Estimating Forest Soil Carbon and Nitrogen Stocks with Double Sampling for Stratification

Abstract

Precise and accurate estimation of C and N in forest soils is important for monitoring long‐term site productivity and C stock changes. Obtaining such estimates remains a major challenge, however, especially because of high natural variability in the forest floor. Although most researchers have used simple random sampling (SRS) for within‐plot soil sampling, double sampling for stratification (DSS) can be used to decrease costs, increase precision, and increase power. Estimates of C and N stocks based on DSS were compared with those estimated by SRS in the humus forms of Douglas‐fir [Pseudotsuga menziesii (Mirb.) Franco] stands in the Cascade Mountains of Oregon. Generally, DSS was 1.34 to 5.11 times more efficient than SRS for total C, and 1.07 to 2.00 for total N. Coefficients of variation estimated from DSS were about one‐half of those estimated by SRS and reported elsewhere in the literature. The cost for sampling using DSS was one‐third to one‐half of that for SRS, depending on the number of strata used. Costs were reduced because fewer samples were required using DSS to provide the same precision as SRS. The DSS design was more powerful than SRS and could detect smaller changes than SRS with the same number of samples. Results suggested that the most efficient design for total C would use two strata where samples were allocated proportional to variance rather than proportional to area. Overall, large gains in efficiency can be realized with a more complex within‐plot sampling design, i.e., DSS, compared with SRS.