Allelopathic Impact of Sorghum and Sunflower on Germinability and Seedling Growth of Cotton (Gossypium hirsutum L.)
Bibliographic record
Abstract
Sorghum and sunflower are considered as highly allelopathic plants with inhibitory efficacy on plants of other species. In a pot study, the phytotoxic potential of sorghum and sunflower shoot and root on germination and seedling growth of cotton was evaluated through soil incorporation of powders and spray of water extracts. The experiment was conducted at | department of Agronomy, Sindh Agriculture University Tandojam during Kharif (summer) 2010 and 2011. The analysis of pooled data suggested that all the powders and water extracts of both allelopathic crops caused substantial suppression of germination and related traits of cotton seedlings as compared to control (untreated). Sorghum shoot powder (10 g kg-1 soil) caused highest allelopathic effects and reduced cotton seed germination by 12.8%, root length by 45.4%, shoot length by 51.9%, fresh weight seedling-1 by 41.7% and dry weight seedling-1 by 36.7%, followed by sunflower shoot powder (10 g kg-1 soil) in phytotoxic efficiency for inhibiting seed germination, seedling growth and weight in contrast to control (untreated). Sorghum showed superiority over sunflower in allelopathic efficiency. Powder of both crops was found more allelopathic in contrast to water extract. Among plant parts phytotoxic potential, shoot proved higher in inhibitory effect than root. However, it was concluded from the results of present study that both sorghum and sunflower possess allelopathic compounds with growth suppressing ability which could be utilized for effective weed management in cotton under field conditions as eco-friendly low-cost alternate of herbicides with wise strategy.
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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".