Risk Factors for Intramammary Infections on Bavarian Dairy Farms—A Herd-Level Analysis
Bibliographic record
Abstract
This cross-sectional study aimed to (a) determine the apparent prevalence of mastitis pathogens and (b) to identify risk factors for intramammary infections (IMIs) at the herd level in dairy herds in Bavaria, Germany. A stratified random sample of 305 herds was selected based on herd size, administrative district, and season. During the farm visits between July 2023 and July 2024, management data were recorded, quarter milk samples (QMSs) from 14,700 lactating cows were collected aseptically and analyzed, and the somatic cell count (SCC) at the quarter level was determined. Risk factors for the within-herd prevalence of Staphylococcus (S.) aureus, Streptococcus (Strep.) uberis, Strep. dysgalactiae, and non-aureus staphylococci (NAS) were analyzed by negative binomial regression, while risk factors for the presence of Escherichia (E.) coli and Strep. agalactiae IMIs on dairy farms were identified by logistic regression. The most frequently detected pathogens were NAS, found in 5.0% of all QMSs (n = 57,251), followed by Strep. uberis (1.9%) and S. aureus (1.8%), Strep. agalactiae (0.2%), and E. coli (0.1%). At the herd level, NAS, Strep. uberis, S. aureus, and Strep. dysgalactiae were found in 92%, 69%, 67%, and 57% of farms, respectively. Risk factors for increased within-herd prevalence included automated milking systems (NAS), organic production (Strep. uberis, S. aureus), straw bedding (Strep. uberis), and lack of bedding or mattress cubicles (Strep. dysgalactiae). The odds for a herd to be positive were increased with audible liner slips (E. coli) and the irregular cleaning of water troughs (Strep. agalactiae), and without a maintenance agreement for milking equipment (Strep. agalactiae). These results provide valuable insights into options for the targeted prevention of IMI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".