Effect of Regulated Deficit Irrigation on Cucumber Growth and Fruit Yield in Greenhouse Conditions
Bibliographic record
Abstract
Water scarcity is an extreme global concern due to changing climate and rapid population growth. Worldwide, freshwater sources are depleting and there is a need to implement water-conserving approaches in water-limited or arid areas for sustainable crop production. Therefore, this study aimed to investigate the impact of deficit irrigation on cucumber plant physiology, growth, and fruit yield traits grown under greenhouse conditions. The experiment was conducted in a complete randomized design with four water regimes, 100%, 80%, 60%, and 40% crop water requirement (ETc). Results showed that the highest plant leaf chlorophyll contents (43.2 µmol m-2), plant biomass (12.43 g), plant height (172.39 cm), number of leaves (19.85), fruit yield (2.49 kg), and total soluble solids (3.73 °Brix) were in plants irrigated at 100% level. In comparison, the lowest plant height (115.73 cm), number of leaves (15.77) and fruit yield (1.81 kg) were recorded at 40% irrigation. Results revealed that reduced irrigation regimes up to 80% or 60% have no significant inhibitory effects on cucumber agro-morphological traits. However, 40% of irrigation levels showed significantly reduced plant morphological and fruit yield traits. Overall, the result demonstrated that regulated deficit irrigation can significantly conserve water without negatively impacting cucumber yield.
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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 | 0.001 |
| 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".