Phosphorus and calcium requirements for bone mineralisation of growing pigs predicted by mechanistic modelling
Bibliographic record
Abstract
Phosphorus (P) is an essential nutrient in livestock feed but can pollute waterways. In order for pig production to become less of a threat to the environment, excreta must contain as little P as possible or be efficiently used by plants. This must be achieved without decreasing the livestock performance. Phosphorus and calcium (Ca) deposition in the bones of growing pigs must be optimised without affecting the muscle gain. This requires precision feeding based on cutting-edge techniques of diet formulation throughout the animal growth phase. Modelling and data mining have become important tools in this quest. In this study, a mechanistic model taking into account the distribution of P between bone and soft tissues was compared to the established factorial models (INRA (Jondreville and Dourmad, 2005) and NRC (National Research Council, 2012)) that predict P (apparent total tract digestible, ATTD-P; or standardised total tract digestible, STTD-P) and Ca (total and STTD) requirements as a function of BW and protein deposition. The requirements for different bone mineralisation scenarios, namely, 100% and 85% of the genetic potential, were compared with these two models. Sobol indices were used to estimate the relative impact of growth-related parameters on mineral requirements at 30, 60 and 120 kg of BW. The INRA showed the highest value of ATTD-P requirement between 29 and 103 kg of BW (6%) and lower for lighter and higher BW. Similarly, the model for 85% bone mineralisation showed lower STTD-P requirement than NRC between 29 and 93 kg of BW (7%) and higher for lighter and higher BW. Contrary to other models, the Ca requirement of the proposed model is not fixed in relation to P. It increases from 95 kg of BW while the others decrease. The INRA showed the highest Ca requirements. The model Ca requirements for 100% bone mineralisation are higher than NRC from 20 to 38 kg of BW similar until 70 kg of BW and then higher again. For 85% objective, the model showed lower Ca requirements from 25 to 82 kg of BW and higher for lighter and higher BW. The potential Ca deposition in bones is the most sensitive parameter (84% to 100% of the variance) of both ATTD-P and Ca at 30, 60 and 120 kg. The second most sensitive parameter is the protein deposition, explaining 1% to 15% of the ATTD-P variance. Studies such as this one will help to usher in a new era of sustainable and eco-friendly livestock production.
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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 | 0.000 |
| 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) | 0.000 | 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".