Wednesday, November 7, 2007
314-4

Estimation of Forest Soil Carbon Stocks Using Double Sampling for Stratification.

Cindy Shaw, Canadian Forest Service, Canadian Forest Service, 5320 122nd Street, Edmonton, AB T6H 3S5, CANADA, James Boyle, 280 Peavy Hall, Oregon State University, Oregon State University, College of Forestry, Corvallis, OR 97331-5703, and A. Y. Omule, Agro Forestry Limited, 1564 Granada Crescent, Victoria, BC V8N 2B8, Canada.

Achieving precise and accurate estimation of carbon (C) in forest soils is a challenge especially because of high natural variability in the forest floor. Although most researchers have used a simple random sampling (SRS) design for within-plot soil sampling, double sampling for stratification (DSS) can be used to decrease costs, increase precision and increase power. We compared estimates of C and N stocks based on DSS to those estimated by SRS, in the humus forms (O plus A horizons) of Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) stands in the Cascade Mountains in Oregon, USA. The DSS was 5 to 66 times more efficient than SRS for estimating soil C stocks. Coefficients of variation estimated from DSS were about one quarter of those estimated by SRS and reported elsewhere in the literature. To achieve the same level of precision with 6 to 16 samples per plot using DSS, 43 to 863 samples per plot for would be required using SRS. The DSS design is more powerful than SRS and therefore permits a scientist to detect smaller changes than SRS with the same number of samples. The estimated costs of sampling for this study using DSS were about one-twentieth of those if SRS had been used to obtain the same precision. These results show that large gains in efficiency were realized by using a more complex within-plot sampling design, i.e. double sampling for stratification, compared with simple random sampling.