Study of the Economic Profitability of Soybean Production in the Collines Department in Benin: An Analysis Using the Direct Costing Method
Bibliographic record
Abstract
In the Collines department in Benin, soybean cultivation is an important agricultural activity that contributes significantly to economic growth. Its production engages various groups of producers and, to do this, its economic evaluation is essential to sustain its development. However, for several years, the development of departmental production has been characterized by a lackluster trend, despite the incentives and signals sent out by the market. Using data collected from a sample of 120 producers chosen at random in the said department, this study assessed, using an approach based on direct costing, the economic profitability of soybean production, and identified the determinants of its improvement based on a logit model. The results obtained show that the activities of the different groups of producers in the study area are economically profitable to varying degrees; and the factors identified as explaining the improvement of this profitability are: the economic situation, climatic and meteorological conditions and rural roads. To this end, as an implication of economic policies, this study suggests that the State play its part as arbiter on the market and contribute to the maintenance of rural roads. In addition, strategies must be developed to deal with climatic challenges, such as irregular rainfall.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".