Technical Efficiency of Soybean Farms and Its Determinants in Saboba and Chereponi Districts of Northern Ghana: A Stochastic Frontier Approach
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Bibliographic record
Abstract
<p>This study analyzes the level and determinants of technical efficiency of soybean farms in the Saboba and Chereponi districts of northern Ghana. A multi-stage sampling technique was used to select 200 soybean farmers from which cross-sectional data was collected using a structured questionnaire. Data collected includes farmers’ socio-economic characteristics such as age and education as well as input and output quantities and prices. Data was analyzed using the stochastic frontier approach. Results showed a mean technical efficiency estimate of 53 percent and the return to scale was 0.75. Location of farm, participation in the Agricultural Value Chain Mentorship Project and age of farmer were found to be important in explaining technical inefficiency among soybean farmers. This implies that farmers in the short run can increase their production by 47 percent by adopting practices of the best soybean farms in Saboba and Chereponi districts of northern Ghana.</p>
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 it