Post-harvest Losses for Urban Fresh Fruits and Vegetables Along the Continuum of Supply Chain Functions: Evidence from Dar es Salaam City - Tanzania
Bibliographic record
Abstract
Despite the significance and efforts put forward to enhance fresh fruits and vegetables, post-harvest losses continue to threaten the supply chain of this trade. The study explores post-harvest losses of urban fresh fruits and vegetables along the supply chain continuum in Dar es Salaam. It further digs into understanding factors contributing to post-harvest losses for fresh fruits and vegetables in the context of supply chain functions of storage, transportation, value addition, and market services. A qualitative research design was adopted and data were drawn from 55 respondents who were selected by purposive and simple random sampling techniques. In-depth interviews, Focus Group Discussions, documentary review and non-participant observation were used in data collection. Findings showed that post-harvest losses for urban fresh fruits and vegetables along the supply chain functions are attributed to deficiencies inherent in the supply chain functions of storage, transportation, value addition and quality improvement as well as market services. Further findings indicate that low technology, inadequate communication and information, inadequate policies and institutions to mention just a few are the underlying factors leading to such loss. The study recommends stakeholders to collectively alleviate poor storage, transportation, value addition, and markets related challenges that lead to post-harvest losses in the sector. These findings contribute to the existing knowledge in the sector, pave policy inputs with regards to minimising post-harvest losses in the agricultural sector, thereby improving food security, traders and the government’s income in general.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".