Simulating the spatial distribution of pollutant loads from pig farming using an agent-based modeling approach

Area of Science:

• Environmental Science
• Agricultural Science
• Computational Modeling

Background:

• Pig farming is a significant source of environmental pollution.
• Understanding farmer behavior is crucial for effective environmental management in agriculture.
• Existing models may not fully capture the complexities of farmer decision-making and its environmental impact.

Purpose of the Study:

• To develop and validate an agent-based model (ABM) for simulating pollutant loads from pig farming.
• To assess the impact of different policy and management scenarios on environmental pollutant discharge.
• To identify key factors influencing farmer behavior regarding manure management.

Main Methods:

• Developed an agent-based model (ABM) incorporating the theory of planned behavior for farmer agents.
• Included household agents, land patches, and environmental parameters in the model.
• Validated the ABM using sensitivity analysis and household survey data.

Main Results:

• The agent-based model demonstrated good predictive ability.
• A positive scenario with enhanced environmental policies reduced pollutant loads in many sub-basins.
• A negative scenario with increased pig numbers and no management improvement led to higher pollutant discharge.

Conclusions:

• Financial and technical support for pig producers is essential for sustainable development and environmental protection.
• Farmer experience and education significantly influence manure management behaviors.
• Environmental communication and awareness-raising are vital alongside technical measures for effective pollution control.