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Chave, J., Andalo, C., Brown, S., Cairns, M. A., Chambers, J. Q., Eamus, D., Folster, H., Fromard, F., Higuchi, N., Kira, T., Lescure, J. P., Nelson, B. W., Ogawa, H., Puig, H., Riera, B., & Yamakura T. (2005). Tree Allometry and Improved Estimation of Carbon Stocks and Balance in Tropical Forests. Oecologia, 145, 87-99.
https://doi.org/10.1007/s00442-005-0100-x

has been cited by the following article:

• TITLE: Analyzing Accuracy of the Power Functions for Modeling Aboveground Biomass Prediction in Congo Basin Tropical Forests

  AUTHORS: Noël Houédougbé Fonton, Vincent Medjibé, Adrien Djomo, Josiane Kondaoulé, Vivien Rossi, Alfred Ngomanda, Hervé Maïdou

  KEYWORDS: Allometric Equation, Allometric Exponents, Adjustment Criteria, Uncertainty, Monte Carlo

  JOURNAL NAME: Open Journal of Forestry, Vol.7 No.4, September 12, 2017

  ABSTRACT: Allometric equation is the common tools for quantifying and monitoring the amount of carbon stored in forest ecosystems. The model used can be one of the major sources of errors that need to be considered for wood biomass estimations. The power function of plants has been questioned by comparing sixteen models. Some adjustment and model selection criteria and prediction of uncertainties have been computed. Published data on biomass studies and plot inventory were used for this analysis. The results highlight that power function is the best model for modeling aboveground biomass and additional effect on logarithm scales of the predictor variables must be prioritized. The power of the logarithm of diameter as predictor variable must be avoided because this leads to worst adjustment and higher prediction uncertainty. Tree height as a third predictor variable gives the best adjustment and reduces the uncertainty on the biomass prediction around 8 t/ha less than model with the two other predictor variables, the diameter and the wood specific density. The adjustment criteria are sufficient for the appreciation of the prediction quality of the models. The exponent of wood density as predictor variable needs better understanding.