The source of performance and excretion data affects the environmental impact of pig rearing estimated by life cycle assessment
Bibliographic record
Abstract
The objective of this study was to calculate the environmental impact of raising pigs fed diets with 192, 182, 172, or 162 g kg−1 crude protein (CP) from 15 to 30 kg live weight, using a life cycle assessment (LCA) based on either predicted or observed performance and excretion data. The predicted data were calculated for two animal profiles: one with feed intake and weight gain data being representative of the national Brazilian average (yp-AVG), and the other being representative of the specific experimental farm conditions (yp-SPC). Performance and excretion of pigs were either measured (yobs) or predicted (yp) for each CP scenario by using InraPorc® model and LCA impacts were calculated from these data. The yp and yobs data of performance, nutrient excretion, and LCA were then compared using a Bayesian paired approach. Whatever the calculation methods, the decrease in dietary CP reduced the environmental impacts per kilogram of weight gain on acidification potential (AP), eutrophication potential (EP), and land occupation. Predicted data from yp-SPC did not differ from yobs data except for nitrogen excretion and AP and EP impacts. Predicted data from yp-AVG differed from yobs for all criteria except for body weight gain, phosphorus excreted, and cumulative energy demand, mainly because of the lower feed conversion ratio for average data. It was concluded that the use of predicted data calculated from specific farm indicators could better explain the environmental impact of pig farming in Brazil compared with the use of national average indicators.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".