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 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".