Chlorophyll Measurements and Nitrogen Nutrition Index for the Evaluation of Corn Nitrogen Status
Bibliographic record
Abstract
Plant‐based diagnostic techniques are used to determine the level of crop N nutrition but there is limited comparative research on the different methods. Our objectives were to establish the relationship between chlorophyll meter (CM) readings and N nutrition index (NNI) during the corn (Zea mays L.) growing season, and to compare both methods as diagnostic tools for predicting grain yield response to N fertilization. The study was established at eight site‐years using four to seven N fertilization rates. The CM readings from the youngest collared leaf were taken on five to eight sampling dates in 2004, 2005, and 2006 along with NNI determinations. Generally, CM readings and NNI increased with increasing N rates. Chlorophyll meter readings and relative CM (RCM) readings were related to NNI, but the intercepts and/or slope of the response curves varied with site‐year. Because they are site‐specific, these relationships may not be reliable indicators of corn N status. The relationship between CM readings and relative grain yield (RY) at stage of development ≈V12 was also site‐specific. Relative CM readings (RY = −0.64 + 1.65 RCM if RCM ≤ 0.98 and RY = 0.97 if RCM > 0.98; R2 = 0.60) and NNI (RY = −0.34 + 1.47 NNI if NNI ≤ 0.88 and RY = 0.96 if NNI > 0.88; R2 = 0.79) at stage of development ≈V12 were related to RY. These two relationships were stable across site‐years and could be used to detect and quantify N deficiencies of corn.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 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".