Combined Organic Ameliorants Mitigate Drought Stress in Watermelon by Enhancing Chlorophyll Retention and Water Use Efficiency
Bibliographic record
Abstract
Watermelon (Citrullus lanatus L.) is highly susceptible to drought stress, which adversely affects its physiological and biochemical performance.This study aimed to assess the effectiveness of organic ameliorants-water hyacinth bokashi, manure, and rice husk biochar-in mitigating the impacts of drought stress on watermelon.A factorial randomized block design (RBD) was employed with two factors: (1) four combinations of organic ameliorants-BoM (bokashi + manure), BoBi (bokashi + biochar), MBi (manure + biochar), and BoMBi (bokashi + manure + biochar); and (2) four levels of drought stress based on field capacity (FC)-100%, 75%, 50%, and 25%.Results demonstrated that certain ameliorant combinations, particularly MBi and BoM under 25% FC (MBi25 and BoM25), significantly sustained higher levels of chlorophyll a (127.57µg/g and 135.84 µg/g, respectively; p < 0.05), chlorophyll b (42.40 µg/g and 48.29 µg/g; p < 0.05), and total chlorophyll (169.97 µg/g and 184.13 µg/g; p < 0.05), indicating improved photosynthetic capacity under severe drought conditions.Additionally, BoM25 maintained a relatively high leaf water content (69.70%), despite root growth inhibition.In contrast, proline, antioxidant activity, and total phenolic content were markedly elevated under extreme drought (25% FC), although the ameliorants had limited impact on these stress markers.Overall, the application of organic ameliorants partially alleviated the negative effects of drought stress by enhancing water retention and preserving chlorophyll content, while biochemical stress indicators remained largely unaffected.
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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".