Social-Ecological Landscape Sustainability in Ghana and Nigeria: An application of a DPSIR-SEL Framework
Bibliographic record
Abstract
Abstract This study evaluates the Land Use and Land Cover (LULC) dynamics and water quality in the Mankran landscape in Ghana (case study-1) and the Doma-Rutu landscape in Nigeria (case study-2) using the Drivers Pressure State Impact Response (DPSIR) for social ecological landscape (SEL) assessment framework (DPSIR-SEL). In the Mankran landscape, between 2008 and 2018, there was a notable shift in land utilization: cash crop cultivation surged to 30% in 2015 before receding to 14.5% by 2018, while subsistence farming was reduced. Water quality analysis revealed deviations from World Health Organization (WHO) standards, with parameters such as Total Suspended Solids (TSS), Pb (lead), and As (arsenic) signaling potential contamination risks. Conversely, in the Doma-Rutu landscape of Nigeria, LULC changes from 2000 to 2022 manifest as expanding residential and agricultural areas and alterations in natural water bodies and vegetation. Water quality concerns have arisen with high levels of electrical conductivity, total dissolved solids, and salinity. Additionally, Focus Group Discussions (FGDs) in Nigeria illuminated the deep-rooted herder-farmer conflicts, constraining crop cultivation due to historical and environmental factors. The intertwined challenges in the Mankran landscape and Doma-Rutu landscape necessitate sustainable and inclusive resource management, adaptive land-use practices, and proactive measures to ensure water quality. Land use land cover (LULC) and water quality evaluations, informed by the DPSIR-SEL framework, underscore the pressing need for integrated and inclusive solutions to address evolving land-use challenges and safeguard water resources in the Mankran and Doma-Rutu landscapes.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".