Comparative life cycle assessment of technologies and strategies to improve nitrogen use efficiency in egg supply chains
Bibliographic record
Abstract
A non-trivial challenge along egg supply chains is inefficient use of nitrogen, which may have a combination of negative economic, human/animal health, and environmental implications. A variety of technologies and management strategies have been proposed to improve nitrogen use efficiency (NUE) and reduce emissions at key supply chain stages. This study considered seven scenarios representing NUE strategies: (1) biochar addition to the soil and (2) application of the “4Rs” approach to fertilizer management in feed input production; (3) a reduced crude protein supplemented with synthetic amino acids diet; (4) use of acid scrubbers in poultry barns; (5) biochar addition to stored manure; (6) manure incorporation at the time of land application; and (7) joint application of all strategies. ISO 14044 compliant environmental life cycle assessment (LCA) along with NUE calculations were performed to analyze and compare these mitigation options. The functional unit was one tonne of egg production at the farm gate in Canada. The most significant increase in NUE (13%) resulted from the application of the scrubber in the layer barn. The scrubber also significantly lowered acidifying (21%) and eutrophying (16%) emissions compared to the baseline. The combined application of all strategies increased NUE by 15% compared to the baseline scenario and reduced acidification, global warming, and eutrophication potential, but at the cost of a large increase in energy consumption. Each strategy might be more or less suitable depending on the considered environmental impacts, as well as NUE outcomes. Use of LCA is essential to informed decision making in this context.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".