Farm-level risk factors for digital dermatitis in dairy cows in mountainous regions

[The Swiss Claw Health Project - an interim assessment after 5 years]

SUBJECT AND AIMS

The Swiss Claw Health Project (SCHP) started on 1 January 2019 and will last a total of 8 years. The main goals of the SCHP are (a) digital recording of claw health data of cattle in Switzerland by 100 professional claw trimmers, (b) development of claw health programs, (c) improvement of cattle claw health in Switzerland, (d) development of breeding values for claw health and (e) reduction of antibiotic use to control claw health problems.

MATERIAL AND METHODS

Data was collected digitally by 45 hoof trimmers who had previously successfully completed the training program for diagnosis and digital documentation in accordance with the ICAR Claw Health Atlas. The national animal health database was further developed for data storage. Livestock farms with unfavorable claw health were offered an on farm analysis with subsequent annual support visits.

RESULTS

A total of 1155 cattle owners joined the project by the end of the 5th project year. The claw health data of 149753 claw trimmings at cow-level was recorded, and 176 individual visits and analyses of farms with claw health issues were carried out. A health program for mechanical and metabolism-related claw disorders was developed and successfully established. Biosecurity measures during claw trimming were analyzed, and a brochure with recommendations for claw trimmers was developed. Risk factors for the occurrence of digital dermatitis in dairy herds were identified. The prevalence of alarm lesions (painful lesions) decreased significantly and continuously in the participating farms over the project years 2 to 4.

CONCLUSIONS

The scientific project support proved to be extremely useful, as the progress of the project was continuously monitored, and new issues could be addressed promptly. Digitally recorded findings during herd hoof trimming are essential for monitoring and improving claw health.

CLINICAL RELEVANCE

This description and critical assessment of the SKGP procedure can be used by other consortia planning a similar project.

Comparisons of recycled manure solids and wood shavings/sawdust as bedding material-Implications for animal welfare, herd health, milk quality, and bedding costs in Swedish dairy herds

Increasing shortages and costs of common bedding materials have led dairy farmers in Sweden to consider using recycled manure solids (RMS), which are readily available and low cost, as an alternative bedding material. The main risks are effects on udder health and milk quality, but RMS could also affect animal welfare and claw health. The advantages and disadvantages of using RMS bedding have not been fully investigated, and findings in other countries cannot be directly applied to Swedish conditions and climate. This observational cross-sectional study investigated the use of RMS as bedding, regarding associations with certain aspects of animal welfare, herd health, milk quality, and bedding costs in Swedish dairy herds. Thirty-four dairy farms using RMS or wood shavings/sawdust (each n = 17) were compared. Each farm was visited 2 times during the housing period from 2020 to 2021, once from October to December and once from March to May. Dairy barns were observed, animal welfare was assessed, and freestall dimensions were measured. Farm owners were interviewed about housing system characteristics, herd performance, and herd management. Data on milk production and herd health were obtained from the Swedish official milk recording scheme for the indoor period from October to March. The prevalence of claw disorders and abnormal claw conformation were collected from the national claw health database for the period from October to May. On each farm visit, composite samples of unused bedding outside the barn and used bedding material from the freestalls, respectively, were taken for total bacterial count and DM analysis. Samples of bulk tank milk for determination of total bacterial count were taken in connection to the visits. In addition, samples of unused and used bedding material and manure from alleys for analysis of 3 Treponema species associated with digital dermatitis (DD) were gathered and analyzed. Total bacterial count was significantly higher in unused (8.50 log10 cfu/g) and used RMS bedding (9.75 log10 cfu/g) than in wood shavings/sawdust (used 4.74; unused 8.63 log10 cfu/g), but there were no significant differences in bulk milk total bacterial count (median 4.07 vs. 3.89 log10 cfu/mL) or SCC (median 243,800 vs. 229,200 cells/mL). The aspects of animal welfare assessed did not differ significantly between the 2 bedding systems, whereas the prevalence of total claw disorders (25.9% vs. 38.0% of trimmed cows), dermatitis (6.9% vs. 16.2% of trimmed cows) and sole ulcers (2.0% vs. 4.0% of trimmed cows) were significantly lower in the RMS herds. Treponema spp. were not detected in unused RMS material, but all RMS herds had presence of DD recorded at foot trimming. An economic assessment based on the interview results and price level from winter 2021 revealed that the costs of RMS bedding varied with amount of RMS produced. Thus, RMS is a potential alternative bedding material for dairy cows in Sweden and can be a profitable option for large dairy herds. However, the high level of total bacteria in the material requires attention to bedding and milking routines as well as regular monitoring of herd health.

First report of the within-farm prevalence of bovine digital dermatitis in Chinese Holstein dairy cows in Jiangsu, China: A Bayesian modelling approach

Digital dermatitis is one of the most important causes of lameness in dairy cattle, particularly in housed, intensively-managed cattle. The number of modern intensive dairy farms in China has increased markedly in recent years; however, we lack research on digital dermatitis in Chinese dairy cattle. This preliminary study aimed to estimate the prevalence of digital dermatitis on three conveniently selected farms in Jiangsu, China. The washed hind feet of all lactating cows on all three farms were examined during milking with the aid of a mobile phone light source. True prevalence was then estimated from the apparent prevalence using a Bayesian superpopulation approach to account for the imperfect nature of identifying digital dermatitis in cows during milking. Despite none of the farms having thought it necessary to implement routine digital dermatitis monitoring or control, the disease was found on all three sampled farms. All lesions observed were either chronic M4 or M4.1 type-lesions, with no M2 lesions (i.e. acute ulcerated lesions) observed. The estimated true prevalences on the farms were 7.3% (95% credible interval [CrI]: 5.4%-9.6%), 8.3% (95%CrI: 6.3%-10.8%), and 29.8% (95%CrI: 22.9%-37.2%).

Evaluation of selected risk factors for different stages of digital dermatitis in Dutch dairy cows

Digital dermatitis (DD) is a painful infectious disease in dairy cattle that causes ulcerative lesions of the skin just above the coronary band, mainly of the hind legs. Estimates for DD prevalence at cow level in the Netherlands range from 20% to 25%. In this study, risk factors for the various stages of DD were identified and quantified. The hind legs of 6766 cows on 88 farms were scored by trained interns, using the M-scoring system (M0-M4.1). Farms in this study were a convenience sample, based on the prevalence of DD as recorded at the latest herd trim, geographical location and willingness of the farmers to participate. A survey with questions about cow environment and herd management was conducted by the intern at the day of scoring. The data were collected between August 2017 and January 2018. DD was found on 38.6% of the scored legs; 49.8% of the cows had DD on at least one leg and M4 was the most frequent stage (20.9%). Not removing manure on a regular basis resulted in lower odds for M2, M4 and M4.1 compared to cleaning by automatic scrapers ten times a day or more (odds ratio [OR]= 0.16, 0.49 and 0.18, respectively). The odds for M2 and M4 lesions were higher in cows aged 3-5 years than in first-calved cows (OR> 1.5 and > 1.7, respectively). Rubber flooring in the passageways resulted in lower odds for both M1 and M2 (OR, 0.06 and 0.32, respectively). Prophylactic use of footbaths treatment with an alternative active compound resulted in significant higher odds for M4 lesions than formalin and a combination of formalin and copper sulphate (OR= 1.69 and 2.04 respectively). The odds for an M4.1 lesion were lower in cows from smaller herds (n = 50-100) compared to large herds (n >100; OR= 0.67).

Prevalence of Painful Lesions of the Digits and Risk Factors Associated with Digital Dermatitis, Ulcers and White Line Disease on Swiss Cattle Farms

The first aim of this study was to calculate the prevalence of painful lesions of the digits ("alarm" lesions; ALs) in Swiss dairy herds and cow-calf operations over a three-year study period. The following ALs were included in the calculation: the M2 stage of digital dermatitis (DD M2), ulcers (U), white line fissures (WLF) of moderate and high severity, white line abscesses (WLA), interdigital phlegmon (IP) and swelling of the coronet and/or bulb (SW). Between February 2020 and February 2023, digit disorders were electronically recorded during routine trimmings by 40 specially trained hoof trimmers on Swiss cattle farms participating in the national claw health programme. The data set used consisted of over 35,000 observations from almost 25,000 cows from 702 herds. While at the herd-level, the predominant AL documented in 2022 was U with 50.3% followed by WLF with 38.1%, at the cow-level, in 2022, it was DD M2 with 5.4% followed by U with 3.7%. During the study period, within-herd prevalences of ALs ranged from 0.0% to a maximum of 66.1% in 2020. The second aim of this study was to determine herd- and cow-level risk factors associated with digital dermatitis (DD), U and white line disease (WL) in dairy cows using data from 2022. While for DD, analysed herd-level factors appeared to have a greater effect on the probability of its occurrence, the presence of U and WL was mainly associated with the analysed cow-level factors. The risk for DD increased with a higher herd trimming frequency. Herds kept in tie stalls had a lower risk for DD and WL and a higher risk for U compared to herds kept in loose housing systems. Herds with predominantly Holstein Friesian cows as well as Holstein Friesian cows had a higher risk for the occurrence of DD compared to herds and cows of other breeds. With increasing parity, cows had a higher risk of developing U and WL, whereas for DD, parity was negatively associated with prevalence. Cows trimmed during the grazing period had a higher risk of U and WL than cows trimmed during the housing period. These findings may contribute to improve management measures affecting the health of the digits in farms with structures similar to those evaluated in the current study, such as small herds with frequent access to pasture. Further research is warranted to demonstrate how measures addressing the current results combined with those of individual herd risk assessments might contribute to an improvement in the health of the digits in the respective dairy herds.

Machine learning approaches to predict and detect early-onset of digital dermatitis in dairy cows using sensor data

The present study aimed to employ machine learning algorithms based on sensor behavior data for (1) early-onset detection of digital dermatitis (DD) and (2) DD prediction in dairy cows. Our machine learning model, which was based on the Tree-Based Pipeline Optimization Tool (TPOT) automatic machine learning method, for DD detection on day 0 of the appearance of the clinical signs has reached an accuracy of 79% on the test set, while the model for the prediction of DD 2 days prior to the appearance of the first clinical signs, which was a combination of K-means and TPOT, has reached an accuracy of 64%. The proposed machine learning models have the potential to help achieve a real-time automated tool for monitoring and diagnosing DD in lactating dairy cows based on sensor data in conventional dairy barn environments. Our results suggest that alterations in behavioral patterns can be used as inputs in an early warning system for herd management in order to detect variances in the health and wellbeing of individual cows.