Evaluation of Canopy Reflectance Technology Using a Delta Yield Approach
Bibliographic record
Abstract
Canopy reflectance measurements have been proposed as a method to variably apply N to corn (Zea mays L.). This study was conducted to determine if normalized difference vegetation index (NDVI) is correlated with corn N response spatially across a field. Whereas most studies use fertilizer N strips to impose N response variation, the current study assesses ability of NDVI to predict N response against naturally occurring soil N levels. The experiment was conducted on commercial corn fields in southern Ontario, Canada in 2006 and 2007. A GreenSeeker System (NTech Industries, Inc., Ukiah, CA) was used to measure NDVI at the 6 to 7, 8, 10, and 11 to 12 leaf stages on 9 by 4.5 m subplots of two treatments receiving 0 and 30 kg N ha−1 starter, respectively. Delta yield estimates that spatially corresponded with NDVI subplot measurements were determined by bordering each subplot plot with a nonlimiting N rate subplot and a zero N rate subplot and measuring differences in yield. Corn response to N fertilizer, as measured by delta yield, was highly variable spatially across each field in both years. Delta yield values ranged between 100 and 7200 kg grain ha−1 in 2006, and −2300 and 5000 kg grain ha−1 in 2007. Similarly NDVI varied spatially with coefficient of variations ranging from 6 to 16% depending on leaf stage and year. Correlations between NDVI measurements and delta yield were not significant at any leaf stage. The relationship between N response and NDVI may be determined by factors other than N or by N mineralization occurring beyond the 11 to 12 leaf stage.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".