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Perinatal mortality (PM) is a common issue on dairy farms, leading to calf losses and increased farming costs. The current knowledge about PM in dairy cattle is, however, limited and previous studies lack comparability. The topic has also primarily been studied in Holstein-Friesian cows and closely related breeds, while other dairy breeds have been largely ignored. Different data collection techniques, definitions of PM, studied variables and statistical approaches further limit the comparability and interpretation of previous studies. This article aims to investigate the factors contributing to PM in two underexplored breeds, Simmental (SIM) and Brown Swiss (BS), while comparing them to German Holstein on German farms, and to employ various modelling techniques to enhance comparability to other studies, and to determine if different statistical methods yield consistent results. A total of 133,942 calving records from 131,657 cows on 721 German farms were analyzed. Amongst these, the proportion of PM (defined as stillbirth or death up to 48 hours of age) was 6.1%. Univariable and multivariable mixed-effects logistic regressions, random forest and multimodel inference via brute-force model selection approaches were used to evaluate risk factors on the individual animal level. Although the balanced random forest did not incorporate the random effect, it yielded results similar to those of the mixed-effect model. The brute-force approach surpassed the widely adopted backwards variable selection method and represented a combination of strengths: it accounted for the random effect similar to mixed-effects regression and generated a variable importance plot similar to random forest. The difficulty of calving, breed and parity of the cow were found to be the most important factors, followed by farm size and season. Additionally, four significant interactions amongst predictors were identified: breed-calving ease, breed-season, parity-season and calving ease-farm size. The combination of factors, such as secondiparous SIM breed on small farms and experiencing easy calving in summer, showed the lowest probability of PM. Conversely, primiparous GH cows on large farms with difficult calving in winter exhibited the highest probability of PM. In order to reduce PM, appropriate management of dystocia, optimal heifer management and a wider use of SIM in dairy production are possible ways forward. It is also important that future studies are conducted to identify farm-specific contributors to higher PM on large farms., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Zablotski et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Regional benchmarking data enables farmers to compare their animal health situation to that of other herds and identify areas with improvement potential. For the udder health status of German dairy cow farms, such data were incomplete. Therefore, the aim of this study was (1) to describe the incidence of clinical mastitis (CM), (2) to describe cell count based udder health indicators [annual mean test day average of the proportion of animals without indication of mastitis (aWIM), new infection risk during lactation (aNIR), and proportion of cows with low chance of cure (aLCC); heifer mastitis rate (HM)] and their seasonal variation, and (3) to evaluate the level of implementation of selected measures of mastitis monitoring. Herds in three German regions (North: n  = 253; East: n  = 252, South: n  = 260) with different production conditions were visited. Data on CM incidence and measures of mastitis monitoring were collected via structured questionnaire-based interviews. Additionally, dairy herd improvement (DHI) test day data from the 365 days preceding the interview were obtained. The median (Q0.1, Q0.9) farmer reported incidence of mild CM was 14.8% (3.5, 30.8%) in North, 16.2% (1.9, 50.4%) in East, and 11.8% (0.0, 30.7%) in South. For severe CM the reported incidence was 4.0% (0.0, 12.2%), 2.0% (0.0, 10.8%), and 2.6% (0.0, 11.0%) for North, East, and South, respectively. The median aWIM was 60.7% (53.4, 68.1%), 59.0% (49.7, 65.4%), and 60.2% (51.5, 67.8%), whereas the median aNIR was 17.1% (13.6, 21.6%), 19.9% (16.2, 24.9%), and 18.3% (14.4, 22.0%) in North, East, and South, respectively with large seasonal variations. Median aLCC was ≤1.1% (≤ 0.7%, ≤ 1.8%) in all regions and HM was 28.4% (19.7, 37.2%), 35.7% (26.7, 44.2%), and 23.5% (13.1, 35.9%), in North, East and South, respectively. Participation in a DHI testing program (N: 95.7%, E: 98.8%, S: 89.2%) and premilking (N: 91.1%, E: 93.7%, S: 90.2%) were widely used. Several aspects of udder health monitoring, including exact documentation of CM cases, regular microbiological analysis of milk samples and the use of a veterinary herd health consultancy service were not applied on many farms. The results of this study can be used by dairy farmers and their advisors as benchmarks for the assessment of the udder health situation in their herds., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Böker, Bartel, Do Duc, Hentzsch, Reichmann, Merle, Arndt, Dachrodt, Woudstra and Hoedemaker.)

Good calf health is crucial for a successfully operating farm business and animal welfare on dairy farms. To evaluate calf health on farms and to identify potential problem areas, benchmarking tools can be used by farmers, herd managers, veterinarians, and other advisory persons in the field. However, for calves, benchmarking tools are not yet widely established in practice. This study provides hands-on application for on-farm benchmarking of calf health. Reference values were generated from a large dataset of the "PraeRi" study, including 730 dairy farms with a total of 13,658 examined preweaned dairy calves. At herd level, omphalitis (O, median 15.9%) was the most common disorder, followed by diarrhea (D, 15.4%) and respiratory disease (RD, 2.9%). Abnormal weight bearing (AWB) was rarely detected (median, 0.0%). Calves with symptoms of more than one disorder at the same time (multimorbidity, M) were observed with a prevalence of 2.3%. The enrolled farms varied in herd size, farm operating systems, and management practices and thus represented a wide diversity in dairy farming, enabling a comparison with similar managed farms in Germany and beyond. To ensure comparability of the data in practice, the reference values were calculated for the whole data set, clustered according to farm size (1-40 dairy cows ( n = 130), 41-60 dairy cows ( n = 99), 61-120 dairy cows ( n = 180), 121-240 dairy cows ( n = 119) and farms with more than 240 dairy cows ( n = 138), farm operating systems (conventional ( n = 666), organic ( n = 64)) and month of the year of the farm visit. There was a slight tendency for smaller farms to have a lower prevalence of disorders. A statistically significant herd-size effect was detected for RD ( p = 0.008) and D ( p < 0.001). For practical application of these reference values, tables, diagrams, and an Excel ® (Microsoft ® ) based calf health calculator were developed as tools for on-farm benchmarking (https://doi.org/10.6084/m9.figshare.c.6172753). In addition, this study provides a detailed description of the colostrum, feeding and housing management of preweaned calves in German dairy farms of different herd sizes and farm type (e.g., conventional and organic)., Competing Interests: Author KB was employed by VetZ GmbH. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Dachrodt, Bartel, Arndt, Kellermann, Stock, Volkmann, Boeker, Birnstiel, Do Duc, Klawitter, Paul, Stoll, Woudstra, Knubben-Schweizer, Müller and Hoedemaker.)