IDEAS home Printed from https://ideas.repec.org/a/eee/ecolec/v201y2022ics0921800922002208.html
   My bibliography  Save this article

Designing forest restoration projects to optimize the application of broadcast burning

Author

Listed:
  • Belavenutti, Pedro
  • Ager, Alan A.
  • Day, Michelle A.
  • Chung, Woodam

Abstract

Active forest restoration programs on western US national forests face multiple challenges to meet their broad ecological goals while designing projects that generate sufficient revenue to build and maintain private forest management capacity needed to expand the scale and scope of treatments. We explored ways to design projects where admixing of treatments along gradients of dry and moist mixed conifer forest types could maximize financial viability while including substantial area where broadcast burning could be applied in conjunction with other treatments. In general, we found that restoration treatments in dry forests that included density reduction thinning and broadcast burning resulted in a net projected cost ranging from $110 to $8000 per ha. By contrast, density reduction thinning in moist mixed conifer forests on more productive microsites generated significant commercial timber volume and projected revenue that ranged from $4000 to $20,000 per ha. We used spatial optimization methods to identify potential project areas that maximized revenue while meeting constraints to treat a minimum proportion of each project with broadcast burning. Multiple project area sizes were also explored to understand the effect of restoration scale on financial outcomes. We found that optimal projects in terms of generating revenue to subsidize density reduction and broadcast burning were 810 ha and contained >50% dry forest area. Larger projects and those with a higher percentage of dry forest area resulted in lower revenue, eliminating revenue when projects reached 2700 ha. Forest restoration programs can use these methods to plan and design restoration projects that are financially viable while addressing the broadcast burn backlog in dry forests that require relatively expensive fuel reduction treatments prior to re-introducing fire.

Suggested Citation

  • Belavenutti, Pedro & Ager, Alan A. & Day, Michelle A. & Chung, Woodam, 2022. "Designing forest restoration projects to optimize the application of broadcast burning," Ecological Economics, Elsevier, vol. 201(C).
  • Handle: RePEc:eee:ecolec:v:201:y:2022:i:c:s0921800922002208
    DOI: 10.1016/j.ecolecon.2022.107558
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0921800922002208
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolecon.2022.107558?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Minas, James P. & Hearne, John W. & Martell, David L., 2014. "A spatial optimisation model for multi-period landscape level fuel management to mitigate wildfire impacts," European Journal of Operational Research, Elsevier, vol. 232(2), pages 412-422.
    2. Miguel Constantino & Isabel Martins & José G. Borges, 2008. "A New Mixed-Integer Programming Model for Harvest Scheduling Subject to Maximum Area Restrictions," Operations Research, INFORMS, vol. 56(3), pages 542-551, June.
    3. Schroder, Svetlana A. (Kushch) & Tóth, Sándor F. & Deal, Robert L. & Ettl, Gregory J., 2016. "Multi-objective optimization to evaluate tradeoffs among forest ecosystem services following fire hazard reduction in the Deschutes National Forest, USA," Ecosystem Services, Elsevier, vol. 22(PB), pages 328-347.
    4. Rummer, Bob, 2008. "Assessing the cost of fuel reduction treatments: A critical review," Forest Policy and Economics, Elsevier, vol. 10(6), pages 355-362, August.
    5. Rodolfo Carvajal & Miguel Constantino & Marcos Goycoolea & Juan Pablo Vielma & Andrés Weintraub, 2013. "Imposing Connectivity Constraints in Forest Planning Models," Operations Research, INFORMS, vol. 61(4), pages 824-836, August.
    6. Paul F. Hessburg & Keith M. Reynolds & R. Brion Salter & James D. Dickinson & William L. Gaines & Richy J. Harrod, 2013. "Landscape Evaluation for Restoration Planning on the Okanogan-Wenatchee National Forest, USA," Sustainability, MDPI, vol. 5(3), pages 1-36, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Dong, Lingbo & Chen, Guanmou & Chung, Woodam & Liu, Zhaogang, 2024. "Variations on the maximum density-size lines to climate and site factors for Larix spp. plantations in northeast China," Ecological Modelling, Elsevier, vol. 498(C).
    2. Alan A Ager & Michelle A Day & Bruno A Aparício & Rachel Houtman & Andrew Stinchfield, 2023. "Optimizing the implementation of a forest fuel break network," PLOS ONE, Public Library of Science, vol. 18(12), pages 1-23, December.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sinha, Ankur & Rämö, Janne & Malo, Pekka & Kallio, Markku & Tahvonen, Olli, 2017. "Optimal management of naturally regenerating uneven-aged forests," European Journal of Operational Research, Elsevier, vol. 256(3), pages 886-900.
    2. Oğuzhan Ahmet Arık, 2021. "Long-term Plantation and Harvesting Planning for Industrial Plantation Forest Areas," SN Operations Research Forum, Springer, vol. 2(2), pages 1-23, June.
    3. Correa, Renata Naoko & Scarpin, Cassius Tadeu & Ferrari, Linamara Smaniotto & Arce, Julio Eduardo, 2020. "Application of relax-and-fix heuristic in the aggregation of stands for tactical forest scheduling," Forest Policy and Economics, Elsevier, vol. 119(C).
    4. Augustynczik, Andrey Lessa Derci & Arce, Julio Eduardo & Yousefpour, Rasoul & da Silva, Arinei Carlos Lindbeck, 2016. "Promoting harvesting stands connectivity and its economic implications in Brazilian forest plantations applying integer linear programming and simulated annealing," Forest Policy and Economics, Elsevier, vol. 73(C), pages 120-129.
    5. Constantino, Miguel & Martins, Isabel, 2018. "Branch-and-cut for the forest harvest scheduling subject to clearcut and core area constraints," European Journal of Operational Research, Elsevier, vol. 265(2), pages 723-734.
    6. Isabel Martins & Filipe Alvelos & Miguel Constantino, 2012. "A branch-and-price approach for harvest scheduling subject to maximum area restrictions," Computational Optimization and Applications, Springer, vol. 51(1), pages 363-385, January.
    7. Brias, Antoine & Munch, Stephan B., 2021. "Ecosystem based multi-species management using Empirical Dynamic Programming," Ecological Modelling, Elsevier, vol. 441(C).
    8. Yuquan Qu & Diego G. Miralles & Sander Veraverbeke & Harry Vereecken & Carsten Montzka, 2023. "Wildfire precursors show complementary predictability in different timescales," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    9. Araya-Córdova, P.J. & Vásquez, Óscar C., 2018. "The disaster emergency unit scheduling problem to control wildfires," International Journal of Production Economics, Elsevier, vol. 200(C), pages 311-317.
    10. Ran Wei & Alan Murray, 2015. "Spatial uncertainty in harvest scheduling," Annals of Operations Research, Springer, vol. 232(1), pages 275-289, September.
    11. Augustynczik, A.L.D. & Arce, J.E. & Silva, A.C.L., 2016. "Aggregating forest harvesting activities in forest plantations through Integer Linear Programming and Goal Programming," Journal of Forest Economics, Elsevier, vol. 24(C), pages 72-81.
    12. Khakzad, Nima, 2021. "Optimal firefighting to prevent domino effects: Methodologies based on dynamic influence diagram and mathematical programming," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    13. Marshalek, Elaina C. & Ramage, Benjamin S. & Potts, Matthew D., 2014. "Integrating harvest scheduling and reserve design to improve biodiversity conservation," Ecological Modelling, Elsevier, vol. 287(C), pages 27-35.
    14. Isabel Martins & Mujing Ye & Miguel Constantino & Maria Conceição Fonseca & Jorge Cadima, 2014. "Modeling target volume flows in forest harvest scheduling subject to maximum area restrictions," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 22(1), pages 343-362, April.
    15. Rashidi, Eghbal & Medal, Hugh & Gordon, Jason & Grala, Robert & Varner, Morgan, 2017. "A maximal covering location-based model for analyzing the vulnerability of landscapes to wildfires: Assessing the worst-case scenario," European Journal of Operational Research, Elsevier, vol. 258(3), pages 1095-1105.
    16. Baghersad, Milad & Emadikhiav, Mohsen & Huang, C. Derrick & Behara, Ravi S., 2023. "Modularity maximization to design contiguous policy zones for pandemic response," European Journal of Operational Research, Elsevier, vol. 304(1), pages 99-112.
    17. Álvarez-Miranda, Eduardo & Goycoolea, Marcos & Ljubić, Ivana & Sinnl, Markus, 2021. "The Generalized Reserve Set Covering Problem with Connectivity and Buffer Requirements," European Journal of Operational Research, Elsevier, vol. 289(3), pages 1013-1029.
    18. Junga Lee & Christopher D. Ellis & Yun Eui Choi & Soojin You & Jinhyung Chon, 2015. "An Integrated Approach to Mitigation Wetland Site Selection: A Case Study in Gwacheon, Korea," Sustainability, MDPI, vol. 7(3), pages 1-28, March.
    19. Teresa Neto & Miguel Constantino & Isabel Martins & João Pedro Pedroso, 2017. "Forest harvest scheduling with clearcut and core area constraints," Annals of Operations Research, Springer, vol. 258(2), pages 453-478, November.
    20. Roland Oliver Hales & Sergio García, 2019. "Congress seat allocation using mathematical optimization," TOP: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 27(3), pages 426-455, October.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:ecolec:v:201:y:2022:i:c:s0921800922002208. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ecolecon .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.