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Handbook of Bioenergy Economics and Policy: Volume II pp 373–399Cite as

Effects of Liquidity Constraints, Risk and Related Time Effects on the Adoption of Perennial Energy Crops

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Part of the book series: Natural Resource Management and Policy ((NRMP,volume 40))

Abstract

This chapter highlights the crucial role of liquidity, risk, and related time effects in explaining farmers’ willingness to grow perennial energy crops as a renewable energy source. I first review the scarce empirical evidence from surveys and focus groups about how liquidity constraints hinder adoption, and present additional results from simulation approaches based on optimization models. Then, I evaluate the extent to which perennial energy crops can be considered as a risky enterprise, and emphasize the importance of assessing risks at farm level to uncover potential diversification benefits. I also show how time considerations generate further related issues, due to intertemporal fluctuations in the income stream, investment irreversibility, and land reallocation. This chapter also highlights relevant policy and contract schemes to overcome the barriers to adoption described above. Establishment grants and cash advance systems are widespread and efficient ways of limiting liquidity effects on adoption as long as moral hazards are managed and conversion back to conventional crops is discouraged. Risk barriers are mostly managed through private long-term production contracts between farmers and biomass processors.

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Notes

  1. 1.

    See Sect. 2.1 of Chap. “Innovation in agriculture…” for a review of theories on technology adoption.

  2. 2.

    The large up-front costs of miscanthus are offset by a high productivity compared to switchgrass (Heaton et al. 2008; Smeets et al. 2009; Boehmel et al. 2008).

  3. 3.

    Tall perennial cane suitable for the Mediterranean region.

  4. 4.

    See section “Risk and uncertainty” of chapter “Innovation in Agriculture: Incentives for Adoption and Development of a Supply Chain for Energy Crops” for more details.

  5. 5.

    See section “Risk and uncertainty” of chapter “Innovation in Agriculture: Incentives for Adoption and Development of a Supply Chain for Energy Crops” for more details about the role of information acquisition and experience in reducing uncertainty.

  6. 6.

    See Hardaker and Gudbrand (2010) for a review of challenges in eliciting probability expectations.

  7. 7.

    Time series econometrics have shown that the linkage between fuel and food prices depends on location, the food and fuels considered, the modeling specification, and whether the data is daily, weekly, or monthly. See Zilberman et al. (2013) and Janda et al. (2012) for a review.

  8. 8.

    If the perennial is a new crop, the experience the farmer gains over time can mitigate these increasing fluctuations.

  9. 9.

    Amacher et al. (2009) gave a very good review of Faustmann models, including assumptions and properties. The authors also provide a comparison with alternatives such as the single-rotation model.

  10. 10.

    See for instance Monti et al. (2007), Larson et al. (2008), Khanna et al. (2008) and Bocquého and Jacquet (2010) for switchgrass; Khanna et al. (2008), Deverell et al. (2009) and Bocquého and Jacquet (2010) for miscanthus; Gasol et al. (2010) and Kasmioui and Ceulemans (2012) for woody crops; and Carriquiry et al. (2011) for a general cost review.

  11. 11.

    Policy and contract tools that are not specifically targeted to liquidity or risk issues are out of the scope of this chapter. Crop insurance schemes and indirect funding through support to renewable energy production are not considered either. A wider range of tools to overcome barriers to adoption is described in Sect. 4.4 of Chap. “Innovation in agriculture…” and in Chap. “Contracting farming in biofuel sector…”.

  12. 12.

    For more theoretical insights about contract farming, please refer to section “A theoretic perspective of contract farming” of chapter “Contracting farming in biofuel sector: A survey”.

  13. 13.

    See Sect. 3 and 4 of Chap. “Innovation in agriculture…” for a complete review of factors affecting energy crop adoption.

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  1. LEF, AgroParisTech, INRA, Nancy, 54000, France

    Géraldine Bocquého

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Editors and Affiliations

  1. Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA

    Madhu Khanna

  2. Department of Agricultural and Resource Economics, University of California at Berkeley, Berkeley, California, USA

    David Zilberman

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Bocquého, G. (2017). Effects of Liquidity Constraints, Risk and Related Time Effects on the Adoption of Perennial Energy Crops. In: Khanna, M., Zilberman, D. (eds) Handbook of Bioenergy Economics and Policy: Volume II. Natural Resource Management and Policy, vol 40. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-6906-7_15

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