Market Foods, Own Production, and the Social Economy: How Food Acquisition Sources Influence Nutrient Intake among Ecuadorian Farmers and the Role of Agroecology in Supporting Healthy Diets
Bibliographic record
Abstract
Rural Ecuadorians are experiencing a double burden of malnutrition, characterized by simultaneous nutrient inadequacies and excesses, alongside the social and environmental consequences of unsustainable agriculture. Agriculture can support farmer nutrition by providing income for market purchases and through the consumption of foods from own production. However, the nutritional contributions of these food acquisition strategies vary by context. We surveyed smallholder women farmers (n = 90) in Imbabura province to assess the dietary contributions of foods obtained through market purchase, own production, and social economy among farmers participating in agroecology—a sustainable farming movement—and neighboring reference farmers. We found that foods from farmers’ own production and the social economy were relatively nutrient-rich, while market foods were calorie-rich. Consumption of foods from own production was associated with better nutrient adequacy and moderation, whereas market food consumption was associated with a worse performance on both. Food acquisition patterns differed between farmer groups: agroecological farmers obtained 44%, 32%, and 23% of their calories from conventional markets, own production, and the social economy, respectively, while reference neighbors obtained 69%, 17%, and 13%, respectively. Our findings suggest that, in this region, farmer nutrition is better supported through the consumption of their own production than through market purchases, and sustainable farming initiatives such as agroecology may be leveraged for healthy diets.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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".