Tillage, Crop Residue and Nitrogen Management Effects on Nitrogen Uptake, Nitrogen Use Efficiency and Yield of Rice
Bibliographic record
Abstract
Field experiment was conducted in three factorial strip split plot design to evaluate the effect of two establishment methods (EM) i.e. transplanted in puddled soil (Pu-TPR) and direct seeded in zero tillage (ZT-DSR), two residue levels i.e. residue kept at 3 t ha -1 (RK) and no residue (RR) with two nitrogen doses i.e. recommended dose (100 kg N ha -1) (RD) and farmers' dose (50 kg N ha-1) (FD) with six replications with individual plot size of 5.4 m × 6.3 m on rice variety Ram-Dhan during the year 2016. Nitrogen uptake, nitrogen use efficiencies and yield of rice were recorded. Straw nitrogen uptake was significantly higher in ZT-DSR than Pu-TPR. Similarly, grain straw and total nitrogen uptake were significantly higher in residue applied and recommended dose of nitrogen than no-residue applied and farmers-nitrogen dose treatments respectively. Nitrogen efficiency ratio and physiological efficiency index were significantly higher in Pu-TPR and no-residue applied treatments while partial factor productivity was higher in residue applied treatment. All nitrogen use efficiencies like partial factor productivity, nitrogen efficiency ratio and physiological efficiency index were significantly influenced by nitrogen dose and seen higher in recommended dose of nitrogen. Establishment methods had no significant effect on grain yield but straw yield was significantly higher in ZT-DSR but harvest index was seen higher in Pu-TPR. Grain yield and straw yield were significantly higher in residue applied treatment and recommended nitrogen but harvest index was higher in farmers-nitrogen dose.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".