Kenaf (Hibiscus cannabinus L.) Impact on Post-Germination Seedling Growth
Bibliographic record
Abstract
The chemical interaction between plants, which is referred to as allelopathy, may result in the inhibition of plant growth and development. The objective of this research was to determine the impact of kenaf (Hibiscus cannabinus L.) plant extracts on the post-germination growth of five plant species. Four concentrations (0, 16.7, 33.3 and 66.7 g/L) of kenaf bark, core, and leaf extracts were applied to the germinated seeds of redroot pigweed (Amaranthus retroflexus L.), green bean (Phaseolus vulgaris L.), tomato (Solanum lycopersicum Mill.), cucumber (Cucumis sativus L.), and Italian ryegrass (Lolium multiflorum Lam.). After 7 days, the developing seedlings were measured to determine the length of their hypocotyls (mm) and radicles (mm), and the number of hair roots. Tomato, Italian ryegrass, and redroot pigweed followed similar negative trends in their responses to the extract source (kenaf bark, core, and leaves) and the impact of extract concentration, whereas, cucumber had a mixed response and green bean reacted positively to the kenaf extracts. Tomato was the most sensitive species tested across all kenaf extracts and concentrations, resulting in decreased hypocotyl, radicle, and root growth. Green bean exhibited no negative effects due to the kenaf extracts, but actually produced increased hypocotyl growth as a result of the kenaf bark, core, and leaf extracts. The kenaf extracts resulted in a mixed response for cucumber. The kenaf leaf and bark extract decreased cucumber radicle growth, whereas, the bark and core extracts increased hypocotyl growth. Italian ryegrass hypocotyl growth decreased across all extract sources (bark, core, and leaf), while the leaf extract also reduced root growth. All kenaf extracts reduced redroot pigweed radicle growth, while the core and leaf extracts reduced hypocotyl growth. The research demonstrated that kenaf leaf extracts were the most allelopathic and the hypocotyls were the most sensitive. Future research should isolate the chemicals responsible for both the negative and positive allelopathic impact on the various plant species, determine if the extracts will influence more mature plants, and pursue cultural practices to utilize these natural allelopathic materials to benefit crop production and limit weed competition.
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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".