Abstract: Objective The purpose of this study is to establish biomass and carbon storage models of Chinese fir plantations suitable for different production areas, so as to provide a basis for accurate estimation of biomass and carbon storage of Chinese fir plantations.Method Based on the measured biomass data of stem, bark, branch, leaf, and root of 109 Chinese fir trees in Sichuan, Guangxi, Jiangxi, and Fujian, and the measured biomass and carbon content of stem, branch, leaf, and root of 40 Chinese fir trees in Sichuan, Guangxi, and Fujian, the additive biomass and carbon storage models of mature forests in different production areas, different forest ages, and comprehensive production areas were established. The seemingly unrelated regression (SUR) is used to jointly estimate the parameters in the additive model system, and the fitting accuracy of the model is tested with the adjusted determination coefficient R²_a, and the total relative error TRE.Result 1) The R²_a of the fir biomass models for the four production areas and different stand ages ranged from 0.635 0 to 0.995 8, with TRE ranging from ?17.88% to 21.39 %, and the R²_a of the stem, bark, and whole plant biomass models were above 0.91, which is suitable for biomass prediction of fir plantation forests in the modeled sites. The biomass models fitted to Guangxi sub-lateral roots had R²_a above 0.80 and TRE of ?5.42% to 7.21%, except for the first-grade lateral roots, which can be used to predict the biomass of lateral roots in Guangxi fir plantation forests. The biomass model fitting accuracy of branches, leaves, and roots was lower than that of stem and bark. 2) The carbon stock model R²_a was from 0.805 0 to 0.994 0 and TRE was from ?19.34% to 19.84% for the three production areas of Sichuan, Guangxi, and Fujian, and the R²_a of the stem, root, and whole plant models was above 0.93, which applied to the prediction of carbon stock in cedar plantation forests in all regions. The carbon stock model fitting accuracy of branches and leaves was lower than that of stem and roots. 3) There are differences in the accuracy of the biomass and carbon stock models across different regions. The biomass model of Sichuan, located in the western part of the central subtropics, has the lowest accuracy. On the other hand, the biomass model of Guangxi, located in the southern subtropics, with the skinned stem and the whole-plant biomass model, has better accuracy. Additionally, the biomass models of Fujian and Jiangxi, located in the eastern part of the central subtropics, have similar accuracy and can be used interchangeably. Regarding the carbon stock models, the one in Guangxi has the highest accuracy, whereas the ones in Sichuan and Fujian are only suitable for predicting the carbon stock in their respective regions. 4) The comprehensive biomass model R²_a is 0.733 5?0.966 9. According to the results of the cross-test, the comprehensive model can accurately predict the biomass of stem with bark and the whole plant of mature forests in different production areas, young forests, and middle-aged forests in Fujian, and the TRE is ?10.47%?19.88%. It can also accurately predict the biomass of organs and whole plants except branches of mature forests in Jiangxi, Fujian, and middle-aged forests in Fujian. The comprehensive carbon storage model R²_a is 0.802 9?0.982 6. Except for the relatively large prediction error of branch carbon storage of Chinese fir plantations in Guangxi, the TRE of other test samples is ?9.57%?15.70%, indicating that the model has good universality and can accurately predict the carbon storage of various organs and whole plants of Chinese fir plantations in different production areas.Conclusion The models established in this study apply to the prediction of biomass and carbon storage in the modeling site. The universality of the model is affected by the difference in production areas. The integrated model can be used to predict biomass and carbon storage in different regions.

Key words: Chinese fir plantations, biomass, carbon storage, different production areas, additive model