Modelling of manure production by pigs and NH3, N2O and CH4 emissions. Part I: animal excretion and enteric CH4, effect of feeding and performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 it