Precision nitrogen management using chlorophyll meter for Improving Growth, Productivity and N Use Efficiency of Rice in Subtropical Climate
Bibliographic record
Abstract
N management must be based on crop demand and supply capacity of the soil. A field experiment was conducted to analyze the effect of chlorophyll meter (SPAD meter) based N management on growth, productivity and agronomic N use efficiency of rice (cv. IR 36) in lateritic soil of India during the wet season of 2010 and 2011. The Experiment contained twelve N management treatments such as farmers’ fertilizer practice, one fixed time N management (FTNM), nine treatments of real time N management (RTNM), and one control. The RTNM is the combination of three SPAD threshold (SPAD: 34, 36 and 38) and three N levels (15, 25 and 35 kg ha-1) for top dressing when the SPAD value of rice leaf falls below the threshold. The grain yield of RTNM was in the range 93 to 105% as that of FTNM, but with lower N application rate. Among RTNM treatments, SPAD 36 with 35 and 25 kg N ha-1 top-dressing could save N fertilizer by 20 to 35% compared to FTNM without reducing grain yield. Agronomic N use efficiency can be increased at high yield level using SPAD meter based N management.
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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".