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Journal Article

Net primary productivity in forests of China: scaling-up of national inventory data and comparison with model predictions


Ni,  J.
Department Biogeochemical Synthesis, Prof. C. Prentice, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Ni, J. (2003). Net primary productivity in forests of China: scaling-up of national inventory data and comparison with model predictions. Forest Ecology and Management, 176(1-3), 485-495.

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A data set of forest leaf area index (LAI), biomass and net primary productivity (NPP) obtained mostly from forest inventory in China between 1989 and 1993 was used to analyze and scale-up biomass and NPP data from plot sites to 0.5° latitude×0.5° longitude grid cells. The plot-based LAI and biomass of Chinese forests have no clear relationships with latitude and longitude, but forest NPP has a significant relationship with latitude and longitude, decreasing with increased latitude and increasing with increased longitude. Forests in China (1248 plots) have large ranges of elevation (10–4240 m, mean=1540), stand age (3–350 years, mean=63) and LAI (0.17–41.78 m2/m2, mean=8.28). The mean biomass is 7173 g C/m2 ranging from 913.5 to 70645.5 g C/m2 and mean NPP 567 g C/m2 per year ranging from 103.5 to 1813.5 g C/m2 per year. The scaling-up of forest LAI, biomass and NPP in China based on the average method also showed similar geographic patterns as plot-based ones. For the 468 grid cells, the mean forest LAI is 8.59 m2/m2 (range 0.17–33.6), mean biomass 7548.8 g C/m2 ranging from 1401.3 to 29701.8 g C/m2, and mean NPP 606.3 g C/m2 per year ranging from 108.5 to 1404.5 g C/m2 per year. The accuracy of the biomass and NPP estimates at the individual grid-cell level varied with the number of represented plots (here 1–7 plots). The gridded forest NPP was compared to LPJ dynamic global vegetation model-based NPP predictions, indicating no significant relationship between them in Chinese forest regions. Reasons of disagreements between model and data (predicted and measured NPP) may arise in four aspects: NPP data quality, model shortcomings, different spatial scales, and human disturbances.