Growth and Fruit Yield of Okro as Influenced by Genotypes and Mulch in the Guinea Savannah Conditions of Ghana
Bibliographic record
Abstract
The experiment was carried out to assess the suitability of different mulch materials in enhancing the growth and fruit yield of okro. Ten okro genotypes were evaluated in a split plot design with 3 replications. Three treatments of mulch (black plastic, grass, and no mulch) represented the main plots with the genotypes as the subplots. The result indicated significant (P<0.05) genotypic variability among the genotypes for all parameters except plant girth. However, genotype and mulch interaction was not significant. The genotype Sasilon had the tallest plants (82.6 cm) and the highest fruit yield under all mulch conditions while Koni had the widest fruits (34.1 mm) with TZ SMN 10-3 having the longest fruits (16.11 cm). Number of fruits per plant ranged from 30 to 11 with an average of 21. Mulching significantly (P<0.05) influenced all parameters except fruit width and mean fruit weight. Plastic mulched plots had the greatest heights while no mulching had the least. The highest average yield was obtained under plastic mulch (3.49 t/ha) which was 4.2% higher than grass (3.34 t/ha) and 11% higher than no mulch (3.11 t/ha). The study has shown that mulching with black plastic or grass ensures vigorous growth and improves the fruit yield of okro.
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) | 0.001 | 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".