Relationship between P and N Concentrations in Corn
Bibliographic record
Abstract
Tools to diagnose P crop status are becoming increasingly important to minimize the risk of surface and groundwater contamination from excessive fertilization while still applying sufficient P to optimize crop yield. The objectives of this study were to establish the relationship between P and N concentrations of corn (Zea mays L.) during the growing season and, in particular, to determine the critical P concentration required to diagnose P deficiency. Shoot biomass and P and N concentrations were determined weekly in an experiment with four to six N rates conducted over 2 yr (2004 and 2005) at three sites with adequate soil P for growth. The P and N concentrations decreased with time and increasing shoot biomass at all sites. The P concentration in relation to N under nonlimiting N conditions is described by a linear relationship (P = 1.00 + 0.094N, R2 = 0.76, P < 0.001, n = 71) in which the concentrations are expressed in g kg−1 dry matter (DM). Under limiting N conditions, the relationship was different with greater P concentrations for a given N concentration. The present study establishes a predictive model for critical P concentration in corn shoots, as a function of the N concentration in the shoot biomass and the degree of N deficiency. This critical P concentration can then be used to quantify the degree of P deficiency during the current growing season.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".