Growth characteristics and biomass model of Cupressus gigantea sapling

Key message

The growth characteristics and biomass model of Cupressus gigantea (34a). The biomass of each part of the tree was ranked in order of size: trunk > main branches > leaves > lateral branches > roots > fruits, with the trunk and main branches dominating, and the biomass model was best fitted by a ternary linear regression model.

Abstract

Biomass models are the most widely used method for forest biomass estimation. Cupressus gigantea, an endemic species of Tibet, is mainly concentrated in southeastern Tibet, but few studies focus on cypress biomass to date. In this paper, The study of the growth characteristics and biomass model can provide a theoretical basis for the protection and field investigation and monitoring of the Cupressus gigantea, and can simplify the method and difficulty of field investigation. Tree data was collected by felling Cupressus gigantea (34a), then measure tree rings, DBH (diameter at breast height) and tree height. Linear and nonlinear regression models were used to eliminate heteroscedasticity using the least square method. Growth characteristics of 0–34 years Cupressus gigantea was obtained, and a biomass model was established. Among the 34-year-old Cupressus gigantea studied, the growth rate was slow from 0 to 10 years old and accelerated from 10 to 30 years old. The average biomass of the whole plant was 369.7 kg, of which the trunk accounted for 47.45%, with the biomass of each part ranking as follows: trunk > main branches > leaves > lateral branches > roots > fruit. The trunk, branches, above ground and below ground parts were modeled according to the basic model, 19 models met the standard for tree models, with R² values above 0.96. The fitting effect of models established in different parts varies greatly. The MPE (Average estimated Error) of the aboveground biomass was between 1.0 and 3.5%, while that of the belowground biomass was above 5%. By comparing the evaluation indexes of various models, it is found that the comprehensive prediction ability of linear model is better than that of nonlinear model. The comprehensive prediction ability of ternary linear model is the best among the five models, but binary linear and nonlinear models are more suitable for practical application.