Biochemical insights into fulvic acid-driven drought tolerance in canola
Bibliographic record
Abstract
Sustainable food production is a critical driver of a consumer-led transformation taking place in agriculture. Among the available strategies, biostimulants represent a critical set of agronomic tools for enhancing crop productivity. This study evaluated the impact of drought stress on canola ( Brassica napus ) and the efficacy of fulvic acid (FA) to mitigate drought-induced damage by linking morphological changes with physiological and metabolic responses. Canola plants were subjected to four treatments: well-watered control (WWC), well-watered with FA (WWFA), drought control (DC), and drought with FA (DFA). FA application significantly improved root growth under drought. Across drought onset (do) and both 7- and 14-days post drought onset (dpdo), FA increased soluble protein, glutathione (GSH), and catalase (CAT) activity (WWC < WWFA < DC < DFA). Drought increased radical scavenging activity and malondialdehyde (MDA), while FA further elevated radical scavenging activity and significantly reduced lipid peroxidation. Proline accumulation was highest in DFA plants, suggesting enhanced osmoprotection. Metabolomic profiling revealed that FA modulated central and secondary metabolism, increasing levels of stress-related metabolites including proline, glucose, glutamine, phenolics (quercetin, ellagic acid), antioxidants, nitrogenous metabolites (trigonelline), and polyamines (spermidine). These shifts were associated with improved oxidative stress management and cellular resilience. Overall, FA treatment conferred broad protective effects against drought in canola by enhancing root growth, antioxidant defenses, and osmoprotection. These findings are supported by increased levels of stress-related metabolites in treated canola, highlighting FA as a promising biostimulant for improving drought tolerance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".