Adaptive Capacity and Coping Strategies in the Face of Climate Change: A Comparative Study of Communities around Two Protected Areas in the Coastal Savanna and Transitional Zones of Ghana
Bibliographic record
Abstract
Modern productivity-enhancing strategies (MPES) are considered to be some of the best adaptation options available to communities in the face of changing climatic conditions. The adaptive capacity of communities living around two protected areas (Kogyae Strict Nature Reserve and Muni-Pomadze Ramsar Site) in Ghana were assessed in relation to MPES by investigating household accessibility to human, social, natural, financial and physical capital. Information was collected from 249 and 250 respondents in Kogyae and Muni respectively. A logit model was used to find out whether adaptive capacity affected adoption of MPES. In both study areas, indigenous coping strategies such as use of simple farm tools, processing of root/tubers and grains and social grouping were practiced. The MPES practiced included application of fertilizers and other agrochemicals, use of high technology machinery and bunding in rice fields. The mean level of adaptive capacity of farm households was low in both areas; 0.30 and 0.27 in Kogyae and Muni respectively. The adoption of MPES was influenced positively by the level of human and physical capacities and farm size and location of protected area, and negatively by farmers’ participation in off-farm activities. Farmers located in Kogyae were more likely to adopt productivity-enhancing strategies than their counterparts in Muni. Considering that access to the resources within the protected areas is restricted and not legally available to support livelihoods of the fringe communities, we conclude that enhancing access to both human and physical capitals is the way forward for climate change adaptation for these two communities.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".