Bibliographic record
Abstract
Estimation of fertilizer N requirements of crops remains a challenge. Numerous field studies have been carried out to calibrate soil tests against yield response to applied fertilizer N. Synthesis and identification of common crop fertilizer N response across large data sets (years, sites) will allow maximum use of this past work and a framework for comparison of future work. The objective of this paper is to define macro-relationships between the economically optimum fertilizer N rate (EONR) and the yield increase at the EONR defined as the delta yield, ΔYec , for large data sets of 2nd- and 3rd-order estimates of fertilizer N response functions with both 0th and 1st-order rate relationships between fertilizer nitrogen use efficiency and applied fertilizer N. The derived macro-relationships are curvilinear, depend on the price ratio R = the ratio of the (price per kilogram of fertilizer N)/(price per kilogram grain), and are similar to measurements from data sets of corn fertilizer N response functions spanning decades (+20 yr) and representing areas in both the United States and Canada. The macro-relationships appear to be robust and therefore useful for quantifying (post-harvest analysis) soil fertility, crop fertilizer N requirement, and comparison/classification of N response functions.Key words: Response function, prediction, efficiency, economic N rate, corn
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.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".