[Construction and precision analysis of individual tree biomass model of Larix olgensis considering random effects]

Accurate estimation of forest biomass in China is crucial for the study of carbon cycle and mechanisms underlying carbon storage in global terrestrial ecosystems. Based on the biomass data of 376 individuals of Larix olgensis in Heilongjiang Province, we used seemingly unrelated regression (SUR) method to build a univariate biomass SUR model with diameter at breast height as the independent variable and considering the random effect at the sampling site level. Then, a seemingly unrelated mixed effect (SURM) model was constructed. As the calculation of random effects of SURM model did not require the empirically measured values of all dependent variables, we analyzed the deviations from the following four types in detail: 1) SURM1, the random effect was calculated according to the measured biomass of stem, branch and foliage; 2) SURM2, the random effect was calculated according to the measured value of tree height (H); 3) SURM3, the random effect was calculated according to the measured crown length (CL); 4) SURM4, the random effect was calculated according to the measured values of H and CL. The results showed that the fitting effect of branch and foliage biomass models was improved significantly after considering the horizontal random effect of the sampling plot, with R² being increased by more than 20%. The fitting effect of stem and root biomass models were improved slightly, with R² being increased by 4.8% and 1.7%, respectively. When using five randomly selected trees to calculate the horizontal random effect of the sampling plot, the prediction performance of SURM model was better than that of SUR model and SURM model considering only fixed effects, especially SURM1 model (MAPE% of stem, branch, foliage and root was 10.4%, 29.7%, 32.1% and 19.5%, respectively). Except for SURM1 model, the deviation of SURM4 in predicting stem, branch, foliage and root biomass was smaller than that of SURM2 and SURM3 models. In actual prediction, although the prediction accuracy of SURM1 model was the highest, it needed to measure aboveground biomass of several trees, and the use cost was relatively high. Therefore, the SURM4 modelled on measured H and CL was recommended to predict the standing tree biomass of L. olgensis.