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Updated: Jun 12, 2026
11:53
Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
Published on: December 9, 2012
Adaptive self-organization of global swidden forests
Sean S Downey1,2,3, Denis Tverskoi4, Shane A Scaggs1,5
1Department of Anthropology, The Ohio State University, Columbus, OH 43210.
Summary
Swidden agriculture exhibits adaptive self-organization, where cultural practices and environmental feedback create balanced landscapes. This study reveals power law patterns in patch sizes, driven by social norms and spatial synchronization.
Area of Science:
- Socio-ecological systems
- Environmental science
- Anthropology
Background:
- Swidden agriculture is a complex human-natural system.
- Understanding its self-organization is key to sustainable land management.
Purpose of the Study:
- To investigate quantitative signatures of adaptive self-organization in swidden agriculture.
- To model the socio-ecological dynamics driving landscape patterns.
Main Methods:
- Analysis of 18,000+ swidden patches from remote sensing data.
- Development of an ethnographically informed agent-based model.
- Statistical analysis of patch size distributions and spatial correlations.
Main Results:
- 16 of 18 swidden areas showed power law patch size distributions (scaling exponent ≈1).
- Spatial synchronization of sites drives power laws; social norms guide landscape disturbance.
- Both mechanisms are crucial for maintaining high harvests and ecosystem productivity.
Conclusions:
- Swidden agriculture demonstrates adaptive self-organization.
- Agent-based modeling reveals mechanisms of landscape pattern formation.
- Adaptive self-organization may be a general characteristic of coupled human and natural systems.
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