Economic feasibility and impacts of replacing tobacco with alternative crops in smallholder cropping systems: evidence from Uganda
Bibliographic record
Abstract
Tobacco is grown on more than 4 million hectares of global agricultural land in at least 124 countries. As a member state to the World Health Organization Framework Convention on Tobacco Control (WHO FCTC) international treaty, Uganda seeks to support economically viable alternative activities as a means to tobacco control because previous efforts with tobacco control that focused on the demand side have failed to control tobacco supply. This study employed the Tradeoff Analysis-Minimum Data (TOA-MD) model to assess tradeoffs and associated economic impacts of switching from tobacco to alternative crops in smallholder cropping systems. Results revealed that switching from tobacco to alternative crops is economically feasible for most of the farms owing to the higher net economic returns. The proportion of farms that expect higher returns from alternative crops is highest for cassava (97%) followed by maize (87%), rice (83%) and the least for beans (74%). Cassava is the best alternative for both small and large farms because of the highest gains in farm net returns, per capita income and poverty reduction. The next feasible crop alternatives for large farms are maize and rice. Despite their economic feasibility, alternative crops are typical of uncertain markets with unorganised structures for production. Since these are important incentives for tobacco farmers, the study recommends development of institutions that foster value chain development for alternative crops. Supporting agro-processors is likely to guarantee markets and provide for lump-sum payments, input credit for production and a reliable extension service system as way of incentivizing a sustainable switch to alternative cropping systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".