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Predicting Soybean Yield with NDVI using a Flexible Fourier Transform Model

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  • Xu, Chang
  • Katchova, Ani

Abstract

We study how to incorporate the Normalized Difference Vegetation Index (NDVI) derived from remote sensing satellites to improve soybean yield predictions in ten major producing states in the United States. Unlike traditional methods which assume that a global OLS model applies to all observations, we account for geographical heterogeneity by using the Flexible Fourier Transform (FFT) model. Results show that there is considerable heterogeneity in how responsive soybean yield is to NDVI over the growing season. Out-of-sample cross-validation indicates that accounting for geographical heterogeneity improves the forecasts in terms of smaller prediction error compared to models assuming away geographical heterogeneity.

Suggested Citation

  • Xu, Chang & Katchova, Ani, 2018. "Predicting Soybean Yield with NDVI using a Flexible Fourier Transform Model," 2018 Annual Meeting, February 2-6, 2018, Jacksonville, Florida 266693, Southern Agricultural Economics Association.
    • Handle: RePEc:ags:saea18:266693
    DOI: 10.22004/ag.econ.266693
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    1. Enders, Walter & Li, Jing, 2015. "Trend-cycle decomposition allowing for multiple smooth structural changes in the trend of US real GDP," Journal of Macroeconomics, Elsevier, vol. 44(C), pages 71-81.
    2. Joshua Woodard, 2016. "Big data and Ag-Analytics," Agricultural Finance Review, Emerald Group Publishing Limited, vol. 76(1), pages 15-26, May.
    3. Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2016. "A new approach to modeling the effects of temperature fluctuations on monthly electricity demand," Energy Economics, Elsevier, vol. 60(C), pages 206-216.
    4. Fenton, Victor M. & Gallant, A. Ronald, 1996. "Qualitative and asymptotic performance of SNP density estimators," Journal of Econometrics, Elsevier, vol. 74(1), pages 77-118, September.
    5. Joseph Cooper & A. Nam Tran & Steven Wallander, 2017. "Testing for Specification Bias with a Flexible Fourier Transform Model for Crop Yields," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 99(3), pages 800-817.
    6. Ralf Becker & Walter Enders & Junsoo Lee, 2006. "A Stationarity Test in the Presence of an Unknown Number of Smooth Breaks," Journal of Time Series Analysis, Wiley Blackwell, vol. 27(3), pages 381-409, May.
    7. Joseph Cooper & A. Nam Tran & Steven Wallander, 2017. "Testing for Specification Bias with a Flexible Fourier Transform Model for Crop Yields," American Journal of Agricultural Economics, John Wiley & Sons, vol. 99(3), pages 800-817, April.
    8. Irwin, Scott H. & Sanders, Dwight R. & Good, Darrel L., 2014. "Evaluation of Selected USDA WAOB and NASS Forecasts and Estimates in Corn and Soybeans," Marketing and Outlook Research Reports 183477, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics.
    9. Kaul, Monisha & Hill, Robert L. & Walthall, Charles, 2005. "Artificial neural networks for corn and soybean yield prediction," Agricultural Systems, Elsevier, vol. 85(1), pages 1-18, July.
    10. A. Ronald Gallant, 1984. "The Fourier Flexible Form," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 66(2), pages 204-208.
    11. Irwin, Scott & Sanders, Dwight & Good, Darrel, 2014. "Evaluation of Selected USDA WAOB and NASS Forecasts and Estimates in Corn and Soybeans," farmdoc daily, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics, vol. 4, January.
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