Significance of specialty protein ingredients in feeding programs for broiler chickens on nitrogen utilization and excretion-A meta-analysis
Bibliographic record
Abstract
Specialty protein ingredients (SPI) such as processed animal proteins (PAP), insect proteins (IP), algae, and further-processed vegetable proteins (PVP), are increasingly incorporated into broiler diets. However, variation in crude protein (CP) intake and the inclusion levels of these ingredients across studies can confound their reported effects. To address this heterogeneity, the present meta-analysis used a mixed-effects meta-regression model that adjusted performance responses for both CP intake and SPI inclusion level, enabling a standardized comparison of ingredient efficacy. The hypothesis was that PVP and PAP would perform similarly to conventional soybean meal (SBM) and outperform IP and algae due to their higher digestibility and more balanced amino acid profiles reported in the literature. Data from 53 peer-reviewed studies were integrated into a mixed-effects model (PROC GLIMMIX, SAS), with study as a random effect and SPI type and CP intake as fixed effects. The model predictions were average daily gain (ADG), average daily feed intake (ADFI), feed conversion ratio (FCR), and nitrogen balance. During the starter and grower phases, PVP, PAP and SBM resulted in the highest ADG and 8-15% reduction in nitrogen excretion (P < 0.001). In the finisher phase, SBM had the highest overall performance, while PVP and PAP had similar results (P < 0.001). The performance response to CP intake revealed that algae had the steepest positive slopes at low intake and the largest quadratic declines at higher intakes (P < 0.001). IP also showed quadratic performance declines at higher intake levels (P < 0.001), particularly above 10 % inclusion. PVP and SBM showed minimal quadratic effects, and PAP showed a moderate decline in nitrogen efficiency in the finisher phase (P < 0.01). In conclusion, PVP and PAP are effective alternative protein sources. In contrast, algae and high inclusion of IP are less suitable because of their lower protein quality. These findings offer a quantitative basis for feed formulation and guide future research aimed at improving the digestibility and amino acid profiles of algae and IP.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.000 | 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".