Suppression of the invasive plant mile-a-minute (Mikania micrantha) by local crop sweet potato (Ipomoea batatas) by means of higher growth rate and competition for soil nutrients
Bibliographic record
Abstract
BACKGROUND: There are a variety of ways of increasing crop diversity to increase agricultural sustainability and in turn having a positive influence on nearby natural ecosystems. Competitive crops may provide potent management tools against invasive plants. To elucidate the competitive mechanisms between a sweet potato crop (Ipomoea batatas) and an invasive plant, mile-a-minute (Mikania micrantha), field experiments were carried out in Longchuan County of Yunnan Province, Southwest China, utilizing a de Wit replacement series. The trial incorporated seven ratios of sweet potato and mile-a-minute plants in 25 m(2) plots. RESULTS: In monoculture, the total biomass, biomass of adventitious root, leafstalk length, and leaf area of sweet potato were all higher than those of mile-a-minute, and in mixed culture the plant height, branch, leaf, stem node, adventitious root, flowering and biomass of mile-a-minute were suppressed significantly (P < 0.05). The relative yield (RY) of mile-a-minute and sweet potato was less than 1.0 in mixed culture, indicating that intraspecific competition was less than interspecific competition. The competitive balance index of sweet potato demonstrated a higher competitive ability than mile-a-minute. Except pH, other soil nutrient contents of initial soil (CK) were significantly higher than those of seven treatments. The concentrations of soil organic matter, total N, total K, available N, available P, available K, exchange Ca, exchange Mg, available Mn, and available B were significantly greater (P < 0.05) in mile-a-minute monoculture soil than in sweet potato monoculture soil, and were reduced by the competition of sweet potato in the mixture. CONCLUSIONS: Evidently sweet potato has a competitive advantage in terms of plant growth characteristics and greater absorption of soil nutrients. Thus, planting sweet potato is a promising technique for reducing infestations of mile-a-minute, providing weed management benefits and economic returns from harvest of sweet potatoes. This study also shows the potential value of replacement control methods which may apply to other crop-weed systems or invaded natural ecosystems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".