Lost harvest: examining the association between postharvest food loss and food insecurity in semi-arid Ghana
Bibliographic record
Abstract
According to the World Resources Institute, about one in every four calories produced for human consumption globally is not eventually consumed by humans. In sub-Saharan Africa, postharvest food loss (PHL) alone accounts for 20–30% of annual production, with an estimated value of 1.6 billion USD. Yet, agricultural policies that target improving food security have largely focused on augmenting productivity with little attention to PHL reduction. That notwithstanding, PHL has the potential to undermine several key dimensions of food insecurity. For instance, it can compromise household food reserves and drive food price hikes. Reductions in food quality may also impact food utilization. The prevalence of PHL and its relationship with food insecurity, however, remains underexplored. Using a cross-sectional survey of smallholder farming households (n = 1100) in Ghana, we examined the association between PHL and food insecurity. Our findings show that, on average, 22% of household harvest is lost postharvest. Nested ordered logistic regression analysis revealed a statistically significant relationship between PHL and food insecurity. A unit increase in PHL (OR = 1.08; p ≤ 0.05) was significantly associated with an 8% increase in the odds of being severely food insecure. Our findings provide an empirical basis for the argument that addressing PHL is a viable entry point for addressing food insecurity in the Global South. While it is crucial to pay attention to production concerns, food policy must simultaneously address postharvest management challenges of smallholder farmers. Policies that prioritize investment in contextually relevant and low-cost solutions to postharvest management will be timely.
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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 | 0.002 |
| 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 | 0.001 | 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".