Understanding barriers impeding the deployment of solar-powered cold storage technologies for post-harvest tomato losses reduction: Insights from small-scale farmers in Tanzania
Why this work is in the frame
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Bibliographic record
Abstract
Postharvest food loss remains one of the major food security challenges in Sub-Saharan Africa (Africa). In Tanzania, it is estimated that about 50 percent of fresh tomatoes perish before reaching consumers due to poor post-harvest management. The lack of cold storage facilities is one of the leading causes of massive post-harvest tomato losses, negatively affecting farmers' livelihoods and the sector's economic contribution. For small-scale farmers in off-grid locations, the adoption of solar-powered cold storage technologies has been found to be a potential solution for reducing losses of highly perishable crops such as tomatoes. However, in Tanzania, the deployment of Solar-powered Cold Storage Technologies (SPCSTs) is limited, leaving the vast majority of rural small-scale farmers without access to such facilities. This study examined barriers impeding the deployment and uptake of Solar-powered Cold Storage Technologies in Tanzania. Farmers' perceptions about SPCSTs and constraints limiting their deployment were examined through semi-structured interviews and Focus Group Discussions (FGD) held between April and June 2021 in Kilolo district, Southeast Tanzania. Participants involved fifty-two ( n = 52) small-scale tomato farmers and twenty-three ( n = 23) experts and key informants from government and non-profit organizations that were purposively selected. The results show that the deployment of solar-powered cold storage technologies is constrained by limited awareness, high investment costs, low-paying capacity among farmers, and consumer preference for non-refrigerated foods. Addressing these barriers demand promoting policies and programs that attract and retain investment in cold storage technologies and improve SPCSTs affordability through flexible payment arrangements.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it