More people, more trees: A reversal of deforestation trends in Southern Ethiopia
Bibliographic record
Abstract
Abstract Despite global commitments to forest restoration, evidence of the pathways through which restoration creates social and ecological benefits remains limited. The objective of this paper is to provide empirical evidence to generate insights on the relationship between forest cover change and key provisioning ecosystem services and reforestation pathways. In Southern Ethiopia, three zones along a gradient of decreasing land cover complexity and tree cover were examined. The land cover change was assessed using satellite remote sensing and complemented ground‐based tree inventory. Perceptions of land cover and ecosystem services change and farmer responses were evaluated through three Participatory Rural Appraisals and eight Focus Group Discussions. Since the 1970s, a landscape shift from a forest‐grassland to a cropland mosaic was associated with increased food production, improved food security, and higher incomes. However, this shift also coincided with reductions in livestock, construction materials, fuelwood and water availability, prompting reforestation efforts designed to recover some of these lost ecosystem services. In particular, some households established Eucalyptus woodlots and encouraged natural regeneration. Natural trees, Eucalyptus woodlots, Ensete plantations (a type of plantain), and grasslands were positively associated with homestead proximity; thus, homestead establishment resulting from population increase in this predominately agricultural landscape appeared to foster a viable forest restoration pathway—that is, 'more people, more trees'. This is a reforestation pathway not previously described in the literature. A return to a more diverse agricultural landscape mosaic provided more secure and diversified income sources along with better provisioning of construction materials, fuelwood, and higher livestock numbers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".