Quantification of the effect of reducing dietary crude protein in broiler chickens on nitrogen flows and litter characteristics by meta-analysis
Bibliographic record
Abstract
In broiler chickens, reducing dietary CP content is an effective strategy to improve the efficiency of dietary nitrogen ( N ) utilisation by broilers while reducing N losses through volatilisation. The purpose of this study was to quantify the effect of lowering dietary CP on N flows (intake, retention, excretion, manure accumulation, and volatilisation). The database included studies that measured N volatilisation using a mass balance approach. A total of nine papers describing 16 trials and 46 observations were found. The effect of CP content on N flow variables was assessed using a linear mixed-effects model with the trial as a random effect. Broilers of the control treatments ingested an average of 4.2 g of N per day and retained an average of 55% of this N. Nitrogen intake that was not retained was excreted (1.9 g/d), and 33% of this excreted N was lost through volatilisation. Reducing dietary CP by 1% point (%-point) decreased N intake by 0.21 g/d ( P < 0.001) without any effect on N retention. Nitrogen excretion and the volatility of excreted N decreased by 0.20 g/d and 4.22%-points, respectively, for each 1%-point reduction in CP content ( P < 0.001). The synergy between the reduced excreted N and its volatility decreased the amount of volatilised N by 0.12 g/d (−23%) for every 1%-point reduction in CP content ( P < 0.001). Reducing CP content also decreased litter mass ( P < 0.05) and increased its DM ( P < 0.01). The data presented show that increasing litter DM content ( P < 0.05) and lowering pH ( P < 0.001) reduces the volatility of excreted nitrogen. The results of this meta-analysis highlight the benefits of this low-CP diet for reducing N losses, and the equations created can be used in future evaluations of the effects of reducing dietary CP content.
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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 | 0.023 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.050 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".