181 Enhancing the efficiency of poultry production by optimising selection objectives and breeding strategies
Bibliographic record
Abstract
Abstract Poultry, in general, have been the most successful farmed species in efficiently producing protein, be it egg laying chickens or meat producing chickens or turkeys. This has been the result of 60–80 years of continuous selection and improvement of traits that effect overall efficiency. The innate physiology of birds, high reproductive rates and relatively short generation intervals have helped speed these improvements. The contribution of feed to production costs has increased efforts to accurately measure feed intake, and selecting for efficient feed utilisation have been significant. Selection for feed efficiency forms an integral part of the breeding objective of the respective breeding programs, but in recent years other components of efficiency have become increasingly important within breeding objectives. Market segmentation, with greater volumes of fully deboned meat for further value added processing, has resulted in the optimisation of yield to feed for both broilers and turkeys. Allometric growth patterns, particularly late stage deposition of breast meat, gives rise to opportunities for optimisation of both slaughter age and weight. Genetic evaluations incorporating genomic information from birds with yield measurements is now implemented across both broilers and turkeys in many breeding programs. The implementation of genomic selection has benefits for not only yield, but also growth and feed efficiency, and we have shown improvements in genetic prediction of 11%, 23% and 31% for growth, breast meat and residual feed intake in turkeys, respectively. Reproductive efficiency has also been improved in broilers, turkeys and particularly layers, by improving evaluations using genomic information. Altering egg production curves to maintain production persistency for increasingly longer time periods has also been important. Layer flocks are now routinely in production for up to 500 days and similar objectives are used for selection in both broiler and turkey reproduction.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".