Optimizing Water Use Efficiency and Cabbage Yield Under Surface and Subsurface Drip Irrigation with Bio-fertilizer WSG
Bibliographic record
Abstract
The current research aims to optimize water consumption in the cabbage crop to contribute to sustainable development in the agricultural sector.A field experiment was conducted in the Heet district, Anbar Governorate, during the autumn season of 2023-2024.The objective was to study the effects of irrigation deficit and bio-fertilizer application on the growth and yield of the cabbage crop under surface and subsurface irrigation systems.A split-split plot design with three replicates was used to distribute the experimental treatments.The main plots consisted of surface and subsurface drip irrigation treatments.The subplots included irrigation water levels, while the subsubplots involved the application of bio-fertilizer (WSG).The total water applied during the growing season was 345.6 mm and 172.8 mm/season for surface drip irrigation at 100% and 50% levels, respectively, and 232.7 mm and 116.35 mm/season for subsurface drip irrigation at the same respective levels.Subsurface drip irrigation at the 50% level with the addition of 10 kg/ha of bio-fertilizer resulted in the highest recorded values for yield (28.6 Mg/ha), field water use efficiency (36.58 kg/m ), plant height (34.93 cm), leaf area (267.33 dm ), and head diameter (23.6 cm).In contrast, surface drip irrigation at the 50% level without bio-fertilizer application recorded the lowest values for yield (7 Mg/ha), plant height (15 cm), leaf area (120.2 dm ), and head diameter (7 cm).The lowest water use efficiency was observed under surface drip irrigation at the 100% level without bio-fertilizer application, with a value of 5.18 kg/m .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".