Response of cucumber to timing of fertilizer application in southwest Nigeria
Bibliographic record
Abstract
A field trial was conducted at the experimental farm of the Department of Crop Protection and Environmental Biology, University of Ibadan, Nigeria, to determine the growth and yield response of cucumber (cv. Poinsett) to timing of fertilizer application. Fertilizer (20:10:10), at the rate of 150 kg N ha-1, was applied at: planting, 3 weeks after planting (WAP) and 6 WAP. Similarly, split applications were given at planting + 3 WAP, at planting + 6 WAP and at 3 WAP + 6 WAP. The experimental design was a randomized complete block with seven treatments (six fertilizer application treatments plus a non fertilizer control) and four replications. Means were separated using Duncan’s Multiple Range Test at the 5% level of significance. In general, plants that were fertilized performed better than control plants in terms of vegetative growth and yield. A split application at 3 WAP + 6 WAP performed the best in terms of vegetative growth and fruit yield. Fertilizer applied once-over at 6 WAP was the least beneficial; application of fertilizer once-over at planting, or a split application at planting + 3 WAP, resulted in a high level of vegetative growth but lower fruit yield compared with the split application at 3 WAP + 6 WAP. Key words: Cucumber, N-P-K., time of application, vegetative growth, fruit yield
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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 | 0.001 |
| 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 | 0.001 |
| 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".