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On Technological Change in Crop Yields

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  • Tor N. Tolhurst
  • Alan P. Ker

Abstract

Technological changes in agriculture tend to alter the mass associated with segments or components of the yield distribution as opposed to simply shifting the entire distribution upwards. We propose modeling crop yields using mixtures with embedded trend functions to account for potentially different rates of technological change in different components of the yield distribution. By doing so we can test some interesting and previously untested hypotheses about the data generating process of yields. For example: (1) is the rate of technological change equivalent across components, and (2) are the probabilities of components constant over time? Our results-technological change is not equivalent across components and probabilities tend not to have changed significantly over time-have implications for modeling yields. We find estimated conditional yield densities are quite different when unique trend functions are embedded inside the mixture components versus estimating the same mixture with detrended data. Also, we prove different rates of technological change in different components lead to nonconstant variance with respect to time (i.e., heteroscedasticity). We present two applications of the proposed yield model. The first application considers climate determinants of component membership, where our results are consistent with the literature for climate determinants of yields. The second application compares the proposed yield model to USDA's current rating methodology for area-yield crop insurance contracts and finds the proposed model may lead to more accurate rates.

Suggested Citation

  • Tor N. Tolhurst & Alan P. Ker, 2015. "On Technological Change in Crop Yields," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 97(1), pages 137-158.
  • Handle: RePEc:oup:ajagec:v:97:y:2015:i:1:p:137-158.
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    File URL: http://hdl.handle.net/10.1093/ajae/aau082
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    References listed on IDEAS

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    Cited by:

    1. Shen, Zhiwei, 2016. "Adaptive local parametric estimation of crop yields: implication for crop insurance ratemaking," 156th Seminar, October 4, 2016, Wageningen, The Netherlands 249984, European Association of Agricultural Economists.
    2. Ramirez, Octavio A. & Shonkwiler, J. Scott, 2017. "A Probabilistic Model of Crop Insurance Purchase Decision," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 0(Number 1), pages 1-17, January.
    3. Ker, Alan. P & Tolhurst, Tor & Liu, Yong, 2015. "Rating Area-yield Crop Insurance Contracts Using Bayesian Model Averaging and Mixture Models," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 205211, Agricultural and Applied Economics Association.
    4. ODonoghue, Erik & Tulman, Sarah, 2016. "The Demand for Crop Insurance: Elasticity and the Effect of Yield Shocks," 2016 Annual Meeting, July 31-August 2, Boston, Massachusetts 235623, Agricultural and Applied Economics Association.
    5. Santeramo, Fabio Gaetano, 2016. "I Learn, You Learn, We Gain. Experience in Crop Insurance Markets," MPRA Paper 86379, University Library of Munich, Germany.
    6. repec:eee:agiwat:v:203:y:2018:i:c:p:333-343 is not listed on IDEAS

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