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

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

Technical note: Evaluation of a system for monitoring individual feeding behavior and activity in beef cattle.

Behavioral observations are important to detect illness in beef cattle. However, traditional observation techniques are time and labor intensive and may be subjective. The objective was to validate a system for monitoring individual feeding behavior and activity in beef cattle (Fedometer [FEDO]; ENGS, Rosh Pina, Israel). Sixteen steers (initial BW ± SD = 326 ± 46 kg) were fitted with data loggers (FEDO) on their left front leg and housed in a pen with a feedbunk equipped with an antenna emitting an electromagnetic field that reached 30 ± 2 cm in front of the feedbunk. Feedbunk attendance (duration of visit and frequency of meals) measured by FEDO was compared with live observations (27 observational periods lasting between 72 and 240 min; mean 126 min). Lying time and frequency of lying bouts were compared with previously validated accelerometers fitted to the hind leg (10 steers equipped for 10 to 12 d; HOBO Pendant G Acceleration Data Logger [HOBO]; Onset Computer Corporation, Pocasset, MA). Step counts were compared with video recordings (15 observations for 6-min intervals in 6 steers). Concordance correlation coefficients (CCC), accounting for repeated measures, and limits of agreement were computed. Comparison between FEDO and observed time at the feedbunk yielded a CCC of 0.98 (95% confidence interval [CI] 0.97-0.99). All 68 meal events observed were recorded by FEDO. However, FEDO recorded 4 meal events during the 27 observational periods that were not observed. Lying time measured by HOBO and FEDO were highly correlated (CCC = 0.98; 95% CI 0.97-0.99). However, frequency of lying bouts measured by FEDO was only moderately correlated to HOBO (CCC = 0.71; 95% CI 0.63-0.77); FEDO underestimating the number of lying bouts (on average, 0.4 fewer bouts per 6 h). Step count by FEDO was moderately correlated to video observations (CCC = 0.75; 95% CI 0.49-0.89); FEDO overestimating the number of steps (on average, 5 more steps per 6 min). In conclusion, the FEDO system accurately measured duration of feedbunk attendance, frequency of meals, and lying time. However, it overestimated the number of steps and underestimated the frequency of lying bouts.

Technical note: Accuracy of an ear tag-attached accelerometer to monitor rumination and feeding behavior in feedlot cattle.

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.

Feeding behavior as an early predictor of bovine respiratory disease in North American feedlot systems.

Bovine respiratory disease (BRD), which can cause substantial losses for feedlot operations, is often difficult to detect based solely on visual observations. The objectives of the current study were to determine a BRD case identification based on clinical and laboratory parameters and assess the value of feeding behavior for early detection of BRD. Auction-derived, mixed-breed beef steers (n = 213) with an average arrival weight of 294 kg were placed at a southern Alberta commercial feedlot equipped with an automated feed bunk monitoring system. Feeding behavior was recorded continuously (1-s intervals) for 5 wk after arrival and summarized into meals. Meals were defined as feeding events that were interrupted by less than 300 s nonfeeding. Meal intake (g) and meal time (min) were further summarized into daily mean, minimum, maximum, and sum and, together with frequency of meals per day, were fit into a discrete survival time analysis with a conditional log-log link. Feedlot staff visually evaluated (pen-checked) health status twice daily. Within 35 d after arrival, 76% (n = 165) of the steers had 1 or more clinical signs of BRD (reluctance to move, crusted nose, nasal or ocular discharge, drooped ears or head, and gaunt appearance). Whereas 41 blood samples could not be processed due to immediate freezing, for 124 of these steers, complete and differential blood cell count, total serum protein, plasma fibrinogen, serum concentration of haptoglobin (HP), and serum amyloid A (SAA) were determined. The disease definition for BRD was a rectal temperature ≥ 40.0°C, at least 2 clinical signs of BRD, and HP > 0.15 mg/mL. It was noteworthy that 94% of the 124 steers identified by the feedlot staff with clinical signs of BRD had HP > 0.15 mg/mL. An increase in mean meal intake, frequency, and mean inter-meal interval was associated with a decreased hazard for developing BRD 7 d before visual identification (P < 0.001). Furthermore, increased mean mealtime, frequency, and mean inter-meal interval were associated with a decreased BRD hazard up to 7 d before feedlot staff noticed clinical symptoms (P < 0.001). In conclusion, mean intake per meal as well as mean meal time and frequency of meals could be used to predict the hazard of BRD in feedlot cattle 7 d before visual detection and could be considered in commercial feedlot settings once a predictive algorithm has been developed.