Opportunities to improve nutrient efficiency in pigs and poultry through breeding
Bibliographic record
Abstract
Efficiency of food and nutrient (including energy) use are considered the key factors in the economic and environmental performance of livestock systems. The aim of this paper is to consider the basis of genetic variation in the components that constitute dietary nutrient efficiency; and to conclude whether there would be benefit, in any relevant terms, in including these components in breeding programmes that aim to improve nutrient efficiency within pig and poultry systems of production. The components considered are (i) external, pre-ingestion losses, such as food spillage and its relation to feeding behaviour traits, (ii) digestive efficiency, (iii) maintenance requirements, (iv) net efficiency of energy and nutrient utilisation and (v) partitioning of scarce resources within productive and between productive and fitness functions. It is concluded that opportunities to exploit genetic variation exist mainly in the potential to improve the digestive efficiency of pigs and to reduce the maintenance requirements for resources mainly in hens, but also potentially in pigs. Current evidence suggests that there are very weak genetic and phenotypic correlations between components of feeding behaviour and productive traits, and little genetic variation in the net efficiency of nutrient utilisation among poultry and pig genotypes. The implication of the latter is that there would be little exploitable genetic variation in the partitioning of scarce nutrients between productive functions. Currently, there is a lack of understanding of the genetic basis of the partitioning of scarce nutrients between productive and fitness functions, and how this may impact upon the efficiency of nutrient use in pig and poultry systems. This is an area of research to which further effort might usefully be devoted.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 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".