Balancing shifting cultivation and forest conservation: lessons from a “sustainable landscape” in southeastern Mexico
Bibliographic record
Abstract
Shifting cultivation is often perceived to be a threat to forests, but it is also central to the culture and livelihoods of millions of people worldwide. Balancing agriculture and forest conservation requires knowledge of how agricultural land uses evolve in landscapes with forest conservation initiatives. Based on a case study from Quintana Roo, Mexico, and remote sensing data, we investigated land use and land cover change (LUCC) in relation to accessibility (from main settlement and road) in search of evidence for agricultural expansion and/or intensification after the initiation of a community forestry program in 1986. Intensification was through a shortening of the fallow period. Defining the sampling space as a function of human needs and accessibility to agricultural resources was critical to ensure a user-centered perspective of the landscape. The composition of the accessible landscape changed substantially between 1976 and 1997. Over the 21-year period studied, the local population saw the accessible landscape transformed from a heterogeneous array of different successional stages including mature forests to a landscape dominated by young fallows. We detected a dynamic characterized by intensification of shifting cultivation in the most accessible areas with milpas being felled more and more from young fallows in spite of a preference for felling secondary forests. We argue that the resulting landscape provides a poorer resource base for sustaining agricultural livelihoods and discuss ways in which agricultural change could be better addressed through participatory land use planning. Balancing agricultural production and forest conservation will become even more important in a context of intense negotiations for carbon credits, an emerging market that is likely to drive future land changes worldwide.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".