Responsiveness of Soil Series in Southwestern Nigeria to Single Super Phosphate Fertilization
Bibliographic record
Abstract
Application of Single Super Phosphate (SSP) fertilizer in soils with inadequate phosphorus (P) should elicit crop response. However, there is an indication of inconsistent response behaviour to SSP fertilizer in some Nigeria soils despite their low available P. This study identified major soil types in Southwestern Nigeria that are unresponsive to P fertilizer application under screenhouse condition and soil properties that are related to this unresponsive behaviour were determined. Soils from twenty locations in Southwestern Nigeria were selected to evaluate their response to four phosphorus fertilizer rates with a control replicated three times in a factorial arrangement laid out in randomized complete block design. Results indicated that most of the locations responded to initial P application except for Fashola 2 which produced significantly higher maize shoot yield in control soils. There were five other locations where no-response was observed. Multiple regression analysis showed that phosphorus sorption index was related to sand, organic matter and clay. Phosphorus accumulation in soil P reserves may explain the behaviour of the P-unresponsive soils irrespective of their available P levels.
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) | 0.000 | 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".