Effects of fertilizer microdosing on soil phosphorus and sulphur availability to Solanum macrocarpon in Southwest Nigeria
Bibliographic record
Abstract
This present study aimed at evaluating the effect of fertilizer rate and time of application on yield, and determined the availability of soil P and S to S. macrocarpon. The experiment was conducted in the derived savanna (Ogbomoso) and the rainforest (Ilesha) in southwest Nigeria. The treatments were arranged in a factorial combination and laid out in a split-plot design with four replicates. The main plots were the fertilizer rates of 0, 20, 40, 60, and 80 \, \mathrm{kg \, N \, ha^{-1}} (without organic fertilizer), with the time of urea application (at planting and two weeks after planting) as a sub-plot. S. macrocarpon was the test crop. Plant fresh weight, P, and S uptake were determined at the first harvest. The results showed that a fertilizer rate of 20 \, \mathrm{kg \, N \, ha^{-1}} produced significantly higher yields and uptake of P and S in the derived savanna ( 4.2 \, \mathrm{t \, ha^{-1}} ) and in the rainforest ( 1.2 \, \mathrm{t \, ha^{-1}} ). Application at two weeks after planting (2 WAP) produced higher yields ( 3.3 \, \mathrm{t \, ha^{-1}} ) in the derived savanna, while the application at planting (AAP) produced the highest yield ( 1.2 \, \mathrm{t \, ha^{-1}} ) in the rainforest. Although the time of fertilizer application did not affect fresh yields and nutrient availability, this study concluded that 60 \, \mathrm{kg \, N \, ha^{-1}} plus 5 \, \mathrm{tons \, ha^{-1}} was the optimum fertilizer combination for S. macrocarpon production in southwest Nigeria.
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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) | 0.000 | 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".