Lameness In Dairy Cows Research Articles

Associations of cow and farm characteristics with cow-level lameness using data from an extensive cross-sectional study across 3 structurally different dairy regions in Germany

The aim of the present study was to evaluate the associations between milk recording data, body condition score (BCS), housing factors, management factors, and lameness in freestall-housed dairy cows in 3 structurally different regions in Germany. These regions substantially vary regarding herd size, breeds, access to pasture, farm management (family run or company owned), and percentage of organic farms. The data used was collected in a large cross-sectional study from 2016 to 2019. A total of 58,144 cows from 651 farms in 3 regions of Germany (North, East, and South) was scored for locomotion and body condition. Additionally, data on milk yield, milk composition, breed, age, as well as information on housing and management were retrieved. One mixed-logistic regression model was fitted per region to evaluate the association of the data with the target variable "lame" and to allow for a comprehensive reflection across different kinds of farming types. In all regions, undercondition (BCS lower than recommended for the lactation stage; North: odds ratio [OR] 2.15, CI 1.96-2.34; East: OR 2.66, CI 2.45-2.88; South: OR 2.45, CI 2.01-2.98) and mid-lactation stage (102-204 d in milk; North: OR 1.15, CI 1.05-1.27; East: OR 1.24, CI 1.17-1.32; South: OR 1.38, CI 1.18-1.62) were associated with higher odds for lameness, whereas overcondition (BCS higher than recommended for the lactation stage; North: OR 0.51, CI 0.44-0.60; East: OR 0.51, CI 0.48-0.54; South: OR 0.65, CI 0.54-0.77) and parity of 1 or 2 was associated with lower odds (parity 1 = North: OR 0.32, CI 0.29-0.35; East: OR 0.19, CI 0.18-0.20; South: OR 0.28, CI 0.24-0.33; parity 2 = North: OR 0.51, CI 0.47-0.46; East: OR 0.41, CI 0.39-0.44; South: OR 0.49, CI 0.42-0.57), irrespective of the regional production characteristics. Low energy-corrected milk yield was associated with higher odds for lameness in South and North (North: OR 1.16, CI 1.05-1.27; South: OR 1.43, CI 1.22-1.69). Further factors such as pasture access for cows (North: OR 0.64, CI 0.50-0.82; and South: OR 0.65, CI 0.47-0.88), milk protein content (high milk protein content = North: OR 1.34, CI 1.18-1.52; East: OR 1.17, CI 1.08-1.28; low milk protein content = North: OR 0.79, CI 0.71-0.88; East: OR 0.84, CI 0.79-0.90), and breed (lower odds for "other" [other breeds than German Simmental and German Holstein] in East [OR 0.47, CI 0.42-0.53] and lower odds both for German Holstein and "other" in South [German Holstein: OR 0.62, CI 0.43-0.90; other: OR 0.46, CI 0.34 - 0.62]) were associated with lameness in 2 regions, respectively. The risk of ketosis (higher odds in North: OR 1.11, CI 1.01-1.22) and somatic cell count (higher odds in East: increased (>39.9 cells × 1,000/mL): OR 1.10; CI 1.03-1.17; high (>198.5 cells × 1,000/mL): OR 1.08; CI 1.01-1.06) altered the odds for lameness in 1 region, respectively. Cows from organic farms had lower odds for lameness in all 3 regions (North: OR 0.18, CI 0.11-0.32; East: OR 0.39, CI 0.28-0.56; South: OR 0.45, CI 0.29-0.68). As the dairy production systems differed substantially between the different regions, the results of this study can be viewed as representative for a wide variety of loose-housed dairy systems in Europe and North America. The consistent association between low BCS and lameness in all regions aligns with the previous literature. Our study also suggests that risk factors for lameness can differ between geographically regions, potentially due to differences in which dairy production system is predominantly used and that region-specific characteristics should be taken into account in comparable future projects.

Identifying housing and management factors associated with lameness in small-scaled mountain dairy farms with different housing systems

The aim of this study was to identify farm-level factors associated with the prevalence of lameness in dairy cows kept in small scale mountain dairy farms. The study population included 1 891 dairy cows (1 069 cows housed in the free stalls and 822 in tie stalls) reared on 204 farms (111 free stalls and 93 tie stalls) in the Alpine region of South Tyrol (Northern Italy). The prevalence of animals showing moderate lameness or severe lameness in tie stalls was 7.8% and 13.9% and for animals housed in free stalls was 5.7% and 11.3%, respectively. A higher prevalence of moderate lameness in free stalls was associated with the use of sawdust bedding (odds ratio (OR) = 2.92; 95% confidence interval (CI) = 1.73–3.89) compared to sand bedding and a lower prevalence with herd size (OR = 0.61; CI = 0.42–0.99, for a 5-cow increase), access to pasture (OR = 0.88; CI = 0.65–0.97) and lime-straw-water bedding (OR =0.63; CI = 0.56–0.89). A higher prevalence of severe lameness in free stalls was associated with the use of wood plates as lying area surface (OR = 2.33; CI = 2.11–2.61) and a lower with the use of rubber mats (OR = 0.22; CI = 0.14–0.46), sand bedding (OR = 0.54; CI =0.41–0.91) and herd size (OR = 0.38; CI = 0.27–0.77, for a 15-cow increase). Furthermore, the results in tie stall farms revealed that a higher prevalence of moderate lameness was associated with the use of sawdust bedding (OR = 1.99; CI) = 1.4–2.66) and herd size (OR = 1.96; CI = 1.41–2.51, for a 5-cow increase), and a lower prevalence with access to pasture (OR = 0.29; CI = 0.22–0.5), increased lying area dimension (OR =0.67; CI = 0.44–0.89) and lime-straw-water bedding (OR=0.22; CI=0.11–0.74). A higher prevalence of severe lameness in tie stalls was associated with the use of wood plates as lying area surface (OR = 3.19; CI = 2.66–4.15) and herd size (OR = 2.14; CI = 1.97–2.87, for a 5-cow increase), and a lower prevalence was associated with the use of rubber mats (OR = 0.37; CI = 0.19–0.61), sand bedding (OR = 0.53; CI = 0.21–0.82) and increased lying area dimension (OR = 0.77; CI = 0.55–0.99). In conclusion, the present findings should provide important insights into housing and management practices relevant to subsequent causal cow-level studies of animal health and welfare in small-scale mountain dairy farms.

Association between Milk Electrical Conductivity Biomarkers with Lameness in Dairy Cows.

Early identification of lameness at all phases of lactation improves milk yield and reduces the incidence of mastitis in the herd. According to the literature we hypothesized that there are associations of electrical conductivity variables of milk flow with lameness in dairy cows. The aim of this study was to determine if blood cortisol and electrical conductivity in the milk flow phases correlate with each other and whether they are related to cow lameness. On one farm, out of 1500 cows, 64 cows with signs of lameness and 56 healthy cows were selected with an average of 2.8 lactations and 60 days in the postpartum period. A local veterinarian who specializes in hoof care treatments identified and scored lameness. During evening milking, the milk flow of all 120 cows was measured using electronic milk flow meters (Lactocorder®, WMB AG, Balgache, Switzerland). Before each milking, two electronic mobile milk flow meters (Lactocorders) were mounted between the milking apparatus and the milking tube to take measurements. We found that the average cortisol concentration in the blood of the studied cows was significantly correlated with the laminitis score. Results of this study indicate that the number of non-lame cows with a milk electrical conductivity level of <6 mS/cm even reached 90.8-92.3% of animals. Milk electrical conductivity indicators ≥ 6 mS/cm were determined in 17.8-29.0% more animals in the group of lame cows compared to the group of non-lame cows. According to our study, we detected that blood cortisol concentration had the strongest positive correlation with milk electrical conductivity indicators. Cows with a greater lameness score had a higher cortisol content and milk conductivity.

Genetics can play a bigger role in breeding out lameness

Introduction: Good husbandry, environment and nutrition all play a part in the fight against dairy cow lameness, but new research has found genetics has a bigger role than was previously thought.

Temporal aggregation network using micromotion features for early lameness recognition in dairy cows

Lameness severely affects dairy cow welfare and potential milk production while reducing dairy farm economics. Most existing computer vision-based lameness recognition methods extract a single class of lameness features, and it is difficult to accurately recognize early lameness in dairy cows with only slight gait abnormalities. In this paper, a temporal aggregation network using micromotion features is proposed, which can capture micromotion features and spatiotemporal features to recognize early lameness in dairy cows. First, a Gaussian filter is used to remove noise in videos of a dairy cow walking. Second, frame-difference images containing motion features are constructed by the frame-difference method. Third, micromotion features in the frame-difference images are extracted using the designed focal convolutional layer. Fourth, the acquired micromotion feature sequence is input into temporal attention mechanisms to extract multigranularity temporal features. Finally, a fully connected layer is used to classify the dairy cow lameness. To evaluate the performance of the proposed model, 621 videos were randomly selected from 888 videos as training videos, 87 videos were randomly selected as validation videos, and the remaining 180 videos were selected as test videos. The lameness recognition accuracy of the proposed model was 98.89%, which was better than state-of-the-art lameness recognition methods. The experimental results showed that the proposed model can extract the micromotion features and multigranularity temporal features in the frame-difference images, thereby improving the early lameness recognition accuracy in dairy cows.

The Association of the Genetic Polymorphism of the stat5a and gh/mspi Genes and the Incidence of Lameness in Holstein Cows in Tunisia

The objective of this study consists in determining the association between genetic polymorphism of the STAT5A and GH genes and the incidence of lameness in dairy cows in Tunisia

Disentangling the relationships between lameness, milking frequency and milk production in Dutch dairy herds using an automatic milking system

Lameness in dairy cattle commonly results in milk production losses. Previous studies have quantified this effect in cows milked with conventional milking systems. The effect of lameness on milk production in herds with automatic milking systems (AMS) might differ because lame cows may visit the AMS less frequently, resulting in an additional milk production loss. This observational study aimed to quantify the direct and indirect effect, through milking frequency, of lameness on milk production in AMS herds by means of a multilevel mediation analysis. Lameness scores, the number of visits to the AMS, and daily milk production records of 1608 dairy cows from 87 Dutch AMS herds were available for analysis. Of those cows, 27 % were mildly lame and 11 % were severely lame. Using a multilevel Poisson regression model, both mildly lame and severely lame cows were identified to visit the AMS less frequently. A separate multilevel linear regression model identified that the direct effect of severe lameness on milk production was 1.4 kg per day. The subsequent multilevel mediation analysis showed that the relationship between severe lameness and milk production was mediated through a reduced milking frequency; 63 % of the total milk production losses (3.9 kg per day) were due to a reduced milking frequency (2.5 kg per day). The mediating effect of a reduced milking frequency was most prominent in cows with parity ≥ 4 and in cows that were ≥ 30 days in lactation. This study suggests that milk production losses due to severe lameness of dairy cows might be higher in AMS herds compared with cows in herds with a conventional milking system, a likely result of a reduced milking frequency of those cows. Prevention of severe lameness may be more cost-effective in AMS herds than in herds with a conventional milking system.

Impact of Calving Difficulty on Lameness in Dairy Cows

The aims of our study were to evaluate the associations between calving difficulty and lameness and their effects on milk yield and quality traits. A total of 4723 calving cases were evaluated for calving difficulty using a 4-point scoring system. Lameness was diagnosed with a visual locomotion score system from 1 to 30 days after calving in 333 fresh dairy cows. Cows were divided into non-lame cows and lame cows. Milk quality traits were registered using Lely Astronaut® A3 milking robots. The normal distribution of all indicators was assessed using the Shapiro–Wilk normality test. Normally distributed milk indicators were expressed as mean ± standard error of the mean. Differences between the mean values of their groups were determined using the Fisher’s least significant difference test. We categorized cows by health status, i.e., lame (LA) and non-lame (HL) cows, and according to calving difficulty (CD) (on a 4-point scale: 1—no problem, 2—slight problem, 3—problems requiring assistance, 4—considerable force and extreme difficulty). In the present study, calving difficulty increased the risk of lameness in cows by 2.09-fold (95% CI = 1.644–2.650, p < 0.001). It was found that the mean standard milk yield in fresh dairy cows with calving difficulty was lower (−6.14 kg, p < 0.001) than in the group where no assistance was required at calving. Similarly, herd affected milk fat (%) and the calving process—herd and the interaction between calving difficulty and herd—and lameness impacted the quantity of milk protein and lactose in cows. We found that severe lameness (3–4 points) (3.88–5.92% of cows) became more prevalent in those cows that had dystocia than those that did not (0.27–2.37% of cows).

Effects of routine treatment with nonsteroidal anti-inflammatory drugs at calving and when lame on the future probability of lameness and culling in dairy cows: A randomized controlled trial

Claw horn lesions (CHL) are reported as the most common cause of lameness in intensive dairy systems. Despite their prevalence, the underlying pathological mechanisms and preventive strategies for CHL remain poorly understood. Recent advances have pointed to the role of inflammation in disease aetiopathogenesis. Moderating inflammation from first calving may lead to long-term benefits and a viable intervention for treating and preventing disease. We conducted a 34-mo randomized controlled trial to investigate the effects of routine treatment with the nonsteroidal anti-inflammatory drug ketoprofen at calving and during treatment for lameness, on the future probability of lameness and culling, caused by exposure to normal farm conditions. A cohort of dairy heifers were recruited from a single, commercial dairy herd between January 8, 2018, and June 22, 2020, and randomly allocated to one of 4 treatment groups before first calving. The lactating herd was lameness scored every 2 wk on a 0 to 3 scale, to identify animals that became lame (single score ≥2a) and hence required treatment. Animals in group 1 received a therapeutic trim and a hoof block on the sound claw (if deemed necessary) every time they were treated for lameness. Animals in group 2 received the same treatment as group 1 with the addition of a 3-d course of ketoprofen (single dose daily) every time they were treated for lameness. Animals in group 3 received the same treatment as group 2 with the addition of a 3-d course of ketoprofen (single dose daily) starting 24 to 36 h after each calving. Animals in group 4 received a 3-d course of ketoprofen (single dose daily) every time they were identified with lameness. No therapeutic trim was administered to this group, unless they were identified as severely lame (a single score ≥3a). Animals were followed for the duration of the study (ending October 23, 2020). Probability of lameness was assessed by a lameness outcome score collected every 14 d. Data on culling was extracted from farm records. One hundred thirty-two animals were recruited to each group, with data from 438 animals included in the final analysis (111 in group 1, 117 in group 2, 100 in group 3, and 110 in group 4). Mixed effect logistic regression models were used to evaluate the effect of treatment group on the ongoing probability of lameness. Compared with the control group (group 1), animals in group 3 were less likely to become lame (odds ratio: 0.66) and severely lame (odds ratio: 0.28). A Cox proportional hazards survival model was used to investigate the effect of treatment group on time to culling. Compared with group 1, animals in groups 2 and 3 were at reduced risk of culling (hazard ratios: 0.55 and 0.56, respectively). The lameness effect size we identified was large and indicated that treating a cohort of animals with the group 3 protocol, would lead to an absolute reduction in population lameness prevalence of approximately 10% and severe lameness prevalence of 3%, compared with animals treated in accordance with conventional best practice (group 1).

Fusion of RGB, optical flow and skeleton features for the detection of lameness in dairy cows

Lameness is common in dairy cows. Methods that used RGB-based images to detect lameness in dairy cows always have low accuracy and poor robustness because of the complex background environment involved. In this study, a lameness detection method for dairy cows based on multiple features, including RGB, optical flow and skeletons, was proposed. The network was divided into three branches according to different inputs: for Branch1 and Branch3, a convolutional neural network (CNN) was used to predict lameness according to the input images and optical flow, and for Branch2, a spatial temporal graph convolutional network (ST-GCN) was used to predict lameness according to the cows’ skeletons. Finally, the weight was adjusted, and the prediction scores of these three branches were fused to complete the lameness detection process. In this research, 680 different videos were used for training and testing. The segmentation ratio of the train set and test set was 6:1. The best ACC was 97.20% when the weight of the RGB, optical flow and skeletons was 1:0.5:0.5. To verify the robustness of the method, the gamma transform was used to adjust the brightness of the image to simulate a change in illumination. Under different illumination settings, the maximum error of the method was 2.65%, which was significantly lower than other methods without skeletons. The results showed that the proposed method was effective for the detection of early-stage lameness, severe lameness, and non-lameness of dairy cows. • Fusion of RGB, optical flow and skeleton features for the detection of lameness in dairy cows. • Cow skeletons are used to increase network attention to cows. • A spatial temporal graph convolutional network (ST-GCN) was used to predict lameness according to the cows' skeletons. • This method is more stable in the antagonism of environmental robustness.

Dairy cow lameness detection using a back curvature feature

• A new deep learning framework to detect dairy cow lameness was proposed. • This method was more stable in the antagonism of environmental robustness. • This method improves the detection accuracy of dairy cow lameness. Manual detection of lameness poses several problems, such as difficulty in finding sudden, severe or early lameness behavior. A dairy cow’s lameness is closely related to curvature of the cow’s back. Focusing on the curvature features of dairy cows’ backs, this study proposes a lameness detection method that combines machine vision technology with a deep learning algorithm. Firstly, the FLYOLOv3 algorithm was used to construct a Cow’s Back Position Extraction (CBPE) model to realize the extraction of the dairy cow’s back position coordinates. Simultaneously, a First to Last Frame Image Difference (FLFID) algorithm was used to construct a Cow’s Object Region Extraction (CORE) model to separate the dairy cow from the image background and obtain the pixel region of the dairy cow. Then, a Cow’s Back Curvature Extraction (CBCE) model was used to extract the dairy cow’s back curvature data from the acquired dairy cow’s back position and pixel region of the dairy cow. Finally, a Noise+ Bilateral Long Short-term Memory (BiLSTM) model was used to predict the curvature data and match the curvature features of the dairy cow’s lameness, so as to classify and detect dairy cow lameness. To verify the effectiveness of the algorithm, 567 videos were used to train the network model in a Long Short-term Memory (LSTM) model, a BiLSTM model, Noise+LSTM model, and the model proposed in this paper, respectively, and 243 videos were used for verification and testing. According to the fitting curvature data of the dairy cows’ back obtained by the algorithm used in this paper, it was found that the average classification accuracy of the model proposed in this research was 8.04%, 2.09%, and 5.78% higher than the average classification accuracy of the LSTM, BiLSTM, Noise+LSTM models, respectively. In the parallel experiment that classified the detection of dairy cow lameness, the average classification accuracy of the model proposed in this paper was 96.61%. The above results show that the lameness of dairy cows can be correctly detected through analysis of the curvature features of dairy cows' backs. The proposed method is a novel, deep learning-based method for dairy cow lameness early detection which may have significant economic impact on the dairy industry, and the proposed method provides an innovative means for detecting dairy cow lameness.

Detection of Treponema phylotypes from digital dermatitis lesions and effect of different phylotypes on lesion size.

Bovine digital dermatitis (BDD) is a contagious infectious disease which causes lameness in dairy cows. It has a multifactorial etiology which is not yet fully understood but Treponema spp. seem to play a significant role in development of BDD lesions. This study evaluated the presence of Treponema phylotypes commonly associated with BDD (T. medium/T. vincentii, T. phagedenis and T. putidum/T. denticola), in four farms different areas in Iran. Single biopsies were taken from 113 Holstein cows with active BDD lesions (scored according to size) on the farms and polymerase chain reaction assays used to detect 16S rRNA nucleotide fragments of three BDD Treponema phylotype groups: ‘‘T. medium/T. vincentii’’, ‘‘T. phagedenis’’ and ‘‘T. putidum/T. denticola’’ (now T. pedis). Over 95.00% of samples were positive for at least one of phylotypes, with 89.00%, 91.00 %, and 66.00% of samples were positive for T. putidum/T. denticola, T. phagedenis and T. medium/T. vincentii, respectively. Out of the 113 samples, 60.00% were positive for all three phylotypes, the detection of T. putidum/T. denticola was positively associated with detection of both T. phagedenis and T. medium/T. vincentii. No association between lesion size and phylotypes identified was found but there were significant differences between farms in the proportion of each phylotypes identified. Further research is required to establish the factors influencing the proportions of individual phylotypes, especially at the farm level.

Producer Perceptions Toward Prevention and Control of Lameness in Dairy Cows in Alberta Canada: A Thematic Analysis.

Lameness in dairy cattle poses both an animal welfare and economic threat to dairy farms. Although the Canadian dairy industry has identified lameness as the most important health issue, lameness prevalence in the province of Alberta has not decreased over the last decade. Factors related to lameness have been reported, but the prevalence remains high. Therefore, this study was conducted to investigate dairy producers' perceptions on lameness and how these perceptions influence lameness prevalence in their cows. Qualitative interviews with open-ended questions were conducted with nine dairy producers in Alberta, Canada presenting farms with a wide variety of lameness prevalence. Thematic analysis of these interviews revealed five major themes, as well as five distinct types of producers regarding their perceptions. All nine producers mentioned similar challenges with lameness prevention and control. Identifying lameness, taking action, delays in achieving success, various approaches to prevention and control strategies, and differences between farms were the challenges encountered. However, producers' attitudes when dealing with these challenges varied. We concluded that understanding producers' perceptions is essential as no “one size fits all”, when advising them regarding how to address lameness, as guidance and support will be most successful when it is aligned with their viewpoint.

Cow- and herd-level risk factors for lameness in partly housed pasture-based dairy cows

Lameness in dairy cows is a major animal welfare concern and has substantial economic impact through reduced production and fertility. Previous risk factor analyses have focused on housed systems, rather than those where cows were grazed for the majority of the year and housed only for the winter period. Therefore, the aim of this observational study was to identify a robust set of cow-level and herd-level risk factors for lameness in a pasture-based system, based on predictors from the housing and grazing periods. Ninety-nine farms were visited during the grazing period (April 2019-September 2019), and 85 farms were revisited during the housing period (October 2019-February 2020). At each visit, all lactating cows were scored for lameness (0 = good mobility, 1 = imperfect mobility, 2 = impaired mobility, 3 = severely impaired mobility), and potential herd-level risk factors were recorded through questionnaires and infrastructure measurements. Routine cow-level management data were also collected. Important risk factors for lameness were derived though triangulation of results from elastic net regression, and from logistic regression model selection using modified Bayesian information criterion. Both selection methods were implemented using bootstrapping. This novel approach has not previously been used in a cow-level or herd-level risk factor analysis in dairy cows, to the authors' knowledge. The binary outcome variable was lameness status, whereby cows with a lameness score of 0 or 1 were classed as non-lame and cows with a score of 2 or 3 were classed as lame. Cow-level risk factors for increased lameness prevalence were age and genetic predicted transmitting ability for lameness. Herd-level risk factors included farm and herd size, stones in paddock gateways, slats on cow tracks near the collecting yard, a sharper turn at the parlor exit, presence of digital dermatitis on the farm, and the farmers' perception of whether lameness was a problem on the farm. This large-scale study identified the most important associations between risk factors and lameness, based on the entire year (grazing and housing periods), providing a focus for future randomized clinical trials.

Machine learning approaches for the prediction of lameness in dairy cows

Lameness is one of the costliest health problems, as well as a welfare concern in dairy cows. However, it is difficult to detect cows with possible lameness, or the ones that are at risk of becoming lame e.g. in the next week or so. In this study, we investigated the ability of three machine learning algorithms, Naïve Bayes (NB), Random Forest (RF) and Multilayer Perceptron (MLP), to predict cases of lameness using milk production and conformation traits. The performance of these algorithms was compared with logistic regression (LR) as the gold standard approach for binary classification. We had a total of 2 535 lameness scores (2 248 sound and 287 unsound) and 29 predictor features from nine dairy herds in Australia to predict lameness incidence. Training was done on 80% of the data within each herd with the remainder used as validation set. Our results indicated that in terms of area under curve of receiver operating characteristics, there were negligible differences between LR (0.67) and NB (0.66) while MLP (0.62) and RF (0.61) underperformed compared to the other two methods. However, the F1-score in NB (27%) outperformed LR (1%), suggesting that NB could potentially be a more reliable method for the prediction of lameness in practice, given enough relevant data are available for proper training, which was a limitation in this study. Considering the small size of our dataset, lack of information about environmental conditions prior to the incidence of lameness, management practices, short time gap between production records and lameness scoring, and farm information, this study proved the concept of using machine learning predictive models to predict the incidence of lameness a priori to its occurrence and thus may become a valuable decision support system for better lameness management in precision dairy farming.

Behaviour, Hygiene, and Lameness of Dairy Cows in a Compost Barn During Cold Seasons in a Subtropical Climate

AbstractThis study aimed to assess the thermal environment, behaviour, hygiene, and lameness in dairy cows with different numbers of lactations housed in a compost-bedded pack barn system in a humid subtropical climate during autumn and winter. Between April and July 2016, behavioural and microclimatic environment assessments were conducted in a compost barn system, and hygiene and lameness assessments were performed. Crossbred cows in lactation (n = 10) were divided into two groups according to the number of lactations: primiparous and multiparous. Air temperature, relative humidity, and air ventilation speed were measured at 09:00, 12:00, and 15:00. The cows’ hygiene and lameness were evaluated using subjective scores ranging from 1 to 4 and 1 to 5, respectively. The daytime behaviour assessment was performed as sampling 0/1, using the focal method. Primiparous cows showed a greater probability of walking (P<0.05) than multiparous cows between 09:20 and 11:20, and at 15:20, and increased the water intake at 14:00. Regarding the eating behaviour and the effect of time of day within each group (P<0.05), the cows ate more in the morning’s early hours. The cows’ hygiene scores were 1 or 2, and most cows presented scores of 1 or 2 for lameness. Our research has shown that microclimatic variables may influence the walking behaviour and water intake of primiparous cows during the hottest periods of the day, even during cold weather, in a humid subtropical climate. The dairy cows showed low hygiene and lameness scores, which indicates that the compost bar positively influenced cleanliness and locomotion comfort.

Dairy cattle lameness prevalence, causes and risk factors in selected farms of southern Ethiopia

A cross-sectional study was conducted from October 2018 to December 2019 to estimate the prevalence of lameness in dairy cows, to assess the causes and associated risk factors in southern Ethiopia. A total of 2009 dairy cows were visually and clinically examined, and the overall prevalence of lameness was found to be 14. 1% (95% CI=12.7-15.7). The prevalence of lameness was significantly (P< 0.05) higher in Arsi Negelle, Yirgalem, and Wolaita Sodo than in Wondo Genet and Hawassa. The prevalence of lameness was also associated with the stages of pregnancy and parity (P < 0.05). Generally, the prevalence of lameness in dairy cows was increasing with the increase in parity and stage of pregnancy. The main abnormalities observed in this study were hooves overgrowth (6%), lesions between hooves (4%), lesions on legs (2.2%), trauma (0.6%), arthritis (0.5%), and a sole ulcer (0.3%). Most of these abnormalities were mainly due to faulty management. Hence, based on this finding it is recommended that hoof management and trimming are very essential components of lameness control. Furthermore, early detection and treatment of lame cows; training of the owners on hoof management; and improvement of the housing are helpful to keep lameness at a lower level.

Impact of Lameness on Attributes of Feeding Registered with Noseband Sensor in Fresh Dairy Cows

We hypothesized that lameness in fresh dairy cows (1–30 days after calving) has an impact on attributes of feeding registered with a noseband sensor. The aim of this study was to investigate the impact of lameness in fresh dairy cows on attributes of feeding (registered with the RumiWatch noseband sensor): rumination time (RT), drinking time (DT), eating time (ET), rumination chews (RC), eating chews (EC), chews per minute (CM), drinking gulps (DG), bolus count (B), and chews per bolus (CB). The measurement registration was started at the first day after calving and continued until 30 days after calving. There were 20 Lithuanian black and white breed cows selected. Lameness diagnosis was performed by trained staff based on a locomotion score system and it was diagnosed on average on the 15th day after calving. The causes of lameness were categorized as sole ulcer, abscess and foot rot. Special attention was paid to attributes of feeding registered 14 days before and 13 days after diagnosis. The 10 lame cows (LG) used in this experiment had a lameness score of 3–4 presented with severe lameness: they were reluctant to move and unwilling to complete weight transfer off the affected limb. The 10 healthy cows (HG) were given a lameness score of 1. We found that lameness of fresh dairy cows has an impact on inline registered ingestive behaviors biomarkers—the mean RT of HG cows was as much as 2.19 times higher than that of LG cows on the day of diagnosis of lameness, later this difference between the groups decreased to the sixth day of treatment, then increased again and decreased at the end of the experiment. The lowest eating time was found on diagnosis day and the highest on the ninth day before determination of lameness. Drinking time was higher in the HG group, with the exception of 10 and 9 days prior to clinical signs of disease in LG cows. A downward trend in rumination chews was observed in LG cows from day 7 until the onset of clinical symptoms. The bolus count decreased from day 3 before diagnosis to day 1 after diagnosis in LG cows. The largest difference in this indicator between groups was found on day of diagnosis. Analysing the pattern of CM values in the LG group, we found a decrease from 10 days before to 2 days after diagnosis. The CB value was almost the same in both groups of cows at the end of the experiment, but largest difference between the groups was found on day 7 after clinical sings of lameness.

The effect of housing tiestall dairy cows in deep-bedded pens during an 8-week dry period on gait and step activity

Increasing locomotor activity can improve leg health and decrease the prevalence of lameness in dairy cows. The dry period offers an opportunity to provide alternative housing to tiestall (TSL) cows that can increase locomotor activity. The objective was to determine whether housing TSL dairy cows in a deep-bedded loose pen (LP) during the 8-wk dry period affected gait and step activity. Twenty cows, paired by parity and calving date, were assigned at dry-off to a deep-bedded LP or a TSL. Step activity was measured by leg-mounted pedometers. Cows were walked 1×/wk on a test corridor, and video recordings of gait were taken. Six aspects of gait were scored on a 0-to-5 scale (interval: 0.1 unit): tracking up, joint flexion, back arch, asymmetric step, swing, and reluctance to bear weight. Overall gait was also scored using a 1-to-5 scale (interval: 0.5 unit). Data for gait were analyzed based on the change in gait between dry-off and calving. Daily step data were averaged per week of the dry period. Analyses were performed using a mixed model with treatment, term, and block as fixed effects and cow nested within treatment and block as a random effect for step data. The same model, omitting the fixed effect of week, was used for gait variable analyses. There was no difference in step activity between LP and TSL cows (842.1 ± 88.86 vs. 799.5 ± 76.92 steps/d, LP vs. TSL, respectively). Only joint flexion yielded a treatment difference, with LP cows improving over time and TSL cows worsening (−0.4 ± 0.15 vs. 0.2 ± 0.15). Possibly owing to individual variation in motivation to perform locomotor activity, higher levels of step activity, independent of treatment, tended to be correlated with improvements in swinging out, tracking up, joint flexion, and overall gait score. The increased space allotted to LP cows may have allowed for a larger range of motion for each step, and the denser lying surface may have provided a cushioning effect when transitioning between rising and lying, all of which can improve joint health, reflected in improved joint flexion. Further investigation is warranted into the potential benefits of alternative housing on cow comfort, movement opportunity, and cow condition.

Identification of Risk Factors for Lameness Detection with Help of Biosensors

In this study we hypothesized that the lameness of early lactation dairy cows would have an impact on inline biomarkers, such as rumination time (RT), milk fat (%), milk protein (%), milk fat/protein ratio (F/P), milk lactose (L, %), milk electrical conductivity of all udder quarters, body weight (BW), temperature of reticulorumen content (TRR), pH of reticulorumen content (pH), and walking activity (activity). All 30 lame cows (LCs) used in this experiment had a score of 3–4, identified according to the standard procedure of Sprecher et al. The 30 healthy cows (HC) showed a lameness score of one. RT, milk fat, MY, milk protein, F/P, L, milk electrical conductivity of all udder quarters, and BW were registered using Lely Astronaut® A3 milking robots each time the cow was being milked. The TRR, cow activity, and pH of the contents of each cow’s reticulorumen were registered using specific smaXtec boluses. The study lasted a total of 28 days. Days “−14” to “−1” denote the days of the experimental period before the onset of clinical signs of lameness (day “0”), and days “1” to “13” indicate the period after the start of treatment. We found that from the ninth day before the diagnosis of laminitis until the end of our study, LCs had higher milk electrical conductivity in all udder quarters, and higher milk fat to protein ratios. On the 3rd day before the onset of clinical signs of the disease until the day of diagnosis, the milk fat of the LC group was reduced. The activity of the LCs decreased sharply from the second day to the first day after treatment. RT in the HC group tended to decrease during the experiment. pH in LCs also increased on the day of the appearance of clinical signs.