Combination of Ascophyllum nodosum Extract and Humic Acid Improve Early Growth and Reduces Post-Harvest Loss of Lettuce and Spinach
Bibliographic record
Abstract
Leafy vegetables like lettuce and spinach are prone to significant post-harvest losses during handling and storage. The pre-harvest treatment of crops with biostimulants offers a sustainable strategy for reducing post-harvest losses. Earlier studies focused on the effect of plant biostimulants applied individually. In this study, we studied the efficacy of a combined application of two commonly used plant biostimulants: Ascophyllum nodosum extract (ANE) and humic acid (HA). Interestingly, the combination of both biostimulants improved early growth of lettuce and spinach compared to ANE and HA alone. Among the combinations used in this study, 0.25% ANE + 0.2% HA produced significantly higher fresh and dry biomass in lettuce and spinach compared to the other treatments and the control. Pre-harvest treatment of combination of 0.25% ANE and 0.2% HA significantly reduced the loss of fresh biomass during post-harvest storage. The combination of 0.25% ANE and 0.2% HA reduced lipid peroxidation during storage with an increase in total ascorbate, phenolic, and antioxidant capacity of spinach and lettuce. These results suggest that a combination of ANE and HA reduces post-harvest losses of spinach and lettuce more effectively than when applied individually.
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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".