Figure 1.
Uncertainty and sensitivity analyses of model output.
Figure 2.
A framework for uncertainty and sensitivity analysis of ABMs of socioecological systems.
Applying variance decomposition to simplify a stochastic model (A), and maintain its exploratory power embodied in outcome variability (B) or improving its explanatory power by reducing its outcome variability (C).
Figure 3.
Study area in Michigan, U.S.
Figure 4.
Agent-based model of enrollment in Conservation Reserve Program.
Figure 5.
Soil rental rates (the southeast fragment of the study area).
Figure 6.
Benefit layers used to calculate six composite EBI surfaces.
Each EBI surface is a sum of one of the N1 layers, one of the N2 layers, and the N3 layer. All N1, N2, and N3 layers are standardized based on their respective point scales [78]. The remaining benefit criteria used in EBI calculation (vegetation and air quality) were not used due to their negligible role in the area of study.
Table 1.
Probability distributions for factors used in ABM simulations.
Figure 7.
Example output land use maps (A), and the frequency of agriculture-to-fallow conversion (B).
For clarity, only the southeast portion of the study area is shown.
Figure 8.
Results of uncertainty (A) and sensitivity (B) analysis for the output variable fallow land area.
Fallow land area is reported in map units (equivalent of 30 m). Factor labels used in text: number of offers accepted by the Farm Service Agency - n, payment reduction used by the farmer agent to increase offer competitiveness - BID, FA's decision rule - OWA, fraction of farmland enrolled in CRP - LAND, FA's retirement status - RETIREMENT, FA's value of production - PRODUCTION, land tenure - TENURE, density of enrollment in the neighborhood - DE, measurement of environmental benefits - EBI, factor interactions - I (Equation 3).