Learning from Community-Based Natural Resource Management (CBNRM) in Ghana and Zambia: lessons for integrated landscape approaches
Bibliographic record
Abstract
Land use in much of sub-Saharan Africa is dominated by legislative frameworks based on a strong colonial legacy, focusing strongly on state control and minimal devolution of management responsibilities to local communities. However, attempts to reconcile conservation and socio-economic development by increasing stakeholder engagement in community-based natural resource management (CBNRM) have been undertaken since the late 1980s. Based on a review of published literature on historical land-use trajectories, the evolution of CBNRM, and key respondent interviews with NRM experts in Ghana and Zambia, this paper asks: What lessons can be learned from CBNRM to inform integrated landscape approaches for more equitable social and ecological outcomes? The paper discusses the positive characteristics and persistent challenges arising from CBNRM initiatives in both countries. The former being, improved rights and resource access, an established institutional structure at the local level, and a conservation approach tailored to the local context. The latter include the absence of multi-scale collaboration, inadequate inclusive and equitable local participation, and limited sustainability of CBNRM initiatives beyond short-term project funding timelines. The paper argues that integrated landscape approaches can address these challenges and improve natural resource management in Ghana and Zambia. We urge landscape practitioners to consider how the lessons learned from CBNRM are being addressed in practice, as they represent both challenges and opportunities for landscape approaches to improve natural resource management.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".