Agricultural Mechanization as an Expansion Factor of Cropland in Benin: The Case of Tractors
Bibliographic record
Abstract
We propose in this paper a methodology based on the vector error correction (VCE) model. This modeling approach makes it possible to use a large database to model the impact of agricultural mechanization on cropland in Benin. The results of the VEC model estimates confirm a positive relationship between agricultural mechanization and the areas planted of paddy rice, millet and yams. Moreover, the findings suggest that agricultural mechanization is still far to boost the land uses of cotton, maize and cassava, despite the importance of cotton in the Beninese economy on the one hand, and the key roles of maize and cassava in diet in Benin, on the other hand. Agricultural mechanization is far from being a reality in Benin's agricultural sector to the extent that public agricultural investments are below the Maputo agreements (Note 1). An effective agricultural mechanization must opt for cereals whose investments in agricultural machinery are less expensive compared to cotton. This strategy of agricultural mechanization makes it possible to better ensure food security, unlike the intensive cotton production, whose terms of trade are always unfavorable and dependent on subsidies from the North.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".