Modelling the cost-effectiveness of mitigation methods for multiple pollutants at farm scale

Area of Science:

• Environmental Science
• Agricultural Science
• Environmental Management

Background:

• Agricultural pollution reduction is critical for meeting national and international policy targets for air and water quality.
• Existing studies often focus on individual mitigation methods and pollutants, limiting comprehensive solutions.
• A need exists for integrated approaches to assess multiple methods and pollutants for optimal pollution control.

Purpose of the Study:

• To present a conceptual model for synthesizing evidence on multiple mitigation methods and pollutants.
• To develop a farm-scale decision support tool for identifying least-cost solutions for multiple policy objectives.
• To demonstrate the tool's application in targeting diffuse pollution control in agriculture.

Main Methods:

• Developed a conceptual model for evidence synthesis.
• Implemented a farm-scale decision support tool incorporating a genetic algorithm for multi-objective optimization.
• Quantified baseline pollutant losses from identifiable sources, areas, and pathways.
• Demonstrated the tool using data from two contrasting farm systems in England and Wales.

Main Results:

• The decision support tool effectively quantifies baseline pollutant losses and identifies optimal suites of mitigation methods.
• Demonstration cases show the tool's utility in targeting mitigation options for diffuse agricultural pollution.
• The tool is generic and adaptable, allowing for the integration of measured data and expanded mitigation libraries.

Conclusions:

• The developed tool provides a novel approach to optimizing agricultural pollution control strategies.
• It can assist in targeting mitigation efforts for diffuse pollution, supporting policy objectives.
• The tool has potential as part of the farm advisory process, facilitating informed decision-making.