Leaf Nitrogen Concentration as an Indicator of Corn Nitrogen Status
Bibliographic record
Abstract
Plant‐based measurements can be used to diagnose the in‐season N status of corn (Zea mays L.). Our objective was to assess the relationship between the corn nitrogen nutrition index (NNI), an index based on whole plant N concentration, and leaf nitrogen concentration of the uppermost collared leaf expressed on the basis of dry matter (DM; NL) or area (NLA). Three to seven N fertilization treatments were used in this study conducted at eight site‐years in Canada and two site‐years in France. Leaf N concentrations were measured from the uppermost collared leaf on three to five sampling dates along with NNI determinations. At all sites and most sampling dates, NL, NLA, and NNI increased with increasing N rates. With data from all sampling dates, NL and NLA were related to NNI, but the intercept of the response curves varied with site‐year. These site‐specific relationships should be used with caution for the in‐season evaluation of corn N status. At stage of development ∼V12, however, NL was strongly related to NNI (NNI = −0.035 + 0.028 NL; R2 = 0.82) and the response curve was not affected by site, year, nor cultural practice; NLA was also related to NNI, but the intercept of the response curve varied with site‐year. Determining N concentration (DM basis) of the uppermost collared leaf at, or near, the V12 stage of development is an alternate method for assessing NNI, hence eliminating the need to measure shoot biomass.
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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 | 0.000 |
| 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) | 0.001 | 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".