Technical Efficiency of Yam Producers: The Case of the Municipality of Glazoue in Benin
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.014 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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