Manual and automatic locomotion scoring systems in dairy cows: A review

Automated dairy cattle lameness detection utilizing the power of artificial intelligence; current status quo and future research opportunities

Lameness represents a major welfare and health problem for the dairy industry across all farming systems. Visual mobility scoring, although very useful, is labour-intensive and physically demanding, especially in large dairies, often leading to inconsistencies and inadequate uptake of the practice. Technological and computational advancements of artificial intelligence (AI) have led to the development of numerous automated solutions for livestock monitoring. The objective of this study was to review the automated systems using AI algorithms for lameness detection developed to-date. These systems rely on gait analysis using accelerometers, weighing platforms, acoustic analysis, radar sensors and computer vision technology. The lameness features of interest, the AI techniques used to process the data as well as the ground truth of lameness selected in each case are described. Measures of accuracy regarding correct classification of cows as lame or non-lame varied with most systems being able to classify cows with adequate reliability. Most studies used visual mobility scoring as the ground truth for comparison with only a few studies using the presence of specific foot pathologies. Given the capabilities of AI, and the benefits of early treatment of lameness, longitudinal studies to identify gait abnormalities using automated scores related to the early developmental stages of different foot pathologies are required. Farm-specific optimal thresholds for early intervention should then be identified to ameliorate cow health and welfare but also minimise unnecessary inspections.

Cow key point detection in indoor housing conditions with a deep learning model

Lameness in dairy cattle is a costly and highly prevalent problem that affects all aspects of sustainable dairy production, including animal welfare. Automation of gait assessment would allow monitoring of locomotion in which the cows' walking patterns can be evaluated frequently and with limited labor. With the right interpretation algorithms, this could result in more timely detection of locomotion problems. This in turn would facilitate timely intervention and early treatment, which is crucial to reduce the effect of abnormal behavior and pain on animal welfare. Gait features of dairy cows can potentially be derived from key points that locate crucial anatomical points on a cow's body. The aim of this study is 2-fold: (1) to demonstrate automation of the detection of dairy cows' key points in a practical indoor setting with natural occlusions from gates and races, and (2) to propose the necessary steps to postprocess these key points to make them suitable for subsequent gait feature calculations. Both the automated detection of key points as well as the postprocessing of them are crucial prerequisites for camera-based automated locomotion monitoring in a real farm environment. Side-view video footage of 34 Holstein-Friesian dairy cows, captured when exiting the milking parlor, were used for model development. From these videos, 758 samples of 2 successive frames were extracted. A previously developed deep learning model called T-LEAP was trained to detect 17 key points on cows in our indoor farm environment with natural occlusions. To this end, the dataset of 758 samples was randomly split into a train (n = 22 cows; no. of samples = 388), validation (n = 7 cows; no. of samples = 108), and test dataset (n = 15 cows; no. of samples = 262). The performance of T-LEAP to automatically assign key points in our indoor situation was assessed using the average percentage of correctly detected key points using a threshold of 0.2 of the head length (PCKh0.2). The model's performance on the test set achieved a good result with PCKh0.2: 89% on all 17 key points together. Detecting key points on the back (n = 3 key points) of the cow had the poorest performance PCKh0.2: 59%. In addition to the indoor performance of the model, a more detailed study of the detection performance was conducted to formulate postprocessing steps necessary to use these key points for gait feature calculations and subsequent automated locomotion monitoring. This detailed study included the evaluation of the detection performance in multiple directions. This study revealed that the performance of the key points on a cows' back were the poorest in the horizontal direction. Based on this more in-depth study, we recommend the implementation of the outlined postprocessing techniques to address the following issues: (1) correcting camera distortion, (2) rectifying erroneous key point detection, and (3) establishing the necessary procedures for translating hoof key points into gait features.

Assessing Alternatives to Locomotion Scoring for Detecting Lameness in Dairy Cattle in Tanzania: Infrared Thermography

Lameness detection is a significant challenge. Locomotion scoring (LS), the most widely used system for detecting lameness, has several limitations, including its subjective nature and the existence of multiple systems, each with its own advantages and disadvantages. Therefore, this study aimed to evaluate whether the foot skin temperature (FST) of hind limbs, as measured using infrared thermography (IRT), could potentially be used as an alternative on Tanzanian dairy farms. Each of the three study farms were visited twice during the afternoon milking on consecutive days, with a total of 170 cows assessed. DairyNZ LS (4-point scale (0-3)) was undertaken on the first day as the cows exited the milking parlour after being milked, while on the following day, the plantar aspect of the hind limbs of the cows was thermally imaged while they were standing in the milking parlour, using a handheld T650sc forward-looking infrared camera. Mean FST was higher for cows with a locomotion score of 1 than those with a score of 0; higher for cows with a locomotion score of 2 than those with a score of 1; and higher for cows with a locomotion score of 3 than those with a score of 2, with each one-unit locomotion score increase being associated with a 0.57 °C increase in mean temperature across all zones. The optimal cut-off point of 38.0 °C for mean temperature across all zones was identified using a receiver operator characteristic curve. This cut-off point had a sensitivity of 73.2% and a specificity of 86.0% for distinguishing cows with a locomotion score ≥ 2 (clinical lameness). The prevalence of clinical lameness across all three farms was 33%, which meant that only 72% of cows with a mean FST across all zones ≥ 38.0 °C had been identified as clinically lame using LS. This study confirmed that IRT has the potential to be used to detect lameness on Tanzanian dairy farms. However, before it can be widely used, improvements in accuracy, especially specificity, are needed, as are reductions in equipment (IR camera) costs.

Applications of Technology to Record Locomotion Measurements in Dairy Cows: A Systematic Review

Lameness within the dairy industry is a concern because of its associated costs and welfare implications. Visual locomotion scoring has been commonly used for assessing cows' locomotion quality, but it can have low reliability and is relatively subjective compared to automated methods of assessing locomotion. Kinematic, kinetic, and accelerometric technologies can provide a greater number of more detailed outcome measurements than visual scoring. The objective of this systematic review was to determine outcome measurements, and the relationships between them, that have been recorded using kinematic, kinetic, and accelerometric technologies, as well as other approaches to evaluating cow locomotion. Following PRISMA guidelines, two databases were searched for studies published from January 2000 to June 2022. Thirty-seven articles were retained after undergoing a screening process involving a title and abstract evaluation, followed by a full-text assessment. Locomotion measurements recorded using these technologies often overlapped, but inconsistencies in the types of technology, the arrangement of equipment, the terminology, and the measurement-recording approaches made it difficult to compare locomotion measurements across studies. Additional research would contribute to a better understanding of how factors regarding the health, environment, and management of dairy cows affect aspects of locomotion, as recorded through the detailed, objective outcome measurements provided by these technologies.

A workflow for automatic, high precision livestock diagnostic screening of locomotor kinematics

Locomotor kinematics have been challenging inputs for automated diagnostic screening of livestock. Locomotion is a highly variable behavior, and influenced by subject characteristics (e.g., body mass, size, age, disease). We assemble a set of methods from different scientific disciplines, composing an automatic, high through-put workflow which can disentangle behavioral complexity and generate precise individual indicators of non-normal behavior for application in diagnostics and research. For this study, piglets (Sus domesticus) were filmed from lateral perspective during their first 10 h of life, an age at which maturation is quick and body mass and size have major consequences for survival. We then apply deep learning methods for point digitization, calculate joint angle profiles, and apply information-preserving transformations to retrieve a multivariate kinematic data set. We train probabilistic models to infer subject characteristics from kinematics. Model accuracy was validated for strides from piglets of normal birth weight (i.e., the category it was trained on), but the models infer the body mass and size of low birth weight (LBW) piglets (which were left out of training, out-of-sample inference) to be "normal." The age of some (but not all) low birth weight individuals was underestimated, indicating developmental delay. Such individuals could be identified automatically, inspected, and treated accordingly. This workflow has potential for automatic, precise screening in livestock management.

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.

Graduate Student Literature Review: A systematic review on the associations between nonsteroidal anti-inflammatory drug use at the time of diagnosis and treatment of claw horn lameness in dairy cattle and lameness scores, algometer readings, and lying times

The objectives of this systematic review were to investigate the association between nonsteroidal anti-inflammatory drug (NSAID) use during the treatment of claw horn lameness in dairy cattle and locomotion score (LS), nociceptive threshold, and lying times. A total of 229 studies were initially identified and had their title and abstract screened. From this, we screened the full text of 23 articles, identifying 6 articles for inclusion in the systematic review. Of these 6, 5 reported LS, 2 reported nociceptor thresholds, and 1 reported lying times. The quality of evidence was assessed using a Cochrane risk-of-bias tool and CONSORT items reported for each included study. Due to heterogeneity between the studies, data were reported following Cochrane's Synthesis without meta-analysis guidelines. Identified heterogeneity between the studies included differences in LS systems and statistical analyses, length of time from enrollment to outcome reported, the NSAID used, concomitant treatments administered, and severity and chronicity of lameness. Recommendations are made with respect to consistency of LS reporting and analysis, along with improvements that may be noted with compulsory reporting guidelines. There were at least some concerns over the risk of bias in 4 of the studies, with risks of bias present in missing outcome data between the study groups. Within the 5 studies included with LS outcomes, there were 22 different pairwise comparisons with either NSAID or NSAID + block as the intervention, with measures of association with presence or absence of lameness as the outcome available for 20 of these comparisons. Animals in the NSAID intervention groups had a lower point estimate lameness risk than animals in the comparison groups in 3 of 8 and 9 of 14 analyses for LS outcomes <10 and ≥10 d post-treatment, respectively. However, there was no difference identified between animals in the NSAID intervention groups compared with the animals in the control group in any of these pairwise comparisons with lameness as the outcome. Twelve pairwise comparisons were reported in the 2 studies with nociceptor threshold as an outcome. Animals in the NSAID intervention groups had a greater nociceptor threshold point estimate compared with animals in the comparison groups in 6 of 6 and 1 of 6 analyses for outcomes <10 and ≥10 d post-treatment, respectively. However, no differences were identified between animals in the NSAID intervention groups and those in the comparison groups. All 4 pairwise comparisons reported in the study with lying times as an outcome found no differences between animals in the NSAID groups and those in the comparison groups. Despite the widespread use of NSAID in the treatment of claw horn lameness, there is a lack of studies of NSAID association with LS, nociceptive thresholds, or lying times. The limited evidence is consistent with no association with NSAID use and those parameters, but comparability across studies was limited by heterogeneity.

Welfare of cattle during transport

In the framework of its Farm to Fork Strategy, the Commission is undertaking a comprehensive evaluation of the animal welfare legislation. The present Opinion deals with protection of cattle (including calves) during transport. Welfare of cattle during transport by road is the main focus, but other means of transport are also covered. Current practices related to transport of cattle during the different stages (preparation, loading/unloading, transit and journey breaks) are described. Overall, 11 welfare consequences were identified as being highly relevant for the welfare of cattle during transport based on severity, duration and frequency of occurrence: group stress, handling stress, heat stress, injuries, motion stress, prolonged hunger, prolonged thirst, respiratory disorders, restriction of movement, resting problems and sensory overstimulation. These welfare consequences and their animal-based measures are described. A variety of hazards, mainly relating to inexperienced/untrained handlers, inappropriate handling, structural deficiencies of vehicles and facilities, poor driving conditions, unfavourable microclimatic and environmental conditions, and poor husbandry practices leading to these welfare consequences were identified. The Opinion contains general and specific conclusions relating to the different stages of transport for cattle. Recommendations to prevent hazards and to correct or mitigate welfare consequences have been developed. Recommendations were also developed to define quantitative thresholds for microclimatic conditions within the means of transport and spatial thresholds (minimum space allowance). The development of welfare consequences over time was assessed in relation to maximum journey duration. The Opinion covers specific animal transport scenarios identified by the European Commission relating to transport of unweaned calves, cull cows, the export of cattle by livestock vessels, the export of cattle by road, roll-on-roll-off ferries and 'special health status animals', and lists welfare concerns associated with these.

Randomized clinical trial investigating the effect of exercise and standing on concrete prior to first calving on time to first lameness event in dairy heifers

This controlled clinical trial investigated if an intervention immediately before the first calving event could reduce lameness incidence in pasture-based dairy heifers. Seven hundred ninety heifers across 6 farms in the Waikato region of New Zealand were randomly enrolled into treatment or control groups at a ratio of 1:1. The treatment consisted of heifers walking approximately 1 km from pasture along the farm race, standing on concrete for one hour, and then walking back to their paddock. This occurred once a day, 5 times a week, for 5 wk before calving. The control heifers were managed solely at pasture before calving. Just before calving, both groups were bought together and managed as one group for the remainder of the study. Heifers were followed for up to 28 wk, with fortnightly lameness scores collected to identify animals with a lameness score of ≥2 (lameness score 0-3). Lameness could also be diagnosed by the farmers, who had no formal lameness scoring training. The primary outcome of interest was time to first lame event. Secondary outcomes included milk solid production, change in body condition score during early lactation, time from onset of breeding season until conception, feasibility of the regimen and change in sole soft tissue thickness and profile. From a total of 782 heifers that had data collected on the outcomes, 102 (13.0%) individual first lameness events were recorded, 53 in heifers in the treatment group and 49 in control heifers. Of those 102 lameness events, 51 were first diagnosed by farmers. No apparent differences were detected in the hazard rate for time to first lame event between heifers in the 2 treatment groups. Treatment heifers had a 1.12 times hazard rate (95% confidence interval: 0.65-1.95) of a lame event compared with control heifers. No associations were identified between heifers in the 2 groups for any of the secondary outcome measures. However, farmers did report that the intervention was practical and easy to implement. It is possible that the intervention did not challenge the hoof enough, and that longer duration and distances walked may have resulted in a different outcome. Although no improvement in lameness outcomes were reported, no negative effects during and after the intervention were noted in animals in the intervention group. Further research into the area of lameness prevention is needed as there are few evidence-based solutions available to reduce lameness incidence in pasture-based systems.

Prevalence of and factors associated with swellings of the ribs in tie stall housed dairy cows in Germany

Swellings of the ribs result from severe injury and affected animals are subjected to considerable and prolonged pain and suffering. The knowledge on rib swellings in dairy cows has yet been very limited. Therefore, the present study aimed at determining the prevalence of rib swellings in tie stall housed dairy cows in Germany as well as at identifying associated factors. Mean animal-level prevalence of rib swellings for 2,134 cows was 7.54% with a mean of 7.00% on farm level (range 0.00% - 37.49%). Multivariable mixed logistic regression models including nested random effects were built and factors associated with swellings of the ribs were evaluated for 1,740 dairy cows on 96 farms in Germany. Out of the initial 22 predictors, 8 factors were selected for the final model. Managing dairy cows on a part-time basis (OR 0.49 [CI 0.25–0.98]) appeared to decrease the odds for rib swellings compared with full-time farming. Cattle breeds other than Simmental entailed lower odds for rib swellings (OR 0.29 [CI 0.14–0.59]). Lame cows (OR 2.59 [CI 1.71–3.93]) and cows with wounds and/or swellings of the hocks (OR 2.77 [CI 1.32–5.84]) had more than two times the odds for rib swellings compared with sound animals. The results of the present study can help raising awareness of rib swellings in dairy cows and contribute to the body of evidence on this condition.

German Farmers' Awareness of Lameness in Their Dairy Herds

Lameness is one of the most challenging problems in the dairy industry. Control is impeded because farmers often underestimate the number of lame cows. The objectives of this study were to assess German farmers' awareness of lameness in their herds and to determine the associations between farmers' awareness and their management practices, farm characteristics as well as with farmers' education, personality traits and attitudes. As a part of a large cross-sectional study, veterinarians visited farms in three structurally different regions of Germany: north (n = 253), east (n = 252), and south (n = 260). The cows (n = 84,998) were scored for locomotion and farmers were asked to estimate the number of cows that were lame or did not walk soundly. The ratio of farmers' estimated prevalence and the veterinarians' observed prevalence (Farmer's Detection Index; FDI) was calculated. The median lameness prevalence assessed by the veterinarians was 23.1, 39.1, and 23.2%, and the median prevalence of lame cows estimated by the farmers was 9.5, 9.5, and 7.1% in the north, east, and south, respectively. On average, farmers were conscious of only 45.3% (north), 24.0% (east), and 30.0% (south) of their lame cows. Farmers managing their herds according to organic principles had a higher FDI than farmers who managed their herds conventionally. Surprisingly, no significant associations between FDI and factors concerning claw health management could be detected. Therefore, increased awareness did not seem to be necessarily linked to improved management. Moreover, the FDI was not significantly associated with farmers' education or herd size. In the south, more extraverted farmers had a lower FDI. Those farmers who totally agreed with the statement, “I am satisfied with my herd's health,” had a lower FDI than farmers who disagreed or were undecided. Moreover, farmers who disagreed or were undecided with the statement, “It affects me to see a cow in pain” had a higher FDI than those farmers who agreed to the statement. The results indicate that poor awareness of lameness was linked to the farmers' attitude and personality. Therefore, new approaches concerning the consultation regarding lameness control, such as the use of Motivational Interviewing, might be useful in the future.

Evaluating Alternatives to Locomotion Scoring for Detecting Lameness in Pasture-Based Dairy Cattle in New Zealand: In-Parlour Scoring

Earlier detection followed by efficient treatment can reduce the impact of lameness. Currently, locomotion scoring (LS) is the most widely used method of early detection but has significant limitations in pasture-based cattle and is not commonly used routinely in New Zealand. Scoring in the milking parlour may be more achievable, so this study compared an in-parlour scoring (IPS) technique with LS in pasture-based dairy cows. For nine months on two dairy farms, whole herd LS (4-point 0−3 scale) was followed 24 h later by IPS, with cows being milked. Observed for shifting weight, abnormal weight distribution, swollen heel or hock joint, and overgrown hoof. Every third cow was scored. Sensitivity and specificity of individual IPS indicators and one or more, two or more or three positive indicators for detecting cows with locomotion scores ≥ 2 were calculated. Using a threshold of two or more positive indicators were optimal (sensitivity > 92% and specificity > 98%). Utilising the IPS indicators, a decision tree machine learning procedure classified cows with locomotion score class ≥2 with a true positive rate of 75% and a false positive rate of 0.2%. IPS has the potential to be an alternative to LS on pasture-based dairy farms.

Weighting the relative importance of behaviors affecting gait score

This research communication evaluates experts' opinions on the importance and weights of six gait aspects. In 2016, a Qualtrics (Qualtrics LLC., Provo, Utah) survey was distributed to lameness experts. Six gait aspects - general symmetry, tracking, spine curvature, head bobbing, speed and abduction as well as adduction were included. Respondents were asked to rank the gait aspects from 1 (most important) to 6 (least important), and to indicate which weight each gait aspect should receive when assessing lameness. For each gait aspect, frequency (percentage %) was used to describe the distribution of rank, and medians as well as 25th and 75th percentiles were used to summarize assigned weights. Thirty-nine percent of respondents ranked general symmetry first, followed by 32% for tracking, and 19% ranked spine curvature third. Head bobbing ranked fourth with 10% whereas, speed, abduction and adduction were not ranked. The median, 25th and 75th percentiles weight for each gait aspect were: general symmetry (25, 15, and 30), tracking (20, 10, and 30), spine curvature (20, 10, and 21), head bobbing (15, 10, and 20), speed (10, 5, and 20), and abduction and adduction (10, 5, and 10). General symmetry and tracking were deemed the most important gait aspects. A composite gait score can be calculated based on weighted importance of different gait aspects to indicate possible lameness.

Identifying cow - level factors and farm characteristics associated with locomotion scores in dairy cows using cumulative link mixed models

Lameness is a tremendous problem in intensively managed dairy herds all over the world. It has been associated with considerable adverse effects on animal welfare and economic viability. The majority of studies have evaluated factors associated with gait disturbance by categorising cows into lame and non-lame. This procedure yet entails a loss of information and precision. In the present study, we extend the binomial response to five categories acknowledging the ordered categorical nature of locomotion assessments, which conserves a higher level of information. A cumulative link mixed modelling approach was used to identify factors associated with increasing locomotion scores. The analysis revealed that a low body condition, elevated somatic cell count, more severe hock lesions, increasing parity, absence of pasture access, and poor udder cleanliness were relevant variables associated with higher locomotion scores. Furthermore, distinct differences in the locomotion scores assigned were identified in regard to breed, observer, and season. Using locomotion scores rather than a dichotomised response variable uncovers more refined relationships between gait disturbances and associated factors. This will help to understand the intricate nature of gait disturbances in dairy cows more deeply.

Evaluating Alternatives to Locomotion Scoring for Lameness Detection in Pasture-Based Dairy Cows in New Zealand: Infra-Red Thermography

Simple Summary

Early detection accompanied by effective treatment is vital to minimise the negative impacts of lameness in dairy cows. Locomotion scoring is commonly used for detecting lameness but can be challenging to implement effectively in cows at pasture-based systems. One potential alternative detection is measuring foot skin temperature using an infrared camera. Data were collected from a 940-cow dairy farm in New Zealand with cows observed at two consecutive afternoon milkings. Locomotion scoring was undertaken at the first milking and thermal imaging of the hind feet at the second milking. As the locomotion score increased, mean foot skin temperature increased, showing that measuring temperature could be a useful alternative to locomotion scoring. However, the process needs to be speeded up and automated if it is to be used widely.

Abstract

Lameness in cattle is a complex condition with huge impacts on welfare, and its detection is challenging for the dairy industry. The present study aimed to evaluate the association between foot skin temperature (FST) measured using infrared thermography (IRT) and locomotion scoring (LS) in dairy cattle kept at pasture. Data were collected from a 940-cow dairy farm in New Zealand. Cows were observed at two consecutive afternoon milkings where LS was undertaken at the first milking (4-point scale (0–3), DairyNZ). The next day, cows were thermally imaged from the plantar aspect of the hind feet using a handheld T650sc forward-looking infrared camera (IRT). The association between FST and locomotion score was analysed using a generalised linear model with an identity link function and robust estimators. ROC curves were performed to determine optimal threshold temperature cut-off values by maximising sensitivity and specificity for detecting locomotion score ≥ 2. There was a linear association between individual locomotion scores and FST. For mean temperature (MT), each one-unit locomotion score increase was associated with a 0.944 °C rise in MT. Using MT at a cut-off point of 34.5 °C produced a sensitivity of 80.0% and a specificity of 92.4% for identifying cows with a locomotion score ≥ 2 (lame). Thus, IRT has a substantial potential to be used on-farm for lameness detection. However, automation of the process will likely be necessary for IRT to be used without interfering with farm operations.

Simulating the mechanics behind sub-optimal mobility and the associated economic losses in dairy production

Hoof disorders and sub-optimal mobility (SOM) are economically important health issues in dairy farming. Although the dynamics of hoof disorders have an important effect on cow mobility, they have not been considered in previous simulation models that estimate the economic loss of SOM. Furthermore, these models do not consider the varying severities of SOM. The objective of this study was to develop a novel bio-economic simulation model to simulate the dynamics of 8 hoof disorders: digital dermatitis (DD), interdigital hyperplasia (HYP), interdigital dermatitis/heel-horn erosion (IDHE), interdigital phlegmon (IP), overgrown hoof (OH), sole haemorrhage (SH), sole ulcer (SU) and white-line disease (WLD), their role in SOM, and estimate the economic loss of SOM in a herd of 125 dairy cows. A Reed-Frost model was used for DD and a Greenwood model for the other 7 hoof disorders. Economic analysis was conducted per mobility score according to a 5-point mobility scoring method (1 = perfect mobility; 5 = severely impaired mobility) by comparing a scenario with SOM and one without SOM. Parameters used in the model were based on literature and expert opinion and deemed credible during model validation rounds. Results showed that the mean cumulative incidence for maximum mobility scores 2-5 SOM episodes were respectively 34, 16, 7 and <1 episodes per 100 cows per pasture period and 39, 19, 8, <1 episodes per 100 cows per housing period. The mean total annual economic loss due to SOM resulting from the hoof disorders under study was €15,342: €122 per cow per year. The economic analysis uncovered direct economic losses that could be directly linked to SOM episodes and indirect economic losses that could not be directly linked to SOM episodes but arose due to the presence of SOM. The mean total annual direct economic loss for maximum mobility score 2-5 SOM episodes was €1129, €3098, €4354 and €480, respectively. The mean total annual indirect economic loss varied considerably between the 5th and 95th percentiles: €-6174 and €19,499, and had a mean of €6281. This loss was composed of additional indirect culling due to SOM (∼65%) and changes in the overall herd milk production (∼35%) because of additional younger replacement heifers entering the herd due to increased culling rates. The bio-economic model presented novel results with respect to indirect economic losses arising due to SOM. The results can be used to stimulate farmer awareness and promote better SOM management.

Intelligent Perception-Based Cattle Lameness Detection and Behaviour Recognition: A Review

Simple Summary

Cattle lameness detection as well as behaviour recognition are the two main objectives in the applications of precision livestock farming (PLF). Over the last five years, the development of smart sensors, big data, and artificial intelligence has offered more automatic tools. In this review, we discuss over 100 papers that used automated techniques to detect cattle lameness and to recognise animal behaviours. To assist researchers and policy-makers in promoting various livestock technologies for monitoring cattle welfare and productivity, we conducted a comprehensive investigation of intelligent perception for cattle lameness detection and behaviour analysis in the PLF domain. Based on the literature review, we anticipate that PLF will develop in an objective, autonomous, and real-time direction. Additionally, we suggest that further research should be dedicated to improving the data quality, modeling accuracy, and commercial availability.

Abstract

The growing world population has increased the demand for animal-sourced protein. However, animal farming productivity is faced with challenges from traditional farming practices, socioeconomic status, and climate change. In recent years, smart sensors, big data, and deep learning have been applied to animal welfare measurement and livestock farming applications, including behaviour recognition and health monitoring. In order to facilitate research in this area, this review summarises and analyses some main techniques used in smart livestock farming, focusing on those related to cattle lameness detection and behaviour recognition. In this study, more than 100 relevant papers on cattle lameness detection and behaviour recognition have been evaluated and discussed. Based on a review and a comparison of recent technologies and methods, we anticipate that intelligent perception for cattle behaviour and welfare monitoring will develop towards standardisation, a larger scale, and intelligence, combined with Internet of things (IoT) and deep learning technologies. In addition, the key challenges and opportunities of future research are also highlighted and discussed.

Review of guidelines for functional claw trimming and therapeutic approach to claw horn lesions in cattle

Lameness is one of the most pressing health and welfare problems in cattle, especially on dairy farms. The most common cause of lameness is claw pathology, often due to lack of appropriate functional claw trimming. Functional claw trimming restores the physiological shape of the claws and distributes weight properly between the claws and over the claw weight-bearing surface. It also allows closer examination of the claws for early signs of pathology. The methods of functional claw trimming described in the previous century are still applicable today, considering some recent findings on the subject. It is essential not to over-trim the claws and to maintain strict hygiene of the trimming tools. Claw horn pathology in the early stages is usually treated effectively by therapeutic claw trimming alone. The stoic nature of cattle and their natural tendency to hide pain often result in delayed treatment of claw diseases, leading to more advanced stages of disease/pathology associated with higher-grade lameness. This situation often leads to the development of neuropathic pain and hyperalgesia requiring multimodal treatment. Because claw horn diseases are multifactorial, veterinarians and others involved in animal management must be familiar with the preventive measures available to improve claw health in a cattle herd. Further research to improve claw horn quality and effectively control/prevent claw infections without polluting the environment or negatively affecting worker and animal health is still needed. This article reviews the latest knowledge on functional claw trimming and treatment of the most common claw horn diseases in cattle.

On-farm detection of claw lesions in dairy cows based on acoustic analyses and machine learning

Claw lesions are a serious problem on dairy farms, affecting both the health and welfare of the cow. Automated detection of lameness with a practical, on-farm application would support the early detection and treatment of lame cows, potentially reducing the number and severity of claw lesions. Therefore, in this study, a method was proposed for the detection of claw lesions based on the acoustic analysis of a cow's gait. A panel was constructed to measure the impact sound of animals walking over it. The recorded impact sound was edited, and 640 sound files from 64 cows were analyzed. The classification of animal-lameness status was performed using a machine-learning process with a random forest algorithm. The gold standard was a 2-point scale of hoof-trimming results (healthy vs. affected), and 38 properties of the recorded sound files were used as influencing factors. A prediction model for classifying the cow lameness was built using a random forest algorithm. This was validated by comparing the reference output from hoof-trimming with the model output concerning the impact sound. Altering the likelihood settings and changing the cutoff value to predict lame animals improved the prediction model. At a cutoff at 0.4, a decreased false-negative rate was generated, and the false-positive rate only increased slightly. This model obtained a sensitivity of 0.81 and a specificity of 0.97. With this procedure, Cohen's Kappa value of 0.80 showed good agreement between model classification and diagnoses from hoof-trimming. In summary, the prediction model enabled the detection of cows with claw lesions. This study shows that lameness can be detected by machine learning from the impact sound of hoofs in dairy cows.

Prediction of lameness using automatically recorded activity, behavior and production data in post-parturient Irish dairy cows

BACKGROUND

Although visual locomotion scoring is inexpensive and simplistic, it is also time consuming and subjective. Automated lameness detection methods have been developed to replace the visual locomotion scoring and aid in early and accurate detection. Several types of sensors are measuring traits such as activity, lying behavior or temperature. Previous studies on automatic lameness detection have been unable to achieve high accuracy in combination with practical implementation in a on farm commercial setting. The objective of our research was to develop a prediction model for lameness in dairy cattle using a combination of remote sensor technology and other animal records that will translate sensor data into easy to interpret classified locomotion information for the farmer. During an 11-month period, data from 164 Holstein-Friesian dairy cows were gathered, housed at an Irish research farm. A neck-mounted accelerometer was used to gather behavioral metrics, additional automatically recorded data consisted of milk production and live weight. Locomotion scoring data were manually recorded, using a one-to-five scale (1 = non-lame, 5 = severely lame). Locomotion scores where then used to label the cows as sound (locomotion score 1) or unsound (locomotion score ≥ 2). Four supervised classification models, using a gradient boosted decision tree machine learning algorithm, were constructed to investigate whether cows could be classified as sound or unsound. Data available for model building included behavioral metrics, milk production and animal characteristics.

RESULTS

The resulting models were constructed using various combinations of the data sources. The accuracy of the models was then compared using confusion matrices, receiver-operator characteristic curves and calibration plots. The model which achieved the highest performance according to the accuracy measures, was the model combining all the available data, resulting in an area under the curve of 85% and a sensitivity and specificity of 78%.

CONCLUSION

These results show that 85% of this model's predictions were correct in identifying cows as sound or unsound, showing that the use of a neck-mounted accelerometer, in combination with production and other animal data, has potential to replace visual locomotion scoring as lameness detection method in dairy cows.

Cow- and herd-level factors associated with lameness in dairy farms in Peninsular Malaysia

Lameness is a major welfare issue in dairy cows. This study was aimed at investigating the cow- and herd -level factors associated with lameness in dairy farms from four states in Peninsular Malaysia. The study population was 1001 lactating cows from 28 dairy farms located in Selangor (n = 9), Perak (n = 8), Negeri Sembilan (n = 6) and Johor (n = 5). Lameness was assessed by locomotion scoring. Individual cow characteristics such as breeds, parity, body condition score (BCS), hock condition, leg hygiene, presence of claw lesion and claw overgrowth were recorded. Data on herd characteristics, management practices and housing design were collected by on-farm inspection and farmers' interview. Mixed-eff ;ects logistic regressions were used to model the data and to assess the factors associated with lameness. Cow-level lameness prevalence was 34.2 % (95 % CI 22.2-50.0 %), with all the farms having at least one case of lameness. Claw lesions were recorded in 470 cows (46.9 %; CI 33.3-63.3 %) of which 296 (62.9 %) of them were lame. Of these, 78.9 % of the lesions were present on the rear feet and 25.5% of the cows had more than one foot affected. The proportion of cows having non-infectious and infectious claw lesions were 81.9 % and 18.1 %, and the predominant claw lesions were sole ulcers (24.9 %), white line disease (19.6 %), sole haemorrhage (10.2%), swelling of coronet (9.6 %), toe ulcers (8.4 %), and digital dermatitis (5.6%). Cows at third or more parities had higher odds of lameness (OR = 2.2; 95 % CI 1.2-4.1) compared to primiparous cows. Low BCS (< 2.5) increased the odds of lameness (OR = 4.8; 95 % CI 2.9-7.9) relative to cows with moderate BCS, and cows with hair loss around the hock (OR = 1.4; 95 % CI 1.1-1.9) relative to those with normal hock condition. Greater odds of being lame was observed in cows having claw lesion (OR = 15.2; 95 % CI 10.4-19.2) and those with overgrown claw (OR = 3.3; 95 % CI 2.4-4.5). There was increased odds of lameness in farms with high stocking density (OR = 1.8; 95 % CI 1.1-3.1), concrete floored walkways (OR = 1.9; 95 % CI 1.0-3.6), dirty floors (OR = 2.3; 95 % CI 1.9-3.7), and practicing preventive claw trimming (OR = 2.3; 95 % CI 1.9-4.6). Based on the high lameness prevalence, these findings could assist dairy farmers to make informed decisions on areas to implement on-farm changes to reduce lameness in the studied herds.

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.

Association between hoof lesions and fertility in lactating Jersey cows

The objectives of this study were to evaluate the association between hoof lesions and fertility in dairy cows. Lactating Jersey cows (n = 1,639) were enrolled at 20 ± 3 d in milk (D20), examined and treated for presence of hoof lesions (HL), and evaluated for body condition score (BCS). Afterward, they were managed according to standard farm procedures, including estrus detection and presynchronization and a 5 d Cosynch-72 protocol for cows that failed to show estrus. Ovaries were scanned at 27 and 41 ± 3 d in milk, and cows with a corpus luteum greater than 20 mm on at least 1 exam were considered cyclic. At 120 ± 3 d in milk (D120), cows were re-examined for HL and BCS. Cows were classified at D20 according to HL status as healthy (n = 1,197) or having HL (n = 429), and according to HL category as healthy (n = 1,197) or having a sole hemorrhage (n = 280), noninfectious HL (sole ulcer, toe ulcer, or white line disease; n = 113), or infectious HL (digital dermatitis and foot rot; n = 36). Cows with HL at D20 had reduced odds of being cyclic (38.3 vs. 51.9%) and a longer interval from calving to first service (58 vs. 51 d) compared with healthy cows. Cows with infectious HL at D20 had reduced odds of pregnancy to first service (16.7 vs. 38.3%) compared with healthy cows. Cows with sole hemorrhage at D20 were more likely to lose pregnancies between d 32 and 64 after the first service postpartum compared with healthy cows (10.5 vs. 5.2%). Cows with sole hemorrhage at D20 had a smaller hazard of pregnancy (67.9 vs. 75.5%) at 150 d in milk and more days open (88 vs. 77d) compared with healthy cows. To assess the relationship between the development of HL and fertility, cows were classified as healthy (no HL at D20 and D120; n = 308), cured (any HL at D20 and no HL at D120; n = 72), new HL (no HL at D20 and any HL at D120; n = 597), and chronic (any HL at D20 and D120; n = 226). Sole hemorrhage accounted for 93% of new HL. The proportions of cows with HL at D20 and D120 were 26.9 and 68.4%, respectively. We found no evidence for a difference in pregnancy hazard at 150 d in milk between cows that remained healthy (n = 308) and cows that developed new HL (n = 597). Hoof lesions at D20, but not new HL, were associated with decreased odds of cyclicity, longer interval from calving to first service postpartum, and reduced pregnancy hazard in Jersey cows. The effect of an HL diagnosis in early lactation and management to reduce chronic HL in dairy cows warrants further investigation.

Effects of case definition and assessment frequency on lameness incidence estimates

The reliability of locomotion scoring is often low, making it unclear how a single gait score should be interpreted. In addition, differences in assessment frequency between longitudinal studies makes it hard to compare results. Our aims were to evaluate how lameness definition and assessment frequency affect measures of lameness incidence. Six dairy farms in British Columbia, Canada, were enrolled, and 262 cows that were sound at dry-off had their locomotion score (LS) assessed weekly from dry-off to calving, using a 1 to 5 scale. Cows were categorized as remaining sound or becoming lame using 3 different case definitions (LAME1: ≥LS3 at least once; LAME2: ≥2 consecutive scores of LS3, or ≥LS4 at least once; and LAME3: ≥3 consecutive scores of LS3, or ≥LS4 at least once). We analyzed the correspondence between the 3 definitions with percent agreement and weighted κ (linear and quadratic weighting). Comparing LAME1 to LAME3 resulted in lower percent agreement (53%) and κ values (linear κ_w = 0.50; quadratic κ_w = 0.64) than comparing LAME2 and LAME3 (85%; linear κ_w = 0.83; quadratic κ_w = 0.89), indicating that cows scored LS3 twice were likely to be scored LS3 a third time. We also compared the 3 case definitions against trim records from trimmings occurring 90 d or less before calving (n = 117), and used logistic regression models to determine sensitivity, specificity, and positive and negative predictive value. Using the LAME1 criterion resulted in high sensitivity (horn lesions = 0.90; infectious lesions = 0.92) and low specificity (horn = 0.21; infectious = 0.24). We observed higher specificity for LAME2 (horn = 0.62; infectious = 0.66) and LAME3 (horn = 0.71; infectious = 0.77), but LAME2 had higher sensitivity than LAME3 (horn = 0.89 vs. 0.64; infectious = 0.69 vs. 0.64). When evaluating the effects of assessment frequency, we obtained 3 data sets by keeping every, every other, and every third locomotion assessment, and using LAME2 as a case definition. More cows were categorized as lame when assessment frequency increased. Of the cows that were classified as lame when assessed weekly, 72% of the mildly lame, and 33% of the severely lame were classified as sound when assessed every third week. Our results suggest that a single LS3 score should not be used as a criterion for lameness in longitudinal studies. To correctly identify new cases of lameness, dairy cows should be assessed at least every 2 wk.

Evaluation of lameness detection using radar sensing in ruminants

BACKGROUND

Lameness is a major health, welfare and production-limiting condition for the livestock industries. The current 'gold-standard' method of assessing lameness by visual locomotion scoring is subjective and time consuming, whereas recent technological advancements have enabled the development of alternative and more objective methods for its detection.

METHODS

This study evaluated a novel lameness detection method using micro-Doppler radar signatures to categorise animals as lame or non-lame. Animals were visually scored by veterinarian and radar data were collected for the same animals.

RESULTS

A machine learning algorithm was developed to interpret the radar signatures and provide automatic classification of the animals. Using veterinary scoring as a standard method, the classification by radar signature provided 85 per cent sensitivity and 81 per cent specificity for cattle and 96 per cent sensitivity and 94 per cent specificity for sheep.

CONCLUSION

This radar sensing method shows promise for the development of a highly functional, rapid and reliable recognition tool of lame animals, which could be integrated into automatic, on-farm systems for sheep and cattle.

Risk Factors and Detection of Lameness Using Infrared Thermography in Dairy Cows – A Review

Lameness in dairy cows is a worldwide problem, usually a consequence of hoof diseases. Hoof problems have a negative impact on animal health and welfare as well as the economy of the farm. Prevention and early diagnosis of lameness should prevent the development of the disease and consequent high costs of animal treatment. In this review, the most common causes of both infectious and noninfectious lesions are described. Susceptibility to lesions is primarily influenced by the quality of the horn. The quality of the horn is influenced by internal and external conditions such as hygiene, nutrition, hormonal changes during calving and lactation, the animal’s age or genetic predisposition. The next part of this review summarizes the basic principles and possibilities of using infrared thermography in the early detection of lameness in dairy cows.

Lameness in Beef Cattle: UK Farmers' Perceptions, Knowledge, Barriers, and Approaches to Treatment and Control

Lameness in the beef industry has received little attention in the UK, despite the fact that it is a well-recognised problem in the dairy industry. The aims of this study were to (i) compare UK beef farmers' estimates of lameness prevalence to that of researchers, (ii) explore beef farmers' attitudes towards lameness, and (iii) help identify farmer reported barriers to lameness control and treatment. Beef farmers (11 finishing units and 10 suckler farms) were recruited from England and Wales. Farmers were asked to estimate their lameness prevalence, before a researcher conducted locomotion scoring using a five point scale, and a Bland Altman analysis performed. Face to face interviews were also conducted using a semi structured interview script aimed at capturing information such as current approaches and protocols as well as their views of lameness importance. Interviews were recorded and transcribed. An inductive thematic analysis was performed. All but two farmers underestimated lameness prevalence on their farms when compared to the researcher. Farmers initially underestimated lameness prevalence compared to the researchers estimates, with a mean underestimate of 7% (95% CI 5–9%). This is an important barrier to lameness detection and treatment. Thematic analysis identified four major themes: (1). Perception of lameness prevalence, (2). Technical knowledge and skills, (3). Perception of the impact of lameness, and (4). Barriers to the treatment and control of lameness. This study highlights that some approaches to lameness treatment are likely to be causing harm, despite being done with the intention to help the animal. There were four key areas of concern identified: recognition of lameness, treatment approaches, the training provided to farmers and confusion over transport and slaughter options available to farmers. This suggests an urgent need for future work to quantify and address the problem, and to provide evidence to justify the role of prevention and potentially start to break down barriers to control and treatment of lameness.

Objective assessment of lameness in cattle after foot surgery

Assessment of lameness in cattle after foot surgery is important to monitor the recovery period, to improve the long-term success and the cows` welfare. This longitudinal multicenter retrospective study was carried out to evaluate the usefulness of automated tools of weight bearing and gait analysis following foot surgery to support the clinician to monitor lameness in cattle. For this purpose, the effect of involvement of different anatomical structures and the use of different surgery methods on gait parameters of post-operative recovery was assessed. The study consisted of 2 experiments and included cattle with unilateral foot pathologies located in the digital region which needed 1 (experiment 1; n = 30) or 2 (experiment 2; n = 4) surgical interventions. The surgical techniques were debridement, joint lavage, partial resection of bones, tendons or synovial structures, total resection of the sesamoid bone and digit amputation. Two accelerometers (400 Hz; kinematic outcome = stance phase duration; kinetic outcome = foot load and toe-off), a 4-scale weighing platform (difference of mean weight distribution across the limbs; Δ_weight) and a subjective locomotion score were used to evaluate gait parameters every 3 to 4 days after surgery. A repeated measures ANOVA was used in experiment 1 and a receiver operator characteristic analysis was used to determine the optimal cutoff values in experiment 2. Results showed that the differences across limbs for the pedogram variables of stance phases and peaks of foot load and toe-off, Δ_weight and the locomotion score were highest if joints or sesamoid bones were involved, suggesting that these cattle were more severely lame compared to cattle with more superficial foot pathologies. There was a significantly lower degree of lameness after surgical debridement and after digit amputation compared to partial and total resection of anatomical structures of the foot. The use of accelerometers and a 4-scale weighing platform represent promising objective tools for post-operative monitoring of lameness and can support the clinician in gait assessment to improve the long-term success of surgical interventions in the area of the foot.

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.

Non-Compliance and Follow-Up in Swedish Official and Private Animal Welfare Control of Dairy Cows

Farmers often have to comply with several sets of animal welfare regulations, since private standards have been developed in addition to legislation. Using an epidemiological approach, we analysed protocols from animal welfare inspections carried out in Swedish dairy herds by the County Administrative Board (CAB; official control of legislation) and by the dairy company Arla Foods (private control of Arlagården standard) during 2010⁻2013 in the county of Västra Götaland. CAB and Arla inspections were not carried out simultaneously. We aimed to identify common non-compliances, quantify risk factors of non-compliance, and investigate if non-compliances were based on animal-, resource-, or management-based requirements, as well as determining the time period allowed for achieving compliance. Non-compliance was found in 58% of CAB cases, and 51% of Arla cases (each case comprising a sequence of one or several inspections). Dirty dairy cattle was one of the most frequent non-compliances in both control systems. However, the differences in control results were large, suggesting a difference in focus between the two systems. Tie-stall housing and winter season (Dec⁻Feb) were common risk factors for non-compliance, and overall organic farms had a lower predicted number of non-compliances compared to conventional farms. The presence of both similarities and differences between the systems underlines the need for transparency, predictability, and clarity of inspections.

A Review of the Relationship Between Hoof Trimming and Dairy Cattle Welfare

A narrative integrative review on the evidence for hoof trimming found 16 articles on efficacy, frequency, and associations with behavior and physiologic parameters. Review of these studies revealed (1) hoof trimming is associated with behavior and physiologic changes; (2) increasing the frequency of hoof trimming seems to decrease the incidence of hoof lesions; (3) there is limited research to support any particular technique; and (4) descriptions of the hoof trimming techniques used is inadequate in most articles. To increase scientific support for hoof trimming practices, current knowledge gaps in technique, timing, and frequency of hoof trimming need to be addressed.

Association between Lameness and Indicators of Dairy Cow Welfare Based on Locomotion Scoring, Body and Hock Condition, Leg Hygiene and Lying Behavior

Simple Summary

Lameness is a major welfare issue in dairy cows. Locomotion scoring (LS) is mostly used in identifying lame cows based on gait and postural changes. However, lameness shares some important associations with body condition, hock condition, leg hygiene and behavioral changes such as lying behavior. These measures are considered animal-based indicators in assessing welfare in dairy cows. This review discusses lameness as a welfare problem, the use of LS, and the relationship with the aforementioned welfare assessment protocols. Such information could be useful in depicting the impact on cow welfare as well as in reducing the occurrence of lameness in dairy herds.

Abstract

Dairy cow welfare is an important consideration for optimal production in the dairy industry. Lameness affects the welfare of dairy herds by limiting productivity. Whilst the application of LS systems helps in identifying lame cows, the technique meets with certain constraints, ranging from the detection of mild gait changes to on-farm practical applications. Recent studies have shown that certain animal-based measures considered in welfare assessment, such as body condition, hock condition and leg hygiene, are associated with lameness in dairy cows. Furthermore, behavioural changes inherent in lame cows, especially the comfort in resting and lying down, have been shown to be vital indicators of cow welfare. Highlighting the relationship between lameness and these welfare indicators could assist in better understanding their role, either as risk factors or as consequences of lameness. Nevertheless, since the conditions predisposing a cow to lameness are multifaceted, it is vital to cite the factors that could influence the on-farm practical application of such welfare indicators in lameness studies. This review begins with the welfare consequences of lameness by comparing normal and abnormal gait as well as the use of LS system in detecting lame cows. Animal-based measures related to cow welfare and links with changes in locomotion as employed in lameness research are discussed. Finally, alterations in lying behaviour are also presented as indicators of lameness with the corresponding welfare implication in lame cows.

Evaluation of two methodologies for lameness detection in dairy cows based on postural and gait abnormalities observed during milking and while restrained at headlock stanchions

Lameness is a critical issue on dairies with an impact on production and animal welfare. Early lameness detection followed by effective treatments could improve prognosis and cure rate of lame cows. Current methods for lameness detection are based on locomotion score (LS) that requires observation of cows walking, preferably at the exit of the milking parlor. This is a time-consuming task that is difficult to implement on large dairies. Therefore, a common methodology for lameness detection is based on milkers' and cow pushers' observations of cows walking to the milking parlor or standing at the milking stall (MPP). Observation of postural abnormalities predictive of lameness while cows are locked at stanchions (S) can be used as an alternative detection method. The objective of this research was to study the association between postural and gait abnormalities observed with S and MPP methodologies and lameness using LS≥3 as the reference method, as well as to evaluate the epidemiological characteristics of those methods as a diagnostic test for lameness. A secondary objective was to describe the type of hoof lesions observed with postural and gait abnormalities detected with LS, MPP, and S methodologies. A cross-sectional study design was performed on 2274 cows from one farm in California (US). Arched back, cow-hocked, wide-stance, and favored-limb postures as well as uneven gait were observed. Both lameness detection methodologies, S and MPP, indicated that arched back and favored-limb were postural abnormalities associated with lameness. However, the epidemiological test characteristics for each of the postures evaluated as a diagnostic test for lameness indicated that both detection methods, S and MPP, had good specificity (>0.91) but poor sensitivity (0.04-0.39). A convenience sample of 104 cows, selected based on LS>3, favored-limb, presence of two or more abnormal postures, and gait anomalies with either S or MPP methods, received a hoof examination. Lesions were observed on cows selected by LS (17/24), MPP (21/30), and S (33/60) criteria, suggesting a lack of concordance between lameness detection methodologies and visible hoof lesions. Nevertheless, due to the lack of acceptance of LS as the lameness detection method on large commercial dairies in California, it is imperative that future research evaluates modifications of S and MPP lameness detection techniques, considering hoof lesion as reference method.