Determinants of Participation Decision in Cassava Marketing by Smallholder Farmers in Taita-Taveta and Kilifi Counties, Kenya
Bibliographic record
Abstract
Cassava is an important food crop with high production potential in different agroecological zones across the world. Cassava is also a drought tolerant crop performing well in arid and semi-arid areas. Cassava has a great potential as both a food security and industrial crop. In addition, as a drought tolerant crop, it is fits very well as a climate smart crop in the face of climate change. However, the cassava industry and value chain in Kenya is still underdeveloped and therefore there are many cassava marketing opportunities that are yet to be exploited. This study analyses factors that influence smallholder farmers’ decision to participate in cassava marketing in Taita-Taveta and Kilifi Counties in Kenya. Data was collected using semi-structured questionnaires from a sample of 250 smallholder cassava farmers. Descriptive statistics were used to analyse the socio-economic characteristics of respondents while a binary Probit model was used to analyse the socio-economic factors that influence farmers’ participation decision in cassava marketing. The results of the binary Probit model show that, sex of the head of a household, access to extension services, price of cassava products and quantity harvested had a positive and significant influence on market participation decision while years of schooling, household size and farm size had a negative and significant influence on the market participation decision. Therefore, based on the findings, the study recommended policy interventions targeting organization and coordination of the cassava marketing system and provision of appropriate incentives to farmers to enhance market participation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".