Exploring linkages between protected-area access and Kenyan pastoralist food security using a new agent-based model
Bibliographic record
Abstract
Pastoral communities living in the arid and semi-arid lands of Kenya raise livestock herds within highly patchy environments, and experience chronic food insecurity and inter-ethnic conflicts linked to resource access. For these primarily rural communities, livestock are a source of calories and income and are therefore crucial to achieving the United Nations’ Sustainable Development Goals (SDGs) associated with food security (SDG 2). Achieving sustainable improvements in household well-being in this region is contingent on understanding how diverse policy decisions complement or undermine the ability of pastoral households to raise livestock. Of near-term relevance is the question of reconciling food security with biodiversity conservation goals (SDG 15) across Kenya’s drylands, which are also known for their exceptional biodiversity. World over, protected areas are associated with diverse impacts on local communities. However, spatial variation in how these areas contribute to pastoral food security and household well-being across Kenya remain poorly understood. Using our newly developed model SPIRALL, we examined spatial variation in changes in household well-being that result when pastoral households across Kenya lose access to neighboring protected areas. SPIRALL is a country-scale, agent-based pastoral household decision-making model. We joined SPIRALL to L-Range, a model that simulates rangeland ecosystem functioning. The resulting coupled model simulates reciprocal interactions between pastoral households and the environment in Kenya and can be used as a scenario analysis tool to understand impacts of broadly defined policies on food security. Our scenario-based analysis showed that loss of protected-area access caused increases in rates of hunger, debt, and trans-boundary movements, particularly among non-sedentary and agropastoral households. These effects were spatially heterogeneous and influenced by county size and proximity to protected areas. We conclude by outlining the policy-implications result of the interactions between SDG 2 and SDG 15 in Kenya. We also highlight additional uses and avenues for improvement for SPIRALL.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".