Reproductive management practices and performance of Canadian dairy herds using automated activity-monitoring systems
Bibliographic record
Abstract
The objectives of this study were to describe the characteristics and motivations of producers who had implemented automated activity-monitoring (AAM) systems and to compare herd reproductive performance before and after the implementation of an AAM system and between herds with AAM and herds managing reproduction based on timed artificial insemination (TAI) or based on other programs. Freestall dairy herds located in Ontario and the western provinces of Canada and enrolled in Dairy Herd Improvement were surveyed through a mail questionnaire between April and July 2010. The data describe the characteristics and reproductive management practices of herds using AAM systems. A total of 505 questionnaires (29%) were returned. On average, 21-d pregnancy risk, conception risk, and 21-d insemination risk did not differ between herds managing reproduction based on an AAM system (18, 39, and 50%, respectively) or a TAI-based program (17, 38, and 49%, respectively). Herds that implemented an AAM system had a significant increase in annual pregnancy risk, from 15 to 17%, and insemination risk increased from 42 to 50%, whereas conception risk was unchanged (37 and 35%) following adoption of the system. The majority of respondents with AAM systems first used the system to manage reproduction in lactating cows. Most herds with AAM were performing artificial insemination twice per day, most commonly with an interval from the estrus alarm to artificial insemination of 7 to 12 h. The most commonly reported reason to adopt an AAM system was a desire to improve reproductive performance. These results support the findings from randomized trials that AAM-based programs can yield comparable reproductive performance to TAI-based programs.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".