Relationship between farm management strategies, reticuloruminal pH variations, and risks of subacute ruminal acidosis
Bibliographic record
Abstract
Low reticuloruminal pH (rpH), often observed in subacute ruminal acidosis (SARA), may negatively affect rumen health and animal performance. To investigate the variability of rpH and the prevalence of SARA on commercial farms, we conducted an observational study on 110 early-lactation Holstein cows of different parities from 12 farms selected to cover a broad range of farm management strategies. The rpH of each cow was continuously monitored for 50 d using wireless boluses. To study the effects of animal and farm management characteristics on rpH, we used a multivariable mixed model analysis with the animal and farm as random effects. Automatic milking system and presence of corn silage in the ration were associated with a decrease in rpH of 0.37 and 0.20 pH units, respectively, whereas monensin supplementation was associated with an increase of 0.27 pH units. The rpH increased by 0.15 pH units during the first 60 d in milk. We defined a SARA-positive day as rpH below 5.8 (SARA5.8) or 6.0 (SARA6.0) for at least 300 min for 1 d. Using those definitions, during our study, a total of 38 (35%) and 65 (59%) cows experienced at least one episode of SARA5.8 and SARA6.0, respectively. The proportion of cows with at least one SARA-positive day varied among farms from 0 to 100%. Automatic milking system was associated with an increased risk of SARA5.8 (odds ratio: 10) and SARA6.0 (odds ratio: 11). The use of corn silage was associated with an increased risk of SARA5.8 (odds ratio: 21), whereas the use of monensin was associated with a decreased risk of SARA5.8 (odds ratio: 0.02). Our study shows that rpH is quite variable among farms, but also among animals on the same farm. We also show that multiple animal and farm characteristics are associated with rpH variability and the risk of SARA under commercial conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".