Life cycle assessment of nitrogen efficiency strategies for Canadian egg supply chains
Bibliographic record
Abstract
Rapid population growth and the associated rising demand for food are driving agricultural expansion and intensification. One important consequence is increased use and loss of reactive nitrogen (Nr) in agriculture – in particular in livestock production - precipitating Nr cascades with multiple impacts on ecosystems. Among livestock production systems, egg production is the fastest growing segment worldwide. Improving nitrogen use efficiency (NUE) is a key priority for enabling more environmentally sustainable egg production. The objective of this research is to provide information to support NUE improvements along egg production supply chains while simultaneously minimizing “systems level” resource consumption and negative environmental impacts. NUE is influenced by feed production practices, feed composition, feed conversion efficiency, and manure management practices. Technological and management changes targeting these variables along egg supply chains to improve NUE can influence system-level environmental performance and resource efficiency. To consider the relative efficacy of Nr emissions mitigation options with respect to improving NUE and reducing resource use and emissions, life cycle assessment (LCA) methodology was employed. Potentially efficacious NUE improvement options (taking into account environmental, technical and economic criteria) were identified based on a literature review. Priority strategies were: “4Rs” practices and biochar addition to cropland for the feed input production stage; reduced crude protein diet via synthetic amino acid supplementation for the feed formulation stage; use of ammonia scrubbers in barns along with use of manure belts; biochar addition to the stored manure; and manure incorporation at time of field application. LCA, NUE, and N footprint results iv indicated that each mitigation option might be more or less suitable depending on kinds of environmental impacts considered. The application of scrubbers and biochar addition to the stored manure resulted in 10% and 3% higher NUE compared to the baseline, with considerable reduction of acidification and eutrophication impacts. However, they resulted in higher energy consumption, abiotic depletion and ozone layer depletion. Results clearly indicate that there is trade-offs among different impact categories and NUE for most NUE strategies. Hence, efforts to improve NUE must take into account a variety of potential resource and environmental impacts to ensure informed decision making.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".