Simulating Maize Response to Split-Nitrogen Fertilization Using Easy-to-Collect Local Features
Bibliographic record
Abstract
Maize (Zea mays) is a high-nitrogen (N)-demanding crop potentially contributing to nitrate contamination and emissions of nitrous oxide. The N fertilization is generally split between sowing time and the V6 stage. The right split N rate to apply at V6 and minimize environmental damage is challenging. Our objectives were to (1) predict maize response to added N at V6 using machine learning (ML) models; and (2) cross-check model outcomes by independent on-farm trials. We assembled 461 N trials conducted in Eastern Canada between 1992 and 2022. The dataset to predict grain yield comprised N dosage, weekly precipitations and corn heat units, seeding date, previous crop, tillage practice, soil series, soil texture, organic matter content, and pH. Random forest and XGBoost predicted grain yield accurately at the V6 stage (R2 = 0.78–0.80; RSME and MAE = 1.22–1.29 and 0.96–0.98 Mg ha−1, respectively). Model accuracy up to the V6 stage was comparable to that of the full-season prediction. The response patterns simulated by varying the N doses showed that grain yield started to plateau at 125–150 kg total N ha−1 in eight out of ten on-farm trials conducted independently. There was great potential for economic and environmental gains from ML-assisted N fertilization.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".