ABSTRACT

Estimating coefficient of variation (CV) and semi-variogram from sample data requires an appropriate sampling spacing. Although some recommendations on the optimal spacings exist, they focused on the simulated random fields. We collected surface soil (0–20 cm) samples from 689 sites based on a regular grid of 100 × 100 m in the Liudaogou watershed (6.89 km²) on the Loess Plateau, China. We grouped the samples by re-sampling at six sampling spacings (100, 200, 300, 400, 500, and 600 m) to determine the characteristic changes in the spatial variability. The mean and CV values varied little among the different spacings (p < 0.05). The sampling spacing can be increased to 300 m if we want to know the possible relationships between OC, TN, and TP with the environmental factors. OC and TN were fitted best by the spherical model whereas TP was fitted best with an exponential model. The optimal model type unchanged as the spacing increased. The apparent parameters of semi-variogram did not differ from their true values when the spacing was less than 200 m for most cases (p < 0.05). Therefore, the spacing should not be greater than 200 m in order to clarify their spatial structures.