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Conditional Distributions of Crop Yields: A Bayesian Approach for Characterizing Technological Change

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  • Ramsey, A.

Abstract

What changes in the distribution of crop yields occur as a result of technological innovation? Viewing observed yields as random variables, estimation of the yield distribution conditional on time provides one approach for characterizing distributional transformation. Yields are also affected by weather and other covariates, spatial correlation, and a paucity of data in any one location. Common parametric and nonparametric methods rarely consider these aspects in a unified manner. Comprehensive solutions for describing the distribution of yields can be considered ideal. We implement a Bayesian spatial quantile regression model for the conditional distribution of yields that is distribution-free, includes weather (covariate) effects, smooths across space, and models the complete quantile process. Results provide insight into the temporal and spatial evolution of crop yields with implications for the measurement of technological change. Acknowledgement :

Suggested Citation

  • Ramsey, A., 2018. "Conditional Distributions of Crop Yields: A Bayesian Approach for Characterizing Technological Change," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 277253, International Association of Agricultural Economists.
    • Handle: RePEc:ags:iaae18:277253
    DOI: 10.22004/ag.econ.277253
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    1. Wagner Piazza Gaglianone & Luiz Renato Lima, 2012. "Constructing Density Forecasts from Quantile Regressions," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 44(8), pages 1589-1607, December.
    2. Ryu, Hang K. & Slottje, Daniel J., 1996. "Two flexible functional form approaches for approximating the Lorenz curve," Journal of Econometrics, Elsevier, vol. 72(1-2), pages 251-274.
    3. Jalan, Jyotsna & Ravallion, Martin, 2001. "Behavioral responses to risk in rural China," Journal of Development Economics, Elsevier, vol. 66(1), pages 23-49, October.
    4. Paul Gallagher, 1987. "U.S. Soybean Yields: Estimation and Forecasting with Nonsymmetric Disturbances," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 69(4), pages 796-803.
    5. Wolfram Schlenker & Michael J. Roberts, 2006. "Nonlinear Effects of Weather on Corn Yields," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 28(3), pages 391-398.
    6. 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.
    7. 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.
    8. Sanglestsawai, Santi & Rejesus, Roderick M. & Yorobe, Jose M., 2014. "Do lower yielding farmers benefit from Bt corn? Evidence from instrumental variable quantile regressions," Food Policy, Elsevier, vol. 44(C), pages 285-296.
    9. Wagner Piazza Gaglianone & Luiz Renato Lima, 2014. "Constructing Optimal Density Forecasts From Point Forecast Combinations," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 29(5), pages 736-757, August.
    10. Buchinsky, Moshe, 1994. "Changes in the U.S. Wage Structure 1963-1987: Application of Quantile Regression," Econometrica, Econometric Society, vol. 62(2), pages 405-458, March.
    11. john M. Antle, 2010. "Asymmetry, Partial Moments, and Production Risk," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 92(5), pages 1294-1309.
    12. Dries F. Benoit & Dirk Van den Poel, 2012. "Binary quantile regression: a Bayesian approach based on the asymmetric Laplace distribution," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(7), pages 1174-1188, November.
    13. Brian J. Reich, 2012. "Spatiotemporal quantile regression for detecting distributional changes in environmental processes," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 61(4), pages 535-553, August.
    14. 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.
    15. Wolfram Schlenker & W. Michael Hanemann & Anthony C. Fisher, 2006. "The Impact of Global Warming on U.S. Agriculture: An Econometric Analysis of Optimal Growing Conditions," The Review of Economics and Statistics, MIT Press, vol. 88(1), pages 113-125, February.
    16. Chak, Pok Man & Madras, Neal & Smith, Barry, 2005. "Semi-nonparametric estimation with Bernstein polynomials," Economics Letters, Elsevier, vol. 89(2), pages 153-156, November.
    17. Terrance M. Hurley & Xudong Rao & Philip G. Pardey, 2014. "Re-examining the Reported Rates of Return to Food and Agricultural Research and Development," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 96(5), pages 1492-1504.
    18. Tony Lancaster & Sung Jae Jun, 2010. "Bayesian quantile regression methods," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(2), pages 287-307.
    19. Vitor A. Ozaki & Sujit K. Ghosh & Barry K. Goodwin & Ricardo Shirota, 2008. "Spatio-Temporal Modeling of Agricultural Yield Data with an Application to Pricing Crop Insurance Contracts," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 90(4), pages 951-961.
    20. Sparger, John Adam & Norton, George W. & Heisey, Paul W. & Alwang, Jeffrey, 2013. "Is the share of agricultural maintenance research rising in the United States?," Food Policy, Elsevier, vol. 38(C), pages 126-135.
    21. Jayson L. Lusk & Jesse Tack & Nathan P. Hendricks, 2018. "Heterogeneous Yield Impacts from Adoption of Genetically Engineered Corn and the Importance of Controlling for Weather," NBER Chapters, in: Agricultural Productivity and Producer Behavior, pages 11-39, National Bureau of Economic Research, Inc.
    22. Michael J. Roberts & Wolfram Schlenker & Jonathan Eyer, 2013. "Agronomic Weather Measures in Econometric Models of Crop Yield with Implications for Climate Change," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 95(2), pages 236-243.
    23. Alan P. Ker & Tor N. Tolhurst & Yong Liu, 2016. "Bayesian Estimation of Possibly Similar Yield Densities: Implications for Rating Crop Insurance Contracts," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 98(2), pages 360-382.
    24. Kim, Tae-Hwan & White, Halbert, 2004. "On more robust estimation of skewness and kurtosis," Finance Research Letters, Elsevier, vol. 1(1), pages 56-73, March.
    25. Brian J. Reich & Luke B. Smith, 2013. "Bayesian Quantile Regression for Censored Data," Biometrics, The International Biometric Society, vol. 69(3), pages 651-660, September.
    26. Carl H. Nelson & Paul V. Preckel, 1989. "The Conditional Beta Distribution as a Stochastic Production Function," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 71(2), pages 370-378.
    27. Rejesus, Roderick M. & Coble, Keith H. & Miller, Mary France & Boyles, Ryan & Goodwin, Barry K & Knight, Thomas O., 2015. "Accounting for Weather Probabilities in Crop Insurance Rating," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 40(2), pages 1-19, May.
    28. Luke B. Smith & Brian J. Reich & Amy H. Herring & Peter H. Langlois & Montserrat Fuentes, 2015. "Multilevel quantile function modeling with application to birth outcomes," Biometrics, The International Biometric Society, vol. 71(2), pages 508-519, June.
    29. Zhu, Ying & Goodwin, Barry K. & Ghosh, Sujit K., 2011. "Modeling Yield Risk Under Technological Change: Dynamic Yield Distributions and the U.S. Crop Insurance Program," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 36(1), pages 1-19, April.
    30. 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.
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