The environmental impacts of commercial poultry production systems using life cycle assessment: a review
Bibliographic record
Abstract
SUMMARYWith the ever-increasing world population, there is a need for the agri-food industry to adopt sustainable practices with a reduced environmental footprint. Life cycle assessment (LCA) is an established methodology to determine and evaluate the environmental impacts of production activities (commodity or service) on human health, ecosystem quality, global warming, resources and water with the ultimate goal of eliminating or decreasing those undesired impacts. Over the past decade, the LCA method has been vastly applied to estimate the environmental loads in various industries, including agri-food, to evaluate all stages of activities, such as extraction, production, transportation, consumption, recycling and reuse. The present work aims to contribute to the ongoing efforts in enhancing the efficiency of agri-food production systems by reviewing the environmental impacts in the commercial poultry industry using the LCA methodology. Our focus is on assessing the environmental impact of meat and egg production across different commercial poultry species, specifically chicken, ostrich and turkey, as well as poultry egg production systems. The findings underscore the considerable role of inputs such as feed and energy, as well as farming processes, in the environmental footprint of commercial poultry systems. To improve sustainability, stakeholders must prioritise enhancing feed and energy efficiency while reducing farm emissions. Future trends and potential applications of LCA are also discussed to advance sustainable practices within poultry production systems. The outcome of the present study provides valuable insights for decision-makers and stakeholders seeking to reduce the environmental footprint of poultry production. By integrating LCA methodologies across all production stages, informed choices can be made to enhance the efficiency and sustainability of agri-food production systems.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".