The environmental impacts of commercial poultry production systems using life cycle assessment: a review
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
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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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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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