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Production and spatial distribution of switchgrass and miscanthus in the United States under uncertainty and sunk cost

Author

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  • Dumortier, Jerome
  • Kauffman, Nathan
  • Hayes, Dermot J.
Abstract
The U.S. cellulosic biofuel mandate has not been enforced in recent years. Uncertainty surrounding the enforcement of the mandate in addition to high production and harvest cost have contributed to a delay in the widespread planting of bioenergy crops such as switchgrass and miscanthus. Previous literature has shown that under uncertainty and sunk cost, an investment threshold is further increased due to the value associated from holding the investment option. In this paper, we extend the previous literature by applying a real option switching model to bioenergy crop production. First, we calculate the county-level break-even price which triggers a switching away from traditional field crops (corn, soybeans, and wheat) to bioenergy crops under various scenarios differing by commodity prices, production cost and biomass price expectations. We show that the resulting break-even prices at the county-level can be substantially higher than previously estimated due to the inclusion of the option value. In a second step, we identify counties that are most likely to grow switchgrass or miscanthus by simulating a stochastic biomass price over time. Our results highlight two issues: First, switchgrass or miscanthus are not grown in the Midwest under any scenario. Under low agricultural residue removal rates, biomass crops are mostly grown in the Southeast. Second, under the assumption of a high removal rates, bioenergy crops are not grown anywhere in the U.S. since the cellulosic biofuel mandate can be covered by agricultural residues.

Suggested Citation

  • Dumortier, Jerome & Kauffman, Nathan & Hayes, Dermot J., 2017. "Production and spatial distribution of switchgrass and miscanthus in the United States under uncertainty and sunk cost," ISU General Staff Papers 201709010700001686, Iowa State University, Department of Economics.
    • Handle: RePEc:isu:genstf:201709010700001686
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    Cited by:

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    2. Adkins, Roger & Paxson, Dean, 2019. "Rescaling-contraction with a lower cost technology when revenue declines," European Journal of Operational Research, Elsevier, vol. 277(2), pages 574-586.
    3. Chris B. Zou & Lixia H. Lambert & Josh Everett & Rodney E. Will, 2022. "Response of Surface Runoff and Sediment to the Conversion of a Marginal Grassland to a Switchgrass ( Panicum virgatum ) Bioenergy Feedstock System," Land, MDPI, vol. 11(4), pages 1-15, April.
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    5. Sharma, Bijay P. & Khanna, Madhu & Miao, Ruiqing, 2022. "Designing Efficient Payments to Incentivize GHG Mitigation Using Energy Crops," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322361, Agricultural and Applied Economics Association.
    6. Hanson, Eilish R. & Nagler, Amy & Ritten, John & Rashford, Benjamin, 2022. "Farm-Level Economics of Bioenergy in the Upper Missouri River Basin," Journal of the ASFMRA, American Society of Farm Managers and Rural Appraisers, vol. 2022.
    7. Jerome Dumortier & Amani Elobeid, 2020. "Assessment of Carbon Tax Policies: Implications on U.S. Agricultural Production and Farm Income," Center for Agricultural and Rural Development (CARD) Publications 20-wp606, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    8. Madhu Khanna & Ruiqing Miao, 2022. "Inducing the adoption of emerging technologies for sustainable intensification of food and renewable energy production: insights from applied economics," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 66(1), pages 1-23, January.
    9. Jiahong Yuan & Xiaoyu Li & Zilai Sun & Junhu Ruan, 2021. "Will the Adoption of Early Fertigation Techniques Hinder Famers’ Technology Renewal? Evidence from Fresh Growers in Shaanxi, China," Agriculture, MDPI, vol. 11(10), pages 1-17, September.
    10. Burli, Pralhad & Lal, Pankaj & Wolde, Bernabas & Jose, Shibu & Bardhan, Sougata, 2019. "Factors affecting willingness to cultivate switchgrass: Evidence from a farmer survey in Missouri," Energy Economics, Elsevier, vol. 80(C), pages 20-29.
    11. Dumortier, Jerome, 2024. "Vehicle electrification and fuel economy policies: Impacts on agricultural land-use in the United States," Land Use Policy, Elsevier, vol. 141(C).
    12. Ewelina Olba-Zięty & Mariusz Jerzy Stolarski & Michał Krzyżaniak, 2021. "Economic Evaluation of the Production of Perennial Crops for Energy Purposes—A Review," Energies, MDPI, vol. 14(21), pages 1-16, November.

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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

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