Smallholder Maize Farmers Need Better Storage for Food Security: An Exploratory Study over the Storage Types Used in Uganda
Bibliographic record
Abstract
Storage is a crucial link in the food supply chain. It helps to even-out fluctuations in food demand and supply. This ensures food availability during the lean periods. Despite the immense contribution of storage, a knowledge gap exists on the storage types used by smallholder maize farmers, how they are acquired, used, and their cost in Uganda. Storage affects the social and economic well-being of smallholder maize farmers. In this study, smallholder maize farmers in three districts of eastern Uganda (Iganga, Manafwa, and Katakwi) were interviewed during the maize storage season of 2014/2015. The aim was to: describe the different storage types; find out how they were acquired and used; the length of storage and the cost. The findings show that sacks were the most used storage type. Storage types were acquired through purchase; however, some were constructed by the smallholder maize farmers. Affordability and accessibility determined the storage type used. Some storage types were not used across all the districts; for example, the granary was used in two out of the three sampled districts. Thus, the findings show that maize storage is a challenge. We recommend that maize storage facilities should be improved with affordable to the farmers.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".