Potato Production in Kenya: Farming Systems and Production Constraints
Bibliographic record
Abstract
Potato (Solanum tuberosum L.) is a major food and cash crop in the Kenyan highlands, widely grown by small-scale farmers. Farmer practices and constraints in potato production differ from region to region. A survey was conducted in three major potato producing districts namely Bomet, Molo and Meru Central with the following objectives: 1) to document farmers’ practices, key potato production and marketing constraints, 2) to determine farmers’ potato cultivar and trait preferences and 3) to assess the prevalence and farmers’ management of bacterial wilt. The survey was carried out between November 2011 and March 2012. During the survey, a semi-structured questionnaire was administered to 253 individual farmers. The results show that the average household farm sizes are less than 2.4 hectares in all the districts. Majority of farmers allocate more than 25% of their farms to potatoes. Potato is produced both for food and cash by 90% of respondents in all districts. In Bomet district the red-skinned Dutch Robyjn is widely grown. In Molo district, the white- skinned Cangi is prominent while in Meru Central, the red-skinned Asante is predominantly grown by farmers. Cultivar preferences are mostly dictated by availability of markets, yield potential and taste. The major potato production constraints are diseases with bacterial wilt being the most prominent.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".