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Title: Sensitivity Analysis of the APEX Model for Assessing Sustainability of Switchgrass Grown for Biofuel Production in Central Texas

Abstract

Crop simulation models are increasingly being used to understand the feasibility of large-scale cellulosic bio-fuel production along with the multi-dimensional impacts on environmental sustainability. However, how the uncertainty in model parameters impacts model performance for sustainability is unclear. In this case study, sensitivity analyses were conducted for three switchgrass sustainability metrics: total biomass production, nitrogen loss, and soil carbon change using the APEX (Agricultural Policy/Environmental eXtender) model. Fifteen out of the 45 parameters (25 crop growth (CROP) parameters and 20 additional model parameters (PARM)) were identified as influential for the three sustainability metrics for three lowland genotypes (WBC, AP13, and KAN) across two locations (Temple, TX, and Austin, TX). Our sensitivity results showed that parameter importance was not dependent on the genotypes but depended on the variables of interest, and differed only slightly between locations. Influential belowground-related CROP and PARM parameters were identified for each sustainability metric, indicating that belowground-related parameters are just as important as commonly measured aboveground CROP parameters. Further investigation of the linear or non-linear relationships and the two-way interactions between each of the individual influential parameters with the three sustainability metrics reflected the functions and characteristics within the APEX model and the interrelations among different processes.more » Strong interactions between the most influential parameters for total biomass, nitrogen loss, and soil carbon change also highlighted the importance of accurately setting these parameters. Identification of influential model parameters for switchgrass sustainability may help guide field measurements and provide further understanding of the interrelated processes in the APEX model. Moreover, future field experiments can be designed to measure these influential parameters and understand the non-linear relationships identified between influential parameters and response variables. More accurate model parameterization will help improve APEX model performance and our understanding of the possible underlying physiological mechanisms.« less

Authors:
ORCiD logo; ; ;
Publication Date:
Research Org.:
Univ. of Texas, Austin, TX (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Basic Energy Sciences (BES); National Science Foundation (NSF)
OSTI Identifier:
1402505
Alternate Identifier(s):
OSTI ID: 1502984; OSTI ID: 1594758
Grant/Contract Number:  
SC0014156; IOS-1444533
Resource Type:
Journal Article: Published Article
Journal Name:
BioEnergy Research
Additional Journal Information:
Journal Name: BioEnergy Research Journal Volume: 11 Journal Issue: 1; Journal ID: ISSN 1939-1234
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
09 BIOMASS FUELS; Panicum virgatum; Sustainability; Total biomass; Nitrogen loss; Soil carbon change; Belowground parameters; Base temperature; Optimum temperature; Uncertainty; Crop models

Citation Formats

Zhang, Li, Juenger, Thomas E., Osorio, Javier M., and Behrman, Kathrine D. Sensitivity Analysis of the APEX Model for Assessing Sustainability of Switchgrass Grown for Biofuel Production in Central Texas. United States: N. p., 2017. Web. doi:10.1007/s12155-017-9878-8.
Zhang, Li, Juenger, Thomas E., Osorio, Javier M., & Behrman, Kathrine D. Sensitivity Analysis of the APEX Model for Assessing Sustainability of Switchgrass Grown for Biofuel Production in Central Texas. United States. https://doi.org/10.1007/s12155-017-9878-8
Zhang, Li, Juenger, Thomas E., Osorio, Javier M., and Behrman, Kathrine D. 2017. "Sensitivity Analysis of the APEX Model for Assessing Sustainability of Switchgrass Grown for Biofuel Production in Central Texas". United States. https://doi.org/10.1007/s12155-017-9878-8.
@article{osti_1402505,
title = {Sensitivity Analysis of the APEX Model for Assessing Sustainability of Switchgrass Grown for Biofuel Production in Central Texas},
author = {Zhang, Li and Juenger, Thomas E. and Osorio, Javier M. and Behrman, Kathrine D.},
abstractNote = {Crop simulation models are increasingly being used to understand the feasibility of large-scale cellulosic bio-fuel production along with the multi-dimensional impacts on environmental sustainability. However, how the uncertainty in model parameters impacts model performance for sustainability is unclear. In this case study, sensitivity analyses were conducted for three switchgrass sustainability metrics: total biomass production, nitrogen loss, and soil carbon change using the APEX (Agricultural Policy/Environmental eXtender) model. Fifteen out of the 45 parameters (25 crop growth (CROP) parameters and 20 additional model parameters (PARM)) were identified as influential for the three sustainability metrics for three lowland genotypes (WBC, AP13, and KAN) across two locations (Temple, TX, and Austin, TX). Our sensitivity results showed that parameter importance was not dependent on the genotypes but depended on the variables of interest, and differed only slightly between locations. Influential belowground-related CROP and PARM parameters were identified for each sustainability metric, indicating that belowground-related parameters are just as important as commonly measured aboveground CROP parameters. Further investigation of the linear or non-linear relationships and the two-way interactions between each of the individual influential parameters with the three sustainability metrics reflected the functions and characteristics within the APEX model and the interrelations among different processes. Strong interactions between the most influential parameters for total biomass, nitrogen loss, and soil carbon change also highlighted the importance of accurately setting these parameters. Identification of influential model parameters for switchgrass sustainability may help guide field measurements and provide further understanding of the interrelated processes in the APEX model. Moreover, future field experiments can be designed to measure these influential parameters and understand the non-linear relationships identified between influential parameters and response variables. More accurate model parameterization will help improve APEX model performance and our understanding of the possible underlying physiological mechanisms.},
doi = {10.1007/s12155-017-9878-8},
url = {https://www.osti.gov/biblio/1402505}, journal = {BioEnergy Research},
issn = {1939-1234},
number = 1,
volume = 11,
place = {United States},
year = {Mon Oct 23 00:00:00 EDT 2017},
month = {Mon Oct 23 00:00:00 EDT 2017}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at https://doi.org/10.1007/s12155-017-9878-8

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