Application of Landscape Approach Principles Motivates Forest Fringe Farmers to Reforest Ghana’s Degraded Reserves
Bibliographic record
Abstract
Research Highlights: Landscape approach principles were developed to address competing claims on resources at local scales. We used the principles to address agricultural expansion in Ghana’s forest reserves. Background and Objectives: Agricultural expansion is a major cause of Ghana’s forest-cover loss. Cultivation has totally deforested some forest reserves. The situation in Ghana illustrates the trade-off between attaining the Sustainable Development Goals (SDGs). SDG 1—reduction of poverty, and 2—achieving food security, are in conflict with SDG 15—protecting and restoring forests. We examined how farmers in forest fringe communities could be engaged in restoring degraded forests using the landscape approach and whether their livelihoods were improved through the use of this approach. Materials and Methods: The Ongwam II Forest Reserve in the Ashanti region of Ghana is encroached by farmers from two communities adjacent to the reserve. We employed the 10 principles of the landscape approach to engage farmers in restoring the degraded reserve. The flexibility of the landscape approach provided a framework against which to assess farmer behaviour. We encouraged farmers to plant trees on 10 ha of the degraded reserve and to benefit through the cultivation of food crops amongst the trees. Results: Access to fertile forest soils for cultivation was the main motivation for the farmers to participate in the reforestation project. The farmers’ access to natural and financial capital increased and they became food secure in the first year of the project’s operation. Conclusions: Effective implementation of several small-scale reforestation projects using the landscape approach could together lead to a forest transition, more trees in agricultural systems and better protection of residual natural forests while improving farmers’ livelihoods, all combining to achieve the SDGs.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".