Precipitation Changes Impact Stream Discharge, Nitrate–Nitrogen Load More Than Agricultural Management Changes
Bibliographic record
Abstract
Nitrate-N losses to surface waters in the Upper Midwest of the Untied States have increased in recent decades, contributing to hypoxia in the Gulf of Mexico. This paper investigates whether increasing nitrate-N export from cropland in the Upper Midwest since the late 1960s results from changes in land use or climate. The Agricultural Drainage and Pesticide Transport (ADAPT) Model simulated current and historical agricultural systems under past and recent wet climate for Seven Mile Creek in Minnesota. Simulations were run with management and climate for three distinctly different periods--namely, 1965 to 1969, 1976 to 1980, and 1999 to 2003 (wettest period). Results showed discharge and nitrate-N losses responded more to changes in climate than management. The wetter period (1999-2003) caused a simulated 70% increase in discharge under 1960s-era management compared with that period's observed climate and a simulated 51% increase in discharge under 1970s-era management compared with the 1976 to 1980 climate. The recent, wetter climate also produced a 62% increase in nitrate-N losses for 1960s-era management compared with the actual climate and a 137% increase in nitrate-N losses for 1978 management conditions compared with actual 1970s climate. Had recent climate been in place and stable since 1965, agricultural changes would have decreased discharge by 6.4% through the late 1970s and then by another 21.1% under modern management but would have increased nitrate-N losses by 184% through the late 1970s and then decreased nitrate-N losses by 13.5% between 1978 and 2001. Management changes that were important drivers included increasing N-fertilizer rates, increases in corn acreage, and increases in crop yield. But the most important factor driving increased nitrate-N losses from agriculture since the 1970s was an increasingly wetter climate.
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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 | 0.001 | 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) | 0.001 | 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 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".