Review of enablers and barriers to the adoption of silvopastoral systems in Latin America
Bibliographic record
Abstract
There is a need to scale out sustainable and environmentally friendly agricultural practices and phase out the detrimental effects of industrialized agriculture. Extensive cattle ranching in Latin America is a major driver of deforestation leading to loss of biodiversity, soil fertility depletion, greenhouse gas emissions, and high climatic vulnerability. Silvopastoral systems (SPS) are a promising agroecological alternative for cattle production which combines trees, shrubs, and forage grasses to enhance cattle production and increase ecosystem services. SPS have great potential for climate change mitigation, productivity increase, and biodiversity conservation; nevertheless, their adoption remains low. Understanding the enablers and barriers to adoption of SPS can be key to scaling out this practice in Latin America, but no synthesis or review on the drivers of adoption of SPS or Latin America yet exists. We conducted a literature review to understand the enablers and barriers to the adoption of SPS in Latin America. We found and reviewed 52 studies, included papers, theses and documents in English, Spanish, and Portuguese, that fulfilled the inclusion criteria. The main factors enabling adoption of SPS were economic incentives, such as payments for ecosystem services (PES), and information transfer capabilities, such as the presence of technical assistance and the strength of social networks. Positive perceptions of ecosystem services provided by SPS also had a positive effect on adoption. The barriers for the adoption of SPS in Latin America are the lack of markets for cattle ranching products generated under SPS, the cost of implementation, and management complexity. Scaling out SPS in Latin America will depend on economic incentives capable of overcoming the initial investment barrier for farmers, information transfer, knowledge on SPS, and the increase of demand for SPS products.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".