Farmer Nutrient Management Decisions: A Study of Farms in the Gully Creek Watershed in Southern Ontario
Bibliographic record
Abstract
In Ontario there has been increased concern regarding harmful algae blooms in Lake Erie, phosphorus has been identified as the growth limiting agent. This research examines farmers’ individual nutrient management decisions to understand if the actual nutrient application rates are equal to the recommended NMAN rate (NE). I use two alternative nutrient rate criteria and a regression analysis to further examine nutrient application decisions. I use a unique data set from the Gully Creek watershed that contains farmers’ individual nutrient application decisions for phosphorus and nitrogen, spanning 2008 to 2013. In corn production, farmers were found to apply nitrogen below the NE, and phosphorus above the NE. In winter wheat production, farmers were found to apply both nitrogen and phosphorus above the NE. The regression analysis identified that larger farms, lower yielding fields, and the application of manure influence farmers’ decisions to apply nutrients in excess of the NE rate.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".