Topographically Controlled Yield Response of Canola to Nitrogen Fertilizer
Bibliographic record
Abstract
The effect of topographical position on the yield response of canola ( Brassica rapa var. Maverick) to N fertilizer was evaluated in a 3‐yr field study in Saskatchewan, Canada. The hummocky, glacial till research site was stratified into three topographically defined landform complexes (convex, linear, and concave). Five N treatments (0, 0.5, 1.0, 1.5, and 2 times the recommended soil test rates; treatments will be referred to as 0×N, 0.5×N, 1×N, 1.5×N, and 2×N, respectively) were randomized in replicated blocks, and each block spanned the three landform complexes. Substantial yield differences occurred among the study years and were associated with year‐to‐year differences in spring soil moisture. Canola seed yields (averaged across years and treatments) increased from 0.53 Mg ha −1 in convex complexes to 0.95 Mg ha −1 in linear, and 1.42 Mg ha −1 in concave landform complexes. The greatest yield responses to N occurred in the concave landform units in years where spring soil moisture was high. The N fertilizer rate required to achieve the economically optimum yield was significantly correlated to spring available moisture (required N rate [kilogram per hectare] = 40.9 + 14.87 [spring available water {in centimeters} water to 60‐cm depth], R 2 = 0.73, sig. = 0.003) but was not significantly related to spring (i.e., preseeding) available soil N. The results confirm earlier studies indicating the importance of spring available water for crop production in the northern Great Plains, and suggest that intensive spring sampling for soil moisture conditions may be the most useful diagnostic tool for the implementation of a variable rate N program in this region.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".