Optimizing nitrogen rates in the midwestern United States for maximum ecosystem value
Bibliographic record
Abstract
The importance of corn production to the midwestern United States cannot be overestimated.However, high production requires high nitrogen fertilization, which carries costs to environmental services such as water quality.Therefore, a trade-off exists between the production of corn yield and water quality.We used the Groundwater Vulnerability Assessment for Shallow depths and Crop Environment Resource Synthesis-Maize models to investigate the nature of this trade-off while testing the Simple Analytic Framework trade-offs featured in this Special Feature.First, we estimated the current levels of yield and water quality production in northeastern Iowa and southern Minnesota at the 1-square-kilometer, county, and regional scales.We then constructed an efficiency frontier from optimized nitrogen application patterns to maximize the production of both yield and water quality.Results highlight the context dependency of this trade-off, but show room for increasing the production of both services to the benefit of all stakeholders.We discuss these results in the context of spatial scale, biophysical limitations to the production of services, and stakeholder outcomes given disparate power balances and biophysical contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 | 0.001 | 0.001 |
| 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".