Socio-economic Support Optimization for Transition from Conventional to Organic Farming Using a Spatiotemporal Agent-based Model

Abstract

Over many decades, conventional farming has increased agricultural production, but in ways that have increased energy usage and environmental pollution as well. An alternative approach is to convert to organic systems, which can be more efficient in both natural resource use and energy consumption. However, this conversion will change the pattern of economic and social activity on farmed landscapes. This research examines this transformation as a complex system in a Multi-agent Model (or Agent-based Model (ABM)). The complexity of the system is due to the interactions between human and natural elements over time and space. We designed an ABM, coupled with a Geographical Information System (GIS), to create a powerful model in which agents are able to evaluate and make decisions about conversion over a period of 32 years. The focus was on economic factors, farmer characteristics, and community relationships, to increase not only economic and social sustainability, but also to reduce environmental pollution. An Ontario sub-watershed with predominantly agricultural land use was selected for this research. The GIS_ABM was applied to scenarios looking at governmental support, social communications, economic factors, and GHG emissions over space and time. Agents (small farm enterprises) are activated and adapt themselves by interacting with other agents. They process input data using knowledge-based rules, including whether to convert from conventional to organic farming or vice versa. Some of the scenarios to optimize economic/social subsidies and their simulation results are discussed in this paper.