Commodity Storage, Post-Harvest Losses, and Food Security: Panel Data Evidence from Ethiopia
Bibliographic record
Abstract
Abstract In Ethiopia, 95 % of total agricultural output comes from some 11 million smallholder farmers. A relatively significant proportion of the food grown in the country is stored at the household level by smallholder farm households, mainly for own consumption. Storage losses, generally perceived to be high, have significant implications for household food security. This study provides a microeconomic perspective of commodity storage, post-harvest losses (PHL), and food security in Ethiopia. It relies on a large-scale household panel dataset, the Ethiopia Socioeconomic Survey (ESS), which comprises 4,000 households in rural areas and small towns that are representative of the most populous regions of Ethiopia. The data were collected as part of the World Bank’s LSMS-ISA project; it involved three “waves” or collection periods: 2011/12, 2013/14, and 2015/16. Data from only the second and third waves were used to estimate a random-effects probit model. Findings show that the magnitude of PHL is substantial: damage is due to rodents and rotting related to traditional storage facilities, poor ventilation, humidity/temperature, and undesirable post-harvest handling. Findings also show that PHL decreases with better market access and improved storage practices. Mitigation measures that improve and promote modern grain storage facilities appear to provide a double dividend – reducing PHL while addressing food insecurity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".