Estrous detection intensity and accuracy and optimal timing of insemination with automated activity monitors for dairy cows
Bibliographic record
Abstract
The objectives of this observational study were to assess the ability of automated activity monitoring (AAM) to detect estrus for first insemination, the accuracy of detection, and the optimum interval from the estrus alert from the AAM system to insemination. Four commercial farms using 1 of 2 commercial AAM systems were studied over 1 yr. Cows were inseminated between 55 and 80 d in milk (DIM) based on AAM only, then by a combination of AAM and timed artificial insemination (AI). Blood progesterone was measured in 1,014 cows at wk 5, 7, and 9 postpartum; purulent vaginal discharge (PVD) was assessed at wk 5; and lameness and BCS at wk 7. Overall, AAM detected 83% of cows in estrus by 80 DIM. Cows that had 3 serum progesterone <1 ng/mL, had PVD, or were both lame and had BCS ≤2.5 has lesser odds of being detected in estrus by 80 DIM (62, 68, and 53%, respectively). Blood samples were collected on the day of 445 AI based on AAM and 323 timed AI. The proportion of cows not in estrus (progesterone >1 ng/mL) on the day of AI was similar between AAM (4 ± 1.8%) and timed AI (3 ± 1.2%). Managers elected, based on subjective criteria, not to inseminate 17% of cows for which an AAM estrus alert was issued, of which 43% were not in estrus. Activity data were extracted from AAM software for 1,399 AI. Onset of estrus was calculated using the same or similar data processing criteria as the AAM system. Producers recorded the time of AI. The interval from onset of estrus to AI was categorized as 0 to 8, 8 to 16, or 16 to 24 h. We found no effect of AAM system on the probability of pregnancy per AI, but noted an interaction of interval with parity. For multiparous cows, the probability of pregnancy per AI was 31%, which did not differ with the interval to AI. For primiparous cows, the odds of pregnancy were greater if AI occurred 0 to 8 h (49%) than 8 to 16 (36%) or 16 to 24 h (31%) after the estrus alert from the AAM. Automated activity monitoring can detect estrus for first AI in just over the length of 1 estrous cycle for over 80% of cows, but the remainder would likely require intervention for timely insemination. For multiparous cows, performing AI based on AAM once per day would not affect pregnancy per AI, but for primiparous cows AI within 8 h of the onset of estrus may be advantageous.
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 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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".