Micro-Dosing of Lime, Phosphorus and Nitrogen Fertilizers Effect on Maize Performance on an Acid Soil in Kenya
Bibliographic record
Abstract
High cost of inorganic fertilizers and lime has precluded their use by smallholder farmers to remedy the problem of soil acidity and infertility in Kenya. To address the problem, we tested a precision technique referred to as micro-dosing, which involves application of small, affordable quantities of inorganic inputs on an acid soil in Busia County, Kenya. Experimental treatments were N-fertilizer (0 and 37.5 kg N ha-1), P-fertilizer (0 and 13 kg P ha-1) and lime (0, 0.77 and 1.55 tons lime ha-1). 37.5 kg N and 13 kg P ha-1 are 50% of the recommended fertilizer rates for maize production in Kenya while 0.77 and 1.55 tons lime ha-1 are 25 and 50% of the actual requirement. Soil chemical changes, maize grain yield and nutrient recovery were determined. Lime and P-fertilizer significantly affected only the top-soil pH, Ca, Mg and available P, while the effects of N-fertilizer were evident on both top- and sub-soil N likely due to its faster mobility than P and lime. Grain P-fertilizer recovery efficiencies were 14 and 16-27% due to 13 kg P and 13 kg P + 0.77-1.55 tons lime ha-1, respectively. N-fertilizer recovery efficiencies were 37 and 42-45% due to 37.5 kg N and 37.5 kg N + 0.77-1.55 tons lime ha-1, respectively. Fertilizers applied to supply 37.5 kg N, 13 kg P and 0.77-1.55 tons lime ha-1 increased grain yield above the control by 134, 39 and 12-22%, respectively, therefore micro-dosing of these inputs can increase maize production on Kenyan acid soils.
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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 | 0.001 |
| 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".