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Agent-based modeling of bioenergy crop adoption and farmer decision-making

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  • Huang, Shiyang
  • Hu, Guiping
  • Chennault, Carrie
  • Su, Liu
  • Brandes, Elke
  • Heaton, Emily
  • Schulte, Lisa
  • Wang, Lizhi
  • Tyndall, John

Abstract

We formulate an agent-based simulation model to analyze farmers' decision-making in bioenergy crops adoption and predict related farmers' group behavior. Agents include farmers and biofuel producers, and each is represented with their own decision-making mechanism. We quantitatively model the decision-making of farmers in Iowa, USA as an optimization model based on values derived from published literature and social survey data; results were further validated with social survey data. Economic and environmental impacts of growing conventional row crops versus dedicated energy crops are considered and evaluated under a series of operational constraints including neighborhood influence. We apply the model to forecast biomass supply in the next three decades and reveal that Iowa has the potential to produce 2.27 billion gallons (8.59 billion liters) of cellulosic biofuel by 2022, which constitutes 14.2% of RFS2 cellulosic ethanol mandate. We also find improved publicity to be more efficient in promoting biomass production than raising the contract price. This paper provides a new approach to study farmers' behavior, and insights we derive on managerial operations may be of interest to bioenergy investors, producers, and government policy makers.

Suggested Citation

  • Huang, Shiyang & Hu, Guiping & Chennault, Carrie & Su, Liu & Brandes, Elke & Heaton, Emily & Schulte, Lisa & Wang, Lizhi & Tyndall, John, 2016. "Agent-based modeling of bioenergy crop adoption and farmer decision-making," Energy, Elsevier, vol. 115(P1), pages 1188-1201.
    • Handle: RePEc:eee:energy:v:115:y:2016:i:p1:p:1188-1201
    DOI: 10.1016/j.energy.2016.09.084
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    Cited by:

    1. Hongbin Liu & Mengyao Wu & Xinhua Liu & Jiaju Gao & Xiaojuan Luo & Yan Wu, 2021. "Simulation of Policy Tools’ Effects on Farmers’ Adoption of Conservation Tillage Technology: An Empirical Analysis in China," Land, MDPI, vol. 10(10), pages 1-23, October.
    2. Burli, Pralhad H. & Nguyen, Ruby T. & Hartley, Damon S. & Griffel, L. Michael & Vazhnik, Veronika & Lin, Yingqian, 2021. "Farmer characteristics and decision-making: A model for bioenergy crop adoption," Energy, Elsevier, vol. 234(C).
    3. Shang, Linmei & Heckelei, Thomas & Gerullis, Maria K. & Börner, Jan & Rasch, Sebastian, 2021. "Adoption and diffusion of digital farming technologies - integrating farm-level evidence and system interaction," Agricultural Systems, Elsevier, vol. 190(C).
    4. Alsaleh, Mohd & Abdul-Rahim, A.S. & Mohd-Shahwahid, H.O., 2017. "Determinants of technical efficiency in the bioenergy industry in the EU28 region," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 1331-1349.
    5. Nicholas R. Magliocca, 2020. "Agent-Based Modeling for Integrating Human Behavior into the Food–Energy–Water Nexus," Land, MDPI, vol. 9(12), pages 1-25, December.
    6. Leibensperger, Carrie & Yang, Pan & Zhao, Qiankun & Wei, Shuran & Cai, Ximing, 2021. "The synergy between stakeholders for cellulosic biofuel development: Perspectives, opportunities, and barriers," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    7. Guo, Miao & van Dam, Koen H. & Touhami, Noura Ouazzani & Nguyen, Remy & Delval, Florent & Jamieson, Craig & Shah, Nilay, 2020. "Multi-level system modelling of the resource-food-bioenergy nexus in the global south," Energy, Elsevier, vol. 197(C).

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