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Changes in Feeding Behavior as Possible Indicators for the Automatic Monitoring of Health Disorders in Dairy Cows
Changes in short-term feeding behavior of dairy cows that occur with the onset of the health disorders ketosis, acute locomotory problems, and chronic lameness were investigated using data collected during previous experiments. The objective of the study was to describe and quantify those changes and to test their suitability as early indicators of disease. Feed intake, feeding time, and number of daily feeder visits were recorded with computerized feeders. Ketosis in 8 cows was characterized by rapid daily decreases in feed intake [−10.4kg of fresh matter (FM)], feeding time (−45.5min), and feeding rate (−25.3g of FM/min) during an average of 3.6 d before diagnosis by farm staff. Acute locomotion disorders in 14 cows showed smaller daily decreases in feed intake (−1.57kg of FM) and feeding time (−19.1min), and a daily increase in feeding rate (+21.6g of FM/min) during an average of 7.7 d from onset to diagnosis. The effects of chronic lameness on short-term feeding behavior were assessed by analyzing changes during the 30 d before and 30 d after all cows were checked for foot lesions and trimmed, and cows were classified as either lame (n = 81) or not lame (n = 62). During the 30 d before trimming, cows classified as lame showed significant changes in daily feeding time, number of daily visits, and feeding rate, but nonlame cows did not. In lame cows, the observed daily changes (slope) for the 30 d before and the 30 d after trimming were −0.75 and +0.32 min/d for daily feeding time, −0.35 and +0.31 for daily number of visits, and +0.77 and −0.35 g/min for feeding rate, respectively. These changes in feeding behavior were not different among cows consuming low or high forage rations. Daily feeding time was the feeding characteristic that changed most consistently in relation to the studied disorders. A simple algorithm was used to identify cows whose daily feeding time was lower than the previous 7-d rolling average minus 2.5 standard deviations. The algorithm resulted in detection of more than 80% of cows with acute disorders at least 1 d before diagnosis by farm staff. Short-term feeding behavior showed very characteristic changes with the onset of disorders, which suggests that a system that monitors short-term feeding behavior can assist in the early identification of sick cows.
Journal Article
Discrimination of the Lame Limb in Horses Using a Machine Learning Method (Support Vector Machine) Based on Asymmetry Indices Measured by the EQUISYM System
Lameness detection in horses is a critical challenge in equine veterinary practice, particularly when symptoms are mild. This study aimed to develop a predictive system using a support vector machine (SVM) to identify the affected limb in horses trotting in a straight line. The system analyzed data from inertial measurement units (IMUs) placed on the horse’s head, withers, and pelvis, using variables such as vertical displacement and retraction angles. A total of 287 horses were included, with 256 showing single-limb lameness and 31 classified as sound. The model achieved an overall accuracy of 86%, with the highest success rates in identifying right and left forelimb lameness. However, there were challenges in identifying sound horses, with a 54.8% accuracy rate, and misclassification between forelimb and hindlimb lameness occurred in some cases. The study highlighted the importance of specific variables, such as vertical head and withers displacement, for accurate classification. Future research should focus on refining the model, exploring deep learning methods, and reducing the number of sensors required, with the goal of integrating these systems into equestrian equipment for early detection of locomotor issues.
Journal Article
Prevalence of Lameness in High-Producing Holstein Cows Housed in Freestall Barns in Minnesota
A cross-sectional study was conducted to estimate the prevalence of clinical lameness in high-producing Holstein cows housed in 50 freestall barns in Minnesota during summer. Locomotion and body condition scoring were performed on a total of 5,626 cows in 53 high-production groups. Cow records were collected from the nearest Dairy Herd Improvement Association test date, and herd characteristics were collected at the time of the visit. The mean prevalence of clinical lameness (proportion of cows with locomotion score ≥3 on a 1-to-5 scale, where 1 = normal and 5 = severely lame), and its association with lactation number, month of lactation, body condition score, and type of stall surface were evaluated. The mean prevalence of clinical lameness was 24.6%, which was 3.1 times greater, on average, than the prevalence estimated by the herd managers on each farm. The prevalence of lameness in first-lactation cows was 12.8% and prevalence increased on average at a rate of 8 percentage units per lactation. There was no association between the mean prevalence of clinical lameness and month of lactation (for months 1 to 10). Underconditioned cows had a higher prevalence of clinical lameness than normal or overconditioned cows. The prevalence of lameness was lower in freestall herds with sand stalls (17.1%) than in freestall herds with mattress stall surfaces (27.9%). Data indicate that the best 10th percentile of dairy farms had a mean prevalence of lameness of 5.4% with only 1.47% of cows with locomotion score = 4 and no cows with locomotion score = 5.
Journal Article
Use of validated objective methods of locomotion characteristics and weight distribution for evaluating the efficacy of ketoprofen for alleviating pain in cows with limb pathologies
In veterinary practice pain alleviation plays a part in managing lameness. The aim of this randomized and placebo-controlled clinical study was to evaluate the effect of a single administration of ketoprofen on locomotion characteristics and weight distribution in cattle with foot (located up to and including the fetlock; n = 31) and (proximal to the fetlock; n = 10) pathologies. Cattle were randomly allocated to either the ketoprofen (group K; intravenous 3 mg/kg of body weight; n = 21) or an equivalent volume of isotonic sterile saline solution (group P; n = 20). Two accelerometers (400 Hz; kinematic outcome = stance phase duration; kinetic outcome = foot load and toe-off), a 4-scale weighing platform (weight distribution and SD of the weight) and a subjective locomotion score were measured before (baseline) and after 1 h and 18 h of treatment. All variables were expressed as differences across contralateral limbs, and the measurements at 1 h and 18 h were compared to the baseline. A repeated measures ANOVA was used to determine the differences between groups K and P. A logistic regression model with a binary outcome (0 = no improvement and 1 = improvement of the differences across the contralateral limbs over time) was calculated. Mean (± SD) of locomotion scores at baseline were not significantly different (P = 0.102) in group K (3.10 ± 0.80) as compared to group P (3.48 ± 0.64). Cattle of group K showed significantly lower differences across contralateral limbs at 1 h as compared to group P for the relative stance phase and the weight distribution. Only the treatment (P versus K) remained a significant factor in the model for relative stance phase (odds ratio (OR) = 6.5; 95% CI = 1.38-30.68) and weight distribution (OR = 6.36; 95% CI = 1.30-31.07). The effects of ketoprofen were evident in improving the differences across contralateral limbs-both for stance phase during walking and weight bearing during standing-after 1 h but not after 18 h of administration.
Journal Article
Treatment of Naturally Occurring Tendon Disease with Allogeneic Multipotent Mesenchymal Stromal Cells: A Randomized, Controlled, Triple-Blinded Pilot Study in Horses
The treatment of tendinopathies with multipotent mesenchymal stromal cells (MSCs) is a promising option in equine and human medicine. However, conclusive clinical evidence is lacking. The purpose of this study was to gain insight into clinical treatment efficacy and to identify suitable outcome measures for larger clinical studies. Fifteen horses with early naturally occurring tendon disease were assigned to intralesional treatment with allogeneic adipose-derived MSCs suspended in serum or with serum alone through block randomization (dosage adapted to lesion size). Clinicians and horse owners remained blinded to the treatment during 12 months (seven horses per group) and 18 months (seven MSC-group and five control-group horses) of follow-up including clinical examinations and diagnostic imaging. Clinical inflammation, lameness, and ultrasonography scores improved more over time in the MSC group. The lameness score difference significantly improved in the MSC group compared with the control group after 6 months. In the MSC group, five out of the seven horses were free of re-injuries and back to training until 12 and 18 months. In the control group, three out of the seven horses were free of re-injuries until 12 months. These results suggest that MSCs are effective for the treatment of early-phase tendon disease and provide a basis for a larger controlled study.
Journal Article
Video-Based Automated Lameness Detection for Dairy Cows
Nowadays, the treatment costs associated with lameness rank second among common diseases of cattle. The standard method for detecting lameness is visual observation of the herd by the farmer. However, these methods are time-consuming and labor-intensive and, due to the qualitative nature of the assessment, involve many discrepancies between different human assessors. This study aims to develop fully automated end-to-end methods for the video-based assessment of lameness in dairy cows using data science. For the study, 832 cows with varying degrees of lameness were recorded. The video recordings were then divided into individual frames, where deep learning detected a single cow and its characteristic anatomical points. A custom 7-point locomotion scoring system, inspired by the commonly used 5-level Sprecher (Zinpro) scale, was introduced and evaluated. This scale was used to assess lameness severity based on processed data, which were analyzed using an expert system, machine-learning methods, and a deep-learning approach. Our solution is based on the analysis of the spine curvature, head position, and distance between pairs of legs. The accuracy of detecting binary lameness (healthy vs. lame) through multiple locomotion features approaches expert-level performance, at 0.821 and 0.872, respectively.
Journal Article
Lameness in dairy cattle: A debilitating disease or a disease of debilitated cattle? A cross-sectional study of lameness prevalence and thickness of the digital cushion
Lameness is the most significant challenge for the dairy industry to overcome, given its obvious disruption of animal welfare and severe economic losses. Sole ulcers and white line abscesses are ubiquitous chronic diseases with the highest associated economic losses among all foot lesions. Their underlying causes are still not fully understood. An observational cross-sectional study was carried out to investigate the association between claw horn lesions and the thickness of the digital cushion. The thickness of the digital cushion was evaluated by ultrasonographic examination of the sole at the typical ulcer site. A total of 501 lactating Holstein dairy cows were enrolled in the study. The prevalence of sole ulcers was 4.2 and 27.8% for parity 1 and parity >1, respectively. The prevalence of white line disease was 1.0 and 6.5% for parity 1 and >1, respectively. The prevalence of lameness (visual locomotion score ≥3) was 19.8 and 48.2% for parity 1 and >1, respectively. The prevalence of sole ulcers and white line diseases was significantly associated with thickness of the digital cushion; cows in the upper quartile of digital cushion thickness had an adjusted prevalence of lameness 15 percentage points lower than the lower quartile. Body condition scores were positively associated with digital cushion thickness. The mean gray value of the sonographic image of the digital cushion had a negative linear association with digital cushion thickness (R2=0.14), indicating that the composition of the digital cushion may have changed with its thickness. Furthermore, digital cushion thickness decreased steadily from the first month of lactation and reached a nadir 120 d after parturition. These results support the concept that sole ulcers and white line abscesses are related to contusions within the claw horn capsule and such contusions are a consequence of the lesser capacity of the digital cushion to dampen the pressure exerted by the third phalanx on the soft tissue beneath.
Journal Article
Evaluation of a Lameness Scoring System for Dairy Cows
Lameness is a major problem in dairy production both in terms of reduced production and compromised animal welfare. A 5-point lameness scoring system was developed based on previously published systems, but optimized for use under field conditions. The scoring system included the words “in most cases” in the descriptions of the clinical signs evaluated. This was done to avoid a situation in which cows might not fit into any of the categories. Additionally, a number of clinical signs used in other lameness scoring systems, considered of less importance in relation to lameness, were not included. Only clinical signs were included that could easily be assessed within a few seconds from a distance. The scoring system was evaluated with intra-and interobserver agreement using kappa statistics. The evaluation was done before and after training 5 observers. Weighted kappa values ranged from 0.38 to 0.78 for intraobserver agreement, with mean kappa values across all observers of 0.60 and 0.53 before and after training, respectively. Weighted kappa values ranged from 0.24 to 0.68 for interobserver agreement, with mean kappa values across all pairs of observers of 0.48 and 0.52 before and after training, respectively. Training had only a limited positive effect on intra- and interobserver agreement. Additionally, how the different lameness categories are distributed along a theoretical scale representing the full spectrum of lameness from “absolutely normal gait” to “as lame as a cow can possibly be” was evaluated. This evaluation was done using the polychoric correlation coefficient. The estimated within-observer polychoric correlation coefficient ranged from 0.76 to 0.96, and there were no significant differences between the thresholds used to classify cows into different lameness categories by different observers before or after training. In conclusion, the results suggest that the lameness categories were not equidistant and the scoring system has reasonable reliability in terms of intra- and interobserver agreement.
Journal Article
Using Sensor Data to Detect Lameness and Mastitis Treatment Events in Dairy Cows: A Comparison of Classification Models
The aim of this study was to develop classification models for mastitis and lameness treatments in Holstein dairy cows as the target variables based on continuous data from herd management software with modern machine learning methods. Data was collected over a period of 40 months from a total of 167 different cows with daily individual sensor information containing milking parameters, pedometer activity, feed and water intake, and body weight (in the form of differently aggregated data) as well as the entered treatment data. To identify the most important predictors for mastitis and lameness treatments, respectively, Random Forest feature importance, Pearson’s correlation and sequential forward feature selection were applied. With the selected predictors, various machine learning models such as Logistic Regression (LR), Support Vector Machine (SVM), K-nearest neighbors (KNN), Gaussian Naïve Bayes (GNB), Extra Trees Classifier (ET) and different ensemble methods such as Random Forest (RF) were trained. Their performance was compared using the receiver operator characteristic (ROC) area-under-curve (AUC), as well as sensitivity, block sensitivity and specificity. In addition, sampling methods were compared: Over- and undersampling as compensation for the expected unbalanced training data had a high impact on the ratio of sensitivity and specificity in the classification of the test data, but with regard to AUC, random oversampling and SMOTE (Synthetic Minority Over-sampling) even showed significantly lower values than with non-sampled data. The best model, ET, obtained a mean AUC of 0.79 for mastitis and 0.71 for lameness, respectively, based on testing data from practical conditions and is recommended by us for this type of data, but GNB, LR and RF were only marginally worse, and random oversampling and SMOTE even showed significantly lower values than without sampling. We recommend the use of these models as a benchmark for similar self-learning classification tasks. The classification models presented here retain their interpretability with the ability to present feature importances to the farmer in contrast to the “black box” models of Deep Learning methods.
Journal Article