Characterisation and Mapping of Soils in Major Coffee Growing Regions of Uganda
Bibliographic record
Abstract
There is limited information on the soil nutrient status, site specific fertilizer and agronomic recommendations for coffee in Uganda hence limiting its production and productivity. Therefore, this study was undertaken to characterize the soils under coffee farms and provide fertilizer and land management recommendations for coffee farming in selected districts in Uganda. 717 soil samples were collected from 45 coffee growing districts that were purposively selected to represent the major coffee growing regions of Eastern, Northern, Western, Mid-west and West Nile. 35 districts were sampled from Robusta coffee growing areas and 10 districts from Arabica coffee growing areas. Parameters considered in the laboratory analysis included; pH, total organic carbon, total nitrogen, available P, exchangeable Calcium (Ca), Sodium (Na), Magnesium (Mg), and Potassium (K) and micro elements: Iron (Fe), Zinc (Zn), Copper (Cu), Manganese (Mn) and Boron, soil texture, bulk density and hydraulic conductivity. Soil nutrient levels distribution maps for Robusta and Arabica coffee growing regions were generated in ArcGIS for the entire country. The soil chemical and physical properties were subjected to analysis of variance using Genstat 14th edition. The soil mapping results showed that, the overall average soil macro-nutrients concentrations were significantly different across regions (p < 0.05) with Eastern having the highest levels of macro elements (CEC of 19.28 meq/100 g, Base Saturation (BS) of 43.40%, pH of 5.78, N of 0.20%, K of 1.64 meq/100 g, P of 107.68 mg/kg and OM of 3.31%) followed by Western region. No significant difference (p > 0.05) was recorded for the micronutrients across the different regions, except Zn (p < 0.05). This study showed that Phosphorus and Potassium were generally moderate to high levels in most of the regions sampled while Nitrogen and organic matter were moderate to low. Results from this study provide a general picture of the nutrient status across all coffee growing regions in Uganda and highlight the required modifications for increased production and productivity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".