Geoscience Reference
In-Depth Information
The application of EC
a
to the SSCM arena is largely due to the past and current research efforts of
Kitchen and colleagues (2003, 2005), Lund and colleagues (1999, 2001), and Jaynes and colleagues
(1995b, 2003, 2005) in the Midwest using EC
a
to delineate productivity zones. Productivity zones
refer to areas of similar productivity potential and are of interest to producers, because some key
management decisions depend upon reliable estimates of expected yield. Productivity zones associ-
ate productivity with a soil property or condition but do not provide the producer with site-specific
information for optimizing yield in low-yield portions of a field. For instance, the productivity zones
of dryland agriculture have been primarily related to available water as affected by soil and topog-
raphy (Jaynes et al., 2003; MacMillan et al., 1998). In contrast, SSMUs are units of soil that can be
managed similarly to optimize yield.
Corwin et al. (2003a) carried the EC
a
-directed soil sampling approach to the next level in SSCM
by integrating crop yield to delineate SSMUs with associated recommendations. This work was
based on the hypothesis that in the field where yield spatially correlates with EC
a
, then geospatial
measurements of EC
a
can be used to identify edaphic and anthropogenic properties that influence
yield. Through spatial statistical analysis, Corwin et al. (2003a) were able to show the influence of
salinity, leaching fraction, θ, and pH on the spatial variation of cotton yield for a 32.4 ha field in the
Broadview Water District of central California. With this information, a crop yield response model
was developed and management recommendations were made that spatially prescribed what could
be done to increase cotton yield at those locations with less than optimal yield. Subsequently, Cor-
win and Lesch (2005a) delineated SSMUs. Highly leached zones were delineated where the leach-
ing fraction (LF) needed to be reduced to <0.5; high salinity areas were defined where the salinity
needed to be reduced below the salinity threshold for cotton, which was established at EC
e
= 7.17
dS m
−1
for this field; areas of coarse texture were defined that needed more frequent irrigations; and
areas were pinpointed where the pH needed to be lowered below a pH of 8 with a soil amendment
such as OM. This work brought an added dimension because it delineated within-field units where
associated site-specific management recommendations would optimize the yield, but it still falls
short of integrating biological, meteorological, economic, and environmental impacts on within-
field crop-yield variation. However, prior to the work by Corwin and colleagues, SSCM applications
of EC
a
had been restricted to the identification of productivity zones (Boydell and McBratney, 1999;
Jaynes et al., 2003, 2005; Kitchen et al., 2005; Ping and Dobermann, 2003) rather than management
zones that vary in some management input or practice.
Because of its ability to spatially characterize soil properties, EC
a
-directed soil sampling easily
transitions into a means of monitoring management-induced spatiotemporal changes through the
interjection of a temporal component (Corwin et al., 2006). However, even though EC
a
-directed soil
sampling is far more efficient and less costly than conventional grid sampling, it is still limited in
the frequency with which spatio-temporal changes can be studied. Highly dynamic changes, such
as those occurring between irrigation or precipitation events or within a crop growing season, are
probably too dynamic to monitor effectively. Gradual changes that occur during the course of soil
reclamation (Lesch et al., 2005) or due to changes in management, such as drainage water reuse
(Corwin et al., 2006), are well suited for EC
a
-directed soil sampling. These typically require moni-
toring at annual intervals or longer.
2.4 pRoGnoSIS of GeophySICAl teChnIQUeS In AGRICUltURe—
fUtURe tRendS And needS
The use of geospatial measurements of EC
a
for directing soil sampling to characterize soil spa-
tial variability will continue to be a useful approach for field and larger spatial extents. There is
considerable potential impact because the characterization of spatial variability is a fundamental
component of a variety of field- and landscape-scale issues, including soil quality assessment, solute
