Baisse de protéine chez le porc en croissance pour la production de protéines animales durables
Bibliographic record
Abstract
Dietary crude protein reduction has been presented as a strategy to reduce environmental impacts of pig production, thanks to improved valorization of protein resources, reducing nitrogen excretion and associated emissions, without impacting pig performance. A meta-analysis and a review of published life cycle assessments showed a lack of data on very low crude protein diets, and an effect of production context on environmental benefits of low crude protein strategies. The objective of this thesis was to evaluate the environmental benefits of crude protein reduction in fattening pig diets, by testing the effect of very low crude protein diets to identify eventual break-off points and taking into account production contexts. Two trials were performed to explore effect of very low crude protein diets on pig performance, nitrogen excretion and gaseous emissions, in Quebec context. Growth performances were constant in the first trial, even with very low crude protein diets. However, a maximum was identified for nitrogen efficiency in the second trial, where performance levels were higher. It limited potential for reduction of nitrogen excretion and ammonia emissions with very low crude protein diets. The second trial confirmed that ammonia emission factor was fixed, but nitrous oxide emission factor increased with crude protein reduction. A modelling approach was then used to perform a life cycle assessment, evaluating the effect of different levels of crude protein reduction on environmental impacts of pig production, in three contrasted contexts (Quebec, Brazil, France). This study showed that only the impacts mainly linked to ammonia emissions were consistently reduced with low crude protein diets (acidification and terrestrial eutrophication). For other impacts, effect of crude protein reduction depended on raw materials used, with positive effects only in the French context. Pig farming practices determined the potential crude protein reduction, as well as current levels of environmental impacts and magnitude of their variation with crude protein reduction. The life cycle assessment methodology used could be perfected, in particular with a better inclusion of the effect of low crude protein diets on fertilizing practices. This thesis identified limits to crude protein reduction as an environmental strategy, influenced by production context, and despite constant growth performances. Evaluating potential of low crude protein strategies combined with other environmental impact reduction strategies, focusing more on raw materials used, such as multiobjective feed formulation, could be an interesting next step.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".