Correction factors can overcompensate log-transformation bias in allometric biomass models
Research paper
Key message
Fitting allometric biomass models using ln-ln transformation approach can produce biased estimates, as in certain conditions correction factors were shown to overcompensate the back transformation bias. However, weighted nonlinear regression approach consistently provided unbiased estimates. Given the difficulty of correctly identifying error distributions, especially with unreliable normality tests, cautious method selection is essential to avoid biased biomass predictions.
Keywords
Logarithmic transformation; Weighted nonlinear model; Biomass prediction bias
Publication
Dutcă, I. Correction factors can overcompensate log-transformation bias in allometric biomass models. Annals of Forest Science 83, 24 (2026). https://doi.org/10.1186/s13595-026-01340-y
Handling editor
Erwin Dreyer