Nitrogen abatement cost comparison for cropping systems under alternative management choices
Bibliographic record
Abstract
There is a need for cost-effective methods to reduce nitrogen pollution from agriculture. Marginal abatement cost (MAC) curves for nitrate-nitrogen pollution in an agricultural watershed are evaluated using estimated crop yield and nitrate pollution production functions for alternative cropping systems. The cropping systems considered in this study included i) two grain corn-based cropping systems; ii) two potato-based cropping systems; and iii) a vegetable-horticulture system, managed under conventional tillage (CT) and no-till (NT). The cost-effective potato-based cropping system which met the Health Canada maximum contaminant limit (MCL) for nitrate-N, with the highest gross margin ($6973 ha-1) and lowest abatement cost ($395 ha-1) was a potato-barley-winter wheat-potato-corn rotation under no-till (PBWPC-NT). Similarly, among the vegetable-horticulture cropping systems, potato-winter wheat-carrot-corn rotation under CT (PWRC-CT) generated the highest gross margin and lowest on-farm abatement cost ($680 ha-1). As the Health Canada allowable limit on nitrate-N pollution was relaxed (i.e., less stringent), the cost-effective corn-based cropping system shifted from a rotation involving corn-corn-alfalfa-alfalfa-alfalfa under CT to corn-corn-corn-alfalfa-alfalfa under NT.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".