Estimating annual bole biomass production using uncertainty analysis

Two common sampling methodologies coupled with a simple statistical model were evaluated to determine the accuracy and precision of annual bole biomass production (BBP) and inter-annual variability estimates using this type of approach. We performed an uncertainty analysis using Monte Carlo methods in conjunction with radial growth core data from trees in three Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) dominated sites (young, mature, and old-growth) in the western Cascades of Oregon. A model based on the mean and standard deviation of annual radial growth from sampled trees was used with and without stratification by tree size to predict radial growth for non-sampled trees. Sample sizes of 64–128 trees per stand were required to achieve accuracy and precision within ±10%. Without stratification the model underestimated annual BBP (Mg ha−1 year−1) in all three age classes by up to 28%, and inter-annual variability by as much as 26%. Applying stratification increased accuracy of estimates at least twofold, and precision of estimates improved by 3–10%, resulting in decreased sample size requirements. The coefficient of variation of error of estimates was half that of inter-annual variability over the study period. Thus, this approach can be used to examine patterns of inter-annual variability of BBP in response to changing climate and land use patterns.