Community perceptions on land and water acquisitions in the Okavango Delta: implications for rural livelihoods
Bibliographic record
Abstract
Large-scale land acquisitions in Africa are increasing, reported often as the transfers of land for food and biofuel crop production. Only reporting agricultural acquisitions underplays potential impacts of other forms of acquisitions like tourism and conservation, which are new engines for economic growth in Southern Africa. While this shift has complex social-ecological implications, there is limited evidence of the multiple ways that land acquisitions unfold in wetland ecosystems, and implications for people and nature. This study aims to investigate local perceptions of implications of land and water acquisitions on local livelihoods in the Okavango Delta, Botswana, using in-depth interviews with 116 local respondents in Etsha 6, Khwai and Tubu villages. Findings revealed that the primary drivers of land acquisitions in the Okavango Delta were tourism and subsistence agriculture, and a new and unique land exchange (we termed land borrowing) was prevalent in Tubu, involving the borrowing of farmland in flood recessions between locals. Concessions, borrowings, and rentals were key perceived land acquisition types. Both positive and negative impacts of land acquisitions on livelihoods surfaced. The diversity of cultural grouping influenced locals’ intricate connection with riparian waters and affected how land was exchanged and governed. The disparities in benefits from land resources have negative implications for equitable resource distribution and natural resource governance, in policy and practice. This research highlights the importance of an expanded view of acquisitions and associated impacts with closer attention to power dynamics which can facilitate more nuanced implementation of targets of the Kunming–Montreal Global Biodiversity framework.
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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 | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".