Relationships between method used for bedding processing and presence of mastitis and nonmastitis pathogens in ready-to-use recycled manure solids bedding on Midwest dairy farms
Bibliographic record
Abstract
<h2>Abstract</h2> Field studies have examined how processing methods affect mastitis pathogen levels in ready-to-use (RTU) recycled manure solids (RMS), but few have assessed their impact on nonmastitis pathogens. This cross-sectional study investigated associations between RMS processing methods and (1) mastitis pathogen levels and (2) the presence of <i>Mycobacterium avium</i> subspecies <i>paratuberculosis</i> (MAP), <i>Salmonella</i> (SAL), and <i>Campylobacter jejuni</i> (CAMP) in RMS from Midwest dairy herds. Twenty-seven dairies in Minnesota and Wisconsin were recruited to represent various RMS processing methods: raw or green solids (GRN; n=6), drum composters (COM; n=3), anaerobic digesters (DIG; n=9), digesters with hot air dryers (DIG+DRY; n=6), digesters with infrared dryers (DIG-IR; n=1), and hot air dryers (DRY; n=2). Farms were visited once in summer 2021 to collect slurry and postprocessed RMS samples before and after each processing step. Samples were tested for MAP (culture and PCR confirmation), CAMP (culture), and SAL (culture). Ready-to-use RMS samples also underwent aerobic culture to determine counts of coliforms, <i>Klebsiella</i> spp., <i>Streptococcus</i> spp., <i>Streptococcus</i> spp. and <i>Streptococcus</i>-like organisms (SSLO), and <i>Staphylococcus</i> spp. (cfu/cm<sup>3</sup>, wet basis). For analysis, dairies were grouped into 4 system types: GRN (n = 6), DIG-only (n = 9), secondary processing only (SEC; DRY or COM; n=5), or DIG combined with SEC (DIG+SEC; n=7). Linear regression assessed associations between processing type and mastitis pathogen counts, and logistic regression evaluated MAP and SAL presence before and after processing. No CAMP was detected. Prevalence of MAP and SAL in raw slurry was high (MAP: 68% [17/25]; SAL: 80% [21/25]). Compared with GRN, DIG-only and SEC-only systems were associated with lower mastitis pathogen counts and reduction of MAP and SAL presence, though these pathogens were still identified in RTU RMS samples. The DIG+SEC systems showed the greatest reduction in mastitis pathogen counts, and MAP and SAL were not detected in RTU RMS. Our results suggest that combining DIG with a secondary processing method (e.g., COM, DRY, or IR) most effectively reduces mastitis and nonmastitis pathogens in RMS bedding.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".