Modelling the variation in performance of a population of growing pig as affected by lysine supply and feeding strategy
Bibliographic record
Abstract
Considerable progress has been made in the nutritional modelling of growth. Most models typically predict (or analyse) the response of a single animal. However, the response to nutrients of a single, representative animal is likely to be different from the response of the herd. To address the variation in response between animals, a stochastic approach towards nutritional modelling is required. In the present study, an analysis method is presented to describe growth and feed intake curves of individual pigs within a population of 192 pigs. This method was developed to allow end-users of InraPorc (a nutritional model predicting and analysing growth in pigs) to easily characterise their animals based on observed data and then use the model to test different scenarios. First, growth and intake data were curve-fitted to characterise individual pigs in terms of BW (Gompertz function of age) and feed intake (power function of BW) by a set of five parameters, having a biological or technico-economical meaning. This information was then used to create a population of virtual pigs in InraPorc, having the same feed intake and growth characteristics as those observed in the population. After determination of the mean lysine (Lys) requirement curve of the population, simulations were carried out for each virtual pig using different feeding strategies (i.e. 1, 2, 3 or 10 diets) and Lys supply (ranging from 70% to 130% of the mean requirement of the population). Because of the phenotypic variation between pigs and the common feeding strategies that were applied to the population, the Lys requirement of each individual pig was not always met. The percentage of pigs for which the Lys requirement was met increased concomitantly with increasing Lys supply, but decreased with increasing number of diets used. Simulated daily gain increased and feed conversion ratio decreased with increasing Lys supply (P < 0.001) according to a curvilinear-plateau relationship. Simulated performance was close to maximum when the Lys supply was 110% of the mean population requirement and did not depend on the number of diets used. At this level of Lys supply, the coefficient of variation of simulated daily gain was minimal and close to 10%, which appears to be a phenotypic characteristic of this population. At lower Lys supplies, simulated performance decreased and variability of daily gain increased with an increasing number of diets (P < 0.001). Knowledge of nutrient requirements becomes more critical when a greater number of diets are used. This study shows the limitations of using a deterministic model to estimate the nutrient requirements of a population of pigs. A stochastic approach can be used provided that relationships between the most relevant model parameters are known.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".