Estimating nitrogen requirement of grain corn in Manitoba using optical spectral reflectance
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it