Effect of low-protein corn and soybean meal-based diets on nitrogen utilization, litter quality, and water consumption in broiler chicken production: insight from meta-analysis
Bibliographic record
Abstract
The growing demand for high-value animal protein must be met using sustainable means that optimize the utilization of nutrients, especially nitrogen (N) so that excreta do not over-fertilize fields and end up causing soil acidification, waterway eutrophication and greenhouse gas emissions. Malodorous N compounds can cause respiratory diseases and poor growth in livestock. The increasing availability of feed-grade amino acids makes it possible to formulate low-protein diets for broilers and thereby reduce N excretion. However, published studies of the effects of such diets on broiler growth performance have been based on reducing CP contents gradually in a variety of ways that have given inconsistent results. Since the amount of published data is now large, a meta-analysis was performed in order to categorize diet formulation strategies and quantify their impact on N balance, water consumption, litter moisture, plasma uric acid. This showed that lowering the CP content of broiler diets generally means replacing some soybean meal with corn and hence increasing the starch content. However, since soybean meal is also a source of potassium, this reduces electrolyte balance. Lowering the CP content from 19% to 17% is associated with a 29% reduction of N excretion in broilers aged 0-21 d, and a 7% increase in N efficiency (N retention/N intake). Reducing the CP content from 19% to 17% decreases daily water consumption by 20.6 mL/bird, litter moisture by 2.2% and plasma uric acid by 0.56 mg/dL. This meta-analysis improves our understanding of the low-protein strategy and allows us to quantify its impact on N balance, litter quality and uric acid. It shows that managing N excretion is wholly beneficial and reduces litter wetness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".