On‐farm comparison of variable rates of nitrogen with uniform application to maize on canopy reflectance, soil nitrate, and grain yield
Bibliographic record
Abstract
Abstract Recent development in canopy optical‐sensing technology provides the opportunity to apply fertilizer variably at the field scale according to spatial variation in plant growth. A field experiment was conducted in Ottawa, Canada, for two consecutive years to determine the effect of fertilizer nitrogen (N) input at variable‐ vs. uniform‐application strategies at the V6–V8 growth stage, on soil mineral N, canopy reflectance, and grain yield of maize (Zea mays L.). The variable N rates were calculated using an algorithm derived from readings of average normalized difference vegetation index (NDVI) of about 0.8 m × 4.6 m, and N fertilizer was then applied to individual patches of the same size of NDVI readings (0.8 m × 4.6 m) within a plot (2184 m2). Canopy reflectance, expressed as NDVI, was monitored with a hand‐held spectrometer, twice weekly before tasseling and once a week thereafter until physiological maturity. Soil mineral N (0–30 cm depth) was analyzed at the V6 and VT growth stages. Our data show that both variable and uniform‐application strategies for N side‐dressings based on canopy‐reflectance mapping data required less amount of N fertilizer (with an average rate of 80 kg N ha–1 as side‐dressing in addition to 30 kg N ha–1 applied at planting), and produced grain yields similar to and higher nitrogen‐use efficiency (NUE) than the preplant fully fertilized (180 kg N ha–1) treatment. No difference was observed in either grain yield or NUE between the variable‐ and uniform‐application strategies. Compared to unfertilized or fully fertilized treatments, the enhancements in grain yield and NUE of the variable‐rate strategy originated from the later N input as side‐dressing rather than the variation in N rates. The variable‐rate strategy resulted in less spatial variations in soil mineral N at the VT growth stage and greater spatial variations in grain yield at harvest than the uniform‐rate strategy. Both variable‐ and uniform‐application strategies reduced spatial variations in soil mineral N at the VT stage and grain yield compared to the unfertilized treatment. The variable‐rate strategy resulted in more sampling points with high soil mineral N than the uniform‐rate strategy at the VT 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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".