Understanding barriers impeding the deployment of solar-powered cold storage technologies for post-harvest tomato losses reduction: Insights from small-scale farmers in Tanzania
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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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".