Biochar mitigation of allelopathic effects in three invasive plants: evidence from seed germination trials
Bibliographic record
Abstract
Many invasive species show allelopathic effects that contribute to competitive impacts on native vegetation for which few control measures exist. We investigated the potential for pyrolized organic material used as a soil amendment (“biochar”) to sorb allelochemicals and mitigate allelopathic effects on seed germination and early seedling development in three common invasive plants in Canada: garlic mustard (Alliaria petiolata), tree of heaven (Ailanthus altissima), and yellow sweetgrass (Melilotus officinalis). We hypothesized that biochars would mitigate effects on germination and early seedling development (radicle extension and cotyledon development) through sorption of allelochemicals. Laboratory assays of seed germination and early seedling development of two agricultural crops (Lactuca sativa and Raphinus raphanistrum) and two native grass species (Andropogon gerardi and Poa palustris) were conducted using water extracts from leaves. Seeds were treated with plant extracts exposed to four different biochars (red oak (Quercus rubra), jack pine (Pinus banksiana), shipping pallet and construction waste, and high-carbon wood ash) using a range of extract and biochar dosages. Treatment of allelopathic plant extracts by biochars significantly (p < 0.05) mitigated effects on seed germination for Alliaria and Melilotus. Effects on seed germination and early seedling development depended on extract concentration, biochar dosage, and the target species assessed. Controls treated with biochar leachate alone showed significantly (p < 0.05) increased germination for two of the biochars tested (jack pine biochar and high-carbon wood ash). Results indicated that biochars can mitigate allelopathic effects of invasive species through sorption of allelochemicals; however, this will be effective in only some cases.
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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) | 0.000 | 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".