Multiattribute optimization of farm plans to improve economic, environmental and social conditions on the example of a farm in the Chesapeake Bay

Abstract.

Modern sustainable agricultural systems are expected to achieve both economic and environmental performance targets. Geospatial analytical tools and precision agricultural data can help farmers and technical advisors design more sustainable farm landscapes. Because agricultural landscapes are highly variable, there are opportunities to enhance performance by modifying field layouts and cropping systems. For example, inclusion of perennial vegetative buffers in floodplains or other unprofitable parts of existing fields can create opportunities for new markets, but poses an operational challenge for farmers. To realize these benefits, a tool is needed that incorporates environmental and social factors in addition to financial profits in a clear and transparent way. In this study, economic, environmental and social factors were analyzed and value functions for multiattribute optimization were defined. The resulting landscape optimization tool has been used to analyze and suggest alternative field designs for a sample farm in the Chesapeake Bay area. The optimal solution is weak Pareto-optimal, meaning that not all factors can be maximized at once. Still, the results show that a compromise between profitability, nature preservation and community development exists. Further research could include a wider variety of sustainability factors and introduce finer spatial scale.