Structuring Markets for Resilient Farming Systems
Bibliographic record
Abstract
Diversified farms have received considerable attention for their potential to contribute to environmentally sustainable, resilient, and socially just food systems. In response, some governments are building new forms of public support for social-ecological services through the creation of mediated markets, such as targeted public food procurement programs. Here, we examine the relationship between farmer participation in Brazil’s National School Feeding Program and farm diversification and household autonomy, as key indicators of farm household resilience. We hypothesized that two key features of the food procurement program—structured demand for diversified food products, and a price premium for certified organic and agroecological production—would increase farm-level agrobiodiversity and the use of agroecological practices. We designed a comparative study between family farmers who do, and do not, participate in Brazil’s National School Feeding Program in the plateau region of Santa Catarina in Southern Brazil. We used semi-structured surveys to collect data on farm agrobiodiversity, management practices, and farm household autonomy, and we conducted land use history assessments. Here, we suggest for the first time that the National School Feeding Program played a role in driving the following: (1) transitions on family farms from low agrobiodiversity, input-intensive farming systems to diversified farming systems (i.e., horticultural production) and (2) a significant increase in the cropped area under diversified farming systems. This transition was supported by making horticultural production an economically viable alternative to field crops typically linked to volatile, unpredictable markets. The convergence of public policies supporting mediated markets, increased farm household autonomy, and farm diversification represents an integrated mechanism with the potential to enhance food system resilience.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".