Determinants of Post-Harvest Losses in Primary Production: Evidence From Vietnamese Lychee Farming
Bibliographic record
Abstract
Despite post-harvest losses in primary production severely threatening food security and farmer livelihoods, information about loss quantification and characteristics remains limited, by focusing exclusively on physical damage while ignoring quality deterioration and economic value losses. This study addresses this gap by examining Vietnamese lychee production from harvesting to primary commercialization, expanding loss quantification beyond conventional physical damage to incorporate intuitive losses representing quality deterioration and economic losses from farmers’ weak market power. Fractional regression models are employed to explore factors affecting post-harvest losses and determine marginal effects across different loss types. Data reveal an average total post-harvest loss of 12%, driven by three primary factor groups: household and farming characteristics, post-harvest handling practices, and market conditions. Notably, Good Agricultural Practice (GAP) implementation and agricultural cooperative participation demonstrate the largest effect magnitudes, followed by cold storage usage and other post-harvest handling practices. Whereas prior studies targeting isolated technical interventions, these findings enable systematic solutions for comprehensive post-harvest loss reduction, including expanded GAP adoption to improve product quality, cooperative development to strengthen market positioning, and shelf-life extension technologies that enable farmers to diversify markets and reduce vulnerability to exploitative trading practices.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".