PSVIII-19 Meta-analysis of genetic parameter estimates for feed efficiency traits in dairy cattle
Bibliographic record
Abstract
Abstract Over 60% of the production costs in dairy cattle are related to feeding. Genetic selection for improved feed efficiency (FE) is a very promising alternative to reduce feeding costs, increase the industry profitability, and reduce the industry environmental footprints. The success of genetic selection relies in part on the heritability (h2) of the indicator traits included in the breeding programs. Several studies have reported h2 estimates for different FE indicator traits, with a broad range of estimates. To obtain more consistent h2 estimates across studies and populations, we performed a meta-analysis of published h2 estimates using four different groups of FE indicator traits commonly used in dairy cattle: 1) energy intake (EI); 2) residual feed intake (RFI); 3) feed (dry-matter) intake (FI); and 4) feed conversion efficiency (FCE). A comprehensive literature review identified 148 h2 estimates across 39 scientific papers from 13 different countries, published between 1991 and 2019. Thereafter, a meta-analysis based on random-effects model was used to summarize and address the variability of the parameter estimates. Our study confirmed that FE indicator traits in dairy cattle are under moderate genetic control. The h2 estimates were 0.18±0.02, 0.19±0.02, 0.29±0.01, and 0.19±0.03 for EI, RFI, FI, and FCE, respectively. In addition, our findings showed that h2 estimates for FE indicator traits in different studies have significant heterogeneity (I2 index estimated for EI, RFI, FI and FCE was 80.5%, 59.8%, 81.7%, and 55.7%, respectively). Among the possible sources of variation that contributed to the heterogeneity across studies are country, type of housing, life stage, and diet. The results reported here summarize the overall level of genetic control of FE in dairy cattle, which are useful for genetic evaluations when reliable h2 estimates for FE are not available in the studied dairy cattle population.
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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 | 0.023 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.046 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".