Sample Sizes to Control Error Estimates in Determining Soil Bulk Density in California Forest Soils

Core Ideas Sample sizes varied from 3 to 17 to achieve ±10% error at a 95% confidence level. Bootstrapping is more robust for estimating sample size than the traditional method. Soil variability must be considered before sampling. Characterizing forest soil properties with high variability is challenging, sometimes requiring large numbers of soil samples. Soil bulk density is a standard variable needed along with element concentrations to calculate nutrient pools. This study aimed to determine the optimal sample size, the number of observation ( n ), for predicting the soil bulk density with a precision of ±10% at a 95% confidence level among different soil types. We determined soil bulk density samples at three depths at 186 points distributed over three different 1‐ha forest sites. We calculated n needed for estimating means of bulk density using a traditional method. This estimate was compared to a bootstrapping method n where the variance was estimated by re‐sampling our original sample over 500 times. The results showed that patterns of soil bulk density varied by sites. Bootstrapping indicated 3 to 17 samples were needed to estimate mean soil bulk density at ± 10% at a 95% confidence level at the three sites and three depths. Sample sizes determined by the bootstrap method were larger than the numbers estimated by the traditional method. Bootstrapping is considered theoretically to be more robust, especially at a site with more variability or for site measures that are not normally distributed.