The Effect of Combined Application of Poultry Manure and Sawdust on the Growth and Yield of Okra
Bibliographic record
Abstract
The effect of combined application of poultry manure and sawdust on soil properties, growth and yield of okra (Abelmoschus esculentus (L.) Moench) were investigated at the main campus of Tai solarin University of Education Ijagun, Ijebu-Ode, Ogun State, Nigeria during 2010/2011 dry season. This size of the plot was 45 m by 5m; the seed was planted with three seed per hole at a spacing of 0.5 m. The total numbers of plots were 27 plots, for the avoidance of doubt; it comprises three treatments and each treatment was replicate three times. The treatments consisted of 0, 5, 10 ton/ha Broiler litter (Poultry manure) and 0, 2, 5 ton/ha (sawdust). The results indicated a significant increase in growth parameters in those plants planted in 0, 2, 5 ton/ha poultry manure plot than sawdust plot. However, treatments were laid out in a randomized complete block design (RCBD) with three replications. Data were collected on growth and yield parameters (plant height, stem girth and number of leaves) were increased significantly (p<0.05) as manure rates increased. Poultry manure at 10 ton/ha has significant increase in fruit yield of okra increase. The combined application of poultry manure and sawdust does not have effect on yield and fruit number of okra but there is a slight effect on plant height. Based on the findings of the experiments it could be deduced that poultry manure seems to promote higher growth and yield of okra. Thus, it should be recommended for farmers growing okra in region.
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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) | 0.001 | 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".