Allelopathic Effects of Three Sweet Potato Cultivars (Ipomoea batatas) on the Invasive Plant Mikania micrantha
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Sweet potato [Ipomoea batatas (L.) Lam] is an important locally grown cash crop in China; it was demonstrated to suppress the invasive plant Mikania micrantha (M. micrantha) H.B.K through strong competitiveness, but its allelopathic effects on this weed were unknown. The present study aimed to explore the allelopathic potential of sweet potato on M. micrantha. MATERIALS AND METHODS: The allelopathic effects of water extracts and soil incorporation from leaves of three sweet potato cultivars (SP1, SP0 and SP9) on the sprout seedling growth of invasive plant M. micrantha in Yunnan Province, China, were studied under laboratory and greenhouse conditions. RESULTS: Stem length, root biomass, aboveground biomass and total biomass of M. micrantha were significantly reduced with increasing concentration in both leaf water extracts and leaf soil incorporation of three sweet potato cultivars. Among these, SP1 had the strongest inhibition and the next highest impact was from SP0 with the lowest effect from SP9. The highest inhibition rates were seen for root biomass, followed by total biomass, whereas the lowest impact was on aboveground biomass. The strong correspondence between results for both leaf water extracts and leaf soil incorporation provided a good demonstration that compounds produced by sweet potato have allelopathic effects on M. micrantha. The general inhibition of M. micrantha by sweet potato followed the order among the three sweet potato cultivars tested as SP1, SP0 and SP9. Moreover, the synthetical allelopathic indices of leaf soil incorporation of three cultivars on M. micrantha were generally higher than these of leaf water extracts. CONCLUSION: Competition and allelopathy have primarily been seen as separate ecological weed management tools, but as these have demonstrated in the case of sweet potato where both mechanisms inhibit weed growth, there is potential for synergism between competition and allelopathy in the reduction of weed infestations.
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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".