Validation of a pressure sensor-based system for measuring eating, rumination and drinking behaviour of dairy cattle

Impacts of heat stress on the accuracy of a noseband sensor for detection of eating and rumination behavior in confined cattle

Precision monitoring of feeding behaviors can aid in dairy herd management. Noseband sensors (RumiWatch System [RW]; Itin + Hoch GmbH) have been established as an automated gold standard for evaluating precision technologies in grazing cows, but more advanced algorithms have not been validated in confinement settings. Additionally, little is known regarding effects of environmental conditions on sensor performance. Therefore, accuracy of RW in quantifying eating and rumination time in confinement was evaluated using 2 versions of the analysis software algorithms (RW Converter V.7.3.2 and V.7.3.36) under thermoneutral (TN; 21.0°C, 64.0% relative humidity [RH], temperature-humidity index [THI] = 67) and heat stress conditions (HS; cyclical daily temperatures to mimic diurnal patterns; 0700-1900 h: 33.6°C, 40.0% RH, THI = 83.5; 1900-0700 h: 23.2°C, 70.0% RH; THI = 70.3). Nine individually housed Holstein × Simmental cross steers were fitted with RW noseband sensors. Agreement for eating time reported by RW and visual observations (1-min scan sampling) was very high in TN regardless of software version (concordance correlation coefficient [CCC]: V.7.3.2 = 0.91; V.7.3.36 = 0.94), and remained high to very high (CCC: V.7.3.2 = 0.89; V.7.3.36 = 0.95) during HS. Agreement for rumination time was very high regardless of software version in both TN (CCC: V.7.3.2 = 0.93; V.7.3.36 = 0.99) and HS (CCC: V.7.3.2 = 0.91; V.7.3.36 = 0.99). Overall, RW accurately quantified eating and ruminating time in confined cattle, and noseband sensors retained accuracy during heat stress. These results indicate RW may serve as a benchmark for future precision technology validations in dairy cattle managed in confinement systems.

Machine learning approaches to predict and detect early-onset of digital dermatitis in dairy cows using sensor data

The present study aimed to employ machine learning algorithms based on sensor behavior data for (1) early-onset detection of digital dermatitis (DD) and (2) DD prediction in dairy cows. Our machine learning model, which was based on the Tree-Based Pipeline Optimization Tool (TPOT) automatic machine learning method, for DD detection on day 0 of the appearance of the clinical signs has reached an accuracy of 79% on the test set, while the model for the prediction of DD 2 days prior to the appearance of the first clinical signs, which was a combination of K-means and TPOT, has reached an accuracy of 64%. The proposed machine learning models have the potential to help achieve a real-time automated tool for monitoring and diagnosing DD in lactating dairy cows based on sensor data in conventional dairy barn environments. Our results suggest that alterations in behavioral patterns can be used as inputs in an early warning system for herd management in order to detect variances in the health and wellbeing of individual cows.

Consider the Source: The Impact of Social Mixing on Drylot Housed Steer Behavior and Productivity

Cattle are a social species in which social mixing can induce physical and psychosocial stress; however, the impact of social mixing on cattle welfare is unknown. Two different sources of genetically similar Angus crossbred steers were transported to the same feedlot and assigned to a pen where they were either socially mixed or housed with individuals from their source herds. Social mixing did not impact average daily gains in pens, feed intake, or feed efficiency; pens of socially mixed steers were more active. Sources differed in their responses to social mixing. One source was unaffected, whereas social mixing negatively impacted productivity for the other source. Irrespective of social mixing, the sources differed in the amount of time per day they spent ruminating and drinking. Group analyses indicated that socially mixing two sources of feedlot steers did not negatively impact group productivity, yet the impacts that were observed at the individual level suggest that prior experiences may influence their ability to cope with social stress, emphasizing the importance of early-life experiences to long-term welfare and productivity. Social mixing was not universally detrimental to cattle welfare, and the source of cattle may have the greatest affect on their performance regardless of whether a social mixing event has occurred.

Influence of pre-grazing herbage mass on bite mass, eating behaviour, and dairy cow performance on pasture

Knowledge about individual daily herbage dry matter (DM) intake (DMI) helps identifying efficient dairy cows and adapting supplementation better to herbage intake and nutrient requirements of grazing dairy cows. With the aid of behavioural characteristics, raw data recorded with the RumiWatch (RW) system and processed with the RW converter 0.7.3.31 (C31), estimation of herbage DMI may be possible. First, C31, which allows differentiation of prehension bites and mastication chews, was validated through direct observation of behavioural characteristics and compared to the previous RW converter 0.7.3.11 (C11). Further, the influence of a low and high pre-grazing herbage mass (HM), with the same target herbage allowance (HA), on bite mass, DMI, number of prehension bites, and milk production was investigated. In total, 24 lactating Holstein cows were pairwise allotted to one of two HM treatments. The cows received a new pasture paddock twice per day with a daily target HA of 22 kg DM per cow/day. On average, low HM (LHM) and high HM (HHM) paddocks had an HM of 589 and 2288 kg DM/ha, respectively, above 6.7 click units (1 CU = 0.5 cm). Overall, LHM cows produced 2.7 kg/day more milk and 2.5 kg/day more energy-corrected milk, had the same herbage DMI and a similar prehension bite mass. The averaged bite mass per week was 0.49 g DM/bite (LHM) or 0.47 g DM/bite (HHM), respectively. A longer eating time (617 vs. 559 min/day) and a shorter rumination time (297 vs. 365 min/day) were observed for the LHM cows compared with the HHM cows. The validation of the RW showed similar results for C11 and C31 apart from prehension bites, where C31 showed a mean absolute deviation of 12.4%. Pre-grazing HM had no effect on relevant behavioural characteristics for prospective intake estimation, namely, bite mass and number of prehension bites.

Improved cattle behaviour monitoring by combining Ultra-Wideband location and accelerometer data

Cattle behaviour is fundamentally linked to the cows' health, (re)production, and welfare. The aim of this study was to present an efficient method to incorporate Ultra-Wideband (UWB) indoor location and accelerometer data for improved cattle behaviour monitoring systems. In total, 30 dairy cows were fitted with UWB Pozyx wearable tracking tags (Pozyx, Ghent, Belgium) on the upper (dorsal) side of the cow's neck. In addition to the location data, the Pozyx tag reports accelerometer data as well. The combination of both sensor data was performed in two steps. In the first step, the actual time spent in the different barn areas was calculated using location data. In the second step, accelerometer data were used to classify cow behaviour using the location information of step 1 (e.g., a cow located in the cubicles cannot be classified as feeding, or drinking). A total of 156 hours of video recordings were used for the validation. For each hour of data, the total time each cow spent in each area and performing which behaviours (feeding, drinking, ruminating, resting, and eating concentrates) were computed using the sensors and compared against annotated video recordings. Bland-Altman plots for the correlation and difference between the sensors and the video recording were then computed for the performance analysis. The overall performance of locating the animals into the correct functional areas was very high. The R² was 0.99 (P < 0.001), and the root-mean-square error (RMSE) was 1.4 min (7.5% of the total time). The best performance was obtained for the feeding and lying areas (R² = 0.99, P < 0.001). Performance was lower in the drinking area (R² = 0.90, P < 0.01) and the concentrate feeder (R² = 0.85, P < 0.05). For the combined location + accelerometer data, high overall performance (all behaviours) was obtained with an R² of 0.99 (P < 0.001) and a RMSE of 1.6 min (12% of the total time). The combination of location and accelerometer data improved the RMSE of the feeding time and ruminating time compared to the accelerometer data alone (2.6-1.4 min). Moreover, the combination of location and accelerometer enabled accurate classification of additional behaviours that are difficult to detect using the accelerometer alone, such as eating concentrates and drinking (R² = 0.85 and 0.90, respectively). This study demonstrates the potential of combining accelerometer and UWB location data for the design of a robust monitoring system for dairy cattle.

Validation of automatic monitoring of feeding behaviours in sheep and goats

Monitoring the feeding and ruminating behaviour of ruminants can be used to assess their health and welfare. The MSR-jaw movement recording system (JAM-R) can automatically record the jaw movements of ruminants. The associated software Viewer2 was developed to classify these recordings in adult cattle and calculate the duration and number of mastications of feeding and ruminating. The purpose of this study was to evaluate the performance of Viewer2 in classifying the behaviour of sheep and goats and assessing their feeding and ruminating. The feeding and ruminating behaviour of ten sheep and ten goats on pasture (observed live) and of five sheep and five goats in the barn (observed by video) were compared with Viewer2 behaviour classifications. To assess the technical and welfare issues of the JAM-R, its application was tested in a feeding experiment with 24 h monitoring of the feeding behaviours of 24 sheep and 24 goats. Viewer2 worked equally well on both species. The mean (95% confidence interval) performance of Viewer2 was at a good level for feeding (accuracy: 0.8-1.0; sensitivity: 0.9-1.0; specificity: 0.6-0.9; precision: 0.7-0.9) and ruminating (accuracy: 0.8-0.9; sensitivity: 0.6-0.8; specificity: 0.8-1.0; precision: 0.9-1.0) compared with human observations, with minor differences between the conditions on pasture and in the barn. The performance improved when recording frequency was increased from 10 Hz to 20 Hz. Applying the JAM-R in a feeding experiment, 71% of the recordings executed were defined as technically error-free and produced plausible values for feeding behaviours. In conclusion, according to the values of accuracy, sensitivity, specificity and precision, the presented JAM-R system with Viewer2 is a reliable and applicable technology for automatic recording of feeding and ruminating behaviour of sheep and goats on pasture and in the barn.

A robust classification system for Southern Yellow cow behavior using 3-DoF accelerometers

Modern methods of monitoring help cow farmers save significantly monitoring time and improve cow health care efficiency. Behavioral changes when cows are sick may include increased or decreased daily activities such as increased lying or decreased walking time. Accelerometer advantages are low power consumption, small size, and lightweight. Thus, accelerometers have been widely used to monitor cow behavior. A cow monitoring system usually includes a central processor for receiving and processing information according to a behavioral classification algorithm through the cows’ movements. This paper introduces an effective classification system for Southern Yellow cow behavior using three degrees of freedom (3-DoF) accelerometers. The proposed classifier applied GBDT algorithm (16 seconds window) with five features, offers the good performance while investigating with four Southern Yellow cattle. The classification achievement was assessed and compared to existing ones regarding sensitivity, accuracy, and positive predictive value.

Industry 4.0 and Precision Livestock Farming (PLF): An up to Date Overview across Animal Productions

Precision livestock farming (PLF) has spread to various countries worldwide since its inception in 2003, though it has yet to be widely adopted. Additionally, the advent of Industry 4.0 and the Internet of Things (IoT) have enabled a continued advancement and development of PLF. This modern technological approach to animal farming and production encompasses ethical, economic and logistical aspects. The aim of this review is to provide an overview of PLF and Industry 4.0, to identify current applications of this rather novel approach in different farming systems for food producing animals, and to present up to date knowledge on the subject. Current scientific literature regarding the spread and application of PLF and IoT shows how efficient farm animal management systems are destined to become. Everyday farming practices (feeding and production performance) coupled with continuous and real-time monitoring of animal parameters can have significant impacts on welfare and health assessment, which are current themes of public interest. In the context of feeding a rising global population, the agri-food industry and industry 4.0 technologies may represent key features for successful and sustainable development.

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.

Evaluation of Wearable Cameras for Monitoring and Analyzing Calf Behavior: A Preliminary Study

Simple Summary

Owing to the small size and lightweight of wearable cameras, they do not affect cattle behavior when attached to their bodies. Thus, this study aimed to evaluate the suitability of wearable cameras for monitoring and analyzing calf behavior. We conclude that wearable cameras are suitable for observing calf behavior, particularly their posture (standing or lying), as well as their ruminating and feeding behaviors.

Abstract

Understanding cattle behavior is important for discerning their health and management status. However, manual observations of cattle are time-consuming and labor-intensive. Moreover, during manual observations, the presence or position of a human observer may alter the normal behavior of the cattle. Wearable cameras are small and lightweight; therefore, they do not disturb cattle behavior when attached to their bodies. Thus, this study aimed to evaluate the suitability of wearable cameras for monitoring and analyzing cattle behavior. From December 18 to 27, 2017, this study used four 2-month-old, group-housed Holstein calves at the Field Science Center of the Obihiro University of Agriculture and Veterinary Medicine, Japan. Calf behavior was recorded every 30 s using a wearable camera (HX-A1H, Panasonic, Japan) from 10:00 to 15:30 and observed directly from 11:00 to 12:00 and 14:00 to 15:00. In addition, the same observer viewed the camera recordings corresponding to the direct observation periods, and the results were compared. The correlation coefficients of all behavioral data from direct and wearable camera video observations were significant (p < 0.01). We conclude that wearable cameras are suitable for observing calf behavior, particularly their posture (standing or lying), as well as their ruminating and feeding behaviors.

The Relationship between Animal Welfare and Antimicrobial Use in Italian Dairy Farms

Information regarding the relationship between animal welfare (AW) and antimicrobial use (AMU) in dairy cows is limited. The current study aimed to investigate this relationship on Italian farms and to identify potential targets of AMU reduction. The study was performed at 79 Italian dairy farms housing over 15,000 cows during 2019. AW was scored with an on-farm protocol assessing farm management and staff training, housing systems, and animal-based measures. AMU was estimated using a defined daily dose per kg of animal biomass (DDDAit/biomass) for Italy. The median AW score was 73% (range: 56.6-86.8%). The median AMU was 4.8 DDDAit/biomass (range: 0-11.8). No relationship between the total AMU and AW was found. Management and staff training were positively associated with the use of the European Medicines Agency's category B antimicrobials, which are critical for human medicine, and with intramammary products for dry cow therapy. In those farms, antimicrobial stewardship should aim to reduce the category B antimicrobials and selective dry cow therapy. Our results underline the importance of implementing both an integrated monitoring system (AW, AMU, etc.) and antimicrobial stewardship tailored to the specific needs of each dairy farm.

What, how, and how much do herbivores eat? The Continuous Bite Monitoring method for assessing forage intake of grazing animals

Determining herbage intake is pivotal for studies on grazing ecology. Direct observation of animals allows describing the interactions of animals with the pastoral environment along the complex grazing process. The objectives of the study were to evaluate the reliability of the continuous bite monitoring (CBM) method in determining herbage intake in grazing sheep compared to the standard double-weighing technique method during 45-min feeding bouts; evaluate the degree of agreement between the two techniques; and to test the effect of different potential sources of variation on the reliability of the CBM. The CBM method has been used to describe the intake behavior of grazing herbivores. In this study, we evaluated a new approach to this method, that is, whether it is a good proxy for determining the intake of grazing animals. Three experiments with grazing sheep were carried out in which we tested for different sources of variations, such as the number of observers, level of detail of bite coding grid, forage species, forage allowance, sward surface height heterogeneity, experiment site, and animal weight, to determine the short-term intake rate (45 min). Observer (Pexp1  = 0.018, Pexp2  = 0.078, and Pexp3  = 0.006), sward surface height (Pexp2  < 0.001), total number of bites observed per grazing session (Pexp2  < 0.001 and Pexp3  < 0.001), and sward depletion (Pexp3  < 0.001) were found to affect the absolute error of intake estimation. The results showed a high correlation and agreement between the two methods in the three experiments, although intake was overestimation by CBM on experiments 2 and 3 (181.38 and 214.24 units, respectively). This outcome indicates the potential of CBM to determining forage intake with the benefit of a greater level of detail on foraging patterns and components of the diet. Furthermore, direct observation is not invasive nor disrupts natural animal behavior.

A Systematic Review on Commercially Available and Validated Sensor Technologies for Welfare Assessment of Dairy Cattle

In order to base welfare assessment of dairy cattle on real-time measurement, integration of valid and reliable precision livestock farming (PLF) technologies is needed. The aim of this study was to provide a systematic overview of externally validated and commercially available PLF technologies, which could be used for sensor-based welfare assessment in dairy cattle. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic literature review was conducted to identify externally validated sensor technologies. Out of 1,111 publications initially extracted from databases, only 42 studies describing 30 tools (including prototypes) met requirements for external validation. Moreover, through market search, 129 different retailed technologies with application for animal-based welfare assessment were identified. In total, only 18 currently retailed sensors have been externally validated (14%). The highest validation rate was found for systems based on accelerometers (30% of tools available on the market have validation records), while the lower rates were obtained for cameras (10%), load cells (8%), miscellaneous milk sensors (8%), and boluses (7%). Validated traits concerned animal activity, feeding and drinking behavior, physical condition, and health of animals. The majority of tools were validated on adult cows. Non-active behavior (lying and standing) and rumination were the most often validated for the high performance. Regarding active behavior (e.g., walking), lower performance of tools was reported. Also, tools used for physical condition (e.g., body condition scoring) and health evaluation (e.g., mastitis detection) were classified in lower performance group. The precision and accuracy of feeding and drinking assessment varied depending on measured trait and used sensor. Regarding relevance for animal-based welfare assessment, several validated technologies had application for good health (e.g., milk quality sensors) and good feeding (e.g., load cells, accelerometers). Accelerometers-based systems have also practical relevance to assess good housing. However, currently available PLF technologies have low potential to assess appropriate behavior of dairy cows. To increase actors' trust toward the PLF technology and prompt sensor-based welfare assessment, validation studies, especially in commercial herds, are needed. Future research should concentrate on developing and validating PLF technologies dedicated to the assessment of appropriate behavior and tools dedicated to monitoring the health and welfare in calves and heifers.

Animal Welfare Implications of Digital Tools for Monitoring and Management of Cattle and Sheep on Pasture

The opportunities for natural animal behaviours in pastures imply animal welfare benefits. Nevertheless, monitoring the animals can be challenging. The use of sensors, cameras, positioning equipment and unmanned aerial vehicles in large pastures has the potential to improve animal welfare surveillance. Directly or indirectly, sensors measure environmental factors together with the behaviour and physiological state of the animal, and deviations can trigger alarms for, e.g., disease, heat stress and imminent calving. Electronic positioning includes Radio Frequency Identification (RFID) for the recording of animals at fixed points. Positioning units (GPS) mounted on collars can determine animal movements over large areas, determine their habitat and, somewhat, health and welfare. In combination with other sensors, such units can give information that helps to evaluate the welfare of free-ranging animals. Drones equipped with cameras can also locate and count the animals, as well as herd them. Digitally defined virtual fences can keep animals within a predefined area without the use of physical barriers, relying on acoustic signals and weak electric shocks. Due to individual variations in learning ability, some individuals may be exposed to numerous electric shocks, which might compromise their welfare. More research and development are required, especially regarding the use of drones and virtual fences.

Validation and Use of the RumiWatch Noseband Sensor for Monitoring Grazing Behaviours of Lactating Dairy Cows

Precision livestock farming (PLF) supports the development of sustainable dairy production. The sensors used in PLF provide valuable information for farm management, but they must be validated to ensure the accuracy. The goal of this study was to validate and use the RumiWatch sensor (RWS; Itin+Hoch GmbH, Liestal, Switzerland) to differentiate prehension bites, eating chews, mastication chews and rumination chews in pressure-based system. Twenty cows were used for 14 days to provide a validation dataset. The concordance correlation coefficient (CCC) was adopted to test the concordance between the RumiWatch sensor and video observation. The RumiWatch sensor performed well in counting prehension bites (CCC = 0.98), eating chews (CCC = 0.95) and rumination chews (CCC = 0.96), while it showed an acceptable concordance in counting mastication chews with video observation (CCC = 0.77). Moderate correlations were found between eating chews and daily milk production: daily milk production (kg/day) = 0.001151 × eating chews (chews/day) − 11.73 (R2 = 0.31; standard error (SE) = 8.88; p = 0.011), and between mastication chews and daily milk production: daily milk production (kg/day) = 0.001935 × mastication chews (chews/day) + 2.103 (R2 = 0.34; SE = 8.70; p = 0.007). Overall, the results indicated that the RumiWatch sensor can be confidently used to quantify and differentiate prehension bites, eating chews and rumination chews; in addition, ingestive behaviours explained up to 34% of the variation in milk production.

Evaluation of the RumiWatch system as a benchmark to monitor feeding and locomotion behaviors of grazing dairy cows

Direct visual observation is a common method for validation of animal behavior technologies; however, visual observations are time consuming and subject to human error. The objective of this study was to evaluate the RumiWatch system (Itin and Hoch GmbH, Liestal, Switzerland), which is composed of a noseband sensor and a pedometer, for monitoring feeding and locomotion behaviors of grazing dairy cows, to determine its accuracy for use as a benchmark in validation studies. The study was conducted at the University of Minnesota West Central Research and Outreach Center in Morris, Minnesota, from May to June 2018. Two experiments were conducted and validated: (1) feeding and locomotion behaviors and (2) rumination cycle and grazing bites. Lactating crossbred dairy cows (n = 12) were offered pasture for 22 h/d, and cows were milked twice daily. Visual observations were recorded by 3 observers with the Pocket Observer app (Noldus Information Technology, Leesburg, VA). The first experiment determined agreement for visual observations and the RumiWatch noseband sensor and pedometer from 144 h of feeding and locomotion behaviors. The second experiment determined agreement for visual observations and the RumiWatch noseband sensor from 17.75 h of rumination cycle and grazing bites. Pearson correlations evaluated associations for visual observations, and the RumiWatch noseband sensor and pedometer and were 0.84 for rumination, 0.76 for grazing, 0.39 for drinking, 0.57 for other activities, 0.83 for standing, 0.91 for lying, and 0.38 for walking. Correlations for visual observations and rumination cycle and grazing bites were -0.13 and 0.47, respectively. The RumiWatch system evaluated rumination, grazing, standing, and lying behaviors with high precision and accuracy, and the RumiWatch system may be used as a benchmark instead of visual observation to validate animal behavior technologies.

Influence of Subclinical Ketosis in Dairy Cows on Ingestive-Related Behaviours Registered with a Real-Time System

Simple Summary

Earlier disease detection may benefit cows by improving responses to treatment. We expected that changes in rumination before evident clinical signs of subclinical ketosis would result in the earlier identification of disease. Cows with subclinical ketosis showed lesser average values for the following parameters: rumination time and rumination chews (1.48 and 1.68 times respectively; p < 0.001), drinking time (1.50 times; p < 0.001), chews per minute, bolus and chews per bolus (1.12, 1.45 and 1.51 times; p < 0.001). From the 15th day before the diagnosis of ketosis, rumination time in the healthy group was greater than that in subclinical ketosis cows from −0.96% (−17 day) to 187.79% (0 days, p < 0.001). Eating time at the beginning of the experiment in healthy cows was 48.92% and at the end of the period was −91.97% lesser compared to subclinical ketosis (p < 0.001).

Abstract

According to the literature, rumination time can be used as biomarker in the diagnosis of subclinical ketosis (SCK). We hypothesized that SCK in cows influences ingestive-related behaviours registered with the real-time system. The aim of the current study was to determine the influence of SCK on dairy cows’ ingestive-related behaviours registered with a real-time system. Twenty Lithuanian Black and White breed dairy cows were selected based on the following criteria: First day after calving, having two or more lactations (on average 3.0 ± 0.13 lactations), and being clinically healthy. The experiment lasted 18 days. Cows were tested 24 h a day for 17.5 days. On the day of diagnosis (day 0), data were recorded for 12 h. During the experimental period, one cow was studied for a total of 420 h. For the registration of rumination behaviour, the RumiWatch system (RWS) was used. It was found that cows with SCK showed lesser average values for the following parameters: rumination time and rumination chews (1.48 and 1.68 times respectively; p < 0.001), drinking time (1.50 times; p < 0.001), chews per minute, bolus and chews per bolus (1.12, 1.45 and 1.51 times; p < 0.001). From the 15th day before the diagnosis of SCK, rumination time in health cows was greater than that in SCK cows from −0.96% (−17 day) to 187.79% (0 days, < 0.001). We estimated the greater average value of drinking time in healthy cows compared with SCK cows from 34.22% on day −17 to −121.67% on day 0 (p < 0.001). Decrease in rumination time was associated with a significant increase in the probability of risk of SCK. Further studies are needed with a larger number of cows with SCK.

An open-source research tool to study triaxial inertial sensors for monitoring selected behaviors in sheep

The use of automated systems for monitoring animal behavior provides information on individual animal behavior and can be used to enhance animal productivity. However, the advancement of this industry is hampered by technology costs, challenges with power supplies, limited data accessibility, and inconsistent testing approaches for confirming the detection of livestock behaviors. Development of open-source research tools similar to commercially available wearable technologies may contribute to the development of more-efficient and affordable technologies. The objective of this study was to demonstrate an open-source, microprocessor-based sensor designed to monitor and enable differentiation among selected behaviors of adult wethers. The sensor was comprised of an inexpensive espressif ESP-32-WROOM-32 microprocessor with Bluetooth communication, a generic MPU92/50 motion sensor that contains a three-axis accelerometer, three-axis magnetometer, a three-axis gyroscope, and a 5-V rechargeable lithium-ion battery. The open-source Arduino IDE software was used to program the microprocessor and to adjust the frequency of sampling, the data packet to send, and the operating conditions. For demonstration purposes, sensors were placed on six housed sheep for three 1-h increments with concurrent visual behavioral observation. Sensor readings (x-, y-, and z-axis) were summarized (mean and SD) within a minute and compared to animal behavior observations (also on a by-minute basis) using a linear mixed-effect model with animal as a random effect and behavioral classifier as a fixed effect. This analysis demonstrated the basic utility of the sensor to differentiate among animal behaviors based on sensed data (P < 0.001). Although substantial additional work is needed for algorithm development, power source testing, and network optimization, this open-source platform appears to be a promising strategy to research wearable sensors in a generalizable manner.

Effect of stage of maturity at harvest for forage pea (Pisum sativum L.) on eating behavior, ruminal fermentation, and digestibility when fed as hay to yearling beef heifers

The objective of this study was to evaluate the stage of maturity at harvest for pea hay (Pisum sativum L., c.v. CDC Horizon) on dry matter intake (DMI), eating behavior, ruminal fermentation, and digestibility when fed to beef heifers. Pea hay was cut at EARLY (defined to occur when flat pods were on one or more nodes), MID (when seeds filled the pods at one or more nodes and the leaves were changing from green to gold), and LATE (yellow dry seeds filled pods on most or all of the nodes and the pods and leaves had a yellow color) phases, and was cured in the field and baled. Six ruminally-cannulated Speckle Park heifers were used in a replicated 3 × 3 Latin square design with three 18-d periods including 12 d for adaptation, 2 d for measurement of ruminal pool sizes, and 4 d for the collection of eating behavior, ruminal pH, ruminal digesta, and feces. For all treatments, the respective pea hay was included at 40% of the dietary DM. Stage of maturity at harvest for pea hay did not affect total DMI, pea hay DMI, or the total short-chain fatty acid concentration in ruminal fluid with averages of 8.6 kg/d, 3.2 kg/d, and 96.55 mM, respectively. The duration of time spent ruminating decreased with advancing pea hay maturity when reported as min/d, min/kg DMI, and min/kg neutral detergent fiber (NDF) (P ≤ 0.01). Mean ruminal pH also decreased with advancing pea maturity (P < 0.01). The ruminal DM and undigested NDF corrected for OM pools were not affected by stage of maturity (P ≥ 0.55) nor was the rate of digestion for NDF. However, NDF passage rate decreased by 0.21%/h with advancing pea hay maturity (P = 0.02). Apparent total tract digestibility of NDF (average = 16.30%, P = 0.41) was not affected, but starch digestibility decreased from 96.10% to 93.08% with advancing pea hay maturity (P = 0.07). Overall, stage of maturity at harvest for pea hay does not appear to affect DMI or NDF digestibililty but decreases chewing activity, apparent total tract starch digestibility, ruminal pH, and ruminal NDF passage rate.

Validation and Implementation of an Automated Chew Sensor-Based Remote Monitoring Device as Tool for Equine Grazing Research

Field studies characterizing equine grazing activity primarily rely on observational protocols, limiting the quantity and accuracy of collected data. The objectives of this study were to validate an automated chew sensor technology, the EquiWatch System (EWS), for detecting grazing behaviors and to demonstrate potential applications of the EWS in equine grazing research. Eight mature standardbred mares were used in this study. EquiWatch System validation was completed in two phases: grazing time was evaluated in experiment 1 and chew counts in experiment 2. The correlation between visual observations and system-recorded grazing time was high (concordance correlation coefficient [CCC] = 0.997). There was also a high agreement between the sum of manually counted bites and chews and total chew counts reported by the EWS (CCC = 0.979). Following validation, a pilot study was conducted using the EWS to assess feeding behaviors of horses with unrestricted pasture access (PAS) versus horses offered ad libitum hay (HAY). Horses spent more time engaged in feeding behavior on PAS (14.79 ± 0.48 hr/d) than HAY (11.98 ± 0.48 hr/d; P < .0001). Chewing rate also differed by forage (PAS 83.92 ± 1.61; HAY 68.50 ± 1.61 chews/min; P < .0001). However, although the magnitude of these behavioral parameters was influenced by treatment, the underlying 24-hour patterns were largely preserved regardless of forage type. These results demonstrate that the EWS can generate data necessary for characterizing feeding behavior in horses. Future studies implementing this tool could provide a greater understanding of biological, environmental, and nutritive factors driving grazing behavior in horses.

Validation of an ear tag-based accelerometer system for detecting grazing behavior of dairy cows

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.

Validation of the RumiWatch Converter V0.7.4.5 classification accuracy for the automatic monitoring of behavioural characteristics in dairy cows

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.

Chewing and Drinking Activity during Transition Period and Lactation in Dairy Cows Fed Partial Mixed Rations

Simple Summary

It is common to feed cows varying levels of forage fibre in the time span before calving to lactation. The resulting changes in chewing time may help to evaluate if diets have adequate fibre content. Using rumination-halters to measure the chewing activities in dairy cows, we found diminished rumination and eating activity, especially around parturition. This indicates ruminal buffering insufficiency and a greater risk for rumen acidification during this period. In addition, reduced eating time in early-lactation cows was accompanied by reduced drinking time. We suggest that monitoring of chewing activity can be useful to assess rumen disorder risks of the cows during the transition period and rumination-halters may also be used as a tool to identify cows which are about to calve.

Abstract

Dairy cows need sufficient physically effective fibre (peNDF) in their diet to induce chewing with the latter stimulating salivation and maintaining rumen health. Thus, monitoring of chewing activity can be a non-invasive tool to assess fibre adequacy, and thus helping in the optimization of the diet. The objective of this study was to investigate and compare chewing activities of cows during transition period and in the course of lactation. Simmental dairy cows, in four different production groups such as dry period (from 8 to 6 weeks ante-calving), calving (24 h before and after calving), early-lactation (7–60 days in milk), and mid-lactation (60–120 days in milk) were used in the study. Cows were fed partial mixed rations supplemented with different amounts of concentrates. The chewing and drinking activity were recorded using rumination-halters (RumiWatch System, Itin+Hoch GmbH, Liestal, Switzerland). Feed data analysis showed that the peNDF content of the partial mixed ration (PMR) was highest during dry period, decreased around parturition, reaching the nadir in the lactation, in all cases, however, exceeding the peNDF requirements. Chewing data analysis showed that rumination time decreased (p < 0.05) in the time around parturition (from 460 min/d during dry period to 363 min/d 24 h before calving) and increased again in early-lactation (505 min/d), reaching a maximum in mid-lactation (515 min/d). Eating time was lowest for cows during early-lactation (342 min/d) and the highest for those in mid-lactation (462 min/d). Moreover, early-lactation cows spent less time (p < 0.05) drinking (8 min/d) compared to other groups (e.g., 24 min/d the day before calving and 20 min/d postpartum). Monitoring of chewing activity might be a useful tool to assess rumen disorder risks and welfare of the cows during the transition period. It further shows promising results to be used as a tool to identify cows that are shortly before calving.

Digestibility contributes to between-animal variation in feed efficiency in beef cows

Residual feed intake (RFI) is an alternative measure of feed efficiency (FE) and is calculated as the difference between actual and expected feed intake. The biological mechanisms underlying animal-to-animal variation in FE are not well understood. The aim of this study was to investigate the digestive ability of beef cows selected for RFI divergence as heifers, using two contrasted diets. Fifteen 4-year-old beef cows were selected from a total of 69 heifers based on their RFI following the feedlot test. The selected heifers were ranked into high-RFI (+ 1.02 ± 0.28, n = 8) and low-RFI (-0.73 ± 0.28, n = 7), and a digestibility trial was performed after their first lactation. Both RFI groups were offered two different diets: 100% hay or a fattening diet which consisted of a DM basis of 67% whole-plant maize silage and 33% high starch concentrates over four experimental periods (two per diet). A diet effect was observed on feed intake and apparent digestibility, whereas no diet × RFI interaction was detected (P > 0.05). Intake and apparent digestibility were higher in cows fed the fattening diet than in those fed the hay diet (P < 0.0001). DM intake (DMI) and organic matter apparent digestibility (OMd) were repeatable and positively correlated between the two subsequent periods of measurements. For the hay and fattening diets, the repeatability between periods was r = 0.71 and r = 0.73 for DMI and r = 0.87 and r = 0.48 for OMd, respectively. Moreover, both intake (r = 0.55) and OMd (r = 0.54) were positively correlated (P < 0.05) between the hay and fattening diets. Significant differences between beef cows selected for divergence in RFI as heifers were observed for digestive traits (P < 0.05), DM and organic matter (OM) apparent digestibility being higher for low-RFI cows. Overall, this study showed that apparent digestibility contributes to between-animal variation in FE in beef cows.

Validation of a noseband pressure sensor algorithm as a tool for evaluation of feeding behaviour in dairy Mediterranean buffalo (Bubalus Bubalis)

This research communication addresses the goal of validating an algorithm to monitor natural occurrence of feeding behaviours in dairy Mediterranean buffalo based on the output of a noseband pressure sensor (RumiWatch®, halter). Several characteristics of the feeding behaviour were detected with a very high (ruminating boluses), high (chews per bolus) and moderate degree of correlation (chews per minute) with video analyses (gold standard). All of them were associated with a low mean difference with the gold standard, and the mean relative measurement error ranged between low (ruminating boluses) and moderate (chews per bolus and chews per minute). The proportion of correctly detected events for the variables rumination and eating time was 98 and 99%, respectively. The collection of data and subsequent evaluation of the parameters investigated may provide objective information on Mediterranean Buffalo behaviours allowing for reliable studies of the animal welfare in this ruminant in the future.

TECHNICAL NOTE: Development of a pressure sensor-based system for measuring rumination time in pre-weaned dairy calves

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.

Herbage dry matter intake estimation of grazing dairy cows based on animal, behavioral, environmental, and feed variables

Information about the individual herbage DMI (HDMI) of grazing dairy cows is important for an efficient use of pasture herbage as an animal feed with a range of benefits. Estimating HDMI, with its multifaceted influencing variables, is difficult but may be attempted using animal, performance, behavior, and feed variables. In our study, 2 types of approaches were explored: 1 for HDMI estimation under a global approach (GA), where all variables measured in the 4 underlying experiments were used for model development, and 1 for HDMI estimation in an approach without information about the amount of supplements fed in the barn (WSB). The accuracy of these models was assessed. The underlying data set was developed from 4 experiments with 52 GA and 50 WSB variables and one hundred thirty 7-d measurements. The experiments differed in pasture size, herbage allowance, pregrazing herbage mass, supplements fed in the barn, and sward composition. In all the experiments, cow behavioral characteristics were recorded using the RumiWatch system (Itin and Hoch GmbH, Liestal, Switzerland). Herbage intake was estimated by applying the n-alkane method. Finally, HDMI estimation models with a minimal relative prediction error of 11.1% for use under GA and 13.2% for use under WSB were developed. The variables retained for the GA model with the highest accuracy, determined through various selection steps, were herbage crude protein, chopped whole-plant corn silage intake in the barn, protein supplement or concentrate intake in the barn, body weight, milk yield, milk protein, milk lactose, lactation number, postgrazing herbage mass, and bite rate performed at pasture. Instead of the omitted amounts of feed intake in the barn and, due to the statistical procedure for model reduction, the unconsidered variables postgrazing herbage mass and bite rate performed at pasture, the WSB model with the highest accuracy retained additional variables. The additional variables were total eating chews performed at pasture and in the barn, total eating time performed at pasture, number of total prehension bites, number of prehension bites performed at pasture, and herbage ash concentration. Even though behavioral characteristics alone did not allow a sufficiently accurate individual HDMI estimation, their inclusion under WSB improved estimation accuracy and represented the most valid variables for the HDMI estimation under WSB. Under GA, the inclusion of behavioral characteristics in the HDMI estimation models did not reduce the root mean squared prediction error. Finally, further adaptation, as well as validation on a more comprehensive data set and the inclusion of variables excluded in this study such as body condition score or gestation, should be considered in the development of HDMI estimation models.

Concentrate Supplement Modifies the Feeding Behavior of Simmental Cows Grazing in Two High Mountain Pastures

Simple Summary

Traditional Alpine husbandry systems require dairy cows to be grazing on mountain pasture during summer and kept indoors during the remaining part of the year. Nowadays, the pasture is not able to fully satisfy the nutritional requirements of cattle; therefore, the use of concentrates is frequently required. From their use, some issues arise: the cows tend to consume the concentrates at the expense of the grass; concentrates are competitive with human diets; concentrates decrease the environmental sustainability of farm. Therefore, in order to minimize their use, it is imperative to obtain data on the grazing behavior of cows. The aim of this study was to assess the effect of concentrate levels on the behavior of dairy cows during summer grazing in two pastures characterized by Poion alpinae and Seslerion caeruleae alliance. Cows were equipped with an electronic device to evaluate feeding behavior (grazing, rumination, and walking). In addition, the plant selection by animals was assessed. In Poion alpinae, a rich pasture, the increased supplement influenced the selectivity of the pasture species, while in Seslerion caeruleae, a poor pasture, supplementation resulted in a reduction in grazing times. The study highlights how the supplement level induced a different grazing behavior depending on pasture type.

Abstract

During grazing on Alpine pastures, the use of concentrates in dairy cows’ diet leads to a reduction of the environmental sustainability of farms, and influences the selective pressure on some plant species. In order to minimize the use of concentrates, it is imperative to obtain data on the grazing behavior of cows. The aim of this study was to assess the effect of concentrate levels on the behavior of dairy cows during grazing. One hundred and ten lactating Italian Simmental cows, that sequentially grazed two pastures characterized by Poion alpinae (Poion) and Seslerion caeruleae (Seslerion) alliance, were considered. For each pasture, eight cows were selected and assigned to two groups: High and Low, supplemented with 4 kg/head/d, and 1 kg/head/d of concentrate respectively. Cows were equipped with a noseband pressure sensor and a pedometer (RumiWatch system, ITIN-HOCH GmbH) to assess grazing, ruminating, and walking behavior. In addition, the plant selection of the animals was assessed. On Poion, increased supplement intake caused a more intense selection of legumes, without affecting feeding and walking times. On Seslerion, grazing time was higher in Low than High. Grazing management in alpine region must take into account the great variability of pastures that largely differ from a floristic and nutritional point of view.

Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle

The aim of the present study was to evaluate empirically confusion matrices in device validation. We compared the confusion matrix method to linear regression and error indices in the validation of a device measuring feeding behaviour of dairy cattle. In addition, we studied how to extract additional information on classification errors with confusion probabilities. The data consisted of 12 h behaviour measurements from five dairy cows; feeding and other behaviour were detected simultaneously with a device and from video recordings. The resulting 216 000 pairs of classifications were used to construct confusion matrices and calculate performance measures. In addition, hourly durations of each behaviour were calculated and the accuracy of measurements was evaluated with linear regression and error indices. All three validation methods agreed when the behaviour was detected very accurately or inaccurately. Otherwise, in the intermediate cases, the confusion matrix method and error indices produced relatively concordant results, but the linear regression method often disagreed with them. Our study supports the use of confusion matrix analysis in validation since it is robust to any data distribution and type of relationship, it makes a stringent evaluation of validity, and it offers extra information on the type and sources of errors.

The use of accelerometer to measure sleeping posture of beef cows

Sleep is one of the essential behaviors for mammals. The aims of this study were to validate the use of accelerometer for measuring sleeping posture of cattle. Duration of sleeping posture of seven Japanese Black cows from 19.00 to 07.00 hours was measured by both accelerometer and video, and a total of 67 accelerometer and video measurement sets were collected. We calculated Cohen's κ coefficient between accelerometer and video measurements and 91.5% of the κ-values were >0.80. Intra- and inter-observer coefficient of variance showed that specific acceleration waveform patterns of sleeping posture could be easily and accurately detected by independent observers. There were no significant differences in the frequency of sleeping posture occurrences between accelerometer and video measurements. We compared averaged sleeping posture bout, and the total sleeping posture time between accelerometer and video measurements using regression. In each trait, the slope was close to 1 and the intercept was not different from 0, which showed a strong agreement between accelerometer and video measurements. This shows that an accelerometer could accurately detect sleeping postures of cattle. We conclude that adequate measurements of sleeping postures can be made using an accelerometer.

Evaluation of the RumiWatchSystem for measuring grazing behaviour of cows

Feeding behaviour is an important parameter of animal performance, health and welfare, as well as reflecting levels and quality of feed available. Previously, sensors were only used for measuring animal feeding behaviour in indoor housing systems. However, sensors such as the RumiWatchSystem can also monitor such behaviour continuously in pasture-based environments. Therefore, the aim of this study was to validate the RumiWatchSystem to record cow activity and feeding behaviour in a pasture-based system. The RumiWatchSystem was evaluated against visual observation across two different experiments. The time duration per hour at grazing, rumination, walking, standing and lying recorded by the RumiWatchSystem was compared to the visual observation data in Experiment 1. Concordance Correlation Coefficient (CCC) values of CCC=0.96 for grazing, CCC=0.99 for rumination, CCC=1.00 for standing and lying and CCC=0.92 for walking were obtained. The number of grazing and rumination bouts within one hour were also analysed resulting in Cohen's Kappa (κ)=0.62 and κ=0.86 for grazing and rumination bouts, respectively. Experiment 2 focused on the validation of grazing bites and rumination chews. The accordance between visual observation and automated measurement by the RumiWatchSystem was high with CCC=0.78 and CCC=0.94 for grazing bites and rumination chews, respectively. These results indicate that the RumiWatchSystem is a reliable sensor technology for observing cow activity and feeding behaviour in a pasture based milk production system, and may be used for research purposes in a grazing environment.

Ingestive mastication in horses resembles rumination but not ingestive mastication in cattle and camels

Horses achieve a higher degree of particle size reduction through ingestive mastication than functional ruminants. We characterized mastication using chew-monitoring halters (RumiWatch) in six domestic horses, cattle, and Bactrian camels each. All animals were offered grass hay of the same batch for 15 min. In cattle and camels, measurements were continued after eating until rumination was observed. Except for one horse, 96% of the horses' ingestive mastication data were identified as "rumination" by the proprietary RumiWatch algorithm, whereas ingestion and rumination by cattle and camels were mostly classified correctly. There were no systematic differences between cattle and camels. In cattle and camels, ingestive mastication was less regular than rumination, indicated by significantly higher standard deviations of chewing peak intervals, peak heights, and peak breadths in intraindividual comparisons. The average standard deviations of these measures were lower in horses than in cattle and camel ingestive mastication, indicating a more consistent chewing pattern in horses. Horse values were similar to those of rumination mastication, suggesting equally regular chewing motions. Regular, rhythmic chewing represents a common feature of horses and functional ruminants, but the less uniform ingestive mastication in functional ruminants represents a deviating pattern, the adaptive value of which remains unclear. In particular, it does not appear to promote a higher ingestion rate. A potential cause may be the avoidance of high tooth wear rates by delaying a more regular, systematic mastication until ingesta has been softened and the grit has been washed off in the forestomach.

A single prolonged milking interval of 24h compromises the well-being and health of dairy Holstein cows

Cows are often shown at dairy shows with overfilled udders to achieve a better show placing. However, it is unclear to what degree "over-bagging" affects the health and well-being of show cows. The goal of this study was to assess the effect of a single prolonged milking interval (PMI) of 24h on the measurable signs of health and well-being in dairy cows in early and mid-lactation and to assess the effect of a nonsteroidal anti-inflammatory drug (NSAID) on well-being during a PMI. Fifteen Holstein cows were studied in early lactation (89.5±2.7d in milk) and were given an NSAID or physiological saline in a crossover design. Ten cows were studied again in mid-lactation (151.6±4.0d in milk). Data on clinical signs of cows' health, behavior, and well-being were collected at 1 or 2h intervals before and during a PMI of 24h. Data from the last 6h of a 12h milking interval were compared with the last 6h of the PMI. Compared with that of a cow in the last 6h of a 12-h milking interval, the behavior of cows in early lactation (saline group) changed during the last 6h of the PMI: we observed decreased eating time (22.4 vs. 16.2min/h), increased ruminating time (13.3 vs. 25.0min/h), and increased hind limb abduction while walking (score 41.7 vs. 62.6) and standing (31.2 vs. 38.9cm). Udder firmness was increased (2.9 vs. 4.5kg) during this period and more weight was placed on the hind limbs (46.4 vs. 47.0%). We also found pathological signs at the end of the PMI: all cows showed milk leaking, and 10 of 15 cows developed edema in the subcutaneous udder tissue. Somatic cell count was significantly increased from 12h to 72h after the PMI. Administration of an NSAID had no influence on measured variables, except that the occurrence of edema was not significantly increased during PMI in the flunixin group (10 of 15 and 6 of 15 cows for the saline and flunixin groups, respectively). In the cows in mid-lactation, different variables were not significantly changed in the PMI compared with baseline values (e.g., eating and ruminating time, occurrence of edema, and abduction). We conclude that the cows' health and well-being were compromised by a single PMI of 24h, because their behavior changed and pathological signs were recorded. Administration of an NSAID had a slight effect on cows' well-being during a PMI. The stage of lactation had more effect on the cows' health and well-being, because fewer variables were changed in mid-lactation.

Use of Extended Characteristics of Locomotion and Feeding Behavior for Automated Identification of Lame Dairy Cows

This study was carried out to detect differences in locomotion and feeding behavior in lame (group L; n = 41; gait score ≥ 2.5) and non-lame (group C; n = 12; gait score ≤ 2) multiparous Holstein cows in a cross-sectional study design. A model for automatic lameness detection was created, using data from accelerometers attached to the hind limbs and noseband sensors attached to the head. Each cow’s gait was videotaped and scored on a 5-point scale before and after a period of 3 consecutive days of behavioral data recording. The mean value of 3 independent experienced observers was taken as a definite gait score and considered to be the gold standard. For statistical analysis, data from the noseband sensor and one of two accelerometers per cow (randomly selected) of 2 out of 3 randomly selected days was used. For comparison between group L and group C, the T-test, the Aspin-Welch Test and the Wilcoxon Test were used. The sensitivity and specificity for lameness detection was determined with logistic regression and ROC-analysis. Group L compared to group C had significantly lower eating and ruminating time, fewer eating chews, ruminating chews and ruminating boluses, longer lying time and lying bout duration, lower standing time, fewer standing and walking bouts, fewer, slower and shorter strides and a lower walking speed. The model considering the number of standing bouts and walking speed was the best predictor of cows being lame with a sensitivity of 90.2% and specificity of 91.7%. Sensitivity and specificity of the lameness detection model were considered to be very high, even without the use of halter data. It was concluded that under the conditions of the study farm, accelerometer data were suitable for accurately distinguishing between lame and non-lame dairy cows, even in cases of slight lameness with a gait score of 2.5.