Improving drought tolerance of <i>Opuntia ficus-indica</i> under field using subsurface water retention technology: changes in physiological and biochemical parameters
Bibliographic record
Abstract
The prickly pear ( Opuntia ficus-indica) is an essential economic and ecological medicinal plant in arid and semi-arid areas, especially in Morocco, where water scarcity affects its survival and growth. Evidence suggests that subsurface water retention technology (SWRT) may enhance crops’ drought resistance. A field experiment was conducted to explore SWRT application effects on cactus cladodes’ growth and physiological and biochemical performance under drought conditions. Two water regimes were applied (rainfed conditions; irrigated with 4 L of water twice a week) with two treatments (without SWRT; with SWRT). The results showed that cactus cladodes’ growth and physiological and biochemical parameters cultivated for 8 months were negatively affected by drought. Drought-exposed cactus cladodes under SWRT application showed an increase in surface area and cladode stomatal densities by 65% and 29%, respectively, compared with no SWRT. This technology reduced drought-induced oxidative stress by mitigating malondialdehyde and hydrogen peroxide excess by 22% and 17%, respectively. Moreover, lower levels of enzymatic and nonenzymatic antioxidant activities were concluded, and soil organic matter and assimilable phosphorus contents were enhanced. In conclusion, our findings highlighted SWRT’s positive impacts on the tested parameters, thus presenting it as a promising technology for cactus growth and development improvement under water deficiency.
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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".