Historical land policies influence contemporary landscape patterns in agropastoral landscapes
Bibliographic record
Abstract
Context Historical land use policies can have enduring impacts on contemporary patterns of land use. However, the role of legacy land use policies on contemporary patterns of land use has been understudied especially in agropastoral landscapes that support the livelihoods of millions of people globally. Objectives In this study, we investigated four distinct historical land policy trajectories in agropastoral landscapes in Narok County, southern Kenya, and how they have shaped spatial patterns of land use over more than four decades. Methods A spatially explicit historical land use policy map was used to guide comparisons of land change beginning with a landscape baseline (circa 1974) derived from historical aerial photographs as well as a time series of Landsat-derived maps (1990, 2000, 2010, 2018). Results Results showed that post-colonial land tenure policies differentially influenced the pace and patterns of land use transitions across the landscape. Collectively, private ownership was associated with large reductions of forest (− 96%) and rangelands (− 40%) due to the expansion of croplands (+ 60%), especially after 2010. In the initial years, forest and rangeland fragmentation coincided with areas under privatization policies, whereas group ranches tended to exhibit greater homogenization alongside the disappearance of small forest patches. In the recent years, private lands become increasingly homogeneous as cropland aggregate while group ranches become fragmented following cropland expansion. Conclusions The changes in spatial patterns affect livelihoods of agropastoralists, either by modifying habitat availability or movement that supports adaptive capacity during periods of stress. Because policy legacies continue to shape land use trajectories and patterns, the study suggests that integrating policy analysis into landscape ecological research is critical for interpreting contemporary landscape services.
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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 | 0.000 | 0.001 |
| 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".