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

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  • 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
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    2. 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.
    3. 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.
    4. 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).
    5. 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).
    6. 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).
    7. Lessa Derci Augustynczik, Andrey & Yousefpour, Rasoul, 2021. "Assessing the synergistic value of ecosystem services in European beech forests," Ecosystem Services, Elsevier, vol. 49(C).

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