Effect of Drip Irrigation on Growth, Physiology, Yield and Water Use of Rice
Bibliographic record
Abstract
The field experiment was conducted to investigate the effect of drip irrigation treatments such as three lateral distances (0.6 m, 0.8 m or 1.0 m lateral distance) on growth parameters physiological characters, yield and water use of rice under two discharge rates drippers (0.6 or 1.0 litre per hour emitters). Among the lateral distances, 0.8 m lateral distance adjudged as optimum spacing for its better performance in growth, physiological and yield attributes than rest of the lateral distances. Between two-discharge rates, 1.0 lph drippers out performed 0.6 lph drippers in grain yield. Interactively, laterals spaced at 0.8 m with 1.0 lph drippers exhibited better performance by way of growth parameters (such as plant height, tiller density, root biomass, total dry matter accumulation), physiological attributes (such as Yw, Chlorophyll content, Catalase activity and Malondialdehyde content), yield and its components (such as productive tillers, spikelet numbers, filled grain percentage, Harvest Index) along with water productivity. Drip irrigation confirms to increase in water productivity in the present study with water saving of 27.4% over the conventional aerobic rice cultivation. Enhanced physiological activities showed increased growth and yield in rice under 0.8 m lateral distance drip irrigation which is a viable tool for balanced source sink relation. Our results indicated that the lateral spacing of 0.8 m with 1.0 lph drippers is best for rice cultivation in enhancing the growth, physiology, grain yield and water productivity.
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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) | 0.001 | 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".