Technological and institutional lock-in and excessive synthetic nitrogen fertilizer use on North American grain and oilseed farms
Bibliographic record
Abstract
This intervention examines commodity grain and oilseed farmers’ over reliance on synthetic nitrogen fertilizer in North America. Most grain and oilseed farmers apply synthetic nitrogen fertilizer at rates higher than necessary in order to ensure maximum yields. At the same time, high fertilizer application rates lead to increased farm input expenses and generate significant amounts of water pollution and excessive greenhouse gas emissions. A number of low-cost alternative approaches have been developed which can significantly reduce or eliminate the need for synthetic nitrogen fertilizer while maintaining farm profitability. But such practices have only seen limited adoption by Canadian and US farmers. This is despite significant production cost savings and environmental benefits. A number technological and institutional factors work in combination to lock farmers into production models requiring large amounts of synthetic nitrogen fertilizer. They include crop varieties bred to thrive in artificially high nitrogen soil conditions, conventional tillage practices, restrictive financial arrangements, largely unenforced water quality laws, and non-diverse marketing outlets. These technological and institutional lock-ins are significant barriers to the adoption of alternative crop production practices that are less reliant upon synthetic nitrogen fertilizer.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".