Metafrontier Analysis of Access to Credit and Technical Efficiency among Smallholder Cocoa Farmers in Southwest Nigeria
Bibliographic record
Abstract
It has been identified that limited access to timely credit has been a major constraint militating against increased agricultural productivity by farmers. Studies have shown that a large percentage of farmers faced with credit constraints have low production efficiencies. This study empirically investigated access to credit and technical efficiency (TE) of cocoa farmers in southwest Nigeria. The Study was conducted in Ogun State, southwest Nigeria. Primary data used for the study were collected in 2012 through a multi-stage sampling technique to select 240 cocoa farmers in the four (4) divisions of Ogun State. Data collected were analyzed using descriptive statistics, and Stochastic Metaproduction Frontier analyses. Tobit Regression Model was used to analyze the relationship between access to credit and technical efficiency among cocoa farmers in the study area. The study revealed that majority of the cocoa farmers did not have access to credit from formal institution implying negative consequences for agricultural productivity and household income generation. Most of the farmers borrowed relatively small size of loans for short duration. Estimates of the stochastic frontier models showed that cocoa farms in Ogun State had mean technical efficiency scores of 0.41, 0.94, 0.32 and 0.71 for farms in Yewa, Ijebu, Remo and Egba respectively. The results for the inefficiency model showed that age, education, experience, membership association, marital status, occupation, livelihood income, household size, and labour cost had signi?cant effect on cocoa farmers in at least one division. The values of the Technology Gap Ration (TGR), together with the technical efficiencies obtained from the divisional stochastic frontiers (TE) and the metafrontier (TE*) were computed for all farmers in Ogun State. These results implied that the mean producer in Ijebu and Egba, if he or she were technically ef?cient, could still increase output by 6% and 29%, if he or she were to adopt the most ef?cient meta-technology for the division. Finally, there was a significant relationship between access to credit and technical efficiency of cocoa farmers. Evidence from the study revealed that majority of the poor cocoa farmers do not have access to credit from formal institution. Evidence from the metaproduction frontier showed that the mean productivity potential and technical efficiency ratios give additional explanation compared to the analysis based only on individual stochastic production frontiers. The productivity potential ratio plays an important part in explaining the ability of cocoa farmers in one division to compete with other farmers from different division at the state level. Based on the results, policy environment whereby individual cocoa farmers may have access to formal credit from banks and other agency, by forming groups, by means of using land use right certificates and also guarantor as a collateral would be a right direction in boosting cocoa production in the sub-region.
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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 | 0.011 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".