Geoscience Reference
In-Depth Information
From Table 4.4 and Table 4.5, what is known about the interrelationship of soil properties influ-
encing the EC
a
measurement for agricultural soils in the arid southwest? First, it is clear that the
inner-correlation structure of the various primary soil properties (EC
e
, SP, θ
w
) determines how
well each property ultimately correlates with the EC
a
signal data. However, the variability of each
soil property also influences the final correlation estimates, because increased variability in any
given soil property directly translates into increased variation in the EC
a
data. Obviously, one may
encounter many diverse types of inner-correlation structures and different degrees of specific soil
property variation as shown in Table 4.4 and Table 4.5. Thus, the ultimate correlation between the
EC
a
signal data and any specific soil property may be quite different from field to field. For example,
this effect is clearly evident in the ln(EMI
ave
) and SP correlation estimates shown in Table 4.4, where
the observed estimates range from −0.33 to 0.84. Second, with respect to EC
e
data, the best scenario
for the prediction of salinity from EC
a
signal data occurs when the EC
e
, SP, and θ
w
cross-correlation
estimates are all positive and high (i.e., near 1), and the SP and θ
w
variation is minimal.
4.4 CloSInG ReMARkS
The need for a means of measuring within-field variation in soil salinity within the root zone in a
quick, reliable, and cost-effective manner resulted in the development of GPS-based mobile ER and
EMI techniques to measure and map EC
a
. However, the measurement of EC
a
is complicated by the
influence of several soil properties aside from soil salinity, including soil texture, temperature, and
water content. This has enabled geospatial measurements of EC
a
to become a tool for directing soil
sampling to characterize spatial variability of soil properties correlated with EC
a
within a given
field. When maps of EC
a
are properly understood, they can be used to (1) provide a graphic inven-
tory of the scope of the soil salinity problem, (2) provide useful spatial information concerning soil
texture and water content, (3) identify potential areas in need of improved irrigation and drainage
management, (4) identify areas in need of reclamation, (5) provide a means of monitoring manage-
ment-induced spatiotemporal changes in soil properties that potentially influence crop production,
or (6) provide a means to identify edaphic factors influencing within-field crop variation.
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