Farmer-Fulani pastoralist conflicts in Northern Ghana: are integrated landscape approaches the way forward?
Bibliographic record
Abstract
Over the past 20 years, recurrent and violent conflicts between farmers and Fulani pastoralists have persisted in Northern Ghana. These conflicts mainly revolve around access to and utilisation of natural resources such as land and water. Conflicts of interest have led to the social marginalisation of the Fulani community, leading to their exclusion from formal landscape governance processes. This paper explores the prospects for better management of these conflicts and the potential for including Fulani pastoralists in landscape governance through the implementation of integrated landscape approaches. Based on a semi-systematic literature review and key informant interviews, we propose a categorisation of conflicts and potential causes and solutions. The experience of Burkina Faso in managing farmer-herder conflicts is presented to inform lessons for Ghana. We argue that adopting more inclusive landscape approaches, with a particular emphaisis on key principles, could contribute to reconciling diverging interests between farming and herding communities and help mitigate conflicts. This requires that constraints such as the negative and pervasive perceptions towards the Fulani, the neglect of pastoral activity in broader development processes, and the lack of inclusion of Fulani pastoralists in multi-stakeholder platforms and decision-making need to be urgently addressed.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".