locations may not adequately represent entire site (e.g., clustered sampling locations limit information on spatial distribution).
Simple Random Sampling
The simple random sampling design assumes each given area has an equal opportunity of being selected (Table 2.3), and this typically involves pre‐selection of sampling points to avoid evaluator bias (Dick et al., 1996). Point selection may be done using a random number table and a coordinate grid overlain on an image of the sampling area, or with a random point generator and GIS (geographic information systems) software package (e.g., ArcGIS). Use of this sampling design minimizes bias associated with point selection.
Though easy to apply, simple random sampling has notable caveats since all points in an area are assumed to have equal importance, regardless of variation in inherent features and/or spatial stratification of management attributes. Spatial distribution of selected points therefore may not adequately represent an entire site, although this concern diminishes as the number of sample points increases. This design may be appropriate when knowledge of the site is limited.
Stratified Random Sampling
Stratified random sampling applies simple random sampling to preselected subareas of a site (Table 2.3). This design requires knowledge of site characteristics to accurately identify the location, extent, and boundary of subareas. This sampling design is most useful at sites with clearly defined soil, landscape, management, and/or contamination zones (Dick et al., 1996).
Assessing soil health indicators within defined subareas will provide a more complete understanding of inherent and/or management‐induced variation. The drawback of this sampling design is a corollary to its strength, since subarea stratification will require more resources than simple random sampling.
Systematic Sampling
Systematic sampling involves selection of predetermined sampling points, typically in a grid‐ or transect‐like pattern across the sampling area (Table 2.3). Use of fixed sampling points simplifies sampling, with the added advantage of being able to return to the same point in the future. The data are amenable to spatial analyses (e.g., geostatistics) which can be used to identify a range of soil properties within a site (Dick et al., 1996). Use of systematic sampling is best suited for soil mapping when knowledge of the site is limited but resources are abundant.
Caveats associated with systematic sampling include bias based on grid selection. If there are large distances between sampling points, nuances in soil condition that would be detected at finer scales can be missed. Furthermore, systematic sampling may also sacrifice sampling efficiency (e.g., sampling by stratified zone) because points are selected in an established pattern. Accordingly, systematic sampling may be less cost effective than other designs.
Composite Sampling
Composite sampling can increase sampling accuracy and reduce analytical costs. Briefly, it involves collecting several homogenous samples in an area surrounding a sampling point and combining those samples into a single bulk (or composite) sample (Dick et al., 1996). For effective composite sampling, it is essential that each sub‐sample contributes equally and that there are no interactions among sub‐samples (Boone et al., 1999). Moreover, all sub‐samples must be collected from the same soil type and depth to be homogenous.
Composite sampling can be used with any of the above sampling designs, although it is less suited to systematic sampling because of the fixed sampling point constraints. Also, since multiple sub‐samples are combined and mixed, compositing samples in an area with a wide variation in physical disturbance should be avoided.
Sampling Depth
Selection of appropriate sampling depths is directly related to the evaluator’s goals. Evaluations of soil biological properties and processes as indictors of soil health, nutrient availability for plant growth, or profile carbon stocks will each require sampling at different but context‐appropriate depth increments. The selection of sampling depth increments must also consider soil forming and/or management‐induced changes, such as depth of tillage, to ensure important features are detected (Wienhold et al., 2006). Furthermore, since variability of some soil properties increases with depth, sampling intensity and the number of depth increments needed to detect differences among treatments or over time will vary by location (Kravchenko and Robertson, 2011). Selecting an appropriate number of depth increments requires a reasonable understanding of how variable a site is, how sensitive the indicators are, and the implications of over‐ or under‐estimating the correct value.
Two common sampling strategies are to use genetic horizons or uniform depth increments. Sampling by horizon is well‐suited for sites under native vegetation, where soil forming processes are readily evident throughout the profile due to distinct differences in morphology (i.e., texture, structure, color). Soils with distinct organic horizons benefit from this approach, because layers of surface litter are generally not amenable to fixed increment sampling (Soil Survey Division Staff, 2017). Sampling based on genetic horizons can significantly reduce observed variation throughout the profile (Boone et al., 1999), but requires knowledge of soil taxonomy.
Uniform depth sampling is distinctly different from horizon sampling because every increment starting at the soil surface is fixed (e.g.,
