Bibliographic record
Abstract
This review article is devoted to the use of feed efficiency traits in dairy cattle breeding. An efficient cow is defined as the one that produces the same amount of milk and milk solids while consuming less feed and remaining healthy and fertile; thus, allowing to reduce costs without decrease in production. Improving feed efficiency is economically important due to the increasing price of fodder. Feed efficiency is a genetically complex trait that can be described as units of product output (e.g., milk yield) per unit of feed input. Nowadays genetic evaluation of dairy cattle for feed efficiency is routinely conducted in several countries, including Australia, USA, Canada, Netherlands, Denmark, Sweden, Finland, Norway and United Kingdom. Different countries use different measures of feed efficiency of dairy cows. The main feed efficiency traits are dry matter intake, gross feed efficiency, residual feed intake, energy balance and feed saved. Genome-wide association studies demonstrated that feed efficiency in polygenic trait. Nevertheless, several genes with large effects on feed efficiency were identified. Estimates of heritability of these traits vary from 0.07 to 0.49 and show the presence of considerable genetic variation of these traits and therefore, the possibility of their genetic improvement under the conditions of inclusion in breeding programs. Changes in diet and rumen microbiome substantially impact feed efficiency of dairy cows. Feed efficiency is related to methane emissions and excess nitrogen excretion. Genetic improvement of feed efficiency requires recording of individual data on feed intake in cows. Such data are limited. Two options exist to solve this problem: use of indirect predictors and genomic prediction. Accuracy of genomic prediction varies from 0.21 to 0.61 across countries. International cooperative projects such as Efficient Dairy Genome Project in Canada were launched to establish large databases and to increase accuracy of feed efficiency traits genomic prediction. Future directions of research are the use of novel technologies: mid-infrared spectroscopy, artificial intelligence, holo-omics.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".