Enhancing the prediction accuracy of bovine lameness models through transformations of limb movement variables

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.

Precision Technologies to Address Dairy Cattle Welfare: Focus on Lameness, Mastitis and Body Condition

Specific animal-based indicators that can be used to predict animal welfare have been the core of protocols for assessing the welfare of farm animals, such as those produced by the Welfare Quality project. At the same time, the contribution of technological tools for the accurate and real-time assessment of farm animal welfare is also evident. The solutions based on technological tools fit into the precision livestock farming (PLF) concept, which has improved productivity, economic sustainability, and animal welfare in dairy farms. PLF has been adopted recently; nevertheless, the need for technological support on farms is getting more and more attention and has translated into significant scientific contributions in various fields of the dairy industry, but with an emphasis on the health and welfare of the cows. This review aims to present the recent advances of PLF in dairy cow welfare, particularly in the assessment of lameness, mastitis, and body condition, which are among the most relevant animal-based indications for the welfare of cows. Finally, a discussion is presented on the possibility of integrating the information obtained by PLF into a welfare assessment framework.

Individual dairy cow management: achievements, obstacles and prospects

This review deals with the prospects and achievements of individual dairy cow management (IDCM) and the obstacles and difficulties encountered in attempts to successfully apply IDCM into routine dairy management. All aspects of dairy farm management, health, reproduction, nutrition and welfare are discussed in relation to IDCM. In addition, new IDCM R&D goals in these management fields are suggested, with practical steps to achieve them. The development of management technologies is spurred by the availability of off-the-shelf sensors and expanded recording capacity, data storage, and computing capabilities, as well as by demands for sustainable dairy production and improved animal wellbeing at a time of increasing herd size and milk production per cow. Management technologies are sought that would enable the full expression of genetic and physiological potential of each cow in the herd, to achieve the dairy operation's economic goals whilst optimizing the animal's wellbeing. Results and conclusions from the literature, as well as practical experience supported by published and unpublished data are analyzed and discussed. The object of these efforts is to identify knowledge and management routine gaps in the practical dairy operation, in order to point out directions and improvements for successful implementation of IDCM in the dairy cows' health, reproduction, nutrition and wellbeing.

Cost benefit analysis of automatic lameness detection systems in dairy herds: A dynamic programming approach

Epidemiological data establish that lameness is second only to mastitis as the dairy industry's most prevalent and costly animal welfare issue. Using an automatic lameness detection (ALD) system in which continuous, accurate detection is coupled with proper treatment, is key for reducing economic losses due to lameness. It is reasonable to assume that the cost of lameness would vary with its severity. Therefore, our first objective was to estimate the cost of different lameness severity levels as a function of milk production, lameness risk, conception probability, and treatment cost using a dynamic programming (DP) model. Our second objective was to conduct a cost benefit analysis for ALD systems which can reduce production losses through early detection and treatment of lameness, when compared to visual-detection (VD; i.e., performed by humans) systems. The default production loss parameters for the VD system used as inputs to the DP model were either sourced from the literature or were estimated based on data from a field trial. The production loss parameters for the ALD system used as inputs to the DP model were based on extrapolations of parameter values used for the VD system. The profit per present cow per year under assumed expenses and revenues decreased from $426.05 (when lameness incidence was assumed to be 0%) to $389.69 when lameness incidence was 19.5 %. Out of the 19.5 % lameness incidence in our default scenario, 9.8 % were moderate cases and 9.7 % were severe cases. Average cost of lameness was $36.36 at 19.5 % incidence. Average cost of lameness increased with increased incidence and was respectively $82.05, $195.05, and $286.87 at the low, medium, and high incidence scenarios. We used an operational framework which compared the lameness costs between the VD and ALD systems with 25 %, 50 % and 75 % net avoided costs (NAC) for the 10 year lifespan of the ALD system, at default, low, medium and high lameness incidence scenarios. The net return per cow per year from using an ALD system over a VD system was $13, at low incidence and 25 % NAC. The net return per cow per year for the ALD system was as high as $99 at high incidence and 75 % NAC. Out of 351 (3 system prices, 3 system efficiencies, 3 levels of lameness incidence and 13 different herd sizes) scenarios tested, 295 resulted in a net profit within the system lifespan of 10 years, thus justifying the investment in ALD systems.

Understanding dairy farmer intentions to make improvements to their management practices of foot lesions causing lameness in dairy cows

Foot lesions causing lameness in dairy cows have been demonstrated to adversely affect milk yield, reproductive performance and longevity, resulting in significant economic burden to individual dairy farmers and the dairy industry. Further, foot lesions compromise dairy cow welfare. Despite this knowledge, foot lesions remain a large problem in many dairy herds woldwide. Therefore, there is potential for dairy farmers to make changes to their current management practices of foot lesions. This study used the social-psychology framework, the Theory of Planned Behavior (TPB), to explore dairy farmers' intentions to make improvements to their current management practices of foot lesions in their dairy cows and to identify the underlying behavioral, normative and control beliefs facilitating and constraining this behavior. In accordance with the theoretical framework, Australian dairy farmers were invited to participate in an online questionnaire which included questions regarding intentions, attitudes, subjective norms and perceived behavioral control. Fifty-six dairy farmers completed the questionnaire. The overall intention of these dairy farmers to make improvements to their management practices of foot lesions in the next year was moderate. Dairy farmers believed improving their current management practices of foot lesions would improve animal welfare, increase milk production and was worth the cost involved (behavioral beliefs). They indicated that the opinions of consumers, staff, and animal welfare groups were important in their decision to make improvements (normative beliefs). Better equipment and facilities, improved knowledge and training, and a favorable cost-benefit ratio were perceived as factors that would enable dairy farmers to improve their management practices (control beliefs). While all of these beliefs may be considered as potential drivers to facilitate dairy farmers to change their management practices, the behavioral beliefs were identified as the priority beliefs that industry should target in the development of strategies to increase dairy farmer intentions to make improvements to their management practices of foot lesions.

Individualised automated lameness detection in dairy cows and the impact of historical window length on algorithm performance

Lameness is an important economic problem in the dairy sector, resulting in production loss and reduced welfare of dairy cows. Given the modern-day expansion of dairy herds, a tool to automatically detect lameness in real-time can therefore create added value for the farmer. The challenge in developing camera-based tools is that one system has to work for all the animals on the farm despite each animal having its own individual lameness response. Individualising these systems based on animal-level historical data is a way to achieve accurate monitoring on farm scale. The goal of this study is to optimise a lameness monitoring algorithm based on back posture values derived from a camera for individual cows by tuning the deviation thresholds and the quantity of the historical data being used. Back posture values from a sample of 209 Holstein Friesian cows in a large herd of over 2000 cows were collected during 15 months on a commercial dairy farm in Sweden. A historical data set of back posture values was generated for each cow to calculate an individual healthy reference per cow. For a gold standard reference, manual scoring of lameness based on the Sprecher scale was carried out weekly by a single skilled observer during the final 6 weeks of data collection. Using an individual threshold, deviations from the healthy reference were identified with a specificity of 82.3%, a sensitivity of 79%, an accuracy of 82%, and a precision of 36.1% when the length of the healthy reference window was not limited. When the length of the healthy reference window was varied between 30 and 250 days, it was observed that algorithm performance was maximised with a reference window of 200 days. This paper presents a high-performing lameness detection system and demonstrates the importance of the historical window length for healthy reference calculation in order to ensure the use of meaningful historical data in deviation detection algorithms.

Automatic lameness detection in cattle

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

The use of data mining methods for dystocia detection in Polish Holstein-Friesian Black-and-White cattle

OBJECTIVE

The aim of this study was to verify the usefulness of artificial neural networks (ANN), multivariate adaptive regression splines (MARS), naïve Bayes classifier (NBC), general discriminant analysis (GDA), and logistic regression (LR) for dystocia detection in Polish Holstein-Friesian Black-and-White heifers and cows and to indicate the most influential predictors of calving difficulty.

METHODS

A total of 1,342 and 1,699 calving records including six categorical and four continuous predictors were used. Calving category (difficult vs easy or difficult, moderate and easy) was the dependent variable.

RESULTS

The maximum sensitivity, specificity and accuracy achieved for heifers on the independent test set were 0.855 (for ANN), 0.969 (for NBC), and 0.813 (for GDA), respectively, whereas the values for cows were 0.600 (for ANN), 1.000 and 0.965 (for NBC, GDA, and LR), respectively. With the three categories of calving difficulty, the maximum overall accuracy for heifers and cows was 0.589 (for MARS) and 0.649 (for ANN), respectively. The most influential predictors for heifers were an average calving difficulty score for the dam's sire, calving age and the mean yield of the farm, where the heifer was kept, whereas for cows, these additionally included: calf sex, the difficulty of the preceding calving, and the mean daily milk yield for the preceding lactation.

CONCLUSION

The potential application of the investigated models in dairy cattle farming requires, however, their further improvement in order to reduce the rate of dystocia misdiagnosis and to increase detection reliability.

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.

Predictive models of lameness in dairy cows achieve high sensitivity and specificity with force measurements in three dimensions

Lameness remains a significant cause of production losses, a growing welfare concern and may be a greater economic burden than clinical mastitis . A growing need for accurate, continuous automated detection systems continues because US prevalence of lameness is 12·5% while individual herds may experience prevalence's of 27·8–50·8%. To that end the first force-plate system restricted to the vertical dimension identified lame cows with 85% specificity and 52% sensitivity . These results lead to the hypothesis that addition of transverse and longitudinal dimensions could improve sensitivity of lameness detection. To address the hypothesis we upgraded the original force plate system to measure ground reaction forces (GRFs) across three directions. GRFs and locomotion scores were generated from randomly selected cows and logistic regression was used to develop a model that characterised relationships of locomotion scores to the GRFs. This preliminary study showed 76 variables across 3 dimensions produced a model with greater than 90% sensitivity, specificity, and area under the receiver operating curve (AUC). The result was a marked improvement on the 52% sensitivity, and 85% specificity previously observed with the 1 dimensional model  or the 45% sensitivities reported with visual observations. Validation of model accuracy continues with the goal to finalise accurate automated methods of lameness detection.

Lameness detection based on multivariate continuous sensing of milk yield, rumination, and neck activity

The objective of this study was to develop and validate a mathematical model to detect clinical lameness based on existing sensor data that relate to the behavior and performance of cows in a commercial dairy farm. Identification of lame (44) and not lame (74) cows in the database was done based on the farm's daily herd health reports. All cows were equipped with a behavior sensor that measured neck activity and ruminating time. The cow's performance was measured with a milk yield meter in the milking parlor. In total, 38 model input variables were constructed from the sensor data comprising absolute values, relative values, daily standard deviations, slope coefficients, daytime and nighttime periods, variables related to individual temperament, and milk session-related variables. A lame group, cows recognized and treated for lameness, to not lame group comparison of daily data was done. Correlations between the dichotomous output variable (lame or not lame) and the model input variables were made. The highest correlation coefficient was obtained for the milk yield variable (rMY=0.45). In addition, a logistic regression model was developed based on the 7 highest correlated model input variables (the daily milk yield 4d before diagnosis; the slope coefficient of the daily milk yield 4d before diagnosis; the nighttime to daytime neck activity ratio 6d before diagnosis; the milk yield week difference ratio 4d before diagnosis; the milk yield week difference 4d before diagnosis; the neck activity level during the daytime 7d before diagnosis; the ruminating time during nighttime 6d before diagnosis). After a 10-fold cross-validation, the model obtained a sensitivity of 0.89 and a specificity of 0.85, with a correct classification rate of 0.86 when based on the averaged 10-fold model coefficients. This study demonstrates that existing farm data initially used for other purposes, such as heat detection, can be exploited for the automated detection of clinically lame animals on a daily basis as well.

Diversity in the magnitude of hind limb unloading occurs with similar forms of lameness in dairy cows

The objective of the study was to evaluate the relationship of veterinary clinical assessments of lameness to probability estimates of lameness predicted from vertical kinetic measures. We hypothesized that algorithm-derived probability estimates of lameness would accurately reflect vertical measures in lame limbs even though vertical changes may not inevitably occur in all lameness. Kinetic data were collected from sound (n=179) and unilaterally lame (n=167) dairy cattle with a 1-dimensional, parallel force plate system that registered vertical ground reaction force signatures of all four limbs as cows freely exited the milking parlour. Locomotion was scored for each hind limb using a 1-5 locomotion score system (1=sound, 5=severely lame). Pain response in the interdigital space was quantified with an algometer and pain response in the claw was quantified with a hoof tester fitted with a pressure gage. Lesions were assigned severity scores (1=minimal pathology to 5=severe pathology). Lameness diminished the magnitude of peak ground reaction forces, average ground reaction forces, Fourier transformed ground reaction forces, stance times and vertical impulses in the lame limbs of unilaterally lame cows. The only effect of lameness on the opposite sound limb was increased magnitude of stance times and vertical impulses in unilaterally lame cows. Symmetry measures of the peak ground reaction forces, average ground reaction forces, Fourier transformed ground reaction forces, stance times and vertical impulses between the left and right hind limbs were also affected in unilateral lameness. Paradoxically, limbs with clinically similar lesion and locomotion scores and pain responses were associated with a broad range of load-transfer off the limb. Substantial unloading and changes in the vertical limb variables occurred in some lameness while minimal unloading and changes in vertical limb variables occurred in other lameness. Corresponding probability estimates of lameness accurately reflected changes in the vertical parameters of limbs and generated low probability estimates of lameness when minimal unloading occurred. Failure to transfer load off limbs with pain reactions, locomotion abnormalities and lesions explained much of the limited sensitivity in lameness detection with vertical limb variables.

The effect of meloxicam on behavior and pain sensitivity of dairy calves following cautery dehorning with a local anesthetic

Effects of a single injection of meloxicam on calf behavior, pain sensitivity, and feed and water intakes were examined following dehorning. Sixty Holstein heifer calves were blocked by age and randomly assigned to receive an i.m. injection of meloxicam (0.5 mg/kg) or a placebo. All calves were given a lidocaine cornual nerve block (5 mL per horn). Treatments and nerve blocks were administered 10 min before cautery dehorning. Continuous sampling of behavior was performed during five 1-h intervals using video recordings, and total daily activity was monitored using an accelerometer. A pain sensitivity test was administered with a pressure algometer, and feed and water intakes were recorded daily. Calves were sham-dehorned 24 h before actual dehorning to establish baseline values, and all variables were assessed at the same times following dehorning and sham dehorning for up to 48 h post-dehorning. Meloxicam-treated calves displayed less ear flicking during the 44 h following dehorning (increases of 4.29+/-1.10 and 1.31+/-0.66 ear flicks/h in the first 24 h, and increases of 3.27+/-0.89 and 0.55+/-0.50 ear flicks/h during the second 24 h, for control and meloxicam calves, respectively) and less head shaking during the first 9 h following dehorning (increase of 2.53+/-0.54 and 0.85+/-0.46 headshakes/h over baseline for control and meloxicam, respectively). Meloxicam-treated calves were less active than controls during the first 5 h following dehorning (activity 34.1+/-3.2 and 30.6+/-2.6 for control and meloxicam, respectively) and displayed less sensitivity to pressure algometry 4 h after dehorning (pressure tolerance of 1.62+/-0.13 kg of force and 2.13+/-0.15 kg of force for control and meloxicam calves, respectively). Changes in behavior suggest that meloxicam was effective for reducing post-surgical pain and distress associated with calf dehorning.

Gait analysis of locomotory impairment in rats before and after neuromuscular injury

We used a gait analysis system (GAS) to measure the changes in locomotion parameters of adult Sprague-Dawley rats after neuromuscular injury, induced by repeated large-strain lengthening contractions of the dorsiflexors muscles. We developed a logistic regression model from test runs of control and permanently impaired (denervation of the dorsiflexor muscles) rats and used this model to predict the probabilities of locomotory impairment in rats injured by lengthening contractions. The data showed that GAS predicts the probability of locomotory impairment with very high reliability, with values close to 100% immediately after injury and close to 0% after several weeks of recovery from injury. The six transformed locomotion parameters most effective in the model were in three domains: frequency, force, and time. We conclude that application of the GAS instrument with our predictive model accurately identifies locomotory changes due to neuromuscular deficits. Use of this technology should be valuable for monitoring the progression of a neuromuscular disease and the effects of therapeutic interventions.