The state of agroecology in Brazil: An indicator-based approach to identifying municipal “bright spots”
Bibliographic record
Abstract
Agroecology is increasingly recognized as a pathway for agricultural transformation that can mitigate environmental harms and improve social equity. Yet, the lack of broad-scale assessments that track agroecological indicators in distinct contexts has been identified as a challenge to scaling agroecology out and up. Here, we identify and assess indicators of agroecology based on the Food and Agriculture Organization’s 10 Elements of Agroecology and Tool for Agroecology Performance Evaluation. We created an agroecological index representing the status of agroecological practices and outcomes on farms in Brazil and mapped the results at the municipal level (the smallest autonomous administrative territorial unit in Brazil) using data from the 2017 agricultural census. We found that the extent of agroecological practice across Brazil’s 26 states exhibited strong spatial variability. Within states with low average levels of agroecological practice, we identified “bright spots” of agroecology, or municipalities that performed better than their state average. Bright spot analyses may provide insights on how other municipalities could improve their agroecological status, as well as illustrate potential factors inhibiting agroecological transitions elsewhere. Based on the analysis of local contexts through a literature review, we found that bright spots corresponded to areas with highly visible activities of grassroots farmer networks and nongovernmental organizations, access to public policies and programs, proximity to urban markets, and maintenance of traditional agricultural practices. This suggests that additional institutional investment and support should be directed toward strengthening these enabling factors for agroecology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".