DayCent data and results for "Robust paths to net greenhouse gas mitigation and negative emissions via advanced biofuels"

Field, John L; Smithwick, E. A.; Cai, H; Laser, M. S.; LeBauer, D. S.; Long, S. P.; Paustian, K.; Qin, Z.; Sheehan, J. J.; Smith, P.; Wang, M. Q.; Lynd, L. R.

doi:10.6084/m9.figshare.5760768

Title: DayCent data and results for "Robust paths to net greenhouse gas mitigation and negative emissions via advanced biofuels"

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Abstract

This zip file contains a UNIX-format DayCent model executable, input files, automation code, and associated directory structure necessary to re-produce the DayCent analysis underlying the manuscript. The main script 'autodaycent.py' (written for Python 2.7) opens an interactive command line routine that facilitates: * Calibrating the DayCent pine growth model. * Initializing DayCent for a set of case studies sites. * Executing an ensemble of model runs representing case study site reforestation, grassland restoration, or conversion to switchgrass cultivation. * Results analysis & generation of manuscript Fig. 3. Note that the interactive analysis code requires that all input files to be contained in the directory structure as uploaded, without modification. Executable versions of the DayCent model (https://www2.nrel.colostate.edu/projects/daycent/) compatible with other operating systems are available upon request.

Authors:

Field, John L; Smithwick, E. A.; Cai, H; Laser, M. S.; LeBauer, D. S.; Long, S. P.; Paustian, K.; Qin, Z.; Sheehan, J. J.; Smith, P.; Wang, M. Q.; Lynd, L. R.

Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Bioenergy Innovation (CBI)

Publication Date:: Tue Aug 18 00:00:00 UTC 2020

DOE Contract Number:: AC05-00OR22725

Research Org.:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Center for Bioenergy Innovation (CBI)

Sponsoring Org.:: USDOE Office of Science (SC), Biological and Environmental Research (BER)

Subject:: 09 BIOMASS FUELS; Ecosystem modeling, Daycent model, negative emissions, natural climate solutions

OSTI Identifier:: 3012650

DOI:: https://doi.org/10.6084/m9.figshare.5760768

Citation Formats


                    Field, John L, Smithwick, E. A., Cai, H, Laser, M. S., LeBauer, D. S., Long, S. P., Paustian, K., Qin, Z., Sheehan, J. J., Smith, P., Wang, M. Q., and Lynd, L. R. DayCent data and results for "Robust paths to net greenhouse gas mitigation and negative emissions via advanced biofuels".  United States: N. p., 2020. 
        Web.  doi:10.6084/m9.figshare.5760768.

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                    Field, John L, Smithwick, E. A., Cai, H, Laser, M. S., LeBauer, D. S., Long, S. P., Paustian, K., Qin, Z., Sheehan, J. J., Smith, P., Wang, M. Q., & Lynd, L. R. DayCent data and results for "Robust paths to net greenhouse gas mitigation and negative emissions via advanced biofuels".  United States.  doi:https://doi.org/10.6084/m9.figshare.5760768

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                    Field, John L, Smithwick, E. A., Cai, H, Laser, M. S., LeBauer, D. S., Long, S. P., Paustian, K., Qin, Z., Sheehan, J. J., Smith, P., Wang, M. Q., and Lynd, L. R. 2020.  
"DayCent data and results for "Robust paths to net greenhouse gas mitigation and negative emissions via advanced biofuels"".  United States.  doi:https://doi.org/10.6084/m9.figshare.5760768.  https://www.osti.gov/servlets/purl/3012650. Pub date:Tue Aug 18 00:00:00 UTC 2020

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@article{osti_3012650,

  title        = {DayCent data and results for "Robust paths to net greenhouse gas mitigation and negative emissions via advanced biofuels"},

  author       = {Field, John L and Smithwick, E. A. and Cai, H and Laser, M. S. and LeBauer, D. S. and Long, S. P. and Paustian, K. and Qin, Z. and Sheehan, J. J. and Smith, P. and Wang, M. Q. and Lynd, L. R.},

  abstractNote = {This zip file contains a UNIX-format DayCent model executable, input files, automation code, and associated directory structure necessary to re-produce the DayCent analysis underlying the manuscript. The main script 'autodaycent.py' (written for Python 2.7) opens an interactive command line routine that facilitates: * Calibrating the DayCent pine growth model. * Initializing DayCent for a set of case studies sites. * Executing an ensemble of model runs representing case study site reforestation, grassland restoration, or conversion to switchgrass cultivation. * Results analysis & generation of manuscript Fig. 3. Note that the interactive analysis code requires that all input files to be contained in the directory structure as uploaded, without modification. Executable versions of the DayCent model (https://www2.nrel.colostate.edu/projects/daycent/) compatible with other operating systems are available upon request.},

  doi          = {10.6084/m9.figshare.5760768},

  journal      = {},

  number       = ,

  volume       = ,

  place        = {United States},

  year         = {Tue Aug 18 00:00:00 UTC 2020},

  month        = {Tue Aug 18 00:00:00 UTC 2020}

}

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DOI: https://doi.org/10.6084/m9.figshare.5760768

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