Geoscience Reference
In-Depth Information
due to increased concentration of free ions in solution. These are counteracting mechanisms con-
tributing to the complexity of EC
a
and soil property relationships. In other words, empirical EC
a
versus soil property functions are expected to be temporally variable unless EC
w
and θ
w
remain
relatively unchanged. As discussed previously (Equation (4.3), the other important soil variable
causing change in EC
a
is temperature, with EC
a
increasing by approximately 1.9 percent per degree
centigrade. This could be significant for shallow depths that may exhibit the greatest temperature
variation. Apparent soil electrical conductivity is a complex physicochemical 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, water content, bulk density, and temperature. Although
the effect of soil static and dynamic factors on spatial variability of EC
a
is of significant importance,
understanding their influence on the temporal variability of EC
a
is equally important. That is par-
ticularly true if delineated EC
a
zones are to be used to manage agricultural inputs across the field
for multiple years. 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 sys-
tems that are dominated by static factors (e.g., texture). In texture-driven systems, spatial patterns
remain consistent because variations in dynamic soil properties (i.e., water content) affect only the
magnitude of measured EC
a
(Johnson et al., 2003). This was clearly demonstrated by Farahani and
Buchleiter (2004), who used multiyear measurements of EC
a
from three irrigated and nonsaline
sandy fields in eastern Colorado and quantified their degree of temporal change. For each field, soil
EC
a
values were highly correlated between measurement days (for periods of a few days to 4 years
between measurements), but significant deviations from the 1:1 line (indicative of temporal variabil-
ity) were exhibited. In spite of the temporal variability of the absolute magnitudes of EC
a
, delineat-
ing spatial patterns of EC
a
into low, medium, and high zones across each field was highly stable over
time, mainly because they reflect the static soil properties. 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. For this reason, it is imperative that the soil properties dominating EC
a
measurements
within a field are established to be able to correctly interpret spatial EC
a
survey data.
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 or properties influencing the EC
a
measurement at that study site. Table 4.1 is a compilation of various laboratory and field studies and
the associated dominant soil property or properties measured.
Many of the misinterpretations and misunderstandings regarding past field EC
a
surveys have
been due to a disregard for the complex and dynamic interrelationship and influence of various
physical and chemical properties on EC
a
, which are quantified in Equation (4.6) through Equation
(4.11). Because EC
a
does not measure an individual soil property such as salinity or water content,
but rather is a product of the influence of several properties, geospatial EC
a
surveys are best used to
direct soil sampling in order to characterize the spatial variability of those properties that correlate
with EC
a
at a given study site (Corwin, 2005). Characterizing spatial variability with EC
a
-directed
soil sampling is based on the hypothesis that when EC
a
correlates with a soil property or proper-
ties, then spatial EC
a
information can be used to identify sites that reflect the range and variability
of the property or properties. In instances where EC
a
correlates with a particular soil property, an
EC
a
-directed soil sampling approach will establish the spatial distribution of that property with
an optimum number of site locations to characterize the variability and keep labor costs minimal
(Corwin et al., 2003a; Lesch, 2005). Also, if EC
a
is correlated with crop yield, then an EC
a
-directed
soil sampling approach can be used to identify those soil properties that are causing the variability
in crop yield (Corwin et al., 2003b).