Introducing pySRTS: The next generation of the Sub-Regional Timber Supply Model
Main Article Content
Keywords
forest economics model, SRTS, timber markets, U.S. South
Abstract
The Sub-Regional Timber Supply (SRTS) model has been a staple in the analysis and research of the Southeastern U.S. timber market for over two decades. In this paper, we take the existing model and modernize it through a ground-up rewrite and reimagining. The updated model includes adding new functionality, such as basin-level price premiums when solving the market equilibrium, and dynamic product weights in the harvest goal program. We document model assumptions, input data, and mathematical formulations in addition to identifying opportunities for users to modify and extend the model. By focusing on flexibility and modularity during the initial development, the pySRTS model is more adaptable to a wide range of research applications. We present a set of illustrative demand scenarios in which a new sawmill opens in North Carolina, U.S. In these scenarios, we explore how the new local prices and model parameterization interact to produce projections of future removals, prices, and forest inventories.
References
Abt RC, Cubbage FW, Abt KL. 2009. Projecting southern timber supply for multiple products by subregion. For Prod J. 59(7-8):7-16.
Abt RC, Cubbage FW, Pacheco G. 2000. Southern forest resource assessment using the subregional timber supply (SRTS) model. For Prod J. 50(4):25-33.
Bardon R. 2022. North Carolina standing timber price report - Q4 2022. NC State Extension Forestry. Available from: https://forestry.ces.ncsu.edu/documents/887/NCPR4q2022.pdf.
Brown R, Zhang D. 2005. The Sustainable Forestry Initiative's impact on stumpage markets in the US South. Can J For Res. 35:2056-2064. https://doi.org/10.1139/x05-135
Carter DR. 1992. Effects of supply and demand determinants on pulpwood stumpage quantity and price in Texas. For Sci. 38(3):652-660. https://doi.org/10.1093/forestscience/38.3.652
Creekmore LT. 2015. Modeling timber supply from planted forests in selected South American markets: applications of the SRTS modeling framework [master's thesis]. Raleigh (NC): North Carolina State University.
Dhungel G, Rossi D, Henderson JD, Abt RC, Sheffield R, Baker J. 2023. Critical market tipping points for high-grade white oak inventory decline in the Central Hardwood Region of the United States. J For. 121(3):224-234. https://doi.org/10.1093/jofore/fvad005
Forest Inventory and Analysis Database. 2026. St. Paul (MN): U.S. Department of Agriculture, Forest Service, Northern Research Station. Available from: https://apps.fs.usda.gov/fia/datamart/datamart.html.
Galik CS, Abt RC, Latta G, Vegh T. 2015. The environmental and economic effects of regional bioenergy policy in the Southeastern U.S. Energy Policy. 85:335-346. https://doi.org/10.1016/j.enpol.2015.05.018.
Guo Z, Hodges DG, Abt RC. 2011. Forest biomass supply for bioenergy production and its impacts on roundwood markets in Tennessee. South J Appl For. 35(2):80-86. https://doi.org/10.1093/sjaf/35.2.80
Harris CR, Millman KJ, van der Walt SJ, Gommers R, Virtanen P, Cournapeau D, Wieser E, et al. 2020. Array programming with NumPy. Nature. 585(7825):357-362. https://doi.org/10.1038/s41586-020-2649-2
Henderson JD, Abt RC, Abt KL. 2024. Forest carbon under increasing product demand and land use change in the US Southeast. For Policy Econ. 167:103296. https://doi.org/10.1016/j.forpol.2024.103296
Henderson JD, Abt RC, Abt KL, Baker J, Sheffield R. 2022. Impacts of hurricanes on forest markets and economic welfare: the case of Hurricane Michael. For Policy Econ. 140:102735. https://doi.org/10.1016/j.forpol.2022.102735
Henderson JD, Parajuli R, Abt RC. 2020. Biological and market responses of pine forests in the US Southeast to carbon fertilization. Ecol Econ. 169:106491. https://doi.org/10.1016/j.ecolecon.2019.106491
Latta GS, Baker JS, Ohrel S. 2018. A land use and resource allocation (LURA) modeling system for projecting localized forest CO2 effects of alternative macroeconomic futures. For Policy Econ. 87:35-48. https://doi.org/10.1016/j.forpol.2017.10.003
Liao X, Zhang Y. 2008. An econometric analysis of softwood production in the US South: a comparison of industrial and nonindustrial forest ownerships. For Prod J. 58(11):69-74.
Manner RH, Guo J, Clutter M, Sodiya OE, Abt RC, Sheffield RM, Baker JS. 2026. Globally informed and locally refined: market, management, and carbon projections for the Southeastern United States forest sector. For Policy Econ. 186:103755. https://doi.org/10.1016/j.forpol.2026.103755
Newman DH. 1987. An econometric analysis of the southern softwood stumpage market: 1950-1980. For Sci. 33(4):932-945. https://doi.org/10.1093/forestscience/33.4.932
pandas-dev-team. 2020. Pandas-dev/pandas: pandas. Zenodo. https://doi.org/10.5281/zenodo.3509134.
Polyakov M, Teeter LD, Jackson JD. 2005. Econometric analysis of Alabama's pulpwood market. For Prod J. 55(1):41-44.
Polyakov M, Wear DN, Huggett RN. 2010. Harvest choice and timber supply models for forest forecasting. For Sci. 56(4):344-355. https://doi.org/10.1093/forestscience/56.4.344
Prestemon JP, Abt RC. 2002. Southern forest resource assessment highlights: the southern timber market to 2040. J For. 100(7):16-22. https://doi.org/10.1093/jof/100.7.16
Restrepo HI, Munro HL, Prisley SP, Schilling E, Radtke PJ, Bush R, Coulston J, Woodall CW. 2025. Population estimate differences for forest biomass, merchantable volume, and carbon pools as a result of modeling system updates in the United States. For Ecol Manag. 595:122965. https://doi.org/10.1016/j.foreco.2025.122965
Robinson VL. 1974. An econometric model of softwood lumber and stumpage markets, 1947-1967. For Sci. 20(2):171-179. https://doi.org/10.1093/forestscience/20.2.171
Sendak PE, Abt RC, Turner RJ. 2003. Timber supply projections for northern New England and New York: integrating a market perspective. North J Appl For. 20(4):175-185. https://doi.org/10.1093/njaf/20.4.175
U.S. Department of Agriculture, Forest Service. 2024. North Carolina state production timber product output survey. National Resource Use Monitoring. Available from: https://research.fs.usda.gov/products/dataandtools/national-resource-use-monitoring-data-downloads.
Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, Burovski E, et al. 2020. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 17:261-272. https://doi.org/10.1038/s41592-019-0686-2
Visser L, Latta G, Pokharel R, Hoefnagels R, Junginger M. 2022. Exploring the limits to sustainable pellet production for international markets: the impact of increasing pellet production in the US Southeast on feedstock use, production cost and carbon sequestration in forest areas. GCB Bioenergy. 14(8):896-917. https://doi.org/10.1111/gcbb.12946
Westfall JA, Coulston JW, Gray AN, Shaw JD, Radtke PJ, Walker DM, Weiskittel AR, et al. 2024. A national-scale tree volume, biomass, and carbon modeling system for the United States. Gen Tech Rep WO-104. Washington (DC): U.S. Department of Agriculture, Forest Service. 104 p. https://doi.org/10.2737/WO-GTR-104
Article Sidebar
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal the right of first publication with the work simultaneously licenced under a Creative Commons Attribution 4.0 International License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in the journal. The journal retains no copyright.
