Technical Efficiency of Yam Producers: The Case of the Municipality of Glazoue in Benin
Bibliographic record
Abstract
In Benin republic, yam plays an important role both in production systems and in people’s food security and trade. In view of the decline in agricultural yields in recent years combined with strong population growth, it is essential today to analyse the technical efficiency of yam producers in order to formulate the best recommendations for relaunching yam production. The objective of this paper is to analyse the technical efficiency of yam producers in Benin and its determinants. To achieve this objective, data were collected from 150 yam producers living in the Municipality of Glazoué. A stochastic production frontier is used to analyse the technical efficiency of the yam producer. The results revealed that the mean efficiency score of producers is around 80%. This implies that yam production could be increase by 20% through better use of available resources such as land, labour, herbicides, taking into account the state of technology. Access to credit and mobile phone ownership increase the inefficiency of actors while experience in agricultural production, age and household size reduce the inefficiency of producers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".