The Institutionalization of Farmer Field Schools in Latin America
Bibliographic record
Abstract
Agricultural systems in Latin America face complex challenges (climate change, socio-political instability, and environmental degradation). These undermine food security and smallholder and Indigenous farming systems resilience. Although local knowledge contributes to adaptation, it is constrained by institutional fragmentation. Approaches such as the Farmer Field School (FFS), based on experiential and collaborative learning, offer promise. However, embedding FFS within national institutional frameworks remains a major challenge. This study explores the institutionalization of FFS in Peru, Colombia, Bolivia, Honduras, and Costa Rica. It examines methodological practices, constraints, and enabling conditions shaping extension and institutional strengthening. A qualitative approach included interviews, field visits, and focus groups with actors from governmental, academic, NGO, and private sectors. Data collection was informed by a literature review and purposive snowball sampling. Findings reveal that institutionalization levels differ across countries, shaped by policy contexts, institutional structures, and actor networks. Success cases showed strong inter-institutional collaboration, curricular integration, and long-term support. Barriers include weak coordination, fragmented policies, and limited institutional capacity. Universities were central in Costa Rica, Colombia, and Honduras, while NGOs and state agencies led in Peru and Bolivia. National alignment and inclusive partnerships were essential to institutionalization efforts. Effective institutionalization requires coherent policies, investment in institutional capacities, and sustained multi-sector collaboration. Embedding FFS into formal education and aligning with rural development agendas enhances their sustainability and transformative potential.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".