Factors Influencing Coffee Farmers’ Decisions to Join Cooperatives
Bibliographic record
Abstract
The role of cooperatives in buffering the effects of imperfect markets on smallholder agriculture especially in developing countries has been widely atoned. However, and in spite of eminent advantages, not all smallholder farmers join cooperatives. We use empirical data from coffee farmers in Northwestern Cameroon to identify key factors driving coffee farmers’ decisions to join or not to join a cooperative. Using a standardized questionnaire, data was collected from 140 randomly selected farmers (members and non-members) in contact with North West Cooperative Association Limited (NWCA). Data collection took place in January 2018, with 2017 as the reference period. We use the data to characterize the coffee sector in the region, and to identify key drivers for cooperative membership. 41.4% and 58.6% were NWCA members and nonmembers respectively. The area of land under coffee cultivation, dominant income source, household size, experience in coffee farming, and timely payment of farmers’ dues by the cooperative were the key factors influencing coffee farmers’ decision to join the cooperative or not (P=0.05). Identifying such drivers from farmers’ perspectives and informing policy decisions can increase competitive advantage of smallholder farmers and reduce the effects of market imperfection, as embedded in cooperative concept. This should render the concept once more attractive and portray it as a rational option to many smallholder farmers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.006 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".