Village saving loan association membership and commercialization among smallholder maize farmers in northern Uganda
Bibliographic record
Abstract
Abstract Smallholder maize farmers are typically engaged in subsistence production with low input use, low yields, and insufficient profits for on-farm investment. Despite the role of Village Savings and Loan Associations (VSLAs) in enhancing financial inclusion and promoting agricultural commercialization, little is known about the factors driving smallholder farmers' participation in these associations and how this affects maize commercialization, particularly in northern Uganda. This study sought to examine VLSA membership and commercialization among smallholder maize farmers in northern Uganda. This study specifically examined the factors influencing maize farmers' participation in VSLAs and assessed the factors influencing the level of maize commercialization among VSLA members and non-members. Data were collected from 420 randomly selected smallholder maize farmers and were analyzed using binary logistic and Tobit regression models. Results revealed that 52% of the farmers participated in VSLA while non-participants had maize commercialization levels of 61%. Further, male gender, age, household size, fresh maize form, hire labor, extension service, market information, phone information, access to credit, and land ownership significantly influenced maize farmers’ participation in VSLAs. Factors influencing the level of maize commercialization among VSLA member farmers include age, educational status, spot market, hire labor, land ownership, farm income, and distance to the output market. The study recommends that the Ministry of Agriculture, Animal Industry, and Fisheries (MAAIF) should implement policies focused on improving access to financial services, promoting gender-sensitive training programs, and investing in rural infrastructure to reduce market access barriers, which will foster maize commercialization and economic development among 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".