Assessing Performance and Economic Efficiency of Table Eggs Production in Southern Togo
Bibliographic record
Abstract
Ensuring better allocation of productive resources necessitates socioeconomic considerations. This study examined the performance of table egg production in southern Togo by determining the breeders’ production efficiency level through the stochastic frontier analysis in table egg production. Consequently, identifying the factors that significantly impact technical and allocative efficiency, and explaining the reasons for the technical inefficiency of table egg production. A parametric approach was used to estimate the technical and allocative efficiency levels from a stochastic frontier analysis. Data were collected from primary sources via a structured questionnaire (open-ended) administered to 88 poultry farms in southern Togo (2021) randomly. The parameters measured in this study were table egg production, the feed consumption during the production (each stage separately), veterinary treatment costs (drugs, vitamins), the flock size, the size of the exploitation, and the related costs of production. The finding indicated that 70% of table egg poultry farms in the Maritime Region of southern Togo are moderately technically efficient, although individual efficiency varies. Factors, such as flock size, labor, and veterinary treatments significantly influence the egg production process. Estimating the stochastic production function frontier revealed that inefficiencies in layer production largely stem from technical inefficiency among producers rather than inefficient resource allocation. The present study shows that poultry farms in Southern Togo exhibit medium technical efficiency but demonstrate effective allocation efficiency. Despite high-capacity facilities and financial constraints, the variation in the poultry breeders' production efficiency is explained by both endogenous and exogenous socioeconomic factors revealed through Tobit analysis. These factors are categorized into two groups, including primary (age, education, active membership, density, conflicts, gender), and secondary (credit, type of feed, association membership). Despite moderate technical efficiency, Southern Togo's poultry farms showed effective resource allocation. Financial constraints hinder full facility optimization, and unregulated input markets contribute to fluctuating costs.
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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 | 0.000 |
| 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 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".