Estimating Field-Level Rotations as Dynamic Cycles
Crop rotation systems are an important part of agricultural production for managing pests, diseases, and soil fertility. Recent interest in sustainable agriculture focuses on low input-use practices which require knowledge of the underlying dynamics of production and rotation systems. Policies to limit chemical application depending on proximity to waterways and flood management require field-level data and analysis. Additionally, many supply estimates of crop production omit the dynamic effects of crop rotations. We estimate a dynamic programming model of crop rotation which incorporates yield and cost intertemporal effects in addition to field-specific factors including salinity and soil quality. Using an Optimal Matching algorithm from the Bioinformatics literature, we determine empirically observed rotations using a geo-referenced panel dataset of 14,000 fields over 13 years. We estimate the production parameters which satisfy the Euler equations of the field-level rotation problem and solve an empirically observed four-crop rotation.
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