Drought frequency, conservancies, and pastoral household well-being
Bibliographic record
Abstract
Portions of group ranches of northern Kenya communally held by pastoralists have been removed from grazing to support wildlife and encourage tourism and the resources that follow. These community-based conservancies (CBCs) were designed to benefit CBC members through regular payments, potential for wages, improved security, etc. We used a coupled-systems simulation approach to quantify potential changes in livestock numbers and pastoral well-being associated with the presence of CBC core and buffer areas, and we did so under the current frequency of droughts and increased frequency associated with climate change. The interannual precipitation coefficient of variation (CV) for our focal CBCs in Samburu County was 22% (706 mm average precipitation). We altered precipitation variability to span from 10% to 60% CV while maintaining the average. Compared to a simulation with observed precipitation and all rangelands available, when herders did not use the CBC core areas and seasonally avoided buffer areas, there was an 11% decline in tropical livestock units supported. More predictable precipitation patterns supported more livestock and improved pastoral well-being. At CVs above 30%, dramatic declines in livestock populations were simulated. When drought was made moderately more frequent (i.e., CV from 22% to 27%) there was a 15% decline in the number of livestock. Members receive a variety of benefits as part of CBC communities, but payments are small for these CBCs, and most households do not receive payments. Our results suggest that, from an economic perspective alone, payments must be raised to make membership of residents in conservancies more tenable. Additional adaptive pathways and perhaps external supports will be needed in the future as the frequency of drought increases and livestock populations decrease.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".