Meta-analysis of the effect of low-protein diets on the growth performance, nitrogen excretion, and fat deposition in broilers
Bibliographic record
Abstract
In broilers, the effects of crude protein (CP) reduction on animal performance are heterogeneous. This could limit the use of this strategy in commercial farms despite its potential to improve the sustainability of production. The objective of this meta-analysis was to study the effect of lowering dietary CP in fast-growing broilers with a focus on growth performance. A database was built from 29 papers published after 2016, for a total of 106 trials and 268 treatments. To be included in the database, trials had to be iso-energy and iso-lysine. Trials in which the growth rate for the control treatment was below 90% of the genetic potential of the birds were not included. The effect of the CP level was analyzed by multiple linear regression, with the trial as a random effect. A subsample of 33 trials (AACON) met the recommended amino acid (AA)-to-lysine ratios for indispensable AAs. In this subdatabase, average daily gain and average daily feed intake were maintained when dietary CP was reduced. The feed conversion ratio increased by 1.3% when CP was reduced by a one percentage point. The same CP reduction decreased daily nitrogen (N) excretion by 10.4%, whereas N retention was not affected by CP. In conclusion, respecting the ideal protein concept with the use of feed-grade AA allows for a decrease in dietary CP and N excretion with a limited impact on growth performance.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.032 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".