Spatial Disaggregation of Agricultural Production Data

In this paper we develop a dynamic data-consistent way for estimating agricultural land use choices at a disaggregate level (district-level), using more aggregate data (regional-level). The disaggregation procedure requires two steps. The first step consists in specifying and estimating a dynamic model of land use at the regional level. In the second step, we disaggregate outcomes of the aggregate model using maximum entropy (ME). The ME disaggregation procedure is applied to a sample of California data. The sample includes 6 districts located in Central Valley and 8 possible crops, namely: Alfalfa, Cotton, Field, Grain, Melons, Tomatoes, Vegetables and Subtropical. The disaggregation procedure enables the recovery of land use at the district-level with an out-sample prediction error of 16%. This result shows that the micro behavior, inferred from aggregate data with our disaggregation approach, seems to be consistent with observed behavior.