Kale Seed Germination and Plant Growth Responses to Two Different Processed Biostimulants from Pyrolysis and Hydrothermal Carbonization
Bibliographic record
Abstract
The cost of producing organic crops is increasing. Agricultural wastes can be used as biostimulants to increase plant growth and productivity and reduce the dependence on chemical fertilizers. A pouch assay and a potted greenhouse experiment were conducted to investigate the effect of pyroligneous acid (PA) and sea lettuce (SL) on kale (Brassica oleracea subsp. acephala (DC.) Metzg) seed germination and growth. Although previous studies have demonstrated that these two biostimulants could promote plant germination and growth, there is little research to compare their effects on seed germination and plant growth. The pouch assay showed that PA liquid affected the seed germination rate under different concentrations; the seed germination rate decreased as the concentration of PA liquid increased. However, the effect of seed germination was less pronounced in SL liquids. Kale seeds treated with 0.01% PA showed the best elongation and seedling growth performance. Moreover, the greenhouse experiment indicates that SL liquids significantly (p < 0.05) affected kale growth production, while PA liquid had less difference on kale growth under various concentrations. The 0.25% PA and 1% SL increased the aboveground fresh weight by ca. 26% and 29%, respectively. Also, the phytochemical contents of kale leaves, including phenolics, flavonoids, ascorbate, and protein, were significantly increased with 0.25% PA and 1% SL application. These results suggest that low concentrations of PA are more suitable for seedling root growth in kale and 1% SL had the most significant growth-promoting effect on kale. Hydrothermal carbonization sea lettuce liquid can be used as a good biostimulant for agricultural production to improve kale germination and growth.
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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".