Research on Six-Axis Sensor-Based Step-Counting Algorithm for Grazing Sheep

Step counting is an effective method to assess the activity level of grazing sheep. However, existing step-counting algorithms have limited adaptability to sheep walking patterns and fail to eliminate false step counts caused by abnormal behaviors. Therefore, this study proposed a step-counting algorithm based on behavior classification designed explicitly for grazing sheep. The algorithm utilized regional peak detection and peak-to-valley difference detection to identify running and leg-shaking behaviors in sheep. It distinguished leg shaking from brisk walking behaviors through variance feature analysis. Based on the recognition results, different step-counting strategies were employed. When running behavior was detected, the algorithm divided the sampling window by the baseline step frequency and multiplied it by a scaling factor to accurately calculate the number of steps for running. No step counting was performed for leg-shaking behavior. For other behaviors, such as slow and brisk walking, a window peak detection algorithm was used for step counting. Experimental results demonstrate a significant improvement in the accuracy of the proposed algorithm compared to the peak detection-based method. In addition, the experimental results demonstrated that the average calculation error of the proposed algorithm in this study was 6.244%, while the average error of the peak detection-based step-counting algorithm was 17.556%. This indicates a significant improvement in the accuracy of the proposed algorithm compared to the peak detection method.

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.

Technology applications in bovine gait analysis: A scoping review

Quantitative bovine gait analysis using technology has evolved significantly over the last two decades. However, subjective methods of gait assessment using visual locomotion scoring remain the primary on-farm and experimental approach. The objective of this review is to map research trends in quantitative bovine gait analysis and to explore the technologies that have been utilized to measure biomechanical parameters of gait. A scoping literature review was conducted according to PRISMA guidelines. A search algorithm based on PICO framework generated three components-bovine, gait, and technology-to address our objectives. Three online databases were searched for original work published from January 2000 to June 2020. A two-step screening process was then conducted, starting with the review of article titles and abstracts based on inclusion criteria. A remaining 125 articles then underwent a full-text assessment, resulting in 82 final articles. Thematic analysis of research aims resulted in four major themes among the studies: gait/claw biomechanics, lameness detection, intervention/comparison, and system development. Of the 4 themes, lameness detection (55% of studies) was the most common reason for technology use. Within the literature identified three main technologies were used: force and pressure platforms (FPP), vision-based systems (VB), and accelerometers. FPP were the first and most popular technologies to evaluate bovine gait and were used in 58.5% of studies. They include force platforms, pressure mapping systems, and weight distribution platforms. The second most applied technology was VB (34.1% of studies), which predominately consists of video analysis and image processing systems. Accelerometers, another technological method to measure gait characteristics, were used in 14.6% of studies. In sum, the strong demand for automatic lameness detection influenced the path of development for quantitative gait analysis technologies. Among emergent technologies, deep learning and wearable sensors (e.g., accelerometers) appear to be the most promising options. However, although progress has been made, more research is needed to develop more accurate, practical, and user-friendly technologies.

Relationship between reticular pH parameters and potential on-farm indicators in the early lactation of dairy cows

Subacute ruminal acidosis (SARA) is an important nutritional disorder affecting animal welfare and economy of milk production. Definitions rely on ruminal pH but due to limitations of its measurement, indicators reflecting low pH are highly desirable. The aim of this study was to investigate the relationship between reticular pH and 18 on-farm indicators in milk, blood, faeces, urine and chewing behaviour in early lactating dairy cows. Ten farms were visited for 3 weeks and in total samples of 100 cows (10 per farm) were taken. The statistics and graphical visualization were performed using Pearson correlation and linear regression models on an animal individual level as well as with linear mixed models. Eight indicators (milk fat, fat-to-protein ratio, rumination time, feed intake time, rumination frequency, rumination boluses, lying time and faecal pH) were statistically significant associated with the daily animal individual reticular pH average. However, none of the models including the potential explanatory variables explained more than 5% of the pH variations. The study confirms the necessity of pH measurement to detect SARA risk animals in early lactation dairy cows.

Behavioral Fingerprinting: Acceleration Sensors for Identifying Changes in Livestock Health

During disease or toxin challenges, the behavioral activities of grazing animals alter in response to adverse situations, potentially providing an indicator of their welfare status. Behavioral changes such as feeding behavior, rumination and physical behavior as well as expressive behavior, can serve as indicators of animal health and welfare. Sometimes behavioral changes are subtle and occur gradually, often missed by infrequent visual monitoring until the condition becomes acute. There is growing popularity in the use of sensors for monitoring animal health. Acceleration sensors have been designed to attach to ears, jaws, noses, collars and legs to detect the behavioral changes of cattle and sheep. So far, some automated acceleration sensors with high accuracies have been found to have the capacity to remotely monitor the behavioral patterns of cattle and sheep. These acceleration sensors have the potential to identify behavioral patterns of farm animals for monitoring changes in behavior which can indicate a deterioration in health. Here, we review the current automated accelerometer systems and the evidence they can detect behavioral patterns of animals for the application of potential directions and future solutions for automatically monitoring and the early detection of health concerns in grazing animals.

Identification of Changes in Rumination Behavior Registered with an Online Sensor System in Cows with Subclinical Mastitis

The aim of the present study is to determine the relationship between subclinical mastitis and rumination behavior registered with an online sensor system. Based on the findings of the general clinical examination of 650 milking cows, 10 cows with subclinical mastitis (SCM) and 10 clinically healthy cows (HG) were selected (without clinical signs of any diseases). Rumination behavior biomarkers were registered with RumiWatch noseband sensors (RWS; ITIN + HOCH GmbH, Fütterungstechnik, Liestal, Switzerland). Sensors were implanted on the first day after calving. Data from the RWS 13 days before diagnosis of SCM and 13 days after diagnosis of SCM were compared with HG data from the same period. Healthy cows were checked alongside SCM cows on the same days. In our study, we found that healthy cows spent more time engaging in rumination and drinking (p < 0.05) and also had more boluses per rumination. Moreover, among cows with subclinical mastitis, rumination time from day 4 to day 0 decreased by 60.91%, drinking time decreased by 48.47%, and the number of boluses per rumination decreased by 8.67% (p < 0.05). The results indicate that subclinical affects time and rumination chews registered with sensor systems. However, additional studies with larger numbers of animals are required to confirm these results. Furthermore, the impact of heat stress, estrus, and other effects on rumination behavior biomarkers should be evaluated.

High Precision Classification of Resting and Eating Behaviors of Cattle by Using a Collar-Fitted Triaxial Accelerometer Sensor

Cattle are less active than humans. Hence, it was hypothesized in this study that transmitting acceleration signals at a 1 min sampling interval to reduce storage load has the potential to improve the performance of motion sensors without affecting the precision of behavior classification. The behavior classification performance in terms of precision, sensitivity, and the F1-score of the 1 min serial datasets segmented in 3, 4, and 5 min window sizes based on nine algorithms were determined. The collar-fitted triaxial accelerometer sensor was attached on the right side of the neck of the two fattening Korean steers (age: 20 months) and the steers were observed for 6 h on day one, 10 h on day two, and 7 h on day three. The acceleration signals and visual observations were time synchronized and analyzed based on the objectives. The resting behavior was most correctly classified using the combination of a 4 min window size and the long short-term memory (LSTM) algorithm which resulted in 89% high precision, 81% high sensitivity, and 85% high F1-score. High classification performance (79% precision, 88% sensitivity, and 83% F1-score) was also obtained in classifying the eating behavior using the same classification method (4 min window size and an LSTM algorithm). The most poorly classified behavior was the active behavior. This study showed that the collar-fitted triaxial sensor measuring 1 min serial signals could be used as a tool for detecting the resting and eating behaviors of cattle in high precision by segmenting the acceleration signals in a 4 min window size and by using the LSTM classification algorithm.

Evaluation of locomotor activity in female Chelonoidis chilensis (Testudinidae, Gray 1870) in response to artificial photoperiod and temperature treatments

Turtles as many other reptiles are capable of orientating their bodies toward the sun. This conduct requires the presence of an internal biological chronometer in the organism that regulates this behavior. Thus, a description of the internal clock in these reptiles is of interest. The assessment of locomotor activity can be considered a reliable indicator of biological clock function. This study aimed to investigate the effect of different artificial photoperiod and ambient temperature schedules on total locomotor activity of female Chelonoidis chilensis and its rhythmicity. Six C. chilensis specimens were exposed to different artificial photoperiods and temperature regimes each fixed for seven days. It was observed that the activity period during the different experimental schedules was close to the 24 hours indicating a daily rhythmicity. Moreover, all tortoises showed a similar total locomotor activity pattern displaying the most of motion during light phase. Under the condition of constant light tortoises exhibited a self-sustaining rhythm not entrained to light and temperature zeitgebers, thus, suggesting its possible endogenous periodicity. Though this study deepens the knowledge on the rhythmic system of C. chilensis, further investigations are needed to achieve a more detailed understanding of tortoise biological clock.

Locomotion behavior of dairy cows on traditional summer mountain farms in comparison with modern cubicle housing without access to pasture

Pasture based systems enable cattle to express their natural behavior and are thus expected to provide better welfare than the majority of confinement systems. The aim of this study was to objectively measure locomotion activity of healthy dairy cattle kept on mountain pastures (n = 44) compared with cows kept in cubicle housing systems (n = 38). Selected cows were equipped with a validated 3D-accelerometer on one hind limb, and locomotion behavior was recorded for 48 hours. The 1-hour summaries of the variables lying time, walking time, standing bouts, walking bouts and number of strides were summed up to 24-hour summaries, and the means of the stride distance and stride duration were weighted by the variable number of strides. Mountain pasture cows had higher locomotor activity levels in comparison to cubicle cows. Mountain pasture cows spent less time lying down (528.1±109.5 min/day vs. 693.3±73.8 min/day; P<0.0001) and more time walking (75.6±25.9 min/day vs. 38.8±15.8 min/day; P <0.0001) than cubicle cows. Lying bout duration was longer in cubicle than in mountain pasture cows (90.9± 15.2 min/bout vs. 74.2 ± 21.1 min/bout; P = 0.0001), whilst the number of walking bouts was higher in mountain pasture cows than cubicle cows (199.1 ± 49.1 vs. 123.8 ± 43.8 bouts per day; P < 0.001). Likewise, the number of strides was higher in mountain pasture cows than cubicle cows (2040.5 ± 825.3 vs. 916.7 ± 408.6; P < 0.001). Mountain pasture cows had shorter stride duration (P < 0.0001) and shorter strides (P = 0.0002) than cubicle cows (1.8 ± 0.1 s/stride vs 2 ± 0.2 s/stride and 126.3 ± 18.1 vs 142.1 ± 17.8 m/stride, respectively). In summary, cows kept on mountain pasture were more active and spent longer than 12 hours / day standing. Lying markedly less than 12 hours per day seems to represent the normal behavior of pastured cows searching for fresh grass. This does not cause any obvious damage to the locomotor system as claws of cattle are well adapted to long periods of movement on mountain pastures.

Wearable Wireless Biosensor Technology for Monitoring Cattle: A Review

The review aimed to collect information about the wearable wireless sensor system (WWSS) for cattle and to conduct a systematic literature review on the accuracy of predicting the physiological parameters of these systems. The WWSS was categorized as an ear tag, halter, neck collar, rumen bolus, leg tag, tail-mounted, and vaginal mounted types. Information was collected from a web-based search on Google, then manually curated. We found about 60 WWSSs available in the market; most sensors included an accelerometer. The literature evaluating the WWSS performance was collected through a keyword search in Scopus. Among the 1875 articles identified, 46 documents that met our criteria were selected for further meta-analysis. Meta-analysis was conducted on the performance values (e.g., correlation, sensitivity, and specificity) for physiological parameters (e.g., feeding, activity, and rumen conditions). The WWSS showed high performance in most parameters, although some parameters (e.g., drinking time) need to be improved, and considerable heterogeneity of performance levels was observed under various conditions (average I² = 76%). Nevertheless, some of the literature provided insufficient information on evaluation criteria, including experimental conditions and gold standards, to confirm the reliability of the reported performance. Therefore, guidelines for the evaluation criteria for studies evaluating WWSS performance should be drawn up.

Training and Validating a Machine Learning Model for the Sensor-Based Monitoring of Lying Behavior in Dairy Cows on Pasture and in the Barn

Simple Summary

There are various systems available for health monitoring and heat detection in dairy cows. By continuously monitoring different behavioral patterns (e.g., lying, ruminating, and feeding), these systems detect behavioral changes linked to health disorders and estrous. Most of the systems were developed for cows kept indoors, and only a few systems are available for pasture-based farms. The systems developed for the barn failed to detect the targeted behavior and thereby its changes on the pasture and vice versa. Therefore, our goal was to train and validate a machine learning model for the automated prediction of lying behavior in dairy cows kept on pastures, as well as indoors. Data collection was conducted on three dairy farms where cows were equipped with the collar-based prototype of the monitoring system and recorded with cameras in parallel. The derived dataset was used to develop the machine learning model. The model performed well in predicting lying behavior in dairy cows both on the pasture and in the barn. Therefore, the building of the model presents a successful first step towards the development of a monitoring system for dairy cows kept on pasture and in the barn.

Abstract

Monitoring systems assist farmers in monitoring the health of dairy cows by predicting behavioral patterns (e.g., lying) and their changes with machine learning models. However, the available systems were developed either for indoors or for pasture and fail to predict the behavior in other locations. Therefore, the goal of our study was to train and evaluate a model for the prediction of lying on a pasture and in the barn. On three farms, 7–11 dairy cows each were equipped with the prototype of the monitoring system containing an accelerometer, a magnetometer and a gyroscope. Video observations on the pasture and in the barn provided ground truth data. We used 34.5 h of datasets from pasture for training and 480.5 h from both locations for evaluating. In comparison, random forest, an orientation-independent feature set with 5 s windows without overlap, achieved the highest accuracy. Sensitivity, specificity and accuracy were 95.6%, 80.5% and 87.4%, respectively. Accuracy on the pasture (93.2%) exceeded accuracy in the barn (81.4%). Ruminating while standing was the most confused with lying. Out of individual lying bouts, 95.6 and 93.4% were identified on the pasture and in the barn, respectively. Adding a model for standing up events and lying down events could improve the prediction of lying in the barn.

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.

Description of Behavioral Patterns Displayed by a Recently Weaned Cohort of Healthy Dairy Calves

Simple Summary

Modern technology has allowed researchers to track the movement patterns of cattle with increasing accuracy in order to gain a greater understanding of both overt and subtle activity trends. The aim of this study was to describe and analyze movement patterns displayed by recently weaned and healthy dairy calves. Three movement pattern clusters were identified, and calves in this study were more active in the afternoon and at night. There was a correlation between the rate of movement, linearity ratio, and the distance traveled. However, turning angles do not have any influence on the distance traveled and the rate of movement across the three cluster-type movements. The findings reported in this study could be used to further develop the interpretation of movement and behavior patterns of calves in order to establish an early detection system for poor health and welfare on dairy farms.

Abstract

Animals display movement patterns that can be used as health indicators. The movement of dairy cattle can be characterized into three distinct cluster types. These are cluster type 1 (resting), cluster type 2 (traveling), and cluster type 3 (searching). This study aimed to analyze the movement patterns of healthy calves and assess the relationship between the variables that constitute the three cluster types. Eleven Holstein calves were fitted with GPS data loggers, which recorded their movement over a two week period during spring. The GPS data loggers captured longitude and latitude coordinates, distance, time and speed. It was found that the calves were most active during the afternoon and at night. Slight inconsistencies from previous studies were found in the cluster movements. Cluster type 2 (traveling) reported the fastest rate of movement, whereas cluster type 1 (resting) reported the slowest. These diverse movement patterns could be used to enhance the assessment of dairy animal health and welfare on farms.

Slightly and Moderately Lame Cows in Tie Stalls Behave Differently From Non-lame Controls. A Matched Case-Control Study

Lameness affects dairy cows worldwide and is usually associated with pain. Behavioral differences in lame compared to non-lame tie-stall-housed dairy cows might be less pronounced than in free-stall-housed, since the principle demands to a cow's locomotor system and thus the impact of lameness on behavior seem to be lower in tie stalls. Behavioral differences between lame and non-lame cows might be used to estimate the impact of lameness on the well-being of tied dairy cows. In the current study, lame cows were categorized as locomotion scoring between 2.25 and 3.25 on a 1–5 scale. The aim was to compare the eating, rumination and lying behavior of lame cows against non-lame tied dairy cows, in order to draw conclusions on the association of lameness, behavior and well-being in tied dairy cows. The eating and rumination behavior of 26, the lying behavior of 30, and the relative upright and lying activities of 25 matched case-control pairs were analyzed, considering the matching criteria farm, breed-type, and parity-group. Lame cows had fewer [mean of the pairwise differences (case–control) (mean_diff) = −2.6 bouts, CI_95% (−3.8–−1.4) bouts, p = 0.001], but longer lying bouts [mean_diff = 26.7 min per bout, CI_95% (10.1–43.4) min per bout, p = 0.006]. The lying time was shorter [mean_diff = −64.7 min, CI_95% (−104.4–−24.9) min, p = 0.006] in lame cows compared to their non-lame controls. Lame cows had a shorter eating time [mean_diff = −27.7 min, CI_95% (−51.5–−4.0) min, p = 0.042] and spent a larger proportion of their upright time ruminating [mean_diff = 7.2%, CI_95% (3.2–11.1)%, p = 0.001] instead of eating. The results of the current study indicate that the eating, rumination, and lying behavior of lame tied dairy cows is altered. These findings indicate that slight and moderate lameness (locomotion score between 2.25 and 3.25 on a 1–5 scale) are likely to be associated with an impaired well-being in affected tied dairy cows. This underlines the need to continuously reduce the lameness prevalence and severity in tied dairy herds.

Accelerometer systems as tools for health and welfare assessment in cattle and pigs - A review

Welfare assessment has traditionally been performed by direct observation by humans, providing information at only selected points in time. Recently, this assessment method has been questioned, as 'Precision Livestock Farming' technologies may be able to deliver more valid, reliable and feasible real-time data at the individual level and serve as early monitoring systems for animal welfare. The aim of this paper is to describe how accelerometers can be used for welfare assessment based on the principles of the Welfare Quality assessment protocol. Algorithm development is based mainly on the detection of behavioural traits. So far, high accuracies have been found for movement and resting behaviours in cows and pigs, while algorithm development for feeding and drinking behaviours in pigs lag behind progress in cows where valid algorithms are already available. Welfare studies have used accelerometer technology to address the effects on behaviour of diet, daily cycle, enrichment, housing, social mixing, oestrus, lameness and disease. Additional aspects to consider before a decision is made upon its use in research and in practical applications include battery life and sensor location. While accelerometer systems for cows are already being used by farmers, application in pigs has mainly remained at the research level.

Grazing Cow Behavior's Association with Mild and Moderate Lameness

Accelerometer-based mobility scoring has focused on cow behaviors such as lying and walking. Accuracy levels as high as 91% have been previously reported. However, there has been limited replication of results. Here, measures previously identified as indicative of mobility, such as lying bouts and walking time, were examined. On a research farm and a commercial farm, 63 grazing cows' behavior was monitored in four trials (16, 16, 16, and 15 cows) using leg-worn accelerometers. Seventeen good mobility (score 0), 23 imperfect mobility (score 1), and 22 mildly impaired mobility (score 2) cows were monitored. Only modest associations with activity, standing, and lying events were found. Thus, behavior monitoring appears to be insufficient to discern mildly and moderately impaired mobility of grazing cows.

The new design of cows' behavior classifier based on acceleration data and proposed feature set

Monitor and classify behavioral activities in cows is a helpful support solution for livestock based on the analysis of data from sensors attached to the animal. Accelerometers are particularly suited for monitoring cow behaviors due to small size, lightweight and high accuracy. Nevertheless, the interpretation of the data collected by such sensors when characterizing the type of behaviors still brings major challenges to developers, related to activity complexity (i.e., certain behaviors contain similar gestures). This paper presents a new design of cows' behavior classifier based on acceleration data and proposed feature set. Analysis of cow acceleration data is used to extract features for classification using machine learning algorithms. We found that with 5 features (mean, standard deviation, root mean square, median, range) and 16-second window of data (1 sample/second), classification of seven cow behaviors (including feeding, lying, standing, lying down, standing up, normal walking, active walking) achieved the overall highest performance. We validated the results with acceleration data from a public source. Performance of our proposed classifier was evaluated and compared to existing ones in terms of the sensitivity, the accuracy, the positive predictive value, and the negative predictive value.

Machine Learning Based Prediction of Insufficient Herbage Allowance with Automated Feeding Behaviour and Activity Data

Sensor technologies that measure grazing and ruminating behaviour as well as physical activities of individual cows are intended to be included in precision pasture management. One of the advantages of sensor data is they can be analysed to support farmers in many decision-making processes. This article thus considers the performance of a set of RumiWatchSystem recorded variables in the prediction of insufficient herbage allowance for spring calving dairy cows. Several commonly used models in machine learning (ML) were applied to the binary classification problem, i.e., sufficient or insufficient herbage allowance, and the predictive performance was compared based on the classification evaluation metrics. Most of the ML models and generalised linear model (GLM) performed similarly in leave-out-one-animal (LOOA) approach to validation studies. However, cross validation (CV) studies, where a portion of features in the test and training data resulted from the same cows, revealed that support vector machine (SVM), random forest (RF) and extreme gradient boosting (XGBoost) performed relatively better than other candidate models. In general, these ML models attained 88% AUC (area under receiver operating characteristic curve) and around 80% sensitivity, specificity, accuracy, precision and F-score. This study further identified that number of rumination chews per day and grazing bites per minute were the most important predictors and examined the marginal effects of the variables on model prediction towards a decision support system.

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.

Automatic lameness detection in cattle

There is an increasing demand for health and welfare monitoring in modern dairy farming. The development of various innovative techniques aims at improving animal behaviour monitoring and thus animal welfare indicators on-farm. Automated lameness detection systems have to be valid, reliable and practicable to be applied in veterinary practice or under farm conditions. The objective of this literature review was to describe the current automated systems for detection of lameness in cattle, which have been recently developed and investigated for application in dairy research and practice. The automatic methods of lameness detection broadly fall into three categories: kinematic, kinetic and indirect methods. The performance of the methods were compared with the reference standard (locomotion score and/or lesion score) and evaluated based on level-based scheme defining the degree of development (level I, sensor technique; level II, validation of algorithm; level III, performance for detection of lameness and/or lesion; level IV, decision support with early warning system). Many scientific studies have been performed on levels I-III, but there are no studies of level IV technology. The adoption rate of automated lameness detection systems by herd managers mainly yields returns on investment by the early identification of lame cows. Long-term studies, using validated automated lameness detection systems aiming at early lameness detection, are still needed in order to improve welfare and production under field conditions.

Application of accelerometers to record drinking behaviour of beef cattle

Accelerometers have been used to record many cattle postures and behaviours including standing, lying, walking, grazing and ruminating but not cattle drinking behaviour. This study explores whether neck-mounted triaxial accelerometers can identify drinking and whether head-neck position and activity can be used to record drinking. Over three consecutive days, data were collected from 12 yearling Brahman cattle each fitted with a collar containing an accelerometer. Each day the cattle were herded into a small yard containing a water trough and allowed 5 min to drink. Drinking, standing (head up), walking and standing (head down) were recorded. Examination of the accelerometer data showed that drinking events were characterised by a unique signature compared with the other behaviours. A linear mixed-effects model identified two variables that reflected differences in head-neck position and activity between drinking and the other behaviours: mean of the z- (front-to-back) axis and variance of the x- (vertical) axis (P &lt; 0.05). Threshold values, derived from Kernel density plots, were applied to classify drinking from the other behaviours using these two variables. The method accurately classified drinking from standing (head up) with 100% accuracy, from walking with 92% accuracy and from standing (head down) with 79% accuracy. The study shows that accelerometers have the potential to record cattle drinking behaviour. Further development of a classification method for drinking is required to allow accelerometer-derived data to be used to improve our understanding of cattle drinking behaviour and ensure that their water intake needs are met.

Smart Animal Agriculture: Application of Real-Time Sensors to Improve Animal Well-Being and Production

Consumption of animal products such as meat, milk, and eggs in first-world countries has leveled off, but it is rising precipitously in developing countries. Agriculture will have to increase its output to meet demand, opening the door to increased automation and technological innovation; intensified, sustainable farming; and precision livestock farming (PLF) applications. Early indicators of medical problems, which use sensors to alert cattle farmers early concerning individual animals that need special care, are proliferating. Wearable technologies dominate the market. In less-value-per-animal systems like sheep, goat, pig, poultry, and fish, one sensor, like a camera or robot per herd/flock/school, rather than one sensor per animal, will become common. PLF sensors generate huge amounts of data, and many actors benefit from PLF data. No standards currently exist for sharing sensor-generated data, limiting the use of commercial sensors. Technologies providing accurate data can enhance a well-managed farm. Development of methods to turn the data into actionable solutions is critical.

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.

Development and validation of an ensemble classifier for real-time recognition of cow behavior patterns from accelerometer data and location data

Behaviors are important indicators for assessing the health and well-being of dairy cows. The aim of this study is to develop and validate an ensemble classifier for automatically measuring and distinguishing several behavior patterns of dairy cows from accelerometer data and location data. The ensemble classifier consists of two parts, our new Multi-BP-AdaBoost algorithm and a data fusion method based on D-S evidence theory. We identify seven behavior patterns: feeding, lying, standing, lying down, standing up, normal walking, and active walking. Accuracy, sensitivity, and precision were used to validate classification performance. The Multi-BP-AdaBoost algorithm performed well when identifying lying (92% accuracy, 93% sensitivity, 82% precision), lying down (99%, 82%, 86%), standing up (99%, 74%, 85%), normal walking (97%, 92%, 86%), and active walking (99%, 94%, 89%). Its results were poor for feeding (80%, 52%, 55%) and standing (80%, 46%, 58%), which are difficult to differentiate using a leg-mounted sensor. Position data made it possible to differentiate feeding and standing. The D-S evidence fusion method for combining accelerometer data and location data in classification was used to fuse two pieces of basic behavior-related evidence into a single estimation model. With this addition, the sensitivity and precision of the two difficult behaviors increased by approximately 20 percentage points. In conclusion, the classification results indicate that the ensemble classifier effectively recognizes various behavior patterns in dairy cows. However, further work is needed to study the robustness of the feature and model by increasing the number of cows enrolled in the trial.

Physiological and behavioural responses of grazing dairy cows to an acute metabolic challenge

Due to seasonal changes in the quality and quantity of herbage, the nutrient supply to grazing dairy cows is not always sufficient, which may increase their metabolic load. To investigate the temporal pattern of behavioural changes in relation to concomitant metabolic alterations, we subjected 15 multiparous early lactating Holstein dairy cows (24 (SD 7.4) days in milk) to a short-term metabolic challenge, which we provoked by abruptly withdrawing concentrate for 1 week. Cows grazed full-time and were supplemented with concentrate in experimental week (EW) 1 and EW 3, whereas concentrate was withdrawn in EW 2. We analysed milk and blood samples to characterise the metabolic changes and found that the total yield of milk and protein decreased (p < 0.05) and fat yield, fat-to-protein ratio and acetone content increased (p < 0.05) from EW 1 to EW 2. Plasma glucose and insulin concentrations were lower (p < 0.05), and concentrations of nonesterified fatty acids and beta-hydroxybutyrate were higher (p < 0.05) in EW 2 compared with EW 1. Apart from ingestive and rumination behaviour and activity, we also monitored the use of an automated brush on pasture. While time spent eating and ruminating increased (p < 0.05) in EW 2 compared with EW 1, time spent idling decreased (p < 0.05). Concomitantly, while time standing and moving increased (p < 0.05) from EW 1 to EW 2, walking time decreased (p < 0.05). The daily proportion of cows using the automated brush decreased (p < 0.05) in EW 2 compared with EW 1, as did the duration of brushing per day. In conclusion, grazing cows experiencing a metabolic challenge try to compensate for the nutrient deficiency by increasing eating time, a behavioural element important for short-term survival. Due to the strong impact of weather conditions, we cannot currently recommend observation of outdoor brushing activity to address short-term alterations in the metabolic state of grazing cows.

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.

Validation of a pedometer algorithm as a tool for evaluation of locomotor behaviour in dairy Mediterranean buffalo

This research communication validates an algorithm to monitor natural occurrence of locomotor behaviours in dairy Mediterranean buffalo based on the output of a 3-dimensional accelerometer (RumiWatch®, pedometer). Several characteristics of the locomotor behaviour were detected with a very high (up-right, lying and standing time) or high degree of correlation (walking time and number of strides) and a low mean difference with the video recording. The proportion of correctly detected events exceeded 99 % for the following variables: stand up and lie down events, as well as number of lying, standing or walking bouts. The mean relative measurement error was less than 10 % for the variables: lying, standing, up-right times and number of strides as compared with gold standard. This new algorithm may represent the base for a future early and real-time disease warning system aiming to gain higher health standard in these ruminants.

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.

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.

Experimentally induced subclinical mastitis: are lipopolysaccharide and lipoteichoic acid eliciting similar pain responses?

Background

Pain accompanying mastitis has gained attention recently as a relevant welfare compromising aspect of disease. Adequate pain recognition and therapy are necessary in the context of a modern and ethically acceptable dairy care. For research purposes mastitis is often induced by intramammary infusion of immunogenic bacterial cell wall components. Lipopolysaccharide (LPS) from Escherichia coli and lipoteichoic acid (LTA) from Staphylococcus aureus are commonly administered to this end. While the immune response to specific immunogenic components has been well characterized, not much is known about their role on the expression of pain indicators. The aim of this study was to trial the effects of an intramammary challenge of LTA or LPS on the degree of pain and discomfort as indicated by both physiological and behavioral variables in cows. The hypothesis was that a similar degree of pain can be identified in LTA as well as in LPS induced mastitis.

Results

On the challenge day, compared to pre-challenge, total pain index increased for all treatment groups (LPS; LTA and control), the LPS group having significantly higher values than the control group (P = 0.01). Similarly, pain visual analogue scale (VAS) increased significantly in all cows following treatment on the challenge day. Furthermore, compared to baseline, higher VAS were found 3, 4 and 5 h after the challenge in cows of the LPS group (P_{3h, 4h} < 0.001 and P_5h = 0.001) and 7 h after the challenge in cows of the LTA group (P_7h = 0.002). In the control group, VAS was higher 5 h after the challenge (P_5h = 0.001). On the challenge day, udder edema was higher in the LPS than in the control group (P = 0.007). Furthermore, 4 h after the challenge, milk cortisol was significantly higher than at baseline in the LPS group (P < 0.001).

Conclusions

When administered at equipotent doses targeting a standard somatic cell count increase, intramammary LPS seems to be accompanied by a higher degree of pain and discomfort than LTA, as suggested by the modifications of the outcome variables total pain index, VAS, udder edema and milk cortisol.

Electronic supplementary material

The online version of this article (doi:10.1186/s13028-017-0306-z) contains supplementary material, which is available to authorized users.

The cow pedogram-Analysis of gait cycle variables allows the detection of lameness and foot pathologies

Changes in gait characteristics are important indicators in assessing the health and welfare of cattle. The aim of this study was to detect unilateral hind limb lameness and foot pathologies in dairy cows using 2 high-frequency accelerometers (400 Hz). The extracted gait cycle variables included temporal events (kinematic outcome = gait cycle, stance phase, and swing phase duration) and several peaks (kinetic outcome = foot load, toe-off). The study consisted of 2 independent experiments. Experiment 1 was carried out to compare the pedogram variables between the lateral claw and respective metatarsus (MT; n = 12) in sound cows (numerical rating system <3, n = 12) and the differences of pedogram variables across limbs within cows between lame cows (numerical rating system ≥3, n = 5) and sound cows (n = 12) using pedogram data that were visually compared with the synchronized cinematographic data. Experiment 2 was carried out to determine the differences across limbs within cows between cows with foot lesions (n = 12) and without foot lesions (n = 12) using only pedogram data. A receiver operator characteristic analysis was used to determine the performance of selected pedogram variables at the cow level. The pedogram of the lateral claw of sound cows revealed similarities of temporal events (gait cycle duration, stance and swing phases) but higher peaks (toe-off and foot load) as compared with the pedogram of the respective MT. In both experiments, comparison of the values between groups showed significantly higher values in lame cows and cows with foot lesions for all gait cycle variables. The optimal cutoff value of the relative stance phase duration for identifying lame cows was 14.79% and for cows with foot lesions was 2.53% with (both 100% sensitivity and 100% specificity) in experiments 1 and 2, respectively. The use of accelerometers with a high sampling rate (400 Hz) at the level of the MT is a promising tool to indirectly measure the kinematic variables of the lateral claw and to detect unilateral hind limb lameness and hind limb pathologies in dairy cows and is highly accurate.

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.