The U.S. consumer phosphorus footprint: where do nitrogen and phosphorus diverge?
Bibliographic record
Abstract
Abstract Phosphorus (P) and nitrogen (N) are essential nutrients for food production but their excess use in agriculture can have major social costs, particularly related to water quality degradation. Nutrient footprint approaches estimate N and P release to the environment through food production and waste management and enable linking these emissions to particular consumption patterns. Following an established method for quantifying a consumer-oriented N footprint for the United States (U.S.), we calculate an analogous P footprint and assess the N:P ratio across different stages of food production and consumption. Circa 2012, the average consumer’s P footprint was 4.4 kg P capita−1 yr−1 compared to 22.4 kg N capita−1 yr−1 for the food portion of the N footprint. Animal products have the largest contribution to both footprints, comprising >70% of the average per capita N and P footprints. The N:P ratio of environmental release based on virtual nutrient factors (kilograms N or P per kilogram of food consumed) varies considerably across food groups and stages. The overall N:P ratio of the footprints was lower (5.2 by mass) than for that of U.S. food consumption (8.6), reinforcing our finding that P is managed less efficiently than N in food production systems but more efficiently removed from wastewater. While strategies like reducing meat consumption will effectively reduce both N and P footprints by decreasing overall synthetic fertilizer nutrient demands, consideration of how food production and waste treatment differentially affect N and P releases to the environment can also inform eutrophication management.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".