IDEAS home Printed from https://ideas.repec.org/a/eee/ecomod/v397y2019icp55-73.html
   My bibliography  Save this article

The process-based forest growth model 3-PG for use in forest management: A review

Author

Listed:
  • Gupta, Rajit
  • Sharma, Laxmi Kant

Abstract

Forests are a critical resource, and need proper management in the face of dire climatic changes facing the world today. Advances in modelling system result in the formulation of numerous forest modelling approaches to provide an estimation of forests services. One such useful and straightforward forest modelling approach is process-based modelling, relying on physiological processes and biophysical parameters of forest ecosystems. It is based on parametric calculations and allometric equations, delivering crucial outputs for forest management. The dynamic 3-PG (Physiological Principles in Predicting Growth) is a process-based model (PBM) based on an ecosystem physiological process-based modelling approach. The various applications and flexible nature of the 3-PG model have resulted in its adoption and utilization over several regions of the world. The 3-PGS (Physiological Principles in Predicting Growth with Satellite) model is a modified and spatial version of the 3-PG model that took advantages of remote sensing & GIS (Geographical Information System) for estimation of biophysical variables like FAPAR (Fraction of absorbed photosynthetically active radiation), LAI (Leaf area index), and Canopy water content (CWC), which are tedious and laborious to calculate manually. The integration of remote sensing & GIS with PBMs offers insights to predict forest biomass and productivity at a regional level. Also, coupling of the 3-PG/3-PGS model with other modelling and statistical approaches in a GIS environment provides insights into the prediction of species distributions and potential disturbances due to climatic changes. The 3-PG model was originally designed for relatively homogenous forests; but with the recent development, the 3-PGmix has extended its use to mixed species forests. In this review, we have tried to emphasize the general overview, structure, applications, and efficacy of the process-based 3-PG model for forest management. In future, forests and their ecosystem services are expected to be rigorously influenced by climatic variations. Therefore, it is important to understand the role and effectiveness of the forest growth model 3-PG under the influence of climate change. The 3-PG model performs well for a diverse range of conditions for many forest types and species, and could be integrated with other models and approaches in order to widen its functions and applications. Areas such as Fertility Rating (FR), sensitivity and uncertainty of outputs to the model inputs in the 3-PG model requires attention to remove the weaker side, and to increase the effectiveness and accuracy of model outputs. In addition, the model performance can be improved by calculating its parameters from the population of interest, rather than using default values or values from extant literature. Furthermore, high-resolution remote sensing datasets and accurate input field data could increase the accuracy of the 3-PG/3-PGS model predictions at a broad regional level. In general, the simple forest growth model 3-PG delivers practical outputs, which are directly used in forest management. Additionally, the functions and applications of the 3-PG/3-PGS/3-PGmix model could be explored to deal with the impacts of climate change on forests and to ensure the sustainable management of forests.

Suggested Citation

  • Gupta, Rajit & Sharma, Laxmi Kant, 2019. "The process-based forest growth model 3-PG for use in forest management: A review," Ecological Modelling, Elsevier, vol. 397(C), pages 55-73.
    • Handle: RePEc:eee:ecomod:v:397:y:2019:i:c:p:55-73
    DOI: 10.1016/j.ecolmodel.2019.01.007
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0304380019300213
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.ecolmodel.2019.01.007?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. Seidl, Rupert & Fernandes, Paulo M. & Fonseca, Teresa F. & Gillet, François & Jönsson, Anna Maria & Merganičová, Katarína & Netherer, Sigrid & Arpaci, Alexander & Bontemps, Jean-Daniel & Bugmann, Hara, 2011. "Modelling natural disturbances in forest ecosystems: a review," Ecological Modelling, Elsevier, vol. 222(4), pages 903-924.
    2. Miehle, Peter & Battaglia, Michael & Sands, Peter J. & Forrester, David I. & Feikema, Paul M. & Livesley, Stephen J. & Morris, Jim D. & Arndt, Stefan K., 2009. "A comparison of four process-based models and a statistical regression model to predict growth of Eucalyptus globulus plantations," Ecological Modelling, Elsevier, vol. 220(5), pages 734-746.
    3. Richard Waring & Nicholas Coops, 2016. "Predicting large wildfires across western North America by modeling seasonal variation in soil water balance," Climatic Change, Springer, vol. 135(2), pages 325-339, March.
    4. Xenakis, Georgios & Ray, Duncan & Mencuccini, Maurizio, 2008. "Sensitivity and uncertainty analysis from a coupled 3-PG and soil organic matter decomposition model," Ecological Modelling, Elsevier, vol. 219(1), pages 1-16.
    5. Song, Xiaodong & Bryan, Brett A. & Paul, Keryn I. & Zhao, Gang, 2012. "Variance-based sensitivity analysis of a forest growth model," Ecological Modelling, Elsevier, vol. 247(C), pages 135-143.
    6. Forrester, David I. & Tang, Xiaolu, 2016. "Analysing the spatial and temporal dynamics of species interactions in mixed-species forests and the effects of stand density using the 3-PG model," Ecological Modelling, Elsevier, vol. 319(C), pages 233-254.
    7. Coops, Nicholas C. & Waring, Richard H. & Schroeder, Todd A., 2009. "Combining a generic process-based productivity model and a statistical classification method to predict the presence and absence of tree species in the Pacific Northwest, U.S.A," Ecological Modelling, Elsevier, vol. 220(15), pages 1787-1796.
    8. Richard H. Waring & Nicholas C. Coops, 2016. "Predicting large wildfires across western North America by modeling seasonal variation in soil water balance," Climatic Change, Springer, vol. 135(2), pages 325-339, March.
    9. Song, Xiaodong & Bryan, Brett A. & Almeida, Auro C. & Paul, Keryn I. & Zhao, Gang & Ren, Yin, 2013. "Time-dependent sensitivity of a process-based ecological model," Ecological Modelling, Elsevier, vol. 265(C), pages 114-123.
    10. Pretzsch, Hans & Forrester, David I. & Rötzer, Thomas, 2015. "Representation of species mixing in forest growth models. A review and perspective," Ecological Modelling, Elsevier, vol. 313(C), pages 276-292.
    11. Adhikari, Arjun & White, Joseph D., 2016. "Climate change impacts on regenerating shrubland productivity," Ecological Modelling, Elsevier, vol. 337(C), pages 211-220.
    12. Coops, Nicholas C. & Waring, Richard H., 2011. "Estimating the vulnerability of fifteen tree species under changing climate in Northwest North America," Ecological Modelling, Elsevier, vol. 222(13), pages 2119-2129.
    13. Forrester, David I., 2014. "A stand-level light interception model for horizontally and vertically heterogeneous canopies," Ecological Modelling, Elsevier, vol. 276(C), pages 14-22.
    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. Lessa Derci Augustynczik, Andrey & Yousefpour, Rasoul, 2021. "Assessing the synergistic value of ecosystem services in European beech forests," Ecosystem Services, Elsevier, vol. 49(C).
    2. Nölte, Anja & Yousefpour, Rasoul & Hanewinkel, Marc, 2020. "Changes in sessile oak (Quercus petraea) productivity under climate change by improved leaf phenology in the 3-PG model," Ecological Modelling, Elsevier, vol. 438(C).
    3. Stephen B. Stewart & Anthony P. O’Grady & Daniel S. Mendham & Greg S. Smith & Philip J. Smethurst, 2022. "Digital Tools for Quantifying the Natural Capital Benefits of Agroforestry: A Review," Land, MDPI, vol. 11(10), pages 1-32, September.
    4. Sam Van Holsbeeck & Mark Brown & Sanjeev Kumar Srivastava & Mohammad Reza Ghaffariyan, 2020. "A Review on the Potential of Forest Biomass for Bioenergy in Australia," Energies, MDPI, vol. 13(5), pages 1-19, March.
    5. Elevitch, Craig R. & Johnson, C. Richard, 2020. "A procedure for ranking parameter importance for estimation in predictive mechanistic models," Ecological Modelling, Elsevier, vol. 419(C).
    6. Barbosa, Lorena Oliveira & dos Santos, Juscelina Arcanjo & Gonçalves, Anny Francielly Ataide & Campoe, Otávio Camargo & Scolforo, José Roberto Soares & Scolforo, Henrique Ferraço, 2023. "Competition in forest plantations: Empirical and process-based modelling in pine and eucalypt plantations," Ecological Modelling, Elsevier, vol. 483(C).
    7. Xie, Yalin & Lei, Xiangdong & Shi, Jingning, 2020. "Impacts of climate change on biological rotation of Larix olgensis plantations for timber production and carbon storage in northeast China using the 3-PGmix model," Ecological Modelling, Elsevier, vol. 435(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. Mathys, A.S. & Coops, N.C. & Simard, S.W. & Waring, R.H. & Aitken, S.N., 2018. "Diverging distribution of seedlings and mature trees reflects recent climate change in British Columbia," Ecological Modelling, Elsevier, vol. 384(C), pages 145-153.
    2. Forrester, David I. & Tang, Xiaolu, 2016. "Analysing the spatial and temporal dynamics of species interactions in mixed-species forests and the effects of stand density using the 3-PG model," Ecological Modelling, Elsevier, vol. 319(C), pages 233-254.
    3. Xie, Yalin & Lei, Xiangdong & Shi, Jingning, 2020. "Impacts of climate change on biological rotation of Larix olgensis plantations for timber production and carbon storage in northeast China using the 3-PGmix model," Ecological Modelling, Elsevier, vol. 435(C).
    4. Pretzsch, Hans & Forrester, David I. & Rötzer, Thomas, 2015. "Representation of species mixing in forest growth models. A review and perspective," Ecological Modelling, Elsevier, vol. 313(C), pages 276-292.
    5. Nölte, Anja & Yousefpour, Rasoul & Hanewinkel, Marc, 2020. "Changes in sessile oak (Quercus petraea) productivity under climate change by improved leaf phenology in the 3-PG model," Ecological Modelling, Elsevier, vol. 438(C).
    6. Simant Rimal & Marc Djahangard & Rasoul Yousefpour, 2022. "Forest Management under Climate Change: A Decision Analysis of Thinning Interventions for Water Services and Biomass in a Norway Spruce Stand in South Germany," Land, MDPI, vol. 11(3), pages 1-18, March.
    7. Pinnschmidt, Arne & Yousefpour, Rasoul & Nölte, Anja & Hanewinkel, Marc, 2023. "Tropical mixed-species plantations can outperform monocultures in terms of carbon sequestration and economic return," Ecological Economics, Elsevier, vol. 211(C).
    8. Lessa Derci Augustynczik, Andrey & Yousefpour, Rasoul, 2021. "Assessing the synergistic value of ecosystem services in European beech forests," Ecosystem Services, Elsevier, vol. 49(C).
    9. Zhao, Gang & Bryan, Brett A. & Song, Xiaodong, 2014. "Sensitivity and uncertainty analysis of the APSIM-wheat model: Interactions between cultivar, environmental, and management parameters," Ecological Modelling, Elsevier, vol. 279(C), pages 1-11.
    10. Gao, Lei & Bryan, Brett A., 2016. "Incorporating deep uncertainty into the elementary effects method for robust global sensitivity analysis," Ecological Modelling, Elsevier, vol. 321(C), pages 1-9.
    11. Simons-Legaard, Erin & Legaard, Kasey & Weiskittel, Aaron, 2015. "Predicting aboveground biomass with LANDIS-II: A global and temporal analysis of parameter sensitivity," Ecological Modelling, Elsevier, vol. 313(C), pages 325-332.
    12. Barbosa, Lorena Oliveira & dos Santos, Juscelina Arcanjo & Gonçalves, Anny Francielly Ataide & Campoe, Otávio Camargo & Scolforo, José Roberto Soares & Scolforo, Henrique Ferraço, 2023. "Competition in forest plantations: Empirical and process-based modelling in pine and eucalypt plantations," Ecological Modelling, Elsevier, vol. 483(C).
    13. Xenia Specka & Claas Nendel & Ralf Wieland, 2019. "Temporal Sensitivity Analysis of the MONICA Model: Application of Two Global Approaches to Analyze the Dynamics of Parameter Sensitivity," Agriculture, MDPI, vol. 9(2), pages 1-29, February.
    14. Silva, Gabriela Cristina Costa & Neves, Júlio César Lima & Marcatti, Gustavo Eduardo & Soares, Carlos Pedro Boechat & Calegario, Natalino & Júnior, Carlos Alberto Araújo & Gonzáles, Duberlí Geomar Ele, 2023. "Improving 3-PG calibration and parameterization using artificial neural networks," Ecological Modelling, Elsevier, vol. 479(C).
    15. Christophe Orazio & Rebeca Cordero Montoya & Margot Régolini & José G. Borges & Jordi Garcia-Gonzalo & Susana Barreiro & Brigite Botequim & Susete Marques & Róbert Sedmák & Róbert Smreček & Yvonne Bro, 2017. "Decision Support Tools and Strategies to Simulate Forest Landscape Evolutions Integrating Forest Owner Behaviour: A Review from the Case Studies of the European Project, INTEGRAL," Sustainability, MDPI, vol. 9(4), pages 1-31, April.
    16. Ameztegui, Aitor & Coll, Lluís & Messier, Christian, 2015. "Modelling the effect of climate-induced changes in recruitment and juvenile growth on mixed-forest dynamics: The case of montane–subalpine Pyrenean ecotones," Ecological Modelling, Elsevier, vol. 313(C), pages 84-93.
    17. Qin Ma & Yanjun Su & Chunyue Niu & Qin Ma & Tianyu Hu & Xiangzhong Luo & Xiaonan Tai & Tong Qiu & Yao Zhang & Roger C. Bales & Lingli Liu & Maggi Kelly & Qinghua Guo, 2023. "Tree mortality during long-term droughts is lower in structurally complex forest stands," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    18. Coops, Nicholas C. & Waring, Richard H., 2011. "Estimating the vulnerability of fifteen tree species under changing climate in Northwest North America," Ecological Modelling, Elsevier, vol. 222(13), pages 2119-2129.
    19. Clasen, Christian & Heurich, Marco & Glaesener, Laurent & Kennel, Eckhard & Knoke, Thomas, 2015. "What factors affect the survival of tree saplings under browsing, and how can a loss of admixed tree species be forecast?," Ecological Modelling, Elsevier, vol. 305(C), pages 1-9.
    20. Jie Ding & Zhipeng Li & Heyu Zhang & Pu Zhang & Xiaoming Cao & Yiming Feng, 2022. "Quantifying the Aboveground Biomass (AGB) of Gobi Desert Shrub Communities in Northwestern China Based on Unmanned Aerial Vehicle (UAV) RGB Images," Land, MDPI, vol. 11(4), pages 1-17, April.

    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:397:y:2019:i:c:p:55-73. 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.