Efficacy of Bomas (Kraals) in Mitigating Livestock Depredation in Maasai Mara Conservancies, Kenya
Bibliographic record
Abstract
Livestock depredation is a major conservation challenge globally, causing significant economic losses to pastoralists and threatening large carnivore species outside protected areas. Our study investigated the temporal and spatial distribution of livestock depredation incidences, carnivore species associated with livestock depredation, and assessed mitigation measures in Maasai Mara Conservancies in Southern Kenya. Using daily monitoring of livestock depredation cases, we made comparisons between livestock attacks occurring in predator-proof bomas and those with traditional kraals. A total of 305 livestock depredation incidents were recorded between January and December 2021, translating to a total tally of 1411 livestock maimed or killed. Most livestock depredation incidents occurred during the day (59%) as opposed to night (41%), but this difference was not significant. Livestock depredation incidents in the nighttime occurred mostly inside traditional kraals (34%) and occurred the least in predator-proof kraals (2%). Lions were responsible for more livestock attacks in the grazing fields compared with leopards, hyenas, and wild dogs. Hyenas were more daring and attacked livestock inside traditional bomas relative to lions and leopards. Our study concludes that predator-proof bomas are more effective in minimizing livestock depredation and can be embraced as a sound intervention for human–carnivore co-existence in communities’ wildlife conservation areas.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".