Comparison of Antimicrobial Treatment Incidence Quantification Based on Detailed Field Data on Animal Level with the Standardized Methodology of the European Medicines Agency in Veal Calves, Switzerland, 2016-2018

Associations of the Swiss national reporting system's antimicrobial use data and management practices in dairy cows on tiestall farms

Antimicrobial use (AMU) in Switzerland is above target and requires reduction, especially in dairy cattle. Measuring AMU is pivotal to identifying starting points for AMU reduction, and so are studies investigating its potential drivers in dairy farms worldwide. However, although AMU in dairy farms is high, studies estimating AMU specifically in tiestall farms are scarce. Tiestalls are a common housing system and their prevalence among dairy farms accounts to approximatively 73%, 41%, and 40% in Canada, the United States, and Switzerland, respectively. The objectives of this cross-sectional, retrospective observational study were to estimate AMU using the newly established Swiss national reporting system for AMU in livestock and to identify associated factors on Swiss tiestall dairy farms. We calculated the treatment incidence (TI) by using the European Medicines Agency methodology and their defined daily dose (DDD) and defined course dose (DCD) standards. Data on factors potentially associated with AMU were obtained through personal interviews with farm managers on 221 farms. Retrospectively, during a 1-year period, data on a total of 7,619 treatments were extracted from the national database. Associations between management factors and TI were analyzed using a generalized linear model with gamma distribution. The mean (± SD) overall TI was 5.46 DDD/cow-year (± 4.10 DDD/cow-year). Intramammary treatment during lactation accounted for highest TI (3.24 ± 3.16 DDD/cow-year), whereas dry-cow therapy accounted for lowest TI (0.44 ± 0.49 DCD/cow-year). We found that 5 of the investigated management factors were significantly associated with TI. Organic production (estimate = -2.16; 95% CI = -3.62 to -0.70) and herd size (estimate = -0.81; 95% CI = -1.23 to -0.39) were negatively associated with TI. Specific cow breeds (Brown Swiss estimate = 1.56; 95% CI = 0.45-2.68) and Holstein Friesian (estimate = 1.42; 95% CI = 0.03-2.82; reference: other breeds) and the use of hygienic powders on the lying area (estimate = 1.10; 95% CI = 0.04-2.17) were positively associated with TI. Overall, the Swiss national reporting system is a valuable tool for AMU estimation. Several herd characteristics and management factors were associated with AMU in tiestall farms. Further studies focusing on factors associated with AMU and which are amenable to intervention will help improve stewardship programs and subsequently reduce AMU in dairy cows.

Evaluation of candidate data-based welfare indicators for veal calves in Switzerland

Welfare assessment protocols have been developed for dairy cows and veal calves during the past decades. One practical use of such protocols may be conducting welfare assessments by using routinely collected digital data (i.e., data-based assessment). This approach can allow for continuous monitoring of animal welfare in a large number of farms. It recognises changes in the animal welfare status over time and enables comparison between farms. Since no comprehensive data-based assessment for veal calves is currently available, the purposes of this review are (i) to provide an overview of single existing data-based indicators for veal calves and (ii) to work out the necessary requirements for data-based indicators to be used in a comprehensive welfare assessment for veal calves in Switzerland. We used the Welfare Quality Protocol® (WQ) for veal calves and the Terrestrial Animal Health Code from the World Organisation of Animal Health for guidance throughout this process. Subsequently, routinely collected data were evaluated as data sources for welfare assessment in Swiss veal operations. The four WQ principles reflecting animal welfare, i.e., 'good feeding', 'good housing', 'good health' and 'appropriate behaviour' were scarcely reflected in routinely available data. Animal health, as one element of animal welfare, could be partially assessed using data-based indicators through evaluation of mortality, treatments, and carcass traits. No data-based indicators reflecting feeding, housing and animal behaviour were available. Thus, it is not possible to assess welfare in its multidimensionality using routinely collected digital data in Swiss veal calves to date. A major underlying difficulty is to differentiate between veal calves and other youngstock using routine data, since an identifying category for veal calves is missing in official Swiss databases. In order to infer animal welfare from routine data, adaptations of data collection strategies and animal identification are required. Data-based welfare assessment could then be used to complement on-farm assessments efficiently and, e.g., to attribute financial incentives for specifically high welfare standards accordingly.