Postharvest Management Practices Among Grain Traders in Burkina Faso and Niger
Bibliographic record
Abstract
Cowpea is an important food security and cash crop in West Africa. At harvest, most of the cowpea produced is sold to grain traders who supply it to consumers throughout the year. Hence, effective postharvest management is essential to maintain the quality and quantity along the cowpea value chain. While most research and development efforts have focused on smallholder farmers, little attention has been given to grain traders. This study examined storage practices, pest control methods, and the use of improved hermetic storage (Purdue Improved Crop Storage-PICS) bags among 201 grain traders in 22 towns across Niger and Burkina Faso. Findings reveal that 79.4% of traders used chemical pesticides during cowpea storage—mainly Phostoxin and Dichlorvos—citing affordability and ease of use. Awareness of PICS bags was high (over 85%), yet use varied significantly by country. In Niger, 38.6% of traders used PICS bags compared to just 7% in Burkina Faso, with barriers including high costs, limited availability, and labor requirements for tying. Traders in Niger stored larger volumes of grain (up to 377 tons) and used hermetic storage more frequently. Economic analysis showed a marginally higher return on investment (ROI) for chemical pesticides compared to PICS bags for a single use. Targeted interventions are needed to create incentives for traders to adopt chemical-free grain storage methods. Key priorities include reducing the labor required to tie bags, improving their availability and affordability, strengthening and enforcing food safety policies, and providing training on the proper use of the technology.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".