Limits to Protein in Layer Diets Relative to Mitigating Ammonia Emission
Bibliographic record
Abstract
Ammonia emissions from poultry farms currently contribute to air pollution and acid rain. There are no regulations in North America regarding emissions of ammonia although regulations are being drawn up in the USA and there is concern about the impacts of animal agricultural on the environment. Low crude protein (CP) diets can be an effective contributor to strategies of ammonia mitigation. Since virtually all ammonia originates from nitrogenous compounds in feed, then any attempt at ammonia mitigation must involve scrutiny of the levels of nitrogen, protein and amino acids (AA). Reducing dietary nitrogen/CP leads to reduced nitrogen in the excreta with less potential for microbial conversion to ammonia. Using low CP diets may be an economical strategy for ammonia emissions since the concept involves no special feed additives other than replacement AAs. Although AA requirements for layer hens are well known, the minimal amount of CP required is less clearly defined. AA requirements should be independent of diet CP, assuming there is adequate nitrogen for protein synthesis. However, the birds / response in terms of reduced egg numbers and growth or change in egg composition, suggest that our estimates of amino acid supply are incorrect under these dietary regimes. Independent of bird age and AA supply, more problems are recorded when CP levels are <14-15%. It is timely to redefine the maintenance AA requirements of layers. Since the composition of eggs should give us direct estimates of needs for production, the only other unknown in formulating low CP diets is the efficiency of utilisation of free amino acids versus intact proteins.
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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 | 0.001 | 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".