Strategies to Reduce Food Loss in the Global South
Bibliographic record
Abstract
Approximately one third of the world’s food is lost, and reducing this represents an important strategy for promoting more sustainable food systems and addressing global food insecurity. This paper presents a preliminary assessment of the socio-economic factors that are significant in causing food loss in developing countries. These countries were chosen because the majority of food waste in poorer nations happens on or around the farm and is due to inefficient storage and processing facilities (by contrast, the majority of food waste in the global north is caused by consumers or retailers and, hence, is a very different problem). To explore this topic, we conducted a multivariate panel data analysis where the volume of food loss in 93 countries over 20 years was used as the dependent variable and a range of socio-economic factors were used as independent variables. Results show that, for the countries in the global south, variables related to wealth, agricultural machinery, transportation, and telecommunications were significant in explaining the amount of lost food. We used these results to model the effectiveness of different hypothetical policies designed to reduce food loss and estimate that up to 49% of food loss could be averted by improving each countries’ performance on these variables. While these results seem to offer huge opportunities to improve the sustainability of global agricultural systems and address global food security, this paper concludes on a note of caution: as countries grow wealthy enough to address the food lost by challenges associated with on-farm issues, these same countries may start to experience more food waste at the consumer/retailer end of the food chain. Therefore, any attempt to reduce on-farm food loss in lower income countries must be met with policies to reduce the emerging problems of food waste amongst consumers and retailers.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".