Modeling heat stress effects on first service to conception rates in Canadian Holstein dairy cattle
Bibliographic record
Abstract
Heat stress (HS) can result in decreased production and poor fertility performance in dairy cattle. There is limited understanding of the point at which heat load begins to affect fertility, causing a major challenge for the industry. The temperature-humidity index (THI) is a metric commonly used to represent the realized heat load on livestock, as it incorporates both ambient temperature and humidity percentage. The objectives of this study were to estimate the threshold of THI at which the interval of days from first service to conception (FSTC) begins to increase due to HS, evaluate the effect of estrus synchronization on that threshold, and identify geographic regions of concern for Canadian dairy farming. Data comprised 2,033,928 FSTC records on 1,239,053 Canadian Holsteins in parities 1, 2, and 3, collected between November 2008 and April 2022. Hourly ambient temperature and relative humidity were extracted from the NASA Prediction of Worldwide Energy Resources (POWER) database (https://power.larc.nasa.gov/; accessed Jan. 7, 2025), and daily THI was calculated by averaging hourly THI across the full 24-h day. Daily THI was averaged across 3 temporal windows relevant for follicular growth and zygotic survival: 3 d before to 2 d after first insemination, 7 d before to 2 d after first insemination, and 10 d before to 2 d after first insemination. The FSTC phenotype was adjusted using a single-trait repeatability model to account for known sources of environmental and genetic effects on the trait. Segmented linear-linear regressions were fit to identify the point at which the residual FSTC began to increase in response to HS effects. Thresholds varied between THI 65 and 68.6 across parities and temporal windows. The window spanning 3 d before to 2 d after first insemination showed the highest overall accuracy, and the average threshold for this window (THI 66) was selected as the representative onset of HS effects on FSTC. These results suggest that shorter windows may be sufficient to evaluate HS effects on fertility. Timed artificial insemination (AI) protocols were found to have a greater sensitivity to HS than heat detection methods, displaying a difference in thresholds (timed AI: 64; heat detection: 72.8). However, heat detection inseminations were found to have a higher rate of increase in FSTC above group-specific thresholds (timed AI: 0.44; heat detection: 3.27). Analysis of weather data indicated that Ontario is a province of concern, with average summer temperatures exceeding THI 66. Additionally, Ontario had an average of 50% of 24-h day with THI above 66 as well as 50% of summer days with average THI above 66. All provinces, except for British Columbia, had an observed hottest season within the past 4 years within the dataset. Results of this study suggest an increase in FSTC above THI 66 with a significant effect of timed AI protocol on HS response. A genetic analysis is needed to evaluate a possible genotype by environment interaction and the corresponding re-ranking of sires.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".