Geostatistical Methods to Better Characterize Chemical Soil Properties of PNW Forest Stands At the Sub-Plot Level.

The characterization of soil properties is imprecise. Soil is a vastly heterogeneous entity that exhibits variability from micro- to megascopic scales. Current state-level soil maps (SSURGO) are based largely on geomorphic features rather than sampled measurements. Those maps were designed as generalized assessments and contain much uncertainty in their predictions of individual soil attributes (such as nutrient content or pH). Even when sampled directly at the plot-level, soils can exhibit high variability. Management in such conditions is challenging as heterogeneous soil mixtures can vary greatly in response to a given treatment over scales as small as 5-10 meters.

This research seeks to more closely examine the causes and implications of small scale anisotropic soil attribute distributions, focusing specifically on Pacific Northwest Douglas-fir plantations. Forests are especially prone to heterogeneous distribution; the forest environment varies greatly in topology, biota distribution, and hydrology.

For this study, a highly precise set of soil measurements is required. Soil sampling has historically been expensive and time-consuming, but an emerging technology has the potential to reduce costs. Using spectroscopy, an infrared (IR) beam reflected off a soil sample generates a spectral signature that can be correlated with a number of chemical and physical soil properties.

IR techniques will be used to generate soil attribute data for two 20-acre fir plantations. At each site, 300 soil samples were gathered (at three depth intervals). Potentially predictable soil properties include total organic carbon, total nitrogen, cation exchange capacity, exchangeable calcium, pH, bulk density, sand, silt, clay, and electrical conductivity. The data will be used to generate more accurate soil maps, gain insight into the environmental processes that control soil pedogenesis and distribution, and identify nutrient hot spot locations. This work will also seek to gain understanding of the uncertainty associated with various soil sampling and mapping schemes.