Effects of Deficit Irrigation on Yield and Quality of Onion Crop
Bibliographic record
Abstract
The broad objective of this study was to test Deficit Irrigation (DI) as an appropriate irrigation management strategy to improve crop water productivity and give optimum onion crop yield. A field trial was conducted with drip irrigation system of six irrigation treatments replicated three times in a randomized complete block design. The crop was subjected to six water stress levels 100% ETc (T100), 90% ETc (T90), 80% ETc (T80), 70% ETc (T70), 60% ETc (T60) and 50% ETc (T50) at vegetative and late season growth stages. The onion yield and quality based on physical characteristics and irrigation water use efficiency were determined. The results indicated that the variation in yield ranged from 34.4 ton/ha to 18.9 ton/ha and the bulb size ranged from 64 mm to 35 mm in diameter for T100 and T50 respectively. Irrigation water use efficiency values decreased with increasing water application level with the highest of 16.2 kg/ha/mm at T50, and the lowest being13.1 kg/ha/mm at T100. It was concluded that DI at vegetative and late growth stages influence yields in a positive linear trend with increasing quantity of irrigation water and decreasing water stress reaching optimum yield of 32.0 ton/ha at 20% water stress (T80) thereby saving 10.7% irrigation water. Onion bulb production at this level optimizes water productivity without significantly affecting yields. DI influenced the size and size distribution of fresh onion bulbs, with low size variation of the fresh bulbs at T80.
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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".