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Estimating Field-Level Rotations as Dynamic Cycles

In: Applied Methods for Agriculture and Natural Resource Management

Author

Listed:
  • Duncan MacEwan

    (ERA Economics)

  • Richard E. Howitt

    (ERA Economics
    University of California at Davis)

Abstract

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.

Suggested Citation

  • Duncan MacEwan & Richard E. Howitt, 2019. "Estimating Field-Level Rotations as Dynamic Cycles," Natural Resource Management and Policy, in: Siwa Msangi & Duncan MacEwan (ed.), Applied Methods for Agriculture and Natural Resource Management, chapter 0, pages 145-169, Springer.
    • Handle: RePEc:spr:nrmchp:978-3-030-13487-7_9
    DOI: 10.1007/978-3-030-13487-7_9
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