Soil Physical Characteristics and Crop Productivity as Affected by Tillage in Rice-Wheat System
Bibliographic record
Abstract
Tillage practices significantly influences soil physical characteristics and crop productivity. The infiltration characteristics, bulk density, porosity, penetration resistance, grain yield of rice and wheat were measured under no-till without residue (NT), roto-tillage (RT), and no-till with residue i.e. happy seeder (HS) in rice-wheat cropping system in sandy loam (SL) and loamy sand (LS) soils. For surface soil layer (0-15 cm) highest soil bulk density (Mg m-3) was recorded in HS (1.58) followed by RT (1.56) and NT (1.55) in LS soil. Soil porosity ranged from 40.6 % to 41.2 % under HS and RT, respectively for 0-15 cm depth in SL soil. The PR (K Pa) varied from 2412 (NT) to 2539 (HS) in SL. In LS soil PR (K Pa) varied from 2433 (NT) to 2603 (RT). Final infiltration rate (FIR) varied from 0.1 to 0.3 cm hr-1 under RT and NT in SL soil, while, in LS soil FIR varied from 0.5 to 0.8 cm hr-1 in RT and HS, respectively. Maximum rice grain yield (t ha-1) was recorded in RT (8.0) followed by NT (7.5) and HS (7.0) in SL soil. The corresponding rice yield (t ha-1) in LS was 6.5, 5.8 and 5.5 in RT, NT and HS, respectively. Wheat straw yield (t ha-1) ranged from 4.9 (NT) to 5.9 (HS) in SL soil. The wheat grain yield (t ha-1) was observed to be maximum in HS (4.5), followed by RT (4.0) and NT (3.9) in SL soil, while in LS soil the wheat grain yield (t ha-1) values were observed to be 2.7, 3.0 and 3.6 in NT, RT and HS, respectively. No significant effect of tillage and residue management practices was observed on grain yield of rice and wheat, however, significant changes were observed on water transmission characteristics and straw yields.
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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".