Bacterial concentrations in bedding and their association with dairy cow hygiene and milk quality
Bibliographic record
Abstract
Comparison of bacterial counts (BCs) among common bedding types used for dairy cows, including straw, is needed. There is concern that the microbial content of organic bedding is elevated and presents risks for dairy cow udder health and milk quality. The objectives of this study were to investigate: (1) % DM and BCs (Streptococcus spp., all gram-negatives and specifically Klebsiella spp.) in different types of bedding sampled, and to investigate housing and farm management factors associated with % DM and BCs; (2) if bedding type was associated with hygiene of cow body parts (lower-legs, udder, upper-legs and flank) and housing and management factors associated with hygiene and (3) bedding types associated with higher BCs in cow milk at the farm level and bulk tank milk and management factors that were associated with highest BCs. Seventy farms (44 free-stall and 26 tie-stall) in Ontario, Canada were visited 3 times, 7 days apart from October 2014 to February 2015. At each visit, composite samples of unused and used bedding were collected for % DM determination and bacterial culture. Used bedding samples were collected from the back third of selected stalls. Data were analyzed using multivariable linear mixed models. Bedding classification for each farm were: new sand (n = 12), straw and other dry forage (n = 33), wood products (shavings, sawdust; n = 17) and recycled manure solids (RMSs)-compost, digestate (n = 8). In used bedding, across all bedding samples, sand was driest, compared to straw and wood, and RMS; higher % DM was associated with lower Streptococcus spp. count. Streptococcus spp. and all Gram-negative bacteria counts increased with increasing days since additional bedding was added. Gram-negative bacteria counts in used bedding varied with type: RMS = 16.3 ln colony-forming units (cfu)/mL, straw = 13.8 ln cfu/mL, new sand = 13.5 ln cfu/mL, and wood = 10.3 ln cfu/mL. Klebsiella spp. counts in used bedding were lower for wood products (5.9 ln cfu/mL) compared to all other bedding types. Mean cow SCC tended to be higher on farms with narrower stalls. Farms with mattress-based stalls had a higher prevalence of cows with dirty udders compared to those using a deep bedding system (often inorganic sand). Wider stalls were associated with lower bulk milk bacteria count. Lower % DM of used bedding was associated with higher bulk milk bacteria count. In conclusion, bedding management may have a profound impact on milk quality, bacterial concentrations in the bedding substrates, and cow hygiene.
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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 | 0.000 |
| 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) | 0.000 | 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".