Performance of automated activity monitoring systems used in combination with timed artificial insemination compared to timed artificial insemination only in early lactation in dairy cows
Bibliographic record
Abstract
Identifying cows in estrus remains a challenge on dairy cattle farms, and tools and technologies have been developed and used to complement or replace visual detection of estrus. Automated activity monitoring (AAM) systems and timed artificial insemination (TAI) are technologies available to dairy farmers, but many factors can influence their relative performance. The objective of the present study was to compare reproductive performance of cows managed with an AAM system combined with TAI, or with a TAI program (Double Ovsynch) for insemination before 88 DIM. From April 2014 to April 2015, 998 cows from 2 herds were randomly assigned either to be inseminated at 85 ± 3 DIM exclusively using the Double Ovsynch protocol for TAI, or to be inseminated based on estrus detection by AAM without hormonal intervention between 50 and 75 DIM; if no alarm was detected by 75 DIM, cows were inseminated following the single Ovsynch protocol (AAM + Ovsynch). The herds used different AAM systems. Parity, hyperketonemia at wk 1 and 2 postpartum (PP), purulent vaginal discharge at wk 5 PP, body condition score at wk 7 PP, and anovulation to wk 9 PP were recorded. These health indicators did not significantly differ between treatments, but did between herds. The effect of treatment on pregnancy at first insemination and by 88 DIM were assessed using logistic regression models. Time to pregnancy was assessed using survival analysis. Results are reported from intention-to-treat analyses. Treatment did not affect pregnancy at first insemination or pregnancy by 88 DIM, but we found significant interactions between treatment and herd for both outcomes. In herd 2, marginal mean pregnancy at first AI was greater with Double Ovsynch (38%) than AAM + Ovsynch (31%), but no difference was observed in herd 1 (Double Ovsynch = 31%; AAM + Ovsynch = 34%). By 88 DIM, a smaller proportion of cows in herd 1 were pregnant in Double Ovsynch (31%) than AAM + Ovsynch (49%), but there was no difference in herd 2 (Double Ovsynch = 38%; AAM + Ovsynch = 38%). We observed a treatment by herd interaction for median (95% confidence interval) time to pregnancy, which were, in herd 1, 110 (106 to 129) and 98 (88 to 113) d, and, in herd 2, 126 (113 to 139) and 116 (105 to 131) d for the Double Ovsynch and AAM + Ovsynch treatments, respectively. The relative performance of AAM-based reproductive management compared with TAI only is likely influenced by herd-specific variables, in particular related to insemination rate when estrus detection is employed.
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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 | 0.001 |
| 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 | 0.001 | 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".