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Crop Yield Distributions: A Reconciliation of Previous Research and Statistical Tests for Normality


  • Ardian Harri
  • Cumhur Erdem
  • Keith H. Coble
  • Thomas O. Knight


This study revisits the large but inconclusive body of research on crop yield distributions. Using competing techniques across 3,852 crop/county combinations we can reconcile some inconsistencies in previous studies. We examine linear, polynomial, and ARIMA trend models. Normality tests are undertaken, with an implementable R-test and multivariate testing to account for spatial correlation. Empirical results show limited support for stochastic trends in yields. Results also show that normality rejection rates depend on the trend specification. Corn Belt corn and soybeans yields are negatively skewed while they tend to become more normal as one moves away from the Corn Belt. Copyright 2009, Oxford University Press.

Suggested Citation

  • 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.
  • Handle: RePEc:oup:revage:v:31:y:2009:i:1:p:163-182

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    Cited by:

    1. Li, Lisha, 2015. "Three essays on crop yield, crop insurance and climate change," ISU General Staff Papers 201501010800005371, Iowa State University, Department of Economics.
    2. Park, Seong C. & Brorsen, B. Wade & Stoecker, Arthur L. & Hattey, Jeffory A., 2012. "Forage Response to Swine Effluent: A Cox Nonnested Test of Alternative Functional Forms Using a Fast Double Bootstrap," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 44(04), pages 593-606, November.
    3. Niklaus Lehmann & Robert Finger & Tommy Klein & Pierluigi Calanca, 2013. "Sample Size Requirements for Assessing Statistical Moments of Simulated Crop Yield Distributions," Agriculture, MDPI, Open Access Journal, vol. 3(2), pages 1-11, April.
    4. Jean-Sauveur Ay, 2015. "Information sur l’hétérogénéité de la terre et délégation de la régulation foncière," Revue d'économie politique, Dalloz, vol. 125(3), pages 453-474.
    5. Yu, Tian, 2011. "Three essays on weather and crop yield," ISU General Staff Papers 201101010800002976, Iowa State University, Department of Economics.
    6. Agarwal, Sandip Kumar, 2017. "Subjective beliefs and decision making under uncertainty in the field," ISU General Staff Papers 201701010800006248, Iowa State University, Department of Economics.
    7. 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.
    8. Buchholz, Matthias & Musshoff, Oliver, 2014. "The role of weather derivatives and portfolio effects in agricultural water management," Agricultural Water Management, Elsevier, vol. 146(C), pages 34-44.
    9. Li, Xiaofei & Tack, Jesse B. & Coble, Keith H. & Barnett, Barry J., 2016. "Can Crop Productivity Indices Improve Crop Insurance Rates?," 2016 Annual Meeting, July 31-August 2, 2016, Boston, Massachusetts 235750, Agricultural and Applied Economics Association.
    10. Rejesus, Roderick M. & Marra, Michele C. & Roberts, Roland K. & English, Burton C. & Larson, James A. & Paxton, Kenneth W, 2013. "Changes in Producers’ Perceptions of Within-Field Yield Variability after Adoption of Cotton Yield Monitors," Journal of Agricultural and Applied Economics, Southern Agricultural Economics Association, vol. 45(02), May.
    11. Nolan, Elizabeth & Santos, Paulo, 2012. "Insurance premiums and GM traits," 2012 Conference, August 18-24, 2012, Foz do Iguacu, Brazil 125942, International Association of Agricultural Economists.
    12. Christopher N. Boyer & B. Wade Brorsen & Emmanuel Tumusiime, 2015. "Modeling skewness with the linear stochastic plateau model to determine optimal nitrogen rates," Agricultural Economics, International Association of Agricultural Economists, vol. 46(1), pages 1-10, January.
    13. Lysa Porth & Milton Boyd & Jeffrey Pai, 2016. "Reducing Risk Through Pooling and Selective Reinsurance Using Simulated Annealing: An Example from Crop Insurance," The Geneva Papers on Risk and Insurance Theory, Springer;International Association for the Study of Insurance Economics (The Geneva Association), vol. 41(2), pages 163-191, September.

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