Effect of Irrigation Methods and Plastic Mulch on Yield and Crop Water Productivity of Okra
Bibliographic record
Abstract
A field experiment was conducted during 2014-15, aiming to observe the efficiency of irrigation methods and plastic mulch on the yield and crop productivity of Okra. Okra seeds (cv. Subzpari) were grown on ridges with plastic under two different irrigation methods i.e. Every Furrow Irrigation (EFI) and Alternate Furrow Irrigation (AFI). The soil physical properties of ridges being affected by plastic mulched were analyzed before sowing and after harvesting. The results revealed that dry density of soil decreased by 0.03 g cm-3 and 0.04 g cm-3 for AFI and EFI methods, respectively. The total volume of irrigation water applied under AFI method (2169.70 m3 ha-1) was calculated to be half of the total irrigation water applied to EFI method (4340.91 m3 ha-1). Yield obtained under EFI method was 8518 kg ha-1 which was 10.5% greater than yield obtained under AFI method (7621 kg ha-1) and 31.40% when compared with traditional method. The crop water productivity (CWP) for AFI method (3.51 kg m-3) was calculated to be greater than CWP obtained under EFI method (1.96 kg m-3). The study concluded that both EFI and AFI methods, under plastic mulched ridges practices were beneficial to increase the crop yield with improved crop 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".