Matti Pastell

CowLog: open-source software for coding behaviors from digital video.

We have developed CowLog, which is open-source software for recording behaviors from digital video and is easy to use and modify. CowLog tracks the time code from digital video files. The program is suitable for coding any digital video, but the authors have used it in animal research. The program has two main windows: a coding window, which is a graphical user interface used for choosing video files and defining output files that also has buttons for scoring behaviors, and a video window, which displays the video used for coding. The windows can be used in separate displays. The user types the key codes for the predefined behavioral categories, and CowLog transcribes their timing from the video time code to a data file. CowLog comes with an additional feature, an R package called Animal, for elementary analyses of the data files. With the analysis package, the user can calculate the frequencies, bout durations, and total durations of the coded behaviors and produce summary plots from the data.

Detection of Clinical Mastitis with the Help of a Thermal Camera

Increasing dairy farm size and increase in automation in livestock production require that new methods are used to monitor animal health. In this study, a thermal camera was tested for its capacity to detect clinical mastitis. Mastitis was experimentally induced in 6 cows with 10 microg of Escherichia coli lipopolysaccharide (LPS). The LPS was infused into the left forequarter of each cow, and the right forequarters served as controls. Clinical examination for systemic and local signs and sampling for indicators of inflammation in milk were carried out before morning and evening milking throughout the 5-d experimental period and more frequently on the challenge day. Thermal images of experimental and control quarters were taken at each sampling time from lateral and medial angles. The first signs of clinical mastitis were noted in all cows 2 h postchallenge and included changes in general appearance of the cows and local clinical signs in the affected udder quarter. Rectal temperature, milk somatic cell count, and electrical conductivity were increased 4 h postchallenge and milk N-acetyl-beta-D-glucosaminidase activity 8 h postchallenge. The thermal camera was successful in detecting the 1 to 1.5 degrees C temperature change on udder skin associated with clinical mastitis in all cows because temperature of the udder skin of the experimental and control quarters increased in line with the rectal temperature. Yet, local signs on the udder were seen before the rise in udder skin and body temperature. The udder represents a sensitive site for detection of any febrile disease using a noninvasive method. A thermal camera mounted in a milking or feeding parlor could detect temperature changes associated with clinical mastitis or other diseases in a dairy herd.

Measurement of acceleration while walking as an automated method for gait assessment in dairy cattle

The aims were to determine whether measures of acceleration of the legs and back of dairy cows while they walk could help detect changes in gait or locomotion associated with lameness and differences in the walking surface. In 2 experiments, 12 or 24 multiparous dairy cows were fitted with five 3-dimensional accelerometers, 1 attached to each leg and 1 to the back, and acceleration data were collected while cows walked in a straight line on concrete (experiment 1) or on both concrete and rubber (experiment 2). Cows were video-recorded while walking to assess overall gait, asymmetry of the steps, and walking speed. In experiment 1, cows were selected to maximize the range of gait scores, whereas no clinically lame cows were enrolled in experiment 2. For each accelerometer location, overall acceleration was calculated as the magnitude of the 3-dimensional acceleration vector and the variance of overall acceleration, as well as the asymmetry of variance of acceleration within the front and rear pair of legs. In experiment 1, the asymmetry of variance of acceleration in the front and rear legs was positively correlated with overall gait and the visually assessed asymmetry of the steps (r ≥ 0.6). Walking speed was negatively correlated with the asymmetry of variance of the rear legs (r=-0.8) and positively correlated with the acceleration and the variance of acceleration of each leg and back (r ≥ 0.7). In experiment 2, cows had lower gait scores [2.3 vs. 2.6; standard error of the difference (SED)=0.1, measured on a 5-point scale] and lower scores for asymmetry of the steps (18.0 vs. 23.1; SED=2.2, measured on a continuous 100-unit scale) when they walked on rubber compared with concrete, and their walking speed increased (1.28 vs. 1.22 m/s; SED=0.02). The acceleration of the front (1.67 vs. 1.72 g; SED=0.02) and rear (1.62 vs. 1.67 g; SED=0.02) legs and the variance of acceleration of the rear legs (0.88 vs. 0.94 g; SED=0.03) were lower when cows walked on rubber compared with concrete. Despite the improvements in gait score that occurred when cows walked on rubber, the asymmetry of variance of acceleration of the front leg was higher (15.2 vs. 10.4%; SED=2.0). The difference in walking speed between concrete and rubber correlated with the difference in the mean acceleration and the difference in the variance of acceleration of the legs and back (r ≥ 0.6). Three-dimensional accelerometers seem to be a promising tool for lameness detection on farm and to study walking surfaces, especially when attached to a leg.

Measures of weight distribution of dairy cows to detect lameness and the presence of hoof lesions

There is increasing interest in automated methods of detecting lame cows. Hoof lesion data and measures of weight distribution from 61 lactating cows were examined in this study. Lame cows were identified with different numerical rating scores (NRS) used as thresholds (NRS >3 and NRS >or=3.5) for lameness. The ratio of weight applied to a pair of legs (LWR) when the cow was standing was calculated using a special weigh scale, and the cows were gait scored using a 1 to 5 NRS. Hoof lesions were scored and the cows placed into 1 of 4 mutually exclusive categories of hoof lesion: a) no lesions, b) moderate or severe hemorrhages, c) digital dermatitis, and d) sole ulcers. Regression analysis and receiver operating characteristic (ROC) curves were used to analyze the relation between hoof lesions and LWR. A clear relationship was found between NRS and LWR for the cows with sole ulcers (R(2)=0.79). The LWR could differentiate cows with sole ulcers from sound cows with no hoof lesions [area under the curve (AUC)=0.87] and lame cows from nonlame cows with lameness thresholds NRS >3 (AUC=0.71) and NRS >or=3.5 (AUC=0.88). There was no relationship between LWR and NRS for cows with digital dermatitis. Measurement of how cows distribute their weight when standing holds promise as a method of automated detection of lameness.

Short communication: Lameness impairs feeding behavior of dairy cows

The automated, reliable, and early detection of lameness is an important aim for the future development of modern dairy operations. One promising indicator of lameness is a change in the feeding behavior of a cow. In this study, the associations between feeding behavior and lameness were evaluated. A herd of 50 cows was investigated during the winter season in a freestall barn. Feeding behavior, feed intake, milk yield, and body weight were monitored using electronic feeding troughs and an automated milking system. Gait scoring every second week was used as a measure of lameness. To analyze the effect of lameness on feeding behavior and milk yield, linear mixed models were used. Cows with more severe lameness spent less time feeding per day (104 ± 4, 101 ± 4, and 91 ± 4 min/d for lameness scores 2, 3, and 4, respectively). An interaction between parity and lameness score was detected, with severely lame primiparous cows spending the least time feeding. Severely lame cows fed faster; however, their body weights were lower than for less-lame cows. Increase in lactation stage was associated with longer daily feeding time, longer duration of feeding bouts, and lower feeding rate. Worsening of gait was associated with lower silage intake and less time spent feeding even before severe lameness was scored. The results indicate that lameness is associated with changes in feeding behavior and that such changes could be considered in the future development of remote monitoring systems. It should also be noted that impaired feeding behavior along with lameness can put the welfare of especially early lactating primiparous cows at risk.

Application of CUSUM charts to detect lameness in a milking robot

In the year 2006 about 4000 farms worldwide used over 6000 milking robots. With increased automation the time that the cattle keeper uses for monitoring animals has decreased. This has created a need for automatic health monitoring systems. Lameness is a crucial welfare and economic issue in modern dairy husbandry. It causes problems especially in loose housing of cattle. This could be greatly reduced by early identification and treatment. A four-balance system for automatically measuring the load on each leg of a cow during milking in a milking robot has been developed. It has been previously shown that the weight distribution between limbs changes when cow get lame. In this paper we suggest CUSUM charts to automatically detect lameness based on the measurements. CUSUM charts are statistical based control charts and are well suited for checking a measuring system in operation for any departure from some target or specified values. The target values for detecting lameness were calculated from the cows own historical data so that each animal had an individual chart. The method enables objective monitoring of the changes in leg health, which is valuable information in veterinary research because it provides means for assessing the severity and impact of different causes of lameness and also evaluating the effect of treatment and medication. So far no objective method for calculating these measures has been available and the methodology presented in this paper seems very promising for the task.

Lameness Detection in Dairy Cows: Part 2. Use of Sensors to Automatically Register Changes in Locomotion or Behavior.

Simple Summary As lame cows produce less milk and tendto have other health problems, finding and treating lame cows is very importantfor farmers. Sensors that measure behaviors associated with lameness in cowscan help by alerting the farmer of those cows in need of treatment. This reviewgives an overview of sensors for automated lameness detection and discussessome practical considerations for investigating and applying such systems inpractice. Abstract Despite the research on opportunities toautomatically measure lameness in cattle, lameness detection systems are notwidely available commercially and are only used on a few dairy farms. However, farmers need to be aware of the lame cows in their herds in order treat themproperly and in a timely fashion. Many papers have focused on the automatedmeasurement of gait or behavioral cow characteristics related to lameness. Inorder for such automated measurements to be used in a detection system, algorithms to distinguish between non-lame and mildly or severely lame cowsneed to be developed and validated. Few studies have reached this latter stageof the development process. Also, comparison between the different approachesis impeded by the wide range of practical settings used to measure the gait or behavioralcharacteristic (e.g., measurements during normal farming routine or duringexperiments; cows guided or walking at their own speed) and by the differentdefinitions of lame cows. In the majority of the publications, mildly lame cowsare included in the non-lame cow group, which limits the possibility of alsodetecting early lameness cases. In this review, studies that used sensortechnology to measure changes in gait or behavior of cows related to lamenessare discussed together with practical considerations when conducting lamenessresearch. In addition, other prerequisites for any lameness detection system onfarms (e.g., need for early detection, real-time measurements) are discussed.

Lameness Detection in Dairy Cows: Part 1. How to Distinguish between Non-Lame and Lame Cows Based on Differences in Locomotion or Behavior

Simple Summary Scoring cattle for lameness based on changes in locomotion or behavior is essential for farmers to find and treat their lame animals. This review discusses the normal locomotion of cows in order to define abnormal locomotion due to lameness. It furthermore provides an overview of various relevant visual locomotion scoring systems that are currently being used as well as practical considerations when assessing lameness on a commercial farm. Abstract Due to its detrimental effect on cow welfare, health and production, lameness in dairy cows has received quite a lot of attention in the last few decades—not only in terms of prevention and treatment of lameness but also in terms of detection, as early treatment might decrease the number of severely lame cows in the herds as well as decrease the direct and indirect costs associated with lameness cases. Generally, lame cows are detected by the herdsman, hoof trimmer or veterinarian based on abnormal locomotion, abnormal behavior or the presence of hoof lesions during routine trimming. In the scientific literature, several guidelines are proposed to detect lame cows based on visual interpretation of the locomotion of individual cows (i.e., locomotion scoring systems). Researchers and the industry have focused on automating such observations to support the farmer in finding the lame cows in their herds, but until now, such automated systems have rarely been used in commercial herds. This review starts with the description of normal locomotion of cows in order to define ‘abnormal’ locomotion caused by lameness. Cow locomotion (gait and posture) and behavioral features that change when a cow becomes lame are described and linked to the existing visual scoring systems. In addition, the lack of information of normal cow gait and a clear description of ‘abnormal’ gait are discussed. Finally, the different set-ups used during locomotion scoring and their influence on the resulting locomotion scores are evaluated.

Circadian variation in ghrelin and certain stress hormones in crib-biting horses

Crib-biting is classified as an oral stereotypy, which may be initiated by stress susceptibility, management factors, genetic factors and gastrointestinal irritation. Ghrelin has been identified in the gastric mucosa and is involved in the control of food intake and reward, but its relationship to crib-biting is not yet known. The aim of this study was to examine the concentration and circadian variation of plasma ghrelin, cortisol, adrenocorticotropic hormone (ACTH) and β-endorphin in crib-biting horses and non-crib-biting controls. Plasma samples were collected every second hour for 24h in the daily environment of eight horses with stereotypic crib-biting and eight non-crib-biting controls. The crib-biting horses had significantly higher mean plasma ghrelin concentrations than the control horses. The circadian rhythm of cortisol was evident, indicating that the sampling protocol did not inhibit the circadian regulation in these horses. Crib-biting had no statistically significant effect on cortisol, ACTH or β-endorphin concentrations. The inter-individual variations in β-endorphin and ACTH were higher than the intra-individual differences, which made inter-individual comparisons difficult and complicated the interpretation of results. Further research is therefore needed to determine the relationship between crib-biting and ghrelin concentration.

Instrumentation, measurement and performance of three air quality measurement systems for dairy buildings

Recent interest in global warming has led to the monitoring of operations such as dairy production that emit pollutants into the atmosphere. However, monitoring systems for indoor air quality in dairy buildings are still uncommon due to high costs involved in designing systems that can withstand high moisture, dust, corrosive gases, and varying temperatures. For studying the performance of measurement systems for dairy buildings, three different air quality measuring systems were built using both affordable and expensive sensors. The measurement systems were 1) a stationary system for longer period on-site measurements, 2) a wireless stationary system for off-site measurement, and 3) a mobile system for periodic air quality measurement. The instrumentation, measurement procedures, and performance of these systems are presented in this article. Spatial air quality survey showed high variation in microclimate conditions in the dairy building. Average deviation of sensors from the true value in the different measurement systems was 1.1°C for temperature, 3.6% for relative humidity, 450 ppm for carbon dioxide, 0.5 m/s for velocity, and 1 ppm for ammonia. Affordable sensors in the systems gave reasonably accurate readings when carefully calibrated. The single most practical location for installing air quality measurement systems was directly above the dairy cows in the center of the dairy building.