Geoscience Reference
In-Depth Information
where PW is the percent water on a gravimetric basis, ρ
b
is the bulk density (Mg m
−3
), SP is the
saturation percentage, EC
w
is average electrical conductivity of the soil water assuming equilibrium
(i.e., EC
w
= EC
sw
= EC
wc
), and EC
e
is the electrical conductivity of the saturation extract (dS m
−1
).
The reliability of Equation (2.17) through Equation (2.22) has been evaluated by Corwin and
Lesch (2003). These equations are reliable except under extremely dry soil conditions. However,
Lesch and Corwin (2003) developed a means of extending equations for extremely dry soil condi-
tions by dynamically adjusting the assumed water content function.
Because of the three pathways of electrical conductance, EC
a
is influenced by several soil physi-
cal and chemical properties: (1) soil salinity, (2) saturation percentage, (3) water content, (4) bulk
density, and (5) temperature. The quantitative influence of each factor is reflected in Equation (2.17)
through Equation (2.22). The SP and ρ
b
are both directly influenced by clay content and organic
matter (OM). Furthermore, the exchange surfaces on clays and OM provide a solid-liquid phase
pathway primarily via exchangeable cations; consequently, clay content and mineralogy, cation
exchange capacity (CEC), and OM are recognized as additional factors influencing EC
a
measure-
ments. Apparent soil electrical conductivity is a complex property that must be interpreted with
these influencing factors in mind.
Field measurements of EC
a
are the product of both static and dynamic factors, which include
soil salinity, clay content and mineralogy, θ, ρ
b
, and temperature. Johnson et al. (2003) described
the observed dynamics of the general interaction of these factors. In general, the magnitude and
spatial heterogeneity of EC
a
in a field are dominated by one or two of these factors, which will vary
from one field to the next, making the interpretation of EC
a
measurements highly site specific. In
instances where dynamic soil properties (e.g., salinity) dominate the EC
a
measurement, temporal
changes in spatial patterns exhibit more fluidity than systems that are dominated by static factors
(e.g., texture). In texture-driven systems, spatial patterns remain consistent because variations in
dynamic soil properties affect only the magnitude of measured EC
a
(Johnson et al., 2003). For this
reason, Johnson et al. (2003) warn that EC
a
maps of static-driven systems convey very different
information from those of less-stable dynamic-driven systems.
Numerous EC
a
studies have been conducted that revealed the site specificity and complexity of
spatial EC
a
measurements with respect to the particular property influencing the EC
a
measurement
at that study site. Table 2.1 is a compilation of various laboratory and field studies and the associated
dominant soil property measured.
The complex nature of EC
a
has a positive benefit. Because of its complexity, geospatial mea-
surements of EC
a
provide a means of potentially characterizing the spatial variability of those soil
properties influencing EC
a
or even soil properties correlated to EC
a
without a direct cause-and-
effect relationship. The characterization of spatial variability of soil properties correlated with EC
a
at a specific field has been achieved through EC
a
-directed soil sampling (Corwin and Lesch, 2005c;
Lesch et al., 1995b).
2.3.3 a
g R i c u l t u R a l
a
P P l i c at i of n s
o f
ec
a
-d
i R e c t e d
s
o i l
s
a M P l i n g
The characterization of soil spatial variability using EC
a
-directed soil sampling has been applied to
a variety of landscape-scale agricultural applications: (1) spatial input for solute transport models of
the vadose zone, (2) mapping edaphic and anthropogenic properties, (3) characterizing and assess-
ing soil quality, (4) delineating site-specific management units (SSMUs) and productivity zones, and
(5) monitoring management-induced spatiotemporal change in soil condition.
To date, the only study to use EC
a
-directed soil sampling to characterize soil variability for use
in the modeling of solute transport in the vadose zone is by Corwin et al. (1999). In a landscape-
scale study modeling salt loading to tile drains in California's San Joaquin Valley, Corwin et al.
(1999) used EC
a
-directed soil sampling to define spatial domains of similar solute transport capac-
ity in the vadose zone. These spatial domains, referred to as stream tubes, were volumes of soil
that are assumed to be isolated from adjacent stream tubes in the field (i.e., no solute exchange) so
