Estimating nitrogen requirement of grain corn in Manitoba using optical spectral reflectance
Bibliographic record
Abstract
Optical sensors can measure optical (visible/near-infrared) reflectance and be used to assess crop canopy conditions. In this study, two hand-held active sensors (GreenSeeker® and CropCircleTM) and a passive aerial sensor (Red-Edge multi-spectral camera) were compared at three growth stages of grain corn (V4, V8 and V12) to predict in-season nitrogen (N) requirement. Active optical sensors have a light source. Passive sensors rely on sunlight; thus, their reflectance measurements are subject to changing sunlight conditions. Here a high reflectance area of canopy non-limited by N was used to standardize for variations in sunlight conditions between measurements days. The Normalized Difference Vegetation Index (NDVI) for all three sensors and the Normalized Difference Red-Edge index (NDRE) using the CropCircleTM and Red-Edge were also compared. Four site-years (2018-2021) of N response trials were combined to capture N response under different meteorological conditions and create a regional response model, adjusted for N fertilizer and corn grain prices to determine the optimum N rate to apply. Measured grain yield significantly increased (adjR2=0.40) with N supply (spring soil nitrate plus N rate). The maximum return to nitrogen (MRTN) using a current high price ratio ($N: $Corn) of 9.15:1 was 177 kg N/ha for 7,986 kg grain/ha. Two methods were used to make N addition recommendations. The first was using a quadratic response model for grain corn yield to N supply. The second was the optical sensor approach compared the difference between a non-limited area and the field estimate using canopy spectral reflectance. The optical sensor approach (187 kg/ha N) was the closest to the determined MRTN of 177 kg N/ha. Standardizing light conditions at V4 provided significant associations of NDVI and NDRE with yield regardless of the sensor. At V8, only Red-Edge NDVI and NDRE were improved by standardization. Standardization had no effect at V12. For determining in-season N addition to grain corn in Manitoba, it is best to determine NDRE using the CropCircle V12 (adjR2 = 0.62). However, it is recommended for Manitoba farmers to standardize reflectance values to an N non-limited crop area as they prefer earlier timing for top or side-dressing corn between the V4 to V8 developmental stages.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".