Accounting for differences between crops and regions reduces estimates of nitrate leaching from nitrogen-fertilized soils
Bibliographic record
Abstract
Nitrate (NO3−) leaching from nitrogen (N) fertilized soils is a significant global concern, affecting both the environment and public health. However, substantial uncertainties and variabilities in NO3− leaching factors (LFs) among regions or crops impede accurate assessments of NO3− leaching. Here we synthesize 2500 field observations worldwide and show that LFs vary by an order of magnitude across regions and crops, primarily driven by hydroclimatic and edaphic conditions rather than N fertilizer management. Global cropland NO3− leaching from synthetic N fertilization, calculated through spatially explicit (15.4, 14.8–16.1 Tg N yr–1) and crop-specific (12.9, 11.0–14.8 Tg N yr–1) LFs, is 41% lower than the Intergovernmental Panel on Climate Change Tier 1 global inventory. Over 47% of this leaching is concentrated in China, India, and the United States, with maize, wheat, rice and vegetables accounting for nearly half of it. Improved regional and crop-specific LFs will provide a benchmark for NO3− leaching abatement by pinpointing potential global hotspots. Almost half of globally leached nitrate from nitrogen-fertilized soil is released in China, India, and the United States, according to a meta-analysis covering 2500 field observations worldwide.
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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".