Leveraging Traditional Agroforestry Practices to Support Sustainable and Agrobiodiverse Landscapes in Southern Brazil
Bibliographic record
Abstract
Integrated landscape approaches have been identified as key to addressing competing social, ecological, economic, and political contexts and needs in landscapes as a means to improve and preserve agrobiodiversity. Despite the consistent calls to integrate traditional and local knowledge and a range of stakeholders in the process of developing integrated landscape approaches, there continues to be a disconnect between international agreements, national policies, and local grassroots initiatives. This case study explores an approach to address such challenges through true transdisciplinary and multi-stakeholder research and outreach to develop solutions for integrated landscapes that value and include the experience and knowledge of local communities and farmers. Working collaboratively with small-scale agroforestry farmers in Southern Brazil who continue to use traditional agroecological practices to produce erva-mate (Ilex paraguariensis), our transdisciplinary team is working to collect oral histories, document local ecological knowledge, and support farmer-led initiatives to address a range of issues, including profitability, productivity, and legal restrictions on forest use. By leveraging the knowledge across our network, we are developing and testing models to optimize and scale-out agroforestry and silvopastoral systems based on our partners’ traditional practices, while also supporting the implementation of approaches that expand forest cover, increase biodiversity, protect and improve ecosystem services, and diversify the agricultural landscape. In so doing, we are developing a strong evidence base that can begin to challenge current environmental policies and commonly held misconceptions that threaten the continuation of traditional agroforestry practices, while also offering locally adapted and realistic models that can be used to diversify the agricultural landscape in Southern Brazil.
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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 | 0.003 | 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 | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".