Estimation of Canadian manure and fertilizer nitrogen application rates at the crop and soil-landscape polygon level
Bibliographic record
Abstract
In support of national environmental and economic modeling of agri-environmental indicators, greenhouse gases, carbon sequestration and policy assessment, fertilizer and manure nitrogen application rates were estimated for individual crops at the scale of the 1:1 m Soil Landscapes of Canada polygons. This database provides an estimate of the amount of nitrogen applied to each crop and is based on provincial fertilization recommendations, the type and number of livestock and manure produced and reported amounts of fertilizer sold. The database is being incorporated into ongoing programs related to international reporting, environmental performance and policy formulation at Agriculture and Agri-Food Canada.This paper describes the procedures developed to estimate fertilizer and manure nitrogen inputs for each crop type within each polygon. These procedures include: (i) the compilation of soil-specific recommended nitrogen application rates from provincial extension guide lines and experts; (ii) the calculation of total manure nitrogen production from animal numbers and excretion rates; (iii) the calculation of manure nitrogen available after land application losses and (iv) the adjustment of total fertilizer nitrogen applied to match reported sales at the provincial level. The calculation procedures were incorporated into the Canadian Agricultural Nitrogen Budget model, with provisions for transferring the data to other models and for other applications. Key words: Fertilizer nitrogen, manure nitrogen, nitrogen application rates, nitrogen model, Soil Landscapes of Canada, Census of Agriculture
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".