Relationships between Soil‐Based Management Zones and Canopy Sensing for Corn Nitrogen Management
Bibliographic record
Abstract
Integrating soil‐based management zones (MZ) with crop‐based active canopy sensors to direct spatially variable N applications has been proposed for improving N fertilizer management of corn (Zea mays L.). Analyses are needed to evaluate relationships between canopy sensing and soil‐based MZ and their combined potential to improve N management. The objectives of this study were to: (i) identify soil variables related to in‐season crop canopy reflectance and yield and use these variables to delineate MZ for N fertilizer management; and (ii) compare corn yield response to different N fertilizer treatments for different MZ. Eight N rates (0–274 kg N ha−1 in 39 kg ha−1 increments) were applied in replicated small plots across six irrigated fields in 2007 to 2008 throughout central Nebraska. Soil variables evaluated for MZ delineation included: apparent soil electrical conductivity (ECa), soil optical reflectance, and landscape topography. Crop response to N was determined via active sensor assessments of in‐season canopy reflectance (chlorophyll index, CI590) and grain yield. Relationships between soil and topography data and crop performance were evaluated, with selected soil variables used to delineate two MZ within four of the six fields. Economic benefits to N application according to soil‐based MZ were observed in fields with silty clay loam and silt loam soils with substantial relief and eroded slopes. Sensor‐based algorithms may need to be adjusted according to MZ to account for differences in crop N response.
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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 | 0.001 | 0.002 |
| 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".