Meta-analysis of phosphorus utilization by growing pigs: effect of dietary phosphorus, calcium and exogenous phytase
Bibliographic record
Abstract
Optimizing phosphorus (P) utilization in pigs requires improving our capacity to predict the amount of P absorbed and retained, with the main modulating factors taken into account, as well as precisely determining the P requirements of the animals. Given the large amount of published data on P utilization in pigs, a meta-analysis was performed to quantify the impact of the different dietary P forms, calcium (Ca) and exogenous phytases on the digestive and metabolic utilization criteria for dietary P in growing pigs. Accordingly, the amount of phytate P (PP) leading to digestible P (g/kg) was estimated to be 21%, compared with 73% for non-phytate P (NPP) from plant ingredients and 80% for NPP from mineral and animal ingredients (P < 0.001). The increase in total digestible dietary P following the addition of microbial phytase (PhytM) from Aspergillus niger (P < 0.001) was curvilinear and about two times higher than the increase following the addition of plant phytase, which leads to a linear response (P < 0.001). The response of digestible P to PhytM also depends on the amount of substrate, PP (PhytM(2) × PP, P < 0.001). The digestibility of dietary P decreased with dietary Ca concentration (P < 0.01) independently of phytase but increased with body weight (BW, P < 0.05). Although total digestible dietary P increased linearly with total NPP concentration (P < 0.001), retained P (g/kg), average daily gain (ADG, g/day) and average daily feed intake (ADFI, g/day) increased curvilinearly (P < 0.001). Interestingly, whereas dietary Ca negatively affected P digestibility, the effect of dietary Ca on retained P, ADG and ADFI depended on total dietary NPP (NPP × Ca, P < 0.01, P < 0.05 and P < 0.01, respectively). Increasing dietary Ca reduced retained P, ADG and ADFI at low NPP levels, but at higher NPP concentrations it had no effect on ADG and ADFI despite a positive effect on retained P. Although the curvilinear effect of PhytM on digestible P increased with PP (P < 0.001), this effect was lessened by total NPP for ADG and ADFI (PhytM × NPP and PhytM(2) × NPP, P < 0.05) and depended on both total NPP and Ca for retained P (PhytM(2) × NPP × Ca, P < 0.01). This meta-analysis improves our understanding of P utilization, with major modulating factors taken into account. The information generated will be useful for the development of robust models to formulate environmentally friendly diets for growing 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 | 0.019 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.054 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 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".