The objective was to investigate infrared thermography (IRT) as a noninvasive diagnostic tool for early detection of foot pathologies in dairy cows. This was achieved by measuring changes in coronary band temperature before and after claw trimming in response to visual detection of abnormalities of the hooves. We hypothesized that by focusing on the coronary band region, IRT is able to detect lesions of the hind limbs of dairy cows associated with lameness. In this study, 626 individual observations were collected from 24 cows before and after claw trimming. Infrared thermography was used to assess the surface temperature of the coronary band (CB) region and skin (S), and the temperature difference (ΔT) between CB and S of the hind limbs. The average, minimum, and maximum surface temperatures were recorded in both regions. Temperatures of CB and S and ΔT were significantly higher in cows ≤200 d in milk than in cows >200 d in milk for all healthy hooves: 31.8±2.7 versus 29.8±3.6; 28.5±2.5 versus 27.2±3.3°C, and 3.31±1.7 versus 2.51±1.3°C, respectively. Temperatures of CB and S regions were positively correlated with ambient temperature. This association was best described by a linear model (R(2)=0.92 and 0.99, respectively). The temperatures of CB and S regions were 30.3±3.2°C and 27.3±2.9°C; 32.1±1.7°C and 28.6±2.1°C; and 33.8±1.3°C and 29.9±1.8°C for parlor temperatures of 12.2, 15.7, and 20.3°C, respectively. In the pre- and post-trimming data analysis, a significant difference was found in temperature of the coronary band between cows with lesions and cows without lesions. A threshold value was established to determine the temperature difference between lesion and nonlesion hind claws on CB at 0.64 and 1.09°C before and after claw trimming (sensitivity=85.7%, specificity=55.9%; and sensitivity=80.0%, specificity=82.9%, respectively) with the aim of detecting hoof lesions. In conclusion, the results demonstrate an increase in surface temperature of the lame limb when a hoof has a lesion.
Routine recording of claw health status at claw trimming of dairy cattle has been established in several countries, providing valuable data for genetic evaluation. In this review, we examine issues related to genetic evaluation of claw health; discuss data sources, trait definitions, and data validation procedures; and present a review of genetic parameters, possible indicator traits, and status of genetic and genomic evaluations for claw disorders. Different sources of data and traits can be used to describe claw health. Severe cases of claw disorders can be identified by veterinary diagnoses. Data from lameness and locomotion scoring, activity information from sensors, and feet and leg conformation traits are used as auxiliary traits. The most reliable and comprehensive information is data from regular hoof trimming. In genetic evaluation, claw disorders are usually defined as binary traits, based on whether or not the claw disorder was present (recorded) at least once during a defined time period. The traits can be specific disorders, composite traits, or overall claw health. Data validation and editing criteria are needed to ensure reliable data at the trimmer, herd, animal, and record levels. Different strategies have been chosen, reflecting differences in herd sizes, data structures, management practices, and recording systems among countries. Heritabilities of the most commonly analyzed claw disorders based on data from routine claw trimming were generally low, with ranges of linear model estimates from 0.01 to 0.14, and threshold model estimates from 0.06 to 0.39. Estimated genetic correlations among claw disorders varied from -0.40 to 0.98. The strongest genetic correlations were found among sole hemorrhage (SH), sole ulcer (SU), and white line disease (WL), and between digital/interdigital dermatitis (DD/ID) and heel horn erosion (HHE). Genetic correlations between DD/ID and HHE on the one hand and SH, SU, or WL on the other hand were, in most cases, low. Although some of the studies were based on relatively few records and the estimated genetic parameters had large standard errors, there was, with some exceptions, consistency among studies. Various studies evaluate the potential of various data soureces for use in breeding. The use of hoof trimming data is recommended for maximization of genetic gain, although auxiliary traits, such as locomotion score and some conformation traits, may be valuable for increasing the reliability of genetic evaluations. Routine genetic evaluation of direct claw health has been implemented in the Netherlands (2010); Denmark, Finland, and Sweden (joint Nordic evaluation; 2011); and Norway (2014), and other countries plan to implement evaluations in the near future.
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.
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