Multi-objective optimization and design of farming systems

► The FarmDESIGN model aims to deal with complexity in mixed farm reconfiguration. ► It combines a bio-economic farm model and a multi-objective optimization algorithm. ► The model was implemented for a complex 96ha mixed farm. ► Trade-offs and synergies among four farmer-defined objectives were quantified. ► A set of farming options performing better in all objectives could be identified. Reconfiguration of farming systems to reach various productive and environmental objectives while meeting farm and policy constraints is complicated by the large array of farm components involved, and the multitude of interrelations among these components. This hampers the evaluation of relations between various farm performance indicators and of consequences of adjustments in farm management. Here we present the FarmDESIGN model, which has been developed to overcome these limitations by coupling a bio-economical farm model that evaluates the productive, economic and environmental farm performance, to a multi-objective optimization algorithm that generates a large set of Pareto-optimal alternative farm configurations. The model was implemented for a 96ha mixed organic farm in the Netherlands that represents an example with relevant complexity, comprising various crop rotations, permanent grasslands and dairy cattle. Inputs were derived from a number of talks with the farmers and from literature. After design-, output- and end-user validation the optimization module of the model was used to explore consequences of reconfiguration. The optimization aimed to maximize the operating profit and organic matter balance, and to minimize the labor requirement and soil nitrogen losses. The model outcomes showed that trade-offs existed among various objectives, and at the same time identified a collection of alternative farm configurations that performed better for all four objectives when compared to the original farm. Relatively small modifications in the farm configuration resulted in considerable improvement of farm performance. This modeling study demonstrated the usefulness of multi-objective optimization in the design of mixed farming systems; the potential of the model to support the learning and decision-making processes of farmers and advisers is discussed.