PSVI-14 Impact of phosphorus, calcium and microbial phytase on growth performance in growing pig; a meta-analysis
Bibliographic record
Abstract
Abstract The requirements for calcium (Ca) and phosphorus (P) differ for growth performance and bone mineralization. A mechanistic model has been developed to predict the requirement for maximum P and Ca deposition, so maximising both soft tissue and bone growth. However, literature showed that high Ca can reduces growth performance which is not considered in the model for the moment. The objective of this study was to quantify the effect of Ca, P and microbial phytase (PhytM) on average daily feed intake (ADFI), average daily gain (ADG) and gain to feed ratio (GF) and add these effects in the model to predict the requirements for growth and bone mineralization. A database built from 42 experiments, including 298 treatments was used in pigs from 35.6 to 59.1 kg BW. The random effect of the experiment has been included in the models. Multiples linear regression models has been fitted using Minitab software. The response of ADG to non-phytate P (NPP) is curvilinear (NPP and NPP x NPP, P < 0.001) while a tendency was found for a curvilinear effect for ADFI (P = 0.06) and GF (P = 0.08). Increasing dietary Ca reduced ADG (P < 0.001) and ADFI (P < 0.001) while GF was not modified. However, this effect is alleviated high NPP diet (Ca x NPP, P < 0.01). The response of growth performance to PhytM tended to be curvilinear for ADG and GF (PhytM x PhytM, P < 0.10). Adding 500 FTU/kg increased ADG by 5%. The effect of PhytM on ADFI depended on Ca (Ca x PhytM, P < 0.01); decreasing Ca increased the effect of PhytM with a 3% increased for 500 FTU in a 5 g Ca/kg diet. For GF the effect of PhytM depended on both Ca and NPP (Ca x NPP x PhytM, P < 0.01) with a PhytM effect of 15% for 500 FTU in a diet low in NPP (1 g/kg) and high Ca diet (8 g/kg). Results of the current meta-analysis showed P deficiency reduces growth performance, and that high Ca aggravated the deficiency mainly through reduced ADFI that induced ADG reduction without modifying GF. PhytM improved growth performance but the improvement is dependent of Ca and NPP levels for ADFI and GF. These results showed that at high NPP, i.e. close to requirement, Ca has no detrimental effect on growth performance. These equations will be implemented in the model predicting Ca and P requirement of pig.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.047 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".