Characterization of indigenous chicken production and related constraints: Insights from smallholder households in rural Kenya
Bibliographic record
Abstract
Indigenous chickens (IC) contribute significantly to nutrition and socioeconomic wellbeing of rural households. However, despite their potential, production remains low. Attempts to improve IC production among smallholder farmers in Makueni county, Eastern Kenya have achieved little success due to a variety of constraints. This paper explores IC production characteristics and compares the ranks assigned to production and marketing constraints across geographic regions and in male and female-headed households. A descriptive quantitative household survey of 1217 respondents drawn from IC rearing households was conducted and the results integrated with qualitative findings from 22 informants. Results showed an average flock size of 14.9 ± 15.94 IC per household, with female- headed households having relatively fewer chicken than male-headed households. However, relatively more chicken (15.9 ± 18.9) were lost per household during the last disease outbreak compared to the number kept at the time of study. Production system was largely free-range in nature with minimal provision of supplementary feeds. Disease (1.13±0.5), predation (3.16±1.9) and low market prices (3.89±1.9) were three top ranked (Mean Rank±SD) constraints in that order. Lack of capital, high cost of inputs, poor access to extension services and poor access to knowledge ranked significantly higher in female-headed households and in remote areas, while low market price ranked higher in male-headed households. Failure to agree on the selling price was the major constraint to marketing, while rejection of IC due to diseases, inability to agree on selling price and rejection due to size ranked higher in female-headed households compared to male-headed households. Interventions modeled towards improving biosecurity measures to curb diseases, financial empowerment and facilitating access to markets for smallholder farmers should be prioritized.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".