4.7 Article

Additive Root Biomass Equations for Betula platyphylla Suk. Plantations in Northeast China

Journal

FORESTS
Volume 13, Issue 5, Pages -

Publisher

MDPI
DOI: 10.3390/f13050661

Keywords

Betula platyphylla; root biomass; additive equation; disaggregated model

Categories

Funding

  1. National Key R&D Program of China [2021YFD1500705]
  2. National Natural Science Foundation of China [31670476]
  3. Fundamental Research Funds for the Central Universities [2572019BA15, 2572020DR02]

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Most existing forest biomass models focus on aboveground biomass, while belowground biomass prediction is insufficient. In this study, extensive destructive sampling was used to measure the root biomass of birch plantation forests. The disaggregated model performed slightly better than the additive model in estimating birch root biomass.
Most of the forest biomass models that have been developed so far focus on the study of the aboveground biomass of forest trees and the prediction of belowground biomass remains obviously insufficient. Moreover, most of the existing studies on the estimation of the belowground biomass of trees have considered roots as a whole, ignoring the differences in composition and function of roots within different diameter classes. In this study, we measured the root biomass of birch plantation forests in northeastern China using extensive destructive sampling, in which we divided the root system into three parts: coarse, medium, and fine roots. We selected the best model base form from three common allometric biomass equations and determined the most appropriate error structure for the two sets of models using likelihood comparisons. The additive and disaggregated models were fitted using maximum likelihood with open-source software. We also added the site factor as a dummy variable into the two models. Finally, the competency of the two models was tested using ten-fold cross-validation. The results showed that both models could provide relatively accurate estimates of birch root biomass but that the disaggregated model performed slightly better than the additive model.

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