Delta yield–based optimal nitrogen rate estimates for corn are often economically sound
Bibliographic record
Abstract
Abstract Corn producers often overapply nitrogen (N) fertilizer to minimize risk of yield loss because of uncertainty regarding actual corn N requirements. On‐farm N trials, which are simple to deploy and interpret, may enable producers to better understand their corn N fertilizer requirements. We analyzed a database of corn yield response to N trials conducted in Ontario, Canada to determine if delta yield (dY) N trials can reliably estimate economic optimum N rates (EONRs) and to assess the financial liability of dY‐EONR estimates. Delta yield is calculated as the yield difference between nonlimiting and very low (starter only) N rates. The dY‐EONR estimation relationship is derived from a rectangular hyperbolic relationship between agronomic efficiency and dY. The derived dY‐EONR relationship has a rapid initial estimated EONR rate of increase that diminishes with increasing dY and that approaches a constant increase rate of 16.1 kg N ha –1 . At a N‐corn price ratio of 7, the dY‐EONR estimation model has RMSE = 29.1 kg N ha –1 ( R 2 = .64). Within range of recent corn and N prices, 64–80% of dY‐EONR estimates in combined calibration and validation data ( n = 746) had return losses less than $25 ha –1 relative to the actual EONR. Return losses exceeding $50 ha –1 occurred for 8–18% of the dY‐EONR estimates, most of which (83–98%) were due to overestimation. Delta yield trials provide an easily implemented method for corn producers to conduct on‐farm yield response trials repeatedly over years in order to obtain a better idea of their N requirements.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".