Food security, sweet potato production, and proximity to markets in northern Ghana
Bibliographic record
Abstract
Debates concerning how to achieve food security tend to fall into one of two camps. The first is that high-technology, market-oriented approaches promise to enhance agricultural productivity and improve food security. The counterargument is that low-technology approaches, when combined with building social and physical infrastructure, are more effective at meeting people’s food needs. Using a survey of 540 farm households in northern Ghana, we assess the level of food security for smallholders by analyzing the influence of a low-technology and low-external-input approach, such as sweet potatoes, and that of the production of an improved, commercially produced crop, such as rice. We also measure the influence of market access. Our results indicate that sweet potato producers are more likely to be food secure than commercial rice producers. However, the proximity to and interaction with markets is also associated with farmer food security, even when controlling for measures of prosperity. These findings suggest that low-technology approaches and high-technology, market-oriented approaches should not be treated as diametrically opposed to each other. Enhancing smallholder production of low-technology staple crops like sweet potatoes is likely to improve well-being. At the same time, interventions to build the physical and social infrastructure necessary to enable market participation would also be likely to enhance smallholder well-being.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".