Investment risk in bioenergy crops
Abstract
Here, perennial, cellulosic bioenergy crops represent a risky investment. The potential for adoption of these crops depends not only on mean net returns, but also on the associated probability distributions and on the risk preferences of farmers. Using 6-year observed crop yield data from highly productive and marginally productive sites in the southern Great Lakes region and assuming risk neutrality, we calculate expected breakeven biomass yields and prices compared to corn (Zea mays L.) as a benchmark. Next we develop Monte Carlo budget simulations based on stochastic crop prices and yields. The crop yield simulations decompose yield risk into three components: crop establishment survival, time to maturity, and mature yield variability. Results reveal that corn with harvest of grain and 38% of stover (as cellulosic bioenergy feedstock) is both the most profitable and the least risky investment option. It dominates all perennial systems considered across a wide range of farmer risk preferences. Although not currently attractive for profit-oriented farmers who are risk neutral or risk averse, perennial bioenergy crops.
- Authors:
-
- Univ. of Florida, Wimauma, FL (United States)
- Michigan State Univ., East Lansing, MI (United States)
- Univ. of Wisconsin, Madison, WI (United States)
- Publication Date:
- Research Org.:
- Univ. of Florida, Wimauma, FL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER)
- OSTI Identifier:
- 1363930
- Grant/Contract Number:
- FC02-07ER64494
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Global Change Biology. Bioenergy
- Additional Journal Information:
- Journal Volume: 8; Journal Issue: 6; Journal ID: ISSN 1757-1693
- Publisher:
- Wiley
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 09 BIOMASS FUELS; bioenergy; cellulosic biomass; energy crops; investment analysis; Monte Carlo simulation; risk; stochastic budgeting
Citation Formats
Skevas, Theodoros, Swinton, Scott M., Tanner, Sophia, Sanford, Gregg, and Thelen, Kurt D. Investment risk in bioenergy crops. United States: N. p., 2015.
Web. doi:10.1111/gcbb.12320.
Skevas, Theodoros, Swinton, Scott M., Tanner, Sophia, Sanford, Gregg, & Thelen, Kurt D. Investment risk in bioenergy crops. United States. https://doi.org/10.1111/gcbb.12320
Skevas, Theodoros, Swinton, Scott M., Tanner, Sophia, Sanford, Gregg, and Thelen, Kurt D. Wed .
"Investment risk in bioenergy crops". United States. https://doi.org/10.1111/gcbb.12320. https://www.osti.gov/servlets/purl/1363930.
@article{osti_1363930,
title = {Investment risk in bioenergy crops},
author = {Skevas, Theodoros and Swinton, Scott M. and Tanner, Sophia and Sanford, Gregg and Thelen, Kurt D.},
abstractNote = {Here, perennial, cellulosic bioenergy crops represent a risky investment. The potential for adoption of these crops depends not only on mean net returns, but also on the associated probability distributions and on the risk preferences of farmers. Using 6-year observed crop yield data from highly productive and marginally productive sites in the southern Great Lakes region and assuming risk neutrality, we calculate expected breakeven biomass yields and prices compared to corn (Zea mays L.) as a benchmark. Next we develop Monte Carlo budget simulations based on stochastic crop prices and yields. The crop yield simulations decompose yield risk into three components: crop establishment survival, time to maturity, and mature yield variability. Results reveal that corn with harvest of grain and 38% of stover (as cellulosic bioenergy feedstock) is both the most profitable and the least risky investment option. It dominates all perennial systems considered across a wide range of farmer risk preferences. Although not currently attractive for profit-oriented farmers who are risk neutral or risk averse, perennial bioenergy crops.},
doi = {10.1111/gcbb.12320},
journal = {Global Change Biology. Bioenergy},
number = 6,
volume = 8,
place = {United States},
year = {Wed Nov 18 00:00:00 EST 2015},
month = {Wed Nov 18 00:00:00 EST 2015}
}
Web of Science
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