Potential of Biochar to Mitigate Allelopathic Effects in Tropical Island Invasive Plants
Bibliographic record
Abstract
Many tropical invasive species have strong allelopathic effects. Pyrolyzed waste biomass (“biochar”) has sorptive properties that can reduce the bioavailability of a variety of toxic organic compounds, including pesticides and naturally occurring phenolic acids; however, sorption of allelochemicals has received little attention. Strawberry guava ( Psidium cattleianum) and lemongrass ( Cymbopogon flexuosa) are important tropical island invasives thought to be allelopathic. Leaf extracts of both species were treated with two biochars (made from maize stalk and coconut husk feedstocks) and applied to maize ( Zea mays) and radish ( Raphanus sativus) seeds in a factorial design involving leaf extract and biochar dosages. Leaf extracts of both species had large inhibitory effects on germination and seedling growth, particularly at higher dosages, consistent with allelopathic effects. Biochar treatments positively affected seed germination and early seedling development consistent with sorption of these allelochemicals; in some cases, “rescue” effects occurred, in which biochar treatments completely counteracted allelopathic effects. Biochar leachates alone also generally had positive effects on seed germination and seedling development. We conclude that biochars have promise as a tool for combatting invasive allelopathic plants in tropical island ecosystems. The relative ease of biochar production using “low-tech” methods, and multiple benefits of biochar in enhancing soil productivity and carbon sequestration, may make such an approach viable in many developing countries.
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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".