Modeling Agricultural Supply Response Using Mathematical Programming and Crop Mixes

Mathematical programming models are widely used in agricultural sector analysis. However, the lack of micro-level data, as well as computational requirements, necessitate the aggregation of individual producers into representative units when working at the sectoral level. This usually leads to unrealistic extreme specialization in supply responses. In 1982, McCarl introduced the "historical crop mixes" approach to avoid extreme specialization. We extend this approach by generating additional synthetic crop mixes using supply response elasticities and systematically varied commodity prices. In addition to avoiding extreme specialization, this approach provides flexibility when future supply responses can be vastly different from past responses. An application to U.S. biofuel policy analysis is presented.