Modelling of manure production by pigs and NH3, N2O and CH4 emissions. Part I: animal excretion and enteric CH4, effect of feeding and performance
Bibliographic record
Abstract
A mathematical model was developed from literature data to predict the volume and composition of pig's excreta (dry and organic matter, C, N, P, K, Cu and Zn contents), and the emission of greenhouse gases (CH4 and CO2) though respiration and from the intestinal tract, for each physiological stage (post-weaning and fattening pigs and lactating and gestating sows). The main sources of variation considered in the model are related to animal performances (feed efficiency, prolificacy, body weight gain, etc.), to water and nutrient intakes and to housing conditions (ambient temperature). Model predictions were validated by using 19 experimental studies, most of them performed in conditions close to those of commercial farms. Validation results showed that the model is precise and robust when predicting slurry volume (R2 = 0.96), slurry N (R2 = 0.91), P (R2 = 0.95) and to a lesser extent dry matter (R2 = 0.75) contents. Faeces and urine composition (minerals and macronutrients) can also be precisely assessed, provided the composition and the digestibility of the feed are well known. Sensitivity analysis showed strong differences in CH4 emission and excretion amounts and composition according to physiological status, animal performance, temperature and diet composition. The model is an efficient tool to calculate nutrient balances at the animal level in commercial conditions, and to simulate the effect of production alternatives, such as feeding strategy or animal performance, on excreta production and composition. This is illustrated by simulations of three feeding strategies, which demonstrates important opportunities to limit environmental risks through diet manipulations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".