Comparison of cattle derived from in vitro fertilization, multiple ovulation embryo transfer, and artificial insemination for milk production and fertility traits
Bibliographic record
Abstract
The use of assisted-reproduction technologies such as in vitro fertilization (IVF) is increasing, particularly in dairy cattle. The question of consequences in later life has not yet been directly addressed by studies on large animal populations. Studies on rodents and early data from humans and cattle suggest that in vitro manipulation of gametes and embryos could result in long-term alteration of metabolism, growth, and fertility. Our goal was to better describe these presumed consequences in the population of dairy cows produced by IVF in Québec (Canada) and to compare them to animals conceived by artificial insemination (AI) or multiple ovulation embryo transfer (MOET). To do so, we leveraged a large phenotypic database (2.5 million animals and 4.5 million lactations) from milk records in Québec aggregated by Lactanet (Sainte-Anne-de-Bellevue, QC, Canada) and spanning 2012 to 2019. We identified 304,163, 12,993, and 732 cows conceived by AI, MOET, and IVF, respectively, for a total of 317,888 Holstein animals from which we retrieved information for 576,448, 24,192, and 1,299 lactations (total = 601,939), respectively. Genetic energy-corrected milk yield (GECM) and Lifetime Performance Index (LPI) of the parents of cows were used to normalize for genetic potential across animals. When compared with the general Holstein population, MOET and IVF cows outperformed AI cows. However, when comparing those same MOET and IVF cows with only herdmates and accounting for their higher GECM in the models, we found no statistical difference between the conception methods for milk production across the first 3 lactations. We also found that the rate of Lifetime Performance Index improvement of the IVF population during the 2012 to 2019 period was less than the rate observed in the AI population. Fertility analysis revealed that MOET and IVF cows also scored 1 point lower than their parents on the daughter fertility index and had a longer interval from first service to conception, with an average of 35.52 d compared with 32.45 for MOET and 31.87 for AI animals. These results highlight the challenges of elite genetic improvement while attesting to the progress the industry has made in minimizing epigenetic disturbance during embryo production. Nonetheless, additional work is required to ensure that IVF animals can maintain their performance and fertility potential.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".