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Pixel Level Cropland Allocation and Marginal Impacts of Biophysical Factors

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  • Song, Jingyu
  • Delgado, Michael
  • Preckel, Paul
  • Villoria, Nelson

Abstract

Despite substantial research and policy interest in pixel level cropland allocation data, few sources are available that span a large geographic area. The data used for much of this research are often derived from complex modeling techniques that may include model simulation and other data processing. We develop a transparent econometric framework that uses pixel level biophysical measurements and aggregate cropland statistics to develop pixel level cropland allocation predictions. Validation exercises show that our approach is effective at predicting cropland allocation at multiple levels of resolution. In addition, the model provides marginal effects of changes in climate and biophysical factors on cropland allocation at the pixel level that can be used in a variety of research and policy contexts.

Suggested Citation

  • Song, Jingyu & Delgado, Michael & Preckel, Paul & Villoria, Nelson, 2016. "Pixel Level Cropland Allocation and Marginal Impacts of Biophysical Factors," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235327, Agricultural and Applied Economics Association.
    • Handle: RePEc:ags:aaea16:235327
    DOI: 10.22004/ag.econ.235327
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    Cited by:

    1. Song, Jingyu & Delgado, Michael & Preckel, Paul, 2017. "Aggregated Fractional Regression Estimation: Some Monte Carlo Evidence," 2017 Annual Meeting, July 30-August 1, Chicago, Illinois 258209, Agricultural and Applied Economics Association.

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