Dairy producers' attitudes toward reproductive management and performance on Canadian dairy farms
Bibliographic record
Abstract
The objectives of this study were to explore Canadian dairy producers' attitudes toward reproductive performance and challenges they perceive to be related to reproduction and reproductive management practices. A survey in both English and French was developed, validated, and administered to Canadian dairy farmers between March and May 2014 to collect general farm, reproduction management, and reproductive performance data, as well as opinions and perceptions about different facets of reproduction. Associations between management practices and the perceived importance of reproduction were tested using a logistic regression model. Thematic network analysis was used to identify themes from the open-ended survey questions about challenges concerning reproduction. Finally, questions that were answered on a Likert scale were graphically represented using diverging stacked bar charts. A total of 832 questionnaires were completed online and by mail, which represents approximately 7% of all dairy farms in Canada. Respondents that ranked reproduction in lactating dairy cows as 1 of the 3 most important challenges faced on their farm (66%) were more likely to house their lactating cows in a tiestall and to have a lower herd annual 21-d pregnancy rate. Estrus detection and conception risk were 2 major themes raised and discussed by the respondents. Other concepts, including housing and milk production, were also perceived to affect estrus detection and conception risk. Whereas analysis of open-ended survey questions does not allow for quantification of the importance of different themes in the sample as a whole, it does show that respondents are aware of the multifactorial complexity of reproductive challenges on dairy farms. Improving performance was the main factor influencing decisions concerning reproduction for 80% of the respondents, and they adopted tools and technologies such as synchronization programs and automated activity monitoring systems to improve herd reproductive performance. More research is required to describe how this performance is defined and perceived by the respondents, and how it relates to the actual variability of performance (i.e., pregnancy rate) among farms.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".