Estimation of the Effect of Cassava Commercialization on Different Household Income Measurements in Kilifi County, Kenya
Bibliographic record
Abstract
The transformation of agricultural production from subsistence to commercially oriented outcomes is a topical matter in the rural and socio-economic development discourse. Cassava crop is being promoted for commercialization because of its tolerance to harsh climatic conditions experienced in arid and semi-arid areas. Furthermore, there is high potential for the tuber crop to improve household income. In Kenya, a number of interventions have been directed towards commercializing cassava. The effect of commercialization on household income has not been established. Distinct from other studies, this study estimated the effect of cassava commercialization on three different income measures namely per capita, annual and per acre revenue. A household survey was conducted in Kilifi County in Kenya where 200 respondents were randomly selected. Data was collected using a structured questionnaire. A two-stage endogenous switching regression model was fitted to determine the effect of commercialization on the different income measures. The proportion of households that commercialized was 69% while the remaining 31% did not. The study found that majority of the households marketed low value-added cassava products. The results reveal that farmers who engaged in cassava commercialization enjoyed relatively more income than their counterparts. Off-farm income, age of the household head and distance to market had a negative significant influence in all the income estimates. Group membership was only significant for the per acre income while household size was negative and statistically significant in both per acre and per capita incomes. Findings point out the importance of promoting policies that will enhance cassava commercialization.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".