dairy cattle. This review paper explored different areas where rumination time could be utilized in monitoring dairy cattle at calving, during the estrus period, during heat stressed conditions, and to detect diseases and transition cow disorders. In conclusion, rumination time could be used as an indicator of the health status in dairy cattle.

Calving Management: A Questionnaire Survey of Veterinary Subject Matter Experts and Non-Experts

Simple Summary

We designed a questionnaire and asked two groups of veterinarians: (1) subject matter experts, who had published on calving management and (2) veterinary practitioners) for their opinion about aspects of calving management. Participants recommended to differentiate between the two stages of parturition and emphasized signs of imminent parturition, such as “restlessness” and “visibility of fetal parts”. There was no consensus on the right time to move the cow to the maternity pen. Almost half of the respondents recommended a 6-h observation interval for prepartum cows in the maternity pen. The two veterinary groups differed little in their knowledge of calving management.

Abstract

Accurate detection of the onset of parturition is a key factor in the prevention of dystocia. In order to establish current best practice recommendations for calving management, we asked subject matter experts (SME) who had published on calving management (by online survey, n = 80) and non-SMEs, veterinary practitioners (by workshop survey, n = 24) for their opinions. For this, we designed a questionnaire on the significance of signs of imminent parturition (SIP), the frequency of calving observation, and influencing factors for the timing of cow movement to a maternity pen. The response rate was 67.5% in the online survey and 100% in the workshop survey. The majority (89.7%) of all respondents agreed that it is beneficial for successful calving management to differentiate between stage I and II of parturition. Of 12 signs of imminent parturition (for stage I and II), “restlessness” and “visibility of fetal parts in vulva” were cited by 56.5% and 73.3% of SME and non-SME respondents, respectively. There was no consensus on the right time to move the cow to the maternity pen; recommendations varied from one to over 21 days. Almost half of the respondents (45.7%) recommended a 6-h observation interval for prepartum cows in the maternity pen. This study identified a strong consensus on the SIP and how and when to observe cows prior to parturition. SMEs and non-SMEs provided broadly similar recommendations, while the SMEs and the non-SMEs differed significantly in the number of publications on calving they authored, they differed little in their knowledge of calving management.

Validation of a Commercial Collar-Based Sensor for Monitoring Eating and Ruminating Behaviour of Dairy Cows

The use of sensor technologies to monitor cows' behavior is becoming commonplace in the context of dairy production. This study aimed at validating a commercial collar-based sensor system, the AFICollar^® (Afimilk, Kibbutz Afikim, Israel), designed to monitor dairy cattle feeding and ruminating behavior. Additionally, the performances of two versions of the software for behavior classification, the current software AFIfarm^® 5.4 and the updated version AFIfarm^® 5.5, were compared. The study involved twenty Holstein-Friesian cows fitted with the collars. To evaluate the sensor performance under different feeding scenarios, the animals were divided into four groups and fed three different types of feed (total mixed ration, long hay, animals allowed to graze). Recordings of hourly rumination and feeding time produced by the sensor were compared with visual observation by scan sampling at 1 minute intervals using Spearman correlation, concordance correlation coefficient (CCC), Bland-Altman plots and linear mixed models for assessing the precision and accuracy of the system. The analyses confirmed that the updated software version V5.5 produced better detection performance than the current V5.4. The updated software version produced high correlations between visual observations and data recorded by the sensor for both feeding (r = 0.85, CCC = 0.86) and rumination (r = 0.83, CCC = 0.86). However, the limits of agreement for both behaviors remained quite wide (feeding: -19.60 min/h, 17.46 min/h; rumination: -15.80 min/h, 15.00 min/h). Type of feed did not produce significant effects on the agreement between visual observations and sensor recordings. Overall, the results indicate that the system can provide farmers with adequately accurate data on feeding and rumination time, and can be used to support herd management decisions. Despite all this, the precision of the system remained relatively limited, and should be improved with further developments in the classification algorithm.

Efficacy of statistical process control procedures to identify deviations in continuously measured physiological and behavioral variables in beef heifers resulting from an experimentally combined viral-bacterial challenge

The objective of this experiment was to determine if statistical process control (SPC) procedures coupled with remote continuous data collection could accurately differentiate between animals experimentally inoculated with a viral-bacterial (VB) challenge or phosphate buffer solution (PBS). Crossbred heifers (N = 38; BW = 230 ± 16.4 kg) were randomly assigned to treatments by initial weight, average daily gain (ADG), bovine herpes virus 1, and Mannheimia haemolytica serum titers. Feeding behavior, dry matter intake (DMI), animal activity, and rumen temperature were continuously monitored remotely prior to and following VB challenge. VB-challenged heifers exhibited decreased (P < 0.01) ADG and DMI, as well as increased (P < 0.01) neutrophils and rumen temperature consistent with a bovine respiratory disease (BRD) infection. However, none of the heifers displayed overt clinical signs of disease. Shewhart and cumulative summation (CUSUM) charts were evaluated, with sensitivity and specificity computed on the VB-challenged heifers (n = 19) and PBS-challenged heifers (n = 19), respectively, and the accuracy was determined as the average of sensitivity and specificity. To address the diurnal nature of rumen temperature responses, summary statistics (mean, minimum, and maximum) were computed for daily quartiles (6-h intervals), and these quartile temperature models were evaluated separately. In the Shewhart analysis, DMI was the most accurate (95%) at deciphering between PBS- and VB-challenged heifers, followed by rumen temperature (94%) collected in the 2nd and 3rd quartiles. Rest was most the accurate accelerometer-based traits (89%), and meal duration (87%) and bunk visit (BV) frequency (82%) were the most accurate feeding behavior traits. Rumen temperature collected in the 3rd quartile signaled the earliest (2.5 d) of all the variables monitored with the Shewhart, followed by BV frequency (2.8 d), meal duration (2.8 d), DMI (3.0 d), and rest (4.0 d). Rumen temperature and DMI remained the most accurate variables in the CUSUM at 80% and 79%, respectively. Meal duration (58%), BV frequency (71%), and rest (74%) were less accurate when monitored with the CUSUM analysis. Furthermore, signal day was greater for DMI, rumen temperature, and meal duration (4.4, 5.0, and 3.7 d, respectively) in the CUSUM compared to Shewhart analysis. These results indicate that Shewhart and CUSUM charts can effectively identify deviations in feeding behavior, activity, and rumen temperature patterns for the purpose of detecting sub-clinical BRD in beef cattle.

Association of days in close up, gestation length, and rumination around time of calving with disease and pregnancy outcomes in multiparous dairy cows

The purpose of this study was to evaluate the effect of rumination times and days spent in a close-up group before calving (DCU) on early-lactation health and reproductive outcomes in dairy cows. Data were gathered for 719 cows located in a single herd. Herd management and reproductive records were analyzed for cows receiving treatment in the first 30 d of lactation (days in milk; DIM) for clinical mastitis, reproductive tract disease, ketosis, milk fever, and displaced abomasum. Rumination times for each cow were downloaded daily from the herd's automated collar system used to generate heat and health alerts for each cow beginning at 21 d precalving until 14 d postcalving. During the first 30 DIM, 121 cows (18%) developed at least 1 disease-any combination of ketosis (40 cows, 5.9% of total), mastitis (17 cows, 2.5%), metritis (75 cows, 11%), milk fever (17 cows, 2.5%), or displaced abomasum (28 cows, 4.1%); 305 cows (45%) were pregnant again at 100 DIM, and an additional 139 cows (20%) were pregnant at 150 DIM. Principal component analysis was used to determine the relationship between gestation length and DCU and their association with the odds of developing disease in early lactation. We did not find any significant association between precalving rumination time and disease within the first 30 DIM. Higher rumination time in the week before calving was shown to be strongly linked to a shorter time to subsequent pregnancy, whereas rumination times postcalving were not associated with changes in the time to pregnancy. Principal component analysis showed that a curvilinear combination of gestation length and DCU (principal component 1) was significantly associated with changes in disease incidence in the first 30 DIM. Gestation length and time spent in close up are important management factors in reducing the incidence of disease in early lactation, and rumination times around calving may help predict future reproductive outcomes.

Rumination time as an early predictor of metritis and subclinical ketosis in dairy cows at the beginning of lactation: Systematic review-meta-analysis

Daily rumination time (RT; min/d) is recognized as an important tool for assessing the health of dairy cows, which may depend on the disease, lactation stage and individual cows. Using a systematic review-meta-analysis, this study evaluated whether the variation in RT is effective for early detection of metritis and subclinical ketosis (SCK) in dairy cows in the pre and post-partum periods (from three weeks before to three weeks after calving). The research was carried out in four electronic databases - Scopus, Science Direct, Pubmed and Web of Science. The main inclusion criteria were original research; evaluation of RT in dairy cows; and use of RT for early identification of metritis and/or SCK in post-partum dairy cows. A random effect meta-analysis (MA) was conducted for each disease (metritis and SCK) separately, with the RT means of healthy and sick groups, measured in the pre and post-partum. The effect size used was the mean difference (MD).Twenty-two trials from six studies were included in the MA, involving 1494 dairy cows. For metritis, four trials from three studies in the pre-partum period were considered as well as five trials from four studies in the post-partum. For SCK, six trials from four studies pre-partum and seven trials from five studies in the post-partum period were taken into consideration. The heterogeneity between studies for metritis was null (I² = 0%) and low (I² = 5.7 %) in the pre-partum and in the post-partum, respectively. The MD of RT between healthy cows and those with metritis was different in the pre (MD =0.411 min/d; P < 0.001) and in the post-partum (MD =0.279 min/d; P < 0.001). In SCK, heterogeneity was high in the pre (I² = 69 %) and in the post-partum (I² = 58.1 %), and the MD of RT was similar between healthy and sick cows (P> 0.05). In a meta-regression, RT from primiparous cows showed a lower predicted value for MD (0.48 min. d; P < 0.05) compared to multiparous cows, and the increment in each unit of milk production decreased the predicted MD value by 0.08 min. d (P < 0.001). Our MA demonstrates that RT is a good predictor for early detection of metritis in pre and post-partum; however, it is not an adequate predictor for SCK. Further investigations using more frequent blood sampling and the same threshold values for BHB are required to assess the adequacy of rumination time to predict SCK.

A field study using different technologies to detect calving at a large-scale hungarian dairy farm

The objective of this study was to investigate three different calving detection systems in order to assess and compare their efficiency. The study was conducted at a large-scale dairy farm involving 54 Holstein-Friesian dairy cows and heifers. Animals were fitted with multiple devices: a rumination measuring device (Ruminact^® (RA)), an intravaginal thermometer (Vel'Phone^® (VP)) and a tail movement sensor (Moocall^® (MC)) 5 to 7 days before expected calving and were removed after parturition. RA detects rumination time (RT) and calculates it in 2-hr intervals. VP detects a decrease in vaginal temperature that might indicate calving within 48 hr and the drop in temperature resulting from the expulsion of the device at calving (EXP message). MC detected increased tail movements and if they persisted for one hour, 1HA message was sent. If they continued during the subsequent hour, then 2HA message was sent. Messages sent by MC within 4 hr before calving (C4) were selected retrospectively as true positives for the prediction of calving, using the significant changes in RT as a baseline. All other messages were categorized as false positive. The mean value of RT decreased in a non-significant manner between interval -22 and -4 before calving. Significant decrease of RT was detectable between the two intervals of -4 and -2 before calving (24.7 ± 18.6 min/2 hr and 14.0 ± 13.0 min/2 hr, respectively). There was no significant difference between RT of primiparous and multiparous animals. EXP messages were accurate (positive predictive value 100%) indicators of the onset of calving. We received on average 12.7 ± 15.2 messages/animal (11.0 ± 10.1 and 16.6 ± 22.2 for cows and heifers, respectively). Positive predictive value was 12.6%. The number of false-positive messages was significantly higher in heifers. All three automatic systems could be used in a large-scale farm environment.

Sensitivity and specificity of a tail-activity measuring device for calving prediction in dairy cattle

Efficient calving surveillance is essential for avoiding stillbirth due to unattended dystocia. Calving sensors can help detect the onset of parturition and thus ensure timely calving assistance if necessary. Tail-raising is an indicator of imminent calving. The objective of this study was to evaluate a tail-mounted inclinometer sensor (Moocall Ltd., Dublin, Ireland) and to monitor skin integrity after sensor attachment. Cows (n = 157) and heifers (n = 23) were enrolled at 275 d post insemination, and a sensor was attached to each cow's tail. Investigators checked for signs indicating the onset of stage II of parturition, verified the position of the sensor, and evaluated the skin integrity of the tail above and below the sensor hourly for 24 h/d. We used 5 different intervals (i.e., 1, 2, 4, 12, and 24 h until calving) to calculate sensitivity and specificity. Sensors continuously remained on the tail (i.e., within 3 cm of the initial attachment position) after initial attachment until the onset of calving in only 13.9% of animals (n = 25). Sensors were reattached until a calving event occurred (51.6%) or the animal was excluded for other reasons (34.4%). In 31 animals the sensor was removed because the tail was swollen or painful. Heifers were significantly less likely than cows to lose a sensor but more likely to experience tail swelling or pain. Depending on the interval preceding the onset of parturition, sensitivity varied from 19 to 75% and specificity from 63 to 96%.

Calving difficulty influences rumination time and inflammatory profile in Holstein dairy cows

Difficult calving may adversely affect dairy cow health and performance. Maternal:fetal disproportion is a major cause of dystocia. Therefore, the main objective of this study was to assess the effects of dam:calf body weight ratio (D:C) on calving difficulty, rumination time, lying time, and inflammatory profile in 25 Holstein dairy cows. Using automatic monitoring systems, we monitored behavior and production in 9 primiparous and 16 pluriparous cows between dry-off and 30 d in milk. During the same period, we collected blood samples to monitor metabolism and inflammatory profile of these cows. Calvings were video recorded to assess calving difficulty and observe the duration of the expulsive stage. After parturition, the cows were separated into 3 classes according to their D:C: easy (E; D:C >17), medium (M; 14 < D:C <17), and difficult (D; D:C <14). The cows in class D showed relatively longer labor durations (108 min vs. 54 and 51 min for classes D, M, and E, respectively) and higher calving assistance rates (50% vs. 0 and 11% of calvings for classes D, M, and E, respectively) than those in the other 2 classes. Compared with the cows in classes M and E, those in class D exhibited shorter rumination times on the day of calving (176 min/d vs. 288 and 354 min/d for classes D, M, and E, respectively) and during the first week of lactation (312 min/d vs. 339 and 434 min/d for classes D, M, and E, respectively) and maintained lower rumination values until 30 DIM (399 min/d vs. 451 and 499 min/d for classes D, M, and E, respectively). Primiparous class D cows had shorter resting times during the first week after calving compared with those in class M (8 vs. 11 h/d for classes D and M, respectively). Interclass differences were found in terms of the levels of inflammation markers such as acute-phase proteins (ceruloplasmin, albumin, retinol, and paraoxonase). Moreover, cows in class D had lower plasma levels of fructosamine and creatinine after calving. Low D:C reduced postcalving rumination time and increased inflammation grade, suggesting a lower welfare of these animals at the onset of lactation. The D:C might serve as a useful index for the identification of cows at relatively higher risk of metabolic and inflammatory disease, thus helping farmers and veterinarians improve the welfare and health of these cows.

Efficacy of statistical process control procedures to identify deviations in continuously measured physiologic and behavioral variables in beef steers experimentally challenged with Mannheimia haemolytica

The objective of this experiment was to determine if statistical process control (SPC) procedures coupled with the remote continuous collection of feeding behavior patterns, accelerometer-based behaviors, and rumen temperature can accurately differentiate between animals experimentally inoculated with Mannheimia haemolytica (MH) or PBS. Thirty-six crossbred steers (BW = 352 ± 23 kg) seronegative for MH were randomly assigned to bronchoselective endoscopic inoculation with MH (n = 18) or PBS (n = 18). Electronic feed bunks were used to measure DMI and feeding behavior traits, accelerometer-based neck collars measured feeding- and activity-behavior traits, and ruminal thermo-boluses measured rumen temperature. Data were collected for 28 d prior to and following inoculation. Steers inoculated with MH exhibited elevated (P < 0.02) levels of neutrophils and rumen temperature indicating that MH challenge effectively stimulated immunologic responses. However, only nine of the MH steers exhibited increased serum haptoglobin concentrations indicative of an acute-phase protein response and one displayed clinical signs of disease. Shewhart charts (SPC procedure) were used for two analyses, and sensitivity was computed using all MH-challenged steers (n = 18), and a subset that included only MH-challenged haptoglobin-responsive steers (n = 9). Specificity was calculated using all PBS steers in both analyses. In the haptoglobin-responsive only analysis, DMI and bunk visit (BV) duration had the greatest accuracy (89%), with accuracies for head-down (HD) duration, BV frequency, time to bunk, and eating rate being less (83%, 69%, 53%, and 61%, respectively). To address the diurnal nature of rumen temperature, data were averaged over 6-h intervals, and quarterly temperature models were evaluated separately. Accuracy for the fourth quarter rumen temperature was higher (78%) than the other quarterly temperature periods (first = 56%, second = 50%, and third = 67%). In general, the accelerometer-based behavior traits were highly specific ranging from 82% for ingestion to 100% for rest, rumination, and standing. However, the sensitivity of these traits was low (0% to 50%), such that the accuracies were moderate compared with feeding behavior and rumen temperature response variables. These results indicate that Shewhart procedures can effectively identify deviations in feeding behavior and rumen temperature patterns to enable subclinical detection of BRD in beef cattle.

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

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

Comparison of different applications of automatic herd control systems on dairy farms - a review

Recent years have seen the rapid development of different devices which can be helpful in the daily work of livestock farmers. The growing size of livestock herds has led farmers to lose individual contact with their animals, while behavioral studies show that breeders can effectively and precisely monitor a herd of up to 100 cows. This was the main motivation for this study, which aims to identify and test various electronic devices which provide useful herd management data, including estrus detection, individual activity and body temperature measurement, monitoring rumen pH levels, milk quality and content as well as milk temperature and somatic cell count measurements. Some devices can detect the metabolic status of animals with a reasonable level of precision. Contemporary animal farms are offered a large number of systems for monitoring the behavior of the animals in the herd and helping to identify those that are intended for insemination or are too active or excessively apathetic. Monitoring devices support herd management and help to reduce costs through the early detection of animal diseases and nutritional problems. This review aims to compile and summarize the information currently available on the use of automatic herd control systems on dairy farms, as well as to discuss the interpretation of the results, providing a useful diagnostic tool in nutritional evaluations of dairy herds. © 2018 Society of Chemical Industry.

The effects of source and concentration of dietary fiber, starch, and fatty acids on the daily patterns of feed intake, rumination, and rumen pH in dairy cows

The daily patterns of feed intake and rumination influence rumen fermentation, rumen pH, and timing of absorbed nutrients in the dairy cow, but the effects of diet composition on these patterns are not well characterized. Data from 3 previously published experiments were examined to determine the influence of dietary starch, fiber, and fatty acids (FA) on daily patterns of intake, rumination, and rumen pH. Dietary neutral detergent fiber (NDF) and starch were investigated in 2 experiments, each with duplicated 4 × 4 Latin square designs with a 2 × 2 factorial arrangement of treatments in cows fed cows 1×/d at 1200 and 1400 h, respectively. To investigate fiber content and digestibility in the first experiment, brown midrib or isogenic conventional corn silage were fed in low- and high-NDF diets (29 and 38%, respectively). To investigate starch source and concentration in the second experiment, ground high-moisture corn or dry ground corn were fed in low- and high-starch diets (21 and 32%, respectively). Effect of fat concentration and saturation was investigated in the third experiment using a replicated 4 × 4 Latin square design that fed cows 1×/d at 0900 h; treatments included a control diet with no added fat and 2.5% added saturated FA, unsaturated FA, or a mixture of the saturated and unsaturated FA. In the first 2 experiments, intake followed a similar daily pattern regardless of starch and NDF concentration or digestibility. Rumination displayed a treatment by time interaction for both NDF and starch concentration, with high-fiber, low-starch diets causing greater rumination overnight but not midday. High-starch diets decreased total daily rumen pH equally across the day, but did not change the daily pattern. Type of corn silage did not affect the daily patterns of rumination or rumen pH, but pH was reduced throughout the day in brown midrib diets. In the third experiment, no interactions between fatty acid supplement and time of day were observed for intake, rumination, or rumen pH. Within all experiments, rumination fit or tended to fit a 24-h rhythm regardless of diet, with the amplitude of the rumination being reduced in low-starch diets and diets containing saturated FA or a mixture of saturated and unsaturated FA. Overall, intake, rumination, and rumen pH follow a daily pattern that was minimally modified by dietary fiber and starch type and level or fat level and fatty acid profile.

Rumination time and monitoring of health disorders during early lactation

The objective was to evaluate the association between changes in daily rumination time (dRT) and early stages of disease during early lactation and to assess the performance of two proposed disease detection indices. This cohort study included 210 multiparous Holstein cows at the University of Florida Dairy Unit. Cows were affixed with a neck collar containing rumination loggers providing rumination time. The occurrence of health disorders (mastitis, metritis, clinical hypocalcemia, depression/dehydration/fever (DDF), digestive conditions, lameness and clinical ketosis) was assessed until 60 days in milk. Two indices were developed to explore the association between dRT and disease: (i) Cow index (CIx), based on changes in dRT in the affected cow during the days before the diagnosis of health disorders; (ii) Mates index (MIx), index based on deviations in dRT relative to previous days and healthy pen mate cohorts. Subsequently, an alert model was proposed for each index (ACIx and AMIx) considering the reference alert cut-off values as the differences between average index values in healthy and sick cows for each specific disease. The sensitivity (SE) of ACIx detecting disease ranged from 42% (digestive conditions and DDF) to 80% (clinical hypocalcemia) with 84% specificity (SP). The SE of AMIx ranged from 46% (digestive conditions and DDF) to 100% (clinical hypocalcemia) with 85% SP. Consistent reductions in rumination activity, both within cow and relative to healthy mate cohorts, were observed for each health disorder at the day of diagnosis. However, the ability of the proposed algorithms for detecting each specific disease was variable.

Prediction of calving time in dairy cattle

This prospective study was carried out to predict the calving time in primiparous (n=11) and multiparous (n=22) Holstein-Friesian cows using the combination of data obtained from the RumiWatch noseband-sensor and 3D-accelerometer. The animals included in the study were fitted with the RumiWatch noseband-sensor and 3D-accelerometer at least 10days before the expected calving day. The calving event was defined as the time of the first appearance of the calves' feet outside the vulva, and this moment was determined by farm staff and/or confirmed by video monitor. As primiparous and multiparous cows behaved differently, two models including data of noseband-sensors and 3D-accelerometers were used to predict the calving time in each group. Lying bouts (LB) increased and rumination chews (RC) decreased similarly in both groups; besides that, boluses (B) decreased and other activities (OA) increased significantly in multiparous and primiparous cows, respectively. The sensitivity (Se) and specificity (Sp) for prediction of the onset of calving within the next 3h were determined with the logistic regression and ROC analysis (Se=88.9%, 85% and Sp=93.3%, 74% for multiparous and primiparous cows, respectively). This pilot study revealed that the RumiWatch system is a useful tool to predict calving time under farm conditions.

Potential of connected devices to optimize cattle reproduction

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

Evaluation of calving indicators measured by automated monitoring devices to predict the onset of calving in Holstein dairy cows

Dystocias are common in dairy cows and often adversely affect production, reproduction, animal welfare, labor, and economics within the dairy industry. An automated device that accurately predicts the onset of calving could potentially minimize the effect of dystocias by enabling producers to intervene early. Although many well-documented indicators can detect the imminence of calving, research is limited on their effectiveness to predict calving when measured by automated devices. The objective of this experiment was to determine if a decrease in vaginal temperature (VT), rumination (RT), and lying time (LT), or an increase in lying bouts (LB), as measured by 3 automated devices, could accurately predict the onset of calving within 24, 12, and 6 h. The combination of these 4 calving indicators was also evaluated. Forty-two multiparous Holstein cows housed in tie-stalls were fitted with a temperature logger inserted in the vaginal cavity 7±2 d before their expected calving date; VT was recorded at 1-min intervals. An ear-attached sensor recorded rumination time every hour based on ear movement while an accelerometer fitted to the right hind leg recorded cow position at 1-min intervals. On average, VT were 0.3±0.03°C lower, and RT and LT were 41±17 and 52±28 min lower, respectively, on the calving day compared with the previous 4 d. Cows had 2±1 more LB on the calving day. Of the 4 indicators, a decrease in VT≥0.1°C was best able to predict calving within the next 24 h with a sensitivity of 74%, specificity of 74%, positive and negative predictive values of 51 and 89%, and area under the curve of 0.80. Combining the indicators enhanced the performance to predict calving within the next 24, 12, and 6 h with best overall results obtained by combining the 3 devices for prediction within the next 24 h (sensitivity: 77%, specificity: 77%, positive and negative predictive values: 56 and 90%, area under the curve: 0.82). These results indicate that a device that could simultaneously measure these 4 calving indicators could not precisely determine the onset of calving, but the information collected would assist dairy farmers in monitoring the onset of calving.

Methods and on-farm devices to predict calving time in cattle

In livestock farming, accurate prediction of calving time is a key factor for profitability and animal welfare. The most accurate and sensitive methods to date for prediction of calving within 24 h are the measurement of pelvic ligament relaxation and assays for circulating progesterone and oestradiol-17β. Conversely, the absence of calving within the next 12-24 h can be accurately predicted by the measurement of incremental daily decrease in vaginal temperature and by the combination of pelvic ligament relaxation and teat filling estimates. Continuous monitoring systems can detect behavioural changes occurring on the actual day of calving, some of them being accentuated in the last few hours before delivery; standing/lying transitions, tail raising, feeding time, and dry matter and water intakes differ between cows with dystocia and those with eutocia. Use of these behavioural changes has the potential to improve the management of calving. Currently, four types of devices for calving detection are on the market: inclinometers and accelerometers detecting tail raising and overactivity, abdominal belts monitoring uterine contractions, vaginal probes detecting a decrease in vaginal temperature and expulsion of the allantochorion, and devices placed in the vagina or on the vulvar lips that detect calf expulsion. The performance of these devices under field conditions and their capacity to predict dystocia require further investigation.

Effect of sensor systems for cow management on milk production, somatic cell count, and reproduction

To improve management on dairy herds, sensor systems have been developed that can measure physiological, behavioral, and production indicators on individual cows. It is not known whether using sensor systems also improves measures of health and production in dairy herds. The objective of this study was to investigate the effect of using sensor systems on measures of health and production in dairy herds. Data of 414 Dutch dairy farms with (n=152) and without (n=262) sensor systems were available. For these herds, information on milk production per cow, days to first service, first calving age, and somatic cell count (SCC) was provided for the years 2003 to 2013. Moreover, year of investment in sensor systems was available. For every farm year, we determined whether that year was before or after the year of investment in sensor systems on farms with an automatic milking system (AMS) or a conventional milking system (CMS), or whether it was a year on a farm that never invested in sensor systems. Separate statistical analyses were performed to determine the effect of sensor systems for mastitis detection (color, SCC, electrical conductivity, and lactate dehydrogenase sensors), estrus detection for dairy cows, estrus detection for young stock, and other sensor systems (weighing platform, rumination time sensor, fat and protein sensor, temperature sensor, milk temperature sensor, urea sensor, β-hydroxybutyrate sensor, and other sensor systems). The AMS farms had a higher average SCC (by 12,000 cells/mL) after sensor investment, and CMS farms with a mastitis detection system had a lower average SCC (by 10,000 cells/mL) in the years after sensor investment. Having sensor systems was associated with a higher average production per cow on AMS farms, and with a lower average production per cow on CMS farms in the years after investment. The most likely reason for this lower milk production after investment was that on 96% of CMS farms, the sensor system investment occurred together with another major change at the farm, such as a new barn or a new milking system. Most likely, these other changes had led to a decrease in milk production that could not be compensated for by the use of sensor systems. Having estrus detection sensor systems did not improve reproduction performance. Labor reduction was an important reason for investing in sensor systems. Therefore, economic benefits from investments in sensor systems can be expected more from the reduction in labor costs than from improvements in measures of health and production in dairy herds.