Revising the Crop Nutrient Uptake and Removal Guidelines for Western Canada
Bibliographic record
Abstract
Yield-based crop nutrient uptake (harvested grain + residue/straw) and removal (harvested portion only) data are critical in making nutrient application decisions to achieve a target crop yield in major crops. Developing new high-yielding cultivars within the Western Canadian prairies, implementing best agronomic management practices, and increasing fertilizer application rates have steadily increased dry matter accumulation, seed yield, and crop nutrient uptake. Thus, estimates based on current nutrient uptake and removal guidelines compiled by the Canadian Fertilizer Institute (CFI, 1998; 2001) may not realistically reflect improvements in current best management practices (BMP) - underestimating the productive potential of new cultivars. Our project aims to determine and revise the nutrient (N, P2O5, K2O, S, B, Cu, and Zn) uptake and removal of crops commonly grown in western Canada from samples across different soil zones. The study will also generate a user-friendly mobile application for nutrient uptake and removal. Preliminary nutrient removal (grain) values from the 2020 and 2021 growing seasons show extreme variability across crop yields. Overall, there was generally lower or similar nutrient concentration in most crops compared to the 2001 CFI estimates of higher yields, underscoring the efficiency of newer crop cultivars. The lower nutrient removal per bushel in our study means farmers could save fertilizer costs if application rates are adjusted to reflect removal in grain.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.008 |
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".