4.7 Article

Growth characteristics of Cunninghamia lanceolata in China

Journal

SCIENTIFIC REPORTS
Volume 12, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41598-022-22809-6

Keywords

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Funding

  1. National Natural Science Foundation of China [42177454]

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Chinese fir, one of the most important native tree species in southern China, has undergone noticeable climate-induced changes. This study collected and reviewed published papers (1978-2020) on tree growth of Chinese fir forests in China, and developed a comprehensive growth dataset from 482 sites. The dataset includes various variables such as mean DBH, mean H, volume, biomass, and related information. The regression equations derived from this study can be used to predict and assess the potential of carbon sequestration and afforestation activities in Chinese fir forests.
Chinese fir (Cunninghamia lanceolata) is one of southern China's most important native tree species, which has experienced noticeable climate-induced changes. Published papers (1978-2020) on tree growth of Chinese fir forests in China were collected and critically reviewed. After that, a comprehensive growth data set was developed from 482 sites, which are distributed between 102.19 degrees and 130.07 degrees E in longitude, between 21.87 degrees and 37.24 degrees N in latitude and between 5 and 2260 m in altitude. The dataset consists of 2265 entries, including mean DBH (cm), mean H (m), volume (m(3)), biomass (dry weight) (kg) (stem (over bark) biomass, branches biomass, leaves biomass, bark biomass, aboveground biomass, roots biomass, total trees biomass) and related information, i.e. geographical location (Country, province, study site, longitude, latitude, altitude, slope, and aspect), climate (mean annual precipitation-MAP and mean annual temperature-MAT), stand description (origin, age, canopy density and stand density), and sample regime (plot size, number and investigation year). Our results showed that (1) the best prediction of height was obtained using nonlinear composite model Height = 1.3 + 34.23*(1-e((-0.01025*DBH1.347))), (R-2 = 0.8715, p < 0.05), (2) the equation Volume = DBH2/(387.8 + 19,190/Height) (R-2 = 0.9833, p < 0.05) was observed to be the most suitable model for volume estimation. Meanwhile, when the measurements of the variables are difficult to carry out, the volume model Volume = 0.03957 - 0.01215*DBH + 0.00118*DBH2 (R-2 = 0.9573, p < 0.05) is determined from DBH only has a practical advantage, (3) the regression equations of component biomass against DBH explained more significant than 86% variability in almost all biomass data of woody tissues, which were ranked as total trees (97.25%) > aboveground (96.55%) > stems (with bark) (96.17%) > barks (88.95%) > roots (86.71%), and explained greater than 64% variability in branch biomass. The foliage biomass equation was the poorest among biomass components (R-2 = 0.6122). The estimation equations derived in this study are particularly suitable for the Chinese fir forests in China. This dataset can provide a theoretical basis for predicting and assessing the potential of carbon sequestration and afforestation activities of Chinese fir forests on a national scale.

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