Modeling Enrollment in the Conservation Reserve Program by Using Agents within Spatial Decision Support Systems: An Example from Southern Illinois

Abstract

Existing models of agricultural decisionmaking based on economic optimization often fall short of capturing the complex dynamics of land-use choices at both individual parcel and watershed-level scales. The complexity arises from an interplay of several factors, as explained by Herbert Simon's model of bounded rationality, the theory of diffusion of innovations through spatial contagion, the role of personal environmental values and local culture, and simple historical momentum. This complexity can be captured using ‘artificial life agents’ that model land-use choice for individual parcels by considering characteristics and personal beliefs of the owner or operator, physical traits of the land, and information obtained via social networks. Agents are therefore able to consider holistically a large number of factors affecting land-use choice. The creation of agent-based models of human behavior described herein is based upon empirical data on the acceptance of Conservation Reserve Program for the Cache River watershed of southern Illinois (USA). These models are interfaced with a geographic information system to produce a spatial decision support system capable of anticipating the effects of policies that affect land-use decisionmaking on a real landscape and their economic performance.

Investigating Impacts of Alternative Crop Market Scenarios on Land Use Change with an Agent-Based Model

“…This approach has several drawbacks [12,23]: (1) it generally assumes the maximization of a single objective (e.g., profit), which is often not appropriate when modeling the adoption of new technologies; (2) it ignores the social aspects of farm households such as communication and interaction among farmers in the same community; and (3) it does not properly capture the heterogeneity of the social behaviors and responses of farmers. In ABMs of agricultural land use decision making, mathematical programming is generally applied at the farm level and combined with heuristic approaches and Bayesian inference or Bayesian probability networks [23,[25][26][27]. Heuristic approaches, such as decision trees or rule-based models, assume limited human cognition, while optimization approaches, such as MP, assume that inefficiency in human decisions comes from external factors, such as the failure of institutions, imperfect markets, and lack of infrastructure or limited information [27].…”

“…In the Midwestern U.S., Sengupta et al [23] employed a hybrid approach to model land enrollment in an agricultural land set aside conservation program, CRP. Different types of farmers with distinct decision making rules were modeled in the Cache River watershed of southern Illinois.…”

“…In Midwestern agricultural landscapes, ABM has been applied in studies on how farmer decision making is impacted by agricultural policies, such as the CRP [23], potential market scenarios of biofuel crops [24], and price incentives for nitrogen and carbon abatement [25,26]. In the first two studies [23,24], the research is focused on how land use changes emerge from farmers' decisions under the assumption that the natural environment is static through time.…”

“…In the first two studies [23,24], the research is focused on how land use changes emerge from farmers' decisions under the assumption that the natural environment is static through time. In the latter two studies [25,26], the focus is on the impacts of farmers' management decisions on water quality.…”

ForestSim Model of Impacts of Smallholder Dynamics: Forested Landscapes of the Upper Peninsula of Michigan

“…Models combining GIS with agent-based models have been used to manage recreational opportunities in North American parks [70,71], to understand forest owners' decision-making in response to changes in tax structures and threats from invasive species in Maine [72], and to measure the impact of fuel wood collection on giant panda habitat in China [12] and the ecological impacts of enrollment patterns in the Conservation Reserve Program for agricultural areas in Illinois [73], among other uses [74,75]. However, given the static nature of most GIS data layers [42], incorporating parcelization when a parcel shapefile defines the agents is problematic.…”