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The relation between wheat, soybean, and hemp acreage: a Bayesian time series analysis

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  • Joohun Han

    (University of Arkansas)

  • John N. Ng’ombe

    (North Carolina A&T State University)

Abstract

The 2018 United States Farm Bill has opened the possibility for farmers to increase their profits through hemp cultivation. The literature suggests hemp has the potential to replace soybeans in soybean–wheat double-cropping because hemp shares key attributes of soybeans as a rotation crop (profitability, potential as an energy crop, and maintenance of soil fertility). Nonetheless, due to a short history of hemp cultivation in the USA, it is difficult to predict a time series relationship between hemp, soybean, and wheat through conventional approaches. In this article, we use Bayesian time series models and data from Statistics Canada and the Alberta Agricultural and Rural Development Department to examine a time series relationship between hemp, wheat, and soybean acreage and therefore predict farmers’ decision when hemp is a legal alternative agricultural commodity. Our results show evidence of complementary and substitution relationships for hemp–wheat and hemp–soybean, respectively. In addition, the results indicate a potential of hemp monoculture as a positive response to self-positive shock on hemp acreage that lasts for years.

Suggested Citation

  • Joohun Han & John N. Ng’ombe, 2023. "The relation between wheat, soybean, and hemp acreage: a Bayesian time series analysis," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 11(1), pages 1-12, December.
    • Handle: RePEc:spr:agfoec:v:11:y:2023:i:1:d:10.1186_s40100-023-00242-1
    DOI: 10.1186/s40100-023-00242-1
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    as
    1. Uhlig, Harald, 2005. "What are the effects of monetary policy on output? Results from an agnostic identification procedure," Journal of Monetary Economics, Elsevier, vol. 52(2), pages 381-419, March.
    2. Rebecca N. Kiwanuka-Lubinda & John N. Ng’ombe & Charles Machethe, 2021. "Impacts of interlocked contractual arrangements on dairy farmers’ welfare in Zambia: a robust Bayesian instrumental variable analysis," Agrekon, Taylor & Francis Journals, vol. 60(1), pages 10-30, January.
    3. Sims, Christopher A., 1988. "Bayesian skepticism on unit root econometrics," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 463-474.
    4. Christiano, Lawrence J. & Eichenbaum, Martin & Evans, Charles L., 1999. "Monetary policy shocks: What have we learned and to what end?," Handbook of Macroeconomics, in: J. B. Taylor & M. Woodford (ed.), Handbook of Macroeconomics, edition 1, volume 1, chapter 2, pages 65-148, Elsevier.
    5. Christopher A. Sims, 1989. "Models and Their Uses," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 71(2), pages 489-494.
    6. Hitaj, Claudia & Suttles, Shellye, 2016. "Trends in U.S. Agriculture's Consumption and Production of Energy: Renewable Power, Shale Energy, and Cellulosic Biomass," Economic Information Bulletin 262140, United States Department of Agriculture, Economic Research Service.
    7. Andrea Carriero & Todd E. Clark & Massimiliano Marcellino, 2015. "Bayesian VARs: Specification Choices and Forecast Accuracy," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(1), pages 46-73, January.
    8. Domenico Giannone & Michele Lenza & Giorgio E. Primiceri, 2015. "Prior Selection for Vector Autoregressions," The Review of Economics and Statistics, MIT Press, vol. 97(2), pages 436-451, May.
    9. Ivan Aleksandrovich Kopytin & Nikolay Petrovich Pilnik & Ivan Pavlovich Stankevich, 2021. "Modelling Five Variables BVAR for Economic Policies and Growth in Azerbaijan, Kazakhstan and Russia: 2005 2020," International Journal of Energy Economics and Policy, Econjournals, vol. 11(5), pages 510-518.
    10. Litterman, Robert B, 1986. "Forecasting with Bayesian Vector Autoregressions-Five Years of Experience," Journal of Business & Economic Statistics, American Statistical Association, vol. 4(1), pages 25-38, January.
    11. Bessler, David A. & Hopkins, Jane C., 1986. "Forecasting an agricultural system with random walk priors," Agricultural Systems, Elsevier, vol. 21(1), pages 59-67.
    12. Jinghao Ma & Yujie Shang & Hongyan Zhang & Zhihan Lv, 2021. "Application of Bayesian Vector Autoregressive Model in Regional Economic Forecast," Complexity, Hindawi, vol. 2021, pages 1-10, May.
    13. Mushtaq, Khalid & Dawson, P. J., 2002. "Acreage response in Pakistan: a co-integration approach," Agricultural Economics, Blackwell, vol. 27(2), pages 111-121, August.
    14. John C. Robertson & Ellis W. Tallman, 1999. "Vector autoregressions: forecasting and reality," Economic Review, Federal Reserve Bank of Atlanta, vol. 84(Q1), pages 4-18.
    15. Lüger, Tim, 2018. "A VAR evaluation of classical growth theory," Darmstadt Discussion Papers in Economics 231, Darmstadt University of Technology, Department of Law and Economics.
    16. Beatrice Dingha & Leah Sandler & Arnab Bhowmik & Clement Akotsen-Mensah & Louis Jackai & Kevin Gibson & Ronald Turco, 2019. "Industrial Hemp Knowledge and Interest among North Carolina Organic Farmers in the United States," Sustainability, MDPI, vol. 11(9), pages 1-17, May.
    17. Favero, Carlo A., 2001. "Applied Macroeconometrics," OUP Catalogue, Oxford University Press, number 9780198296850.
    18. Papana, A. & Kyrtsou, K. & Kugiumtzis, D. & Diks, C.G.H., 2014. "Identifying causal relationships in case of non-stationary time series," CeNDEF Working Papers 14-09, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    19. T. Randall Fortenbery & Michael Bennett, 2004. "Opportunities for Commercial Hemp Production," Review of Agricultural Economics, Agricultural and Applied Economics Association, vol. 26(1), pages 97-117.
    20. Nazlioglu, Saban & Erdem, Cumhur & Soytas, Ugur, 2013. "Volatility spillover between oil and agricultural commodity markets," Energy Economics, Elsevier, vol. 36(C), pages 658-665.
    21. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    22. Ayla OguÅŸ Binatli & Niloufer Sohrabji, 2019. "Monetary Policy Transmission in the Euro Zone," Athens Journal of Business & Economics, Athens Institute for Education and Research (ATINER), vol. 5(1), pages 79-92, January.
    23. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    24. Søren Johansen, 2005. "Interpretation of Cointegrating Coefficients in the Cointegrated Vector Autoregressive Model," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 67(1), pages 93-104, February.
    25. Schnitkey, Gary & Swanson, K. & Zulauf, Carl & Coppess, Jonathan & Paulson, Nick & Baltz, Jim, 2022. "Production from Double-Crop Soybean Rotations," farmdoc daily, University of Illinois at Urbana-Champaign, Department of Agricultural and Consumer Economics, vol. 12(71), May.
    26. Kilian,Lutz & Lütkepohl,Helmut, 2018. "Structural Vector Autoregressive Analysis," Cambridge Books, Cambridge University Press, number 9781107196575, January.
    27. Parvez, Ashak Mahmud & Lewis, Jonathan David & Afzal, Muhammad T., 2021. "Potential of industrial hemp (Cannabis sativa L.) for bioenergy production in Canada: Status, challenges and outlook," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    28. David A. Bessler & John L. Kling, 1986. "Forecasting Vector Autoregressions with Bayesian Priors," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 68(1), pages 144-151.
    29. Ng'ombe, John N. & Boyer, Tracy A., 2019. "Determinants of earthquake damage liability assignment in Oklahoma: A Bayesian Tobit censored approach," Energy Policy, Elsevier, vol. 131(C), pages 422-433.
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