Corn Yield Response to Nitrogen at Multiple In‐Field Locations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Improving N management for corn ( Zea mays L.) production with precision agriculture technologies requires that spatial N recommendations adequately represent in‐field variability in N availability. Our objective was to evaluate corn response to increasing N rates in several in‐field locations that represented the range of soil organic matter (OM) content in the field. In a 2‐yr study, three center pivot–irrigated fields were selected in south‐central Kansas and south‐central Nebraska. Four or five locations were selected within each field. At each location, five or six N treatments (0–336 kg N ha −1 ) were surface‐applied early in the growing season. The minimum N rate to achieve maximum yield varied by as much as 130 kg N ha −1 among in‐field locations at three site‐years. The least amount of N to achieve maximum yield did not coincide with locations representing greater soil OM. Yield response at two site‐years was the same among in‐field locations; however, mean yield among in‐field locations varied by as much as 4.2 Mg ha −1 , representing potential for improvement in N use efficiency. Leaf tissue N was below the critical threshold for 60 to 100% of observations at three different in‐field locations but below the critical threshold for <35% of the observations at all other in‐field locations. The reason for the discrepancy in N availability among in‐field locations was not conclusively identified but was not only related to soil OM content. Variable N recommendations based only on soil OM is too simplistic to reflect variability in N availability within a field.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.004 | 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