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FOREST, RANGE & WILDLAND SOILS

Estimating Root Biomass in Rocky Soils using Pits, Cores, and Allometric Equations

College of Environ. Science and Forestry, State University of New York, one Forestry Dr., Syracuse, NY 13210

Matthew A. Vadeboncoeur and Steven P. Hamburg

Center for Environmental Studies, Brown University, Providence, RI 02912

^* Corresponding author (rdyanai{at}mailbox.syr.edu).

Measuring root biomass is time consuming and prone to sampling error. We compared three different methods of measuring root biomass in six northern hardwood stands at the Bartlett Experimental Forest. We found that root coring, the most common method of root sampling, yields estimates of fine root biomass about 27% greater than the estimates based on roots sampled in soil pits. Soil compaction contributes about 10% to this difference; the other contributing factor is that cores cannot be taken through obstructions such as rocks and coarse roots. Pits are the only method allowing characterization of root distribution by depth in rocky soil. If the depth and diameter distribution of roots are not required, allometric equations, if available, provide the easiest method of estimating total root biomass. Equations developed at the nearby Hubbard Brook Experimental Forest predicted root mass measured in soil pits with a mean absolute error of 32%. Allometric equations systematically underpredicted observed soil pit root mass in the young stands, presumably because of mature root systems remaining from the previous cohort, and systematically overpredicted observed root mass in the oldest stands. Soil pits can accurately characterize roots up to about 2 cm; coarser roots are encountered too rarely to be estimated by this method. Soil cores sample only fine roots (up to 1–2-mm diameter) but are much less work than excavating soil pits. Root mass estimates made using cores are more accurate if larger diameter corers are used (5 cm rather than 2.5 cm); subsampling before picking roots can help to control labor costs in the face of larger sample sizes.