IDEAS home Printed from https://ideas.repec.org/p/ags/aaea13/151269.html
   My bibliography  Save this paper

On Technological Change in Crop Yields

Author

Listed:
  • Tolhurst, Tor
  • Ker, Alan P.

Abstract

Technological change in plant research rarely shifts the entire yield distribution upwards as assumed in the agricultural economics literature. Rather, technologies have been targeted at a specific subpopulation of the yield distribution--for example, drought resistant seeds or so-called racehorse seeds--therefore, it is unlikely technological advancements are equal across subpopulations. In this manuscript we introduce a mixture model of crop yields with an embedded trend function in the component means, which allows different rates of technological change in each mixture or subpopulation. By doing so, we can test some interesting hypotheses that have been previously untestable. While previous literature assumes an equivalent rate of technological change across subpopulations we reject the null in 84.0%, 82.3%, and 64.0% of the counties for corn, soybean, and wheat respectively. Conversely, with respect to stable subpopulations through time (i.e. climate change) we reject in only 12.0%, 5.4%, and 4.6% of the counties for corn, soybean, and wheat respectively. These results have implications for modelling yields, directing funds regarding plant science research, and explaining the prevalence of heteroscedasticity in yield data.

Suggested Citation

  • Tolhurst, Tor & Ker, Alan P., 2013. "On Technological Change in Crop Yields," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 151269, Agricultural and Applied Economics Association.
  • Handle: RePEc:ags:aaea13:151269
    as

    Download full text from publisher

    File URL: http://purl.umn.edu/151269
    Download Restriction: no

    Other versions of this item:

    References listed on IDEAS

    as
    1. Roger Claassen & Richard E. Just, 2010. "Heterogeneity and Distributional Form of Farm-Level Yields," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(1), pages 144-160.
    2. Miller, Edward M, 1977. "Risk, Uncertainty, and Divergence of Opinion," Journal of Finance, American Finance Association, vol. 32(4), pages 1151-1168, September.
    3. Robert Finger, 2010. "Revisiting the Evaluation of Robust Regression Techniques for Crop Yield Data Detrending," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(1), pages 205-211.
    4. Coble, Keith H. & Heifner, Richard G. & Zuniga, Manuel, 2000. "Implications Of Crop Yield And Revenue Insurance For Producer Hedging," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 25(02), December.
    5. Jerry R. Skees & J. Roy Black & Barry J. Barnett, 1997. "Designing and Rating an Area Yield Crop Insurance Contract," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(2), pages 430-438.
    6. Featherstone, Allen M. & Kastens, Terry L., 2000. "Non-Parametric and Semi-Parametric Techniques for Modeling and Simulating Correlated, Non-Normal Price and Yield Distributions: Applications to Risk Analysis in Kansas Agriculture," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 32(02), pages 267-281, August.
    7. Xiaodong Du & David A. Hennessy & Cindy L. Yu, 2012. "Testing Day's Conjecture that More Nitrogen Decreases Crop Yield Skewness," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(1), pages 225-237.
    8. Alan P. Ker & Keith Coble, 2003. "Modeling Conditional Yield Densities," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(2), pages 291-304.
    9. Richard E. Just & Quinn Weninger, 1999. "Are Crop Yields Normally Distributed?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 81(2), pages 287-304.
    10. Gallagher, Paul W., 1987. "U.S. Soybean Yields: Estimation and Forecasting with Non-Symmetric Disturbances," Staff General Research Papers Archive 10779, Iowa State University, Department of Economics.
    11. Clifton B. Luttrell & R. Alton Gilbert, 1976. "Crop Yields: Random, Cyclical, or Bunchy?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 58(3), pages 521-531.
    12. Ker, Alan P. & McGowan, Pat, 2000. "Weather-Based Adverse Selection And The U.S. Crop Insurance Program: The Private Insurance Company Perspective," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 25(02), December.
    13. Bruce J. Sherrick & Fabio C. Zanini & Gary D. Schnitkey & Scott H. Irwin, 2004. "Crop Insurance Valuation under Alternative Yield Distributions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(2), pages 406-419.
    14. Ardian Harri & Cumhur Erdem & Keith H. Coble & Thomas O. Knight, 2009. "Crop Yield Distributions: A Reconciliation of Previous Research and Statistical Tests for Normality," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 31(1), pages 163-182.
    15. Barnwal, Prabhat & Kotani, Koji, 2013. "Climatic impacts across agricultural crop yield distributions: An application of quantile regression on rice crops in Andhra Pradesh, India," Ecological Economics, Elsevier, vol. 87(C), pages 95-109.
    16. Ardian Harri & Keith H. Coble & Alan P. Ker & Barry J. Goodwin, 2011. "Relaxing Heteroscedasticity Assumptions in Area-Yield Crop Insurance Rating," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 93(3), pages 703-713.
    17. Octavio A. Ramírez & Tanya McDonald, 2006. "Ranking Crop Yield Models: A Comment," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 88(4), pages 1105-1110.
    18. Bailey Norwood & Matthew C. Roberts & Jayson L. Lusk, 2004. "Ranking Crop Yield Models Using Out-of-Sample Likelihood Functions," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 86(4), pages 1032-1043.
    19. Pease, James W., 1992. "A Comparison Of Subjective And Historical Crop Yield Probability Distributions," Southern Journal of Agricultural Economics, Southern Agricultural Economics Association, vol. 24(02), December.
    20. Arellano, M, 1987. "Computing Robust Standard Errors for Within-Groups Estimators," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 49(4), pages 431-434, November.
    21. Jesse Tack & Ardian Harri & Keith Coble, 2012. "More than Mean Effects: Modeling the Effect of Climate on the Higher Order Moments of Crop Yields," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 94(5), pages 1037-1054.
    22. Octavio A. Ramirez & Sukant Misra & James Field, 2003. "Crop-Yield Distributions Revisited," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(1), pages 108-120.
    23. Barry K. Goodwin & Alan P. Ker, 1998. "Nonparametric Estimation of Crop Yield Distributions: Implications for Rating Group-Risk Crop Insurance Contracts," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 80(1), pages 139-153.
    24. Pease, James W., 1992. "A Comparison of Subjective and Historical Crop Yield Probability Distributions," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 24(02), pages 23-32, December.
    25. Joseph Atwood & Saleem Shaik & Myles Watts, 2003. "Are Crop Yields Normally Distributed? A Reexamination," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 85(4), pages 888-901.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shen, Zhiwei, 2016. "Adaptive local parametric estimation of crop yields: implication for crop insurance ratemaking," 156th Seminar, October 4, 2016, Wagenigen, The Netherlands 249984, European Association of Agricultural Economists.
    2. ODonoghue, Erik & Tulman, Sarah, 2016. "The Demand for Crop Insurance: Elasticity and the Effect of Yield Shocks," 2016 Annual Meeting, July 31-August 2, 2016, Boston, Massachusetts 235623, Agricultural and Applied Economics Association.
    3. 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. 42(1), January.
    4. Santeramo, Fabio Gaetano, 2016. "I Learn, You Learn, We Gain. Experience in Crop Insurance Markets," MPRA Paper 86379, University Library of Munich, Germany.
    5. 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;Western Agricultural Economics Association.

    More about this item

    Keywords

    Crop Production/Industries;

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ags:aaea13:151269. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (AgEcon Search). General contact details of provider: http://edirc.repec.org/data/aaeaaea.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.