Determinants of Loan Repayment among Small Holder Cooperative Farmers in Remo Division, Ogun State, Nigeria
Bibliographic record
Abstract
<p>This study investigated the factors that are crucial in improving small holder cooperative farmers’ loan repayment in Remo Division of Ogun state, Nigeria. Primary data used for the study were collected with the aid of well-structured questionnaire. Multi-stage sampling techniques were used to select the 120 respondents. The data were analyzed using descriptive statistics and probit regression model. The results of the descriptive analysis showed that about 56% of the respondents were able to repay their loans promptly while the rest were not. The respondents’ mean age stood at 47 years, the majority of them are males and married with fair level of education. The majority of smallholder farmers in the study area had been farming for more than 20 years, while the household size for the majority of them was 4-6 members with average family size of 5. The results of the probit regression analysis revealed that age, level of education, farming experience, net farm income and loan size obtained were the major factors that increase the likelihood of loan repayment, while the number of family dependants reduces the probability of repayment. To improve loan repayment ability in the study area, this study recommended improvement in human capacity development as well as sensitization of the farmers in the study area about the importance of education.</p>
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 | 0.001 |
| 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".