Postharvest food loss reduction and agriculture policy framework in Tanzania: status and way forward
Bibliographic record
Abstract
Abstract In 2014, Tanzania became a signatory of the African Union Postharvest Loss Management Strategy (AU-PHLMS) under the Malabo Declaration, a policy framework of the African Union aimed at reducing the continent's postharvest food losses by 50 percent by 2025. Though Tanzania has several agriculture development policies, very little research exists on to what extent the postharvest food loss agenda is reflected and integrated into Tanzania's agriculture policy framework, making it difficult to assess Tanzania's commitment and progress made to realize these ambitious targets in 2025. Using a scoping review method, this study reviews agriculture-food security policies and programs enacted by the government of Tanzania from the 1990s to 2022. Findings reveal that despite high postharvest food losses, policies, and agriculture development programs in favor of increasing food production remain the central focus of the government, while interventions to eliminate food loss and waste have not been prioritized. Results also show that with nearly half of the food produced not reaching consumers, Tanzania's ambitions to be food secure may only be realized if policy measures to increase crop productivity go hand in hand with preventing postharvest food losses. The study calls for full policy integration of postharvest management programs and more investment in farmer-focused interventions to reduce food loss and waste in Tanzania.
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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 | 0.023 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| 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".