Responses of Corn(Zea mays L.) Nitrogen Status Indicators to Nitrogen Rates and Soil Moisture
Bibliographic record
Abstract
Plants usually experience fluctuating water supply during their life cycle due to continuous changes in climatic factors.Soil water content(SWC) is one of the most critical factors affecting nitrogen(N) availability,movement,and uptake by crops.Consequently,SWC levels may confound the assessment of crop N status.The present study compared the sensitivity of tissue N concentration,SPAD readings,Dualex readings,and SPAD/Dualex ratios for assessing corn(Zea mays L.) N status under different water supply conditions.A greenhouse trial was conducted with four N fertilizer application rates(0,50,50+75,and 200 kg ha?1) and three watering levels(drought,drought followed by rewatering,and fully-watered).Tissue N concentration,SPAD,Dualex,and SPAD/Dualex values were influenced significantly by N rates and by SWC.Tissue N concentration,SPAD,and SPAD/Dualex increased with N rates,whereas Dualex decreased.In the first phase of reaction to drought,tissue N concentration,SPAD and SPAD/Dualex decreased rapidly but Dualex increased;however,the opposite pattern of response was observed in the long term.Under rewatering,tissue N concentration,Dualex and SPAD/Dualex gradually recovered,whereas SPAD values did not change significantly as they did in the drought treatment.There were highly significant relationships between SPAD(r = 0.92),Dualex(r = ?0.86),or SPAD/Dualex(r = 0.63) and tissue N concentration.However,SPAD and Dualex were better predictors of tissue N concentration under drought conditions(SPAD:r = 0.90;Dualex:r = ?0.83) than under fully-watered conditions(SPAD:r = 0.39;Dualex:r = ?0.44) at the end of the trial.Among the indicators,Dualex is better able to discriminate N treatments,with consistent results across SWC levels.
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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 | 0.000 | 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) | 0.000 | 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".