Abstract
The accuracy and precision with which carbon amounts have been accounted for in forests have been questioned. As countries seek to comply with agreements to reduce global warming and industries seek to maximize bioenergy potential, this matter has increased international concern. White spruce (Picea glauca (Moench) Voss) stand density management trials in the Petawawa Research Forest, Ontario, Canada, were sampled to evaluate carbon concentration variation within trees and plots of differing stand density. Sample-drying methodologies were also tested to compare freeze-dried carbon (FDC) and oven-dried carbon (ODC) measurements. The average FDC was 51.80 ± 1.19%, and the corrected freeze-dried carbon content (FDCCOR) was 51.76 ± 1.33%. The average ODC was 49.10 ± 0.92%, and the average volatile carbon fraction (Cvol) was 2.67 ± 1.71%. FDC was higher than ODC (mean of the differences = 2.52) and generally more variable. ODC significantly decreased radially and longitudinally. FDC was significantly affected by thinning, where heavy treatments resulted in the highest FDC amounts compared to medium, light, and control treatments. In addition to reducing carbon content (CC), drying influences wood CC in many ways that are still to be elucidated. The results of this study suggest that ODC should continue to be used within the bioenergy industry, while FDC must become the preferred standard for carbon accounting protocols.
Highlights
Forest carbon accounting has been gaining global attention as countries seek to comply with agreements to reduce global warming [1]
When segregating trees based on maturity and testing for differences with a Welch two-sample t-test, no difference was found between juvenile wood and mature wood for freeze-dried carbon (FDC) and oven-dried carbon (ODC) (Figure 3)
White spruce carbon content (CC) increased with thinning, but this increase was only significant for heavy thinning
Summary
Forest carbon accounting has been gaining global attention as countries seek to comply with agreements to reduce global warming [1]. Established methods for measuring forest metrics (e.g., volume, biomass) to estimate carbon content in forests exist and are reasonably accurate, they carry many sources of imprecision [1,3]. Some of these sources of error are related to sampling design (related to tree selection, plot size, number, and location), measurement error (related to tree shape and equipment accuracy), and regression error (related to the substantial changes with the equation used) [1,3]. Carbon concentration variation still needs to be accurately described to improve our estimation of carbon stocks in forest stands
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