The nitrogen legacy: emerging evidence of nitrogen accumulation in anthropogenic landscapes
Bibliographic record
Abstract
Watershed and global-scale nitrogen (N) budgets indicate that the majority of the N surplus in anthropogenic landscapes does not reach the coastal oceans.While there is general consensus that this 'missing' N either exits the landscape via denitrification or is retained within watersheds as nitrate or organic N, the relative magnitudes of these pools and fluxes are subject to considerable uncertainty.Our study, for the first time, provides direct, large-scale evidence of N accumulation in the root zones of agricultural soils that may account for much of the 'missing N' identified in mass balance studies.We analyzed long-term soil data (1957-2010) from 2069 sites throughout the Mississippi River Basin (MRB) to reveal N accumulation in cropland of 25-70 kg ha -1 yr -1 , a total of 3.8±1.8Mt yr -1 at the watershed scale.We then developed a simple modeling framework to capture N depletion and accumulation dynamics under intensive agriculture.Using the model, we show that the observed accumulation of soil organic N (SON) in the MRB over a 30 year period (142 Tg N) would lead to a biogeochemical lag time of 35 years for 99% of legacy SON, even with complete cessation of fertilizer application.By demonstrating that agricultural soils can act as a net N sink, the present work makes a critical contribution towards the closing of watershed N budgets.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".