IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v316y2015icp62-77.html
   My bibliography  Save this article

Identifying interactions among reforestation success drivers: A case study from the Philippines

Author

Listed:
  • Le, Hai Dinh
  • Smith, Carl
  • Herbohn, John

Abstract

Reforestation is an expensive undertaking. It is a long-term, complex, and trans-disciplinary process and it involves uncertainties and changing conditions. There is also a complex array of drivers (including biophysical, technical, socio-economic, institutional, and management drivers) that affect reforestation success. Previous research has documented the independent effects of biophysical and technical, environmental and socio-economic drivers on reforestation success. However, research over the last decade has revealed that the outcome of multiple factor interactions is commonly non-additive (i.e. synergies and antagonisms). Therefore, in order to provide better decision support for reforestation planning and policy setting it is necessary to understand the interactive effects that drivers have on reforestation success. To understand these interactive effects, we developed a Bayesian network model based on data collected from 43 reforestation projects on Leyte Island, the Philippines. Non-additive interactions among reforestation success drivers (i.e. synergies and antagonisms) were found to account for up to 90% of interactions tested. This result suggests an urgent need to account for these non-additive interactions in reforestation policy and planning in order to avoid unanticipated outcomes, wasted effort and missed opportunities.

Suggested Citation

  • Le, Hai Dinh & Smith, Carl & Herbohn, John, 2015. "Identifying interactions among reforestation success drivers: A case study from the Philippines," Ecological Modelling, Elsevier, vol. 316(C), pages 62-77.
  • Handle: RePEc:eee:ecomod:v:316:y:2015:i:c:p:62-77
    DOI: 10.1016/j.ecolmodel.2015.08.005
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380015003531
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ecolmodel.2015.08.005?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. Marcot, Bruce G., 2012. "Metrics for evaluating performance and uncertainty of Bayesian network models," Ecological Modelling, Elsevier, vol. 230(C), pages 50-62.
    2. Uusitalo, Laura, 2007. "Advantages and challenges of Bayesian networks in environmental modelling," Ecological Modelling, Elsevier, vol. 203(3), pages 312-318.
    3. Cain, J. D. & Jinapala, K. & Makin, I. W. & Somaratna, P. G. & Ariyaratna, B. R. & Perera, L. R., 2003. "Participatory decision support for agricultural management. A case study from Sri Lanka," Agricultural Systems, Elsevier, vol. 76(2), pages 457-482, May.
    4. Unknown, 2005. "Forward," 2005 Conference: Slovenia in the EU - Challenges for Agriculture, Food Science and Rural Affairs, November 10-11, 2005, Moravske Toplice, Slovenia 183804, Slovenian Association of Agricultural Economists (DAES).
    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. Yitbarek, Tibebe Weldesemaet & Wilson, John R.U. & Dehnen-Schmutz, Katharina, 2023. "A governance framework for the design and evaluation of tree planting schemes," Forest Policy and Economics, Elsevier, vol. 152(C).
    2. Dinh Le, Hai & Thi Mai Anh, Tran & Thi Hai Hien, Vo & Thi Van, Luu & Thi Mai, Ngo, 2024. "Analyzing determinants of long-rotation plantation decisions by local households in Quang Tri Province, Vietnam with Bayesian Networks," Land Use Policy, Elsevier, vol. 138(C).

    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. Moe, S. Jannicke & Haande, Sigrid & Couture, Raoul-Marie, 2016. "Climate change, cyanobacteria blooms and ecological status of lakes: A Bayesian network approach," Ecological Modelling, Elsevier, vol. 337(C), pages 330-347.
    2. Leonel Lara-Estrada & Livia Rasche & L. Enrique Sucar & Uwe A. Schneider, 2018. "Inferring Missing Climate Data for Agricultural Planning Using Bayesian Networks," Land, MDPI, vol. 7(1), pages 1-13, January.
    3. Meyer, Spencer R. & Johnson, Michelle L. & Lilieholm, Robert J. & Cronan, Christopher S., 2014. "Development of a stakeholder-driven spatial modeling framework for strategic landscape planning using Bayesian networks across two urban-rural gradients in Maine, USA," Ecological Modelling, Elsevier, vol. 291(C), pages 42-57.
    4. Anna Sperotto & Josè Luis Molina & Silvia Torresan & Andrea Critto & Manuel Pulido-Velazquez & Antonio Marcomini, 2019. "Water Quality Sustainability Evaluation under Uncertainty: A Multi-Scenario Analysis Based on Bayesian Networks," Sustainability, MDPI, vol. 11(17), pages 1-34, August.
    5. Alessandro Pagano & Irene Pluchinotta & Raffaele Giordano & Anna Bruna Petrangeli & Umberto Fratino & Michele Vurro, 2018. "Dealing with Uncertainty in Decision-Making for Drinking Water Supply Systems Exposed to Extreme Events," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(6), pages 2131-2145, April.
    6. Barton, David N. & Benjamin, Tamara & Cerdán, Carlos R. & DeClerck, Fabrice & Madsen, Anders L. & Rusch, Graciela M. & Salazar, Álvaro G. & Sanchez, Dalia & Villanueva, Cristóbal, 2016. "Assessing ecosystem services from multifunctional trees in pastures using Bayesian belief networks," Ecosystem Services, Elsevier, vol. 18(C), pages 165-174.
    7. Dinh Le, Hai & Thi Mai Anh, Tran & Thi Hai Hien, Vo & Thi Van, Luu & Thi Mai, Ngo, 2024. "Analyzing determinants of long-rotation plantation decisions by local households in Quang Tri Province, Vietnam with Bayesian Networks," Land Use Policy, Elsevier, vol. 138(C).
    8. Marcot, Bruce G., 2017. "Common quandaries and their practical solutions in Bayesian network modeling," Ecological Modelling, Elsevier, vol. 358(C), pages 1-9.
    9. Guo, Kai & Zhang, Xinchang & Kuai, Xi & Wu, Zhifeng & Chen, Yiyun & Liu, Yi, 2020. "A spatial bayesian-network approach as a decision-making tool for ecological-risk prevention in land ecosystems," Ecological Modelling, Elsevier, vol. 419(C).
    10. Gieder, Katherina D. & Karpanty, Sarah M. & Fraser, James D. & Catlin, Daniel H. & Gutierrez, Benjamin T. & Plant, Nathaniel G. & Turecek, Aaron M. & Robert Thieler, E., 2014. "A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features," Ecological Modelling, Elsevier, vol. 276(C), pages 38-50.
    11. Ropero, R.F. & Aguilera, P.A. & Rumí, R., 2015. "Analysis of the socioecological structure and dynamics of the territory using a hybrid Bayesian network classifier," Ecological Modelling, Elsevier, vol. 311(C), pages 73-87.
    12. Junquera, Victoria & Meyfroidt, Patrick & Sun, Zhanli & Latthachack, Phokham & Grêt-Regamey, Adrienne, 2020. "From global drivers to local land-use change: Understanding the northern Laos rubber boom," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 109, pages 103-115.
    13. Liedloff, Adam C. & Smith, Carl S., 2010. "Predicting a ‘tree change’ in Australia's tropical savannas: Combining different types of models to understand complex ecosystem behaviour," Ecological Modelling, Elsevier, vol. 221(21), pages 2565-2575.
    14. Zorrilla-Miras, Pedro & Mahamane, Mansour & Metzger, Marc J. & Baumert, Sophia & Vollmer, Frank & Luz, Ana Catarina & Woollen, Emily & Sitoe, Almeida A. & Patenaude, Genevieve & Nhantumbo, Isilda & Ry, 2018. "Environmental Conservation and Social Benefits of Charcoal Production in Mozambique," Ecological Economics, Elsevier, vol. 144(C), pages 100-111.
    15. Lim, R.B.H. & Liew, J.H. & Kwik, J.T.B. & Yeo, D.C.J., 2018. "Predicting food web responses to biomanipulation using Bayesian Belief Network: Assessment of accuracy and applicability using in-situ exclosure experiments," Ecological Modelling, Elsevier, vol. 384(C), pages 308-315.
    16. McLaughlin, Douglas B. & Reckhow, Kenneth H., 2017. "A Bayesian network assessment of macroinvertebrate responses to nutrients and other factors in streams of the Eastern Corn Belt Plains, Ohio, USA," Ecological Modelling, Elsevier, vol. 345(C), pages 21-29.
    17. Pilar Lopez-Llompart & G. Mathias Kondolf, 2016. "Encroachments in floodways of the Mississippi River and Tributaries Project," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 81(1), pages 513-542, March.
    18. Cheng, Jianquan & Bertolini, Luca, 2013. "Measuring urban job accessibility with distance decay, competition and diversity," Journal of Transport Geography, Elsevier, vol. 30(C), pages 100-109.
    19. M. De Donno & M. Pratelli, 2006. "A theory of stochastic integration for bond markets," Papers math/0602532, arXiv.org.
    20. Prilly Oktoviany & Robert Knobloch & Ralf Korn, 2021. "A machine learning-based price state prediction model for agricultural commodities using external factors," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 44(2), pages 1063-1085, December.

    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:ecomod:v:316:y:2015:i:c:p:62-77. 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.journals.elsevier.com/ecological-modelling .

    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.