Phosphorus Sorption and Lime Requirements of Maize Growing Acid Soils of Kenya
Bibliographic record
Abstract
In Kenya, maize (Zea mays L.) is mainly grown on acid soils in high rainfall areas. These soils are known for low available phosphorus (P), partly due to its sorption by aluminium (Al) and iron oxides. The study determined soil P sorption, lime requirements and the effects of lime on soil pH, Al levels and available P on the main maize growing acids soils in the highlands east and west of Rift Valley (RV), Kenya. Burnt lime containing 21% calcium oxide was used. The soils were strongly to extremely acid (pH 4.85-4.07), had high exchangeable Al3+ (> 2 cmol Al kg-1) and Al saturation (> 20% Al), which most maize germplasm grown in Kenya are sensitive to. The base cations, cation exchange capacity and available P (< 10 mg P kg-1 bicarbonate extractable P) were low, except at one site in the highlands east of RV indicative with history of high fertilizer applications. Highlands east of RV soils had higher P sorption (343-402 mg P kg-1) than the west (107-258 mg P kg-1), probably because of their high Al3+ ions and also the energies of bonding between the soil colloids and phosphate ions. Highlands east of RV also had higher lime requirements (11.4-21.9 tons lime ha-1) than the west (5.3-9.8 tons lime ha-1). Due to differences in soil acidity, Al levels and P sorption capacities within and between highlands east and west of RV, blanket P fertilizer and lime recommendations may not serve all soils equally well.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".