Estimation of biomass and net primary productivity of major planted forests in China based on forest inventory data

Abstract

Reforestation and afforestation have been suggested as an important land use management in mitigating the increase in atmospheric CO 2 concentration under Kyoto Protocol of UN Framework Convention on climate change. Forest inventory data (FID) are important resources for understanding the dynamics of forest biomass, net primary productivity (NPP) and carbon cycling at landscape and regional scales. In this study, more than 300 data sets of biomass, volume, NPP and stand age for five planted forest types in China ( Larix, Pinus tabulaeformis, Pinus massoniana, Cunninghamia lanceolata, Pouulus) from literatures were synthesized to develop regression equations between biomass and volume, and between NPP and biomass, and stand age. Based on the fourth FID (1989–1993), biomass and NPP of five planted forest types in China were estimated. The results showed that total biomass and total NPP of the five types of forest plantations were 2.81 Pg (1 Pg = 10 15 g) and 235.65 Mg ha −1 yr −1 (1 Mg = 10 6 g), respectively. The area-weighted mean biomass density (biomass) and NPP of different forest types varied from 44.43 ( P. massoniana) to 146.05 Mg ha −1 ( P. tabulaeformis) and from 4.41 ( P. massoniana) to 7.33 Mg ha −1 yr −1 ( Populus), respectively. The biomass and NPP of the five planted forest types were not distributed evenly across different regions in China. Larix forests have the greatest variations in biomass and NPP, ranging from 2.7 to 135.37 Mg ha −1 and 0.9 to 10.3 Mg ha −1 yr −1, respectively. However, biomass and NPP of Populus forests in different region varied less and they were approximately 50 Mg ha −1 and 7–8 Mg ha −1 yr −1, respectively. The distribution pattern of biomass and NPP of different forest types closely related with stand ages and regions. The study provided not only with an estimation biomass and NPP of major planted forests in China but also with a useful methodology for estimating forest carbon storage at regional and global levels.