167 Effect of Reducing Dietary Crude Protein on Growth Performance of Fattening Pigs: A Meta-Analysis
Bibliographic record
Abstract
Abstract Reducing dietary crude protein (CP) is a nutritional strategy implemented to improve the sustainability of pig production. Its effect on growth performance has been broadly studied in fattening pigs but the literature is lacking a quantitative summary of the knowledge acquired. A meta-analysis was performed to assess the effects of reducing dietary CP on pig growth performance. Articles included in the meta-analysis studied effects of CP reduction, with at least 3 CP levels, on the growth performance of fattening pigs (20-115 kg) and were published from 1990 to 2019. The database contained 42 articles, which correspond to 67 trials. Diet composition was recalculated with INRA-AFZ (2004) tables. Trials using iso-digestible lysine and iso-net energy diets with constant or above requirements levels of essential amino acids (EAA, METEX NØØVISTAGO recommendations) were selected for the final analysis, with a 5% acceptance limit. The final selection contained 11 trials and 44 treatments. The general linear model procedure of MINITAB (2019) was used to build regression models, including a fixed trial effect. Dietary CP reduction tested ranged between 1.6 and 7.5 percentage points (%pt) and was performed by reducing soybean meal and increasing cereals inclusion in all trials except one using rapeseed meal. Reducing dietary CP did not significantly impacted feed intake, average daily gain or feed conversion ratio when EAA were adequately supplied. Nitrogen efficiency was improved by 2.7 %pt per %pt of CP reduction (P < 0.001). No statistical analysis could be performed on carcass composition data as there were only 6 trials remaining after selection for these parameters, but fatter carcasses were observed in half of the trials. This study highlighted that few CP reduction studies have been performed while controlling energy and EAA levels. There was no effect of reducing dietary CP on growth performance when those parameters were controlled but more research is needed on very low CP diets for fattening pigs.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.045 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".