Environmental Impacts of the Brazilian Egg Industry: Life Cycle Assessment of the Battery Cage Production System
Bibliographic record
Abstract
Brazil stands as one of the world’s leading producers of animal protein, ranking sixth in global egg production. However, estimated growth in production demand, along with environmental impacts, represents a potential threat to the sustainability of the food system. Methods for assessing and quantifying the environmental impacts generated by Brazilian egg production remain scarce, lacking current reports on comparative effects or guiding standards. The objective of this study was to conduct a life cycle assessment from cradle to farm gate, adhering to ISO 14040 and 14044 standards, for the battery cage egg production systems and associated products in Brazil, with the aim of supporting and promoting sustainability improvements in the Brazilian egg industry. The entire life cycle modeling and process sustainability analysis were executed using the openLCA software, integrated with the Ecoinvent database. Emissions related to egg production yielded results of 65.06 kg SO2 eq., 27.74 kg N eq., 3086.71 kg CO2 eq., 75,152.66 CTUe, 2.75 × 10−5 CFC-11 eq., and 10,044.68 kg MJ eq. per ton of eggs produced. These findings can serve as comparative benchmarks for future studies and for analyzing data across different egg production systems in Brazil.
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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 | 0.000 |
| 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) | 0.001 | 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".