Intensive agriculture, nitrogen legacies, and water quality: intersections and implications
Bibliographic record
Abstract
Abstract More than a century of land-use changes and intensive agriculture across the Mississippi River Basin (MRB) has led to a degradation of soil and water resources. Nitrogen (N) leaching from the excess application of fertilizers has been implicated in algal blooms and the development of large, coastal ‘dead zones’. It is, however, increasingly recognized that water quality today is a function not only of the current-year inputs but also of legacy N within the watershed—legacy that has accumulated in soil and groundwater over decades of high-input agricultural practices. Although attempts have been made to quantify the extent to which soil organic nitrogen (SON) is being sequestered in agricultural soils with intensive fertilization, improved residue management, and the adoption of conservation tillage practices, the controls on accumulation dynamics as well as linkages between legacy N accumulation and water quality remain unclear. Here, we have used the process-based model CENTURY to quantify accumulation and depletion trajectories for soil N across a range of climate and soil types characteristic of the MRB. The model was calibrated against crop yield data and soil N accumulation data from a long-term field site. Model runs highlighted that under current management scenarios, N accumulation is greatest in regions with the highest crop yield, and this can be attributed to the higher residue rates with greater yields. We thus find that humans, through management practices, have homogenized spatial patterns of SON across the landscape by increasing SON magnitudes in warmer and drier regions. Results also suggest a regime shift in the relationship between soil organic N and N mineralization fluxes, such that N fluxes are greater now than in the 1930s, despite similar soil organic N magnitudes, mainly due to higher proportions of labile, unprotected soil organic matter. This regime shift leads to elevated N leaching to tiles and groundwater in landscapes under intensive agriculture.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".