Farm Production Diversification and Dietary Diversity among Subsistence Farming Households: Panel Data Evidence from South Africa
Bibliographic record
Abstract
Improving the production of a variety of foods by subsistence farmers has been identified as a key strategy for improving dietary diversity. However, there is limited evidence in South Africa on how one’s own production is linked to dietary diversity. This study relies on nationally representative panel data to investigate the extent to which farm production diversity is correlated with dietary diversity. The data indicated a moderate level of household dietary diversity that has been on a declining trend between 2008 and 2017. The farm households produced three food groups (meat, cereals, and vegetables), suggesting more reliance on food purchases than own production. The study found a positive relationship between own production diversification and dietary diversity and that dietary diversity varied by demographics and socio-economic characteristics of households. However, production diversity was not significantly associated with the consumption of micronutrient-rich foods such as fruits or vegetables. Higher levels of education, income per capita, food expenditure, and geographic location were some of the key drivers of dietary diversity among subsistence households. The findings suggest that encouraging subsistence farming households to produce various crop and animal species can be an effective strategy to improve dietary diversity among poor households in South Africa.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".