Evaluation of Soil Chemical Properties under Paddy Production System in Central Kenya: Soil Exchangeable Cations
Bibliographic record
Abstract
Lowland irrigated schemes contribute the most rice produced in Kenya. However, production is low and highly variable due to management problems. Production could be increased with appropriate soil management which requires that baseline fertility status of the soils and how they vary be known. This study examined the variability of selected soil chemical properties in the Mwea Irrigation Scheme in Central Kenya. Soil samples were collected from the top 0-15 cm depth in August 2013 and 2014 and analysed for pH, electrical conductivity (EC) and the exchangeable cations potassium (K+), calcium (Ca2+), magnesium (Mg2+) and sodium (Na+). Significant variability in soil EC as well as soil cation concentration was observed among units. Overall results showed soil pH ranged from 4.56 (very strongly acidic) to 8.05 (moderately alkaline). Soil EC varied from 0.17 to 1.52 dS m-1 with higher elevation areas recording lower values (< 0.50 dS m-1) and lower elevation areas recording higher EC values (> 0.50 dS m-1). On average, exchangeable Ca2+ was 38.17 cmolc kg-1, Mg2+ 23.80 cmolc kg-1, Na+ 1.24 cmolc kg-1 and K+ 0.35 cmolc kg-1. The soil exchange complex was mainly dominated by Ca2+ and Mg2+ and cation concentration in the soil was in the order Ca2+ > Mg2+ > Na+ > K+. Soil K is low and severe cation imbalances exist with regard to K+ and other cations thus making K+ deficient for plant uptake. Management practices and farming systems which enhance soil K status should be encouraged to help boost and sustain rice 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".