Spatial variability of barley (<i>Hordeum vulgare</i>) and corn (<i>Zea mays</i> L.) yields, yield response to fertilizer N and soil N test levels
Bibliographic record
Abstract
Identification of management units for the variable application of fertilizer N is a critical component for the implementation of a site-specific N management program. Field studies were conducted to examine the spatial variability of soil nitrate levels, spring barley (Hordeum vulgare) and corn (Zea mays L.) yields and yield responses to fertilizer N applications on two sites in southwestern Ontario, Canada. Soil sampling on a 3 × 10 m grid indicated that soil NO3-N test values had a log-normal distribution and varied considerably at both sites with CVs exceeding 57% on the untransformed data. Ranges of spatial correlation varied from 20 to 95 m with 30 to 80% of the total variance of the ln-transformed data existing as either random or unsampled variance, and these parameters were not temporally stable. Although NO3-N tended to increase at lower slope positions in two of the 3 site-years, considerable within-slope variability of soil NO3-N levels was also observed. Spatial variations in soil N test levels, crop yields and crop yield responses to applied fertilizer N were not strongly related to one another indicating that it would be unlikely that either soil N test level or yield would adequately delineate management zones for the variable application of N fertilizer at these sites. Key words: Variogram, topography, site-specific crop management
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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) | 0.000 | 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 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".