The nitrogen footprint of organic food in the United States
Bibliographic record
Abstract
Abstract We estimated the reactive nitrogen (Nr) lost per unit food Nr consumed for organic food production in the United States and compared it to conventional production. We used a nitrogen footprint model approach, which accounts for both differences in Nr losses as well as differences in productivity of the two systems. Additionally, we quantified the types of Nr inputs (new versus recycled) that are used in both production systems. We estimated Nr losses from organic crop and animal production to be of comparable magnitude to conventional production losses, with the exception of beef. While Nr losses from organic vegetables are possibly higher (+37%), Nr losses from organic grains, starchy roots, legumes are likely of similar magnitude to conventional production (+7%, +6%, −12%, respectively). Nr losses from organic poultry, pigmeat, and dairy production are also likely comparable to conventional production (+9%, +10%, +12%, respectively), while Nr losses from organic beef production were estimated to be higher (+124%). Due to the high variability and high uncertainty in Nr efficiency in both systems we cannot make conclusions yet on the statistical significance of these potential differences. Conventional production relies heavily on the creation of new Nr (70%–90% of inputs are from new Nr sources like synthetic fertilizer), whereas organic production primarily utilizes already existing Nr (0%–50% of organic inputs are from new Nr sources like leguminous N fixation). Consuming organically produced foods has little impact on an individual’s food N footprint but changes the percentage of new versus recycled Nr in the footprint. With the exception of beef, Nr losses from organic production per unit N in product are comparable to conventional production. However, organic production requires the creation of less new Nr, which could reduce global Nr pollution.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".