Social-ecological landscape sustainability in West Africa: applying the driver pressure state impact response framework in Ghana and Nigeria
Bibliographic record
Abstract
This study interrogates the state of social-ecological landscapes (SEL) in West Africa, focusing on two case studies: the Mankran SEL in Ghana (case study 1) and the Doma–Rutu SEL in Nigeria (case study 2). Using a mix of methods, the assessment was framed by the Drivers Pressure State Impact Response (DPSIR) model tailored for SEL evaluation (DPSIR-SEL). In the Mankran landscape, land use patterns shifted significantly from 2008 to 2018, with cash crop cultivation peaking at 30% in 2015 before declining to 14.5% by 2018. Water quality assessments in the Mankran micro-watershed indicated that several parameters, including Total Suspended Solids (TSS) at 914.41 ± 1974 mg/L, lead at 18.73 ± 17.26 µg/L, and arsenic at 53.41 ± 86.66 µg/L, exceeded World Health Organization (WHO) standards, raising concerns about potential contamination. In contrast, the Doma–Rutu landscape in Nigeria experienced land use and land cover (LULC) changes from 2000 to 2022, characterized by the expansion of residential and agricultural areas alongside modifications to natural water bodies and vegetation. Water quality issues have emerged, with elevated levels of electrical conductivity, total dissolved solids, and salinity. Furthermore, Focus Group Discussions (FGDs) revealed persistent herder-farmer conflicts in Nigeria, which have historically constrained crop production due to various environmental and social factors. The intertwined challenges faced by both the Mankran and Doma–Rutu landscapes underscore the urgent need for sustainable and inclusive resource management, adaptive land-use strategies, and proactive measures to safeguard water quality.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".