Efficiency of feed utilisation by livestock — Implications and benefits of genetic improvement
Bibliographic record
Abstract
Genetic improvement strategies in the past have concentrated on traits associated with outputs. Traits that directly affect input costs, such as those related to the efficiency of feed utilisation, have only recently started to receive some attention. This paper examines the current state of knowledge, benefits and challenges associated with genetic improvement of feed utilisation by livestock. Current information indicates the existence of genetic variation in feed efficiency and moderate heritability for most feed efficiency traits in all livestock species. However, there is a paucity of information on the genetic relationships among feed efficiency traits and other traits at different phases of the production cycle. The challenge is to develop breeding programs that exploit genetic variation in efficiency of feed utilisation to improve whole production system efficiency. The cost of recording feed intake (used to compute feed efficiency traits) is high, making it uneconomical, in some species, to measure feed intake in all potential seedstock animals. However, where a two-stage selection approach has been adopted, the inclusion of feed efficiency traits in genetic improvement schemes has yielded substantial additional benefits over and above the benefits from existing schemes that do not include feed efficiency traits. Under a two-stage selection strategy all potential seedstock animals are first evaluated for the production traits of interest, after which strategically selected individuals undergo feed efficiency testing. Where physiological or genetic markers for feed efficiency exist, such information is included in the first stage selection decision. There is theoretical evidence that selection for some feed efficiency traits, such as residual feed intake, will have additional benefit of reducing greenhouse emissions by livestock and improving environmental sustainability. The challenge is to generate the necessary empirical data to support this assertion. Key words: Feed efficiency, genetics, physiology, economic benefit, environmental sustainability
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".