Factors Enhancing Household Nutrition Outcomes in Potato Value Chain in South-Western Uganda
Bibliographic record
Abstract
In Uganda, agricultural commercialization has been promoted to reduce poverty and improve household food security. South-western Uganda, the major producer of potato, has been considered the food basket of the country but it has one of the highest prevalence rates of stunting in children under 5. This study considered potato enterprise as a key pathway for enhancing household food and nutrition security because it has become a major income source and staple in the diets of many households in the area and most urban areas in the country. The objective was to determine factors that influence farm household nutrition and food security outcomes. Through a survey, data were collected from 434 randomly selected potato farmer households. Descriptive and econometric methods were used in data analysis. Results show that household dietary diversity score was low (3.2) for most (57%) of the households. Only 38% were food secure. The main factors enhancing household nutrition outcomes were size of land, livestock units owned, proportion of household income spent on food, and education of household head, while farmer’s experience in potato production had a negative effect. The size of land owned, crop diversification, income from potato, age and education of household head, and a famer being male enhanced household food security outcomes. The study recommends promoting improved production practices to maximize land productivity, integration of livestock in potato production, and training women and men in household food and nutrition and related use of income.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".