Spring wheat (<i>Triticum aestivum</i>) yield and grain protein responses to N fertilizer in topographically defined landscape positions
Bibliographic record
Abstract
A 3-yr field study was initiated in 1996 to examine the different grain yield and grain protein responses of wheat to varied N fertilizer rates in a typical glacial till landscape in Saskatchewan, Canada. Our objective was to assess the agronomic and economic feasibility of variable rate fertilizer (VRF) N application for wheat. Results suggest that spring soil water status largely determined the yield and the protein content of wheat both within different years of the study and between different landscape positions within a given year. Although grain yield was strongly related to spring soil water and was predictable on that basis, the grain yield response of wheat to fertilizer N additions was highly variable due, in part, to the dual role that N played in determining both grain yield and grain protein content. As a consequence of the unpredictable nature of the varied response of wheat to N fertilizer additions, there was little economic rationale for using VRF strategies in the 3 yr of this study. However, in the long-term, we believe that VRF N application strategies can be employed to manage N inputs from the perspective of managing and replacing harvested N. Key words: Variable rate fertilizer application, precision farming, nitrogen application, N fertilizer, Saskatchewan
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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 | 0.000 |
| 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".