Factors associated with Ontario dairy producers' management and care of down dairy cattle
Bibliographic record
Abstract
The objective of this study was to better understand current management practices for down cows in Ontario, Canada, and to identify factors associated with the adoption of acceptable practices. An online survey was distributed to all dairy producers in Ontario, Canada (n = 3,367) and was available from November 2020 to March 2021, inclusive. Dairy producers were identified through their provincial dairy organization and contacted via email, and the survey was also promoted via social media. The survey was comprised of 134 questions, 31 of which were related to down-cow management. Descriptive statistics were evaluated, and 2 logistic regression models were generated using Stata 17, exploring factors associated with (1) relocating down cows with hip lifters and (2) assisting cows to stand within 1 h after discovering a down-cow. A total of 226 producers responded (7.4%). Participants were predominantly male (68%), farm owners (78%), and 30 to 39 yr old (29%). Producers reported relocating down cows with a boat or sled (32.6%), front-end loader bucket (31.4%), hip lifters (28.0%), or "other" (with a text box to further describe; 8.0%). The median time to relocating a down-cow after identifying her was 1 h (range 0-17 h). Farms that relocated a down-cow sooner after identifying her as down, were more likely to use appropriate methods to move the cow. However, we also found that farms that provided feed and water sooner to down cows, were more likely to use an inappropriate method (hip lifters) to move her. Farms that used hip lifters to move cows had higher odds of assisting a cow to stand within 1 h following the discovery of recumbency. Additionally, producers who waited longer to relocate a down-cow were less likely to assist the cow to stand within 1 h of finding them down. Research has identified effective management practices for down cows, yet there remains a gap in understanding the implementation and the decision-making process of producers. Data from this study will be helpful in designing future research that further explores the barriers and motivations of producers when implementing evidence-based management plans to care for down dairy cows and may help inform current industry extension efforts.
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 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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".