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Aboveground Biomass Models in the Combretum-Terminalia Woodlands of Ethiopia: Testing Species and Site Variation Effects

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  • Amsalu Abich

    (Wondo Genet College of Forestry and Natural Resources, Hawassa University, Shashemene P.O. Box 128, Ethiopia
    College of Agriculture and Environmental Sciences, University of Gondar, Gondar P.O. Box 196, Ethiopia)

  • Mesele Negash

    (Wondo Genet College of Forestry and Natural Resources, Hawassa University, Shashemene P.O. Box 128, Ethiopia)

  • Asmamaw Alemu

    (College of Agriculture and Environmental Sciences, University of Gondar, Gondar P.O. Box 196, Ethiopia)

  • Temesgen Gashaw

    (College of Agriculture and Environmental Sciences, Bahir Dar University, Bahir Dar P.O. Box 1289, Ethiopia)

Abstract

The Combretum-Terminalia woodlands and wooded grasslands (CTW) are widely distributed in East Africa. While these landscapes may have the potential to act as key global carbon sinks, relatively little is known about their carbon storage capacity. Here we developed a set of novel aboveground biomass (AGB) models and tested for species and site variation effects to quantify the potential for CTW to store carbon. In total, 321 trees were sampled from 13 dominant tree species, across three sites in the Northwest lowlands of Ethiopia. Overall, fitted species-specific models performed the best, with diameter at breast height explaining 94–99% of the AGB variations. Interspecific tree allometry differences among species were more substantial than intraspecific tree allometry among sites. Incorporating wood density and height in the mixed-species models significantly improved the model performance relative mean absolute error (MAPE) of 2.4–8.0%, while site variation did not affect the model accuracy substantially. Large errors (MAPE%) were observed when using existing pantropical models, indicating that model selection remains an important source of uncertainty. Although the estimates of selected site-specific models were accurate for local sites, mixed-species and species-specific models performed better when validation data collated from different sites were incorporated together. We concluded that including site- and species-level data improved model estimates of AGB for the CTW of Ethiopia.

Suggested Citation

  • Amsalu Abich & Mesele Negash & Asmamaw Alemu & Temesgen Gashaw, 2022. "Aboveground Biomass Models in the Combretum-Terminalia Woodlands of Ethiopia: Testing Species and Site Variation Effects," Land, MDPI, vol. 11(6), pages 1-23, May.
  • Handle: RePEc:gam:jlands:v:11:y:2022:i:6:p:811-:d:827857
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    References listed on IDEAS

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    1. Geoffrey B. West & James H. Brown & Brian J. Enquist, 1999. "A general model for the structure and allometry of plant vascular systems," Nature, Nature, vol. 400(6745), pages 664-667, August.
    2. Chris Chatfield, 1995. "Model Uncertainty, Data Mining and Statistical Inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 158(3), pages 419-444, May.
    3. Tadesse Mucheye & Mekuanent Tebkew & Yohannis G/Mariam & Amsalu Abich, 2021. "Long-term dynamics of woodland vegetation with response of climate variability in the lowlands of north western part of Ethiopia," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(1), pages 123-132, January.
    4. Brian J. Enquist & Karl J. Niklas, 2001. "Invariant scaling relations across tree-dominated communities," Nature, Nature, vol. 410(6829), pages 655-660, April.
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