Geoscience Reference
In-Depth Information
that a one-dimensional, vertical solute transport model can be applied to each stream tube without
concern for lateral flow of water and transport of solute. The application of a functional, tipping-
bucket, layer-equilibrium model to each stream tube resulted in the prediction of salt loading to
within 30% over a five-year study period.
Mapping soil properties with EC
a
-directed soil sampling has been conducted by a limited num-
ber of researchers because this approach is comparatively new. The earliest work was conducted
by Lesch et al. (1995b) mapping soil salinity. Johnson et al. (2001, 2004) used an EC
a
-directed
stratified sampling approach to delineate within-field variability of physical, chemical, and bio-
logical properties and to relate observations made at different experimental scales. Corwin and
Lesch (2005c) used EC
a
-directed soil sampling to map a variety of properties for a saline-sodic soil,
including salinity, clay content, and sodium adsorption ratio. Triantafilis and Lesch (2005) mapped
clay content over a 300 km
2
area. Lesch et al. (2005) used EC
a
-directed soil sampling (1) to map
and monitor salinity during the reclamation of a field by leaching, (2) to map soil texture and soil
type classification, and (3) to identify and locate buried tile lines of a drainage system. Sudduth
et al. (2005) provide the most comprehensive compilation relating EC
a
to soil properties covering
the north-central United States.
An extension of the ability to map individual soil properties is the ability to characterize and
assess soil condition based on a compilation of spatial data for individual soil properties influencing
the intended function of a soil. The application of EC
a
-directed soil sampling to characterize and
assess soil condition has been largely found in the Great Plains area and the southwestern United
States. Using EC
a
maps to direct soil sampling, Johnson et al. (2001) and Corwin et al. (2003b)
spatially characterized the overall soil quality of physical and chemical properties thought to affect
yield potential. To characterize the soil quality, Johnson et al. (2001) used a stratified soil sampling
design with allocation into four geo-referenced EC
a
ranges. Correlations were performed between
EC
a
and the minimum data set of physical, chemical, and biological soil attributes proposed by
Doran and Parkin (1996). Their results showed a positive correlation of EC
a
with percentage clay,
ρ
b
, pH, and EC
1:1
over a soil depth of 0 to 30 cm, and a negative correlation with soil moisture, total
and particulate organic matter, total C and N, microbial biomass C, and microbial biomass N. No
relationship of the soil properties to crop yield was determined. Corwin et al. (2003b) characterized
the soil quality of a saline-sodic soil using a SRSS design. A positive correlation was found between
EC
a
and the properties of volumetric water content; electrical conductivity of the saturation extract
(EC
e
); Cl
−
, NO
3
−
, SO
4
−
, Na
+
, K
+
, and Mg
+2
in the saturation extract; SAR (sodium adsorption ratio),
exchangeable sodium percentage (ESP); B; Se; Mo; CaCO
3
; and inorganic and organic C. The posi-
tive correlation indicated that the spatial variability of soil properties would be accurately character-
ized. Most of these properties are associated with soil quality for arid zone soils. A number of soil
properties (i.e., ρ
b
; percentage clay; pH
e
; SP; HCO
3
−
and Ca
+2
in the saturation extract; exchangeable
Na
+
, K
+
, and Mg
+2
; As; CEC; gypsum; and total N) did not correlate well with EC
a
measurements,
indicating that the SRSS sample design would not accurately characterize the spatial variability of
these particular properties. Johnson et al. (2001) and Corwin et al. (2003b) did not actually relate the
spatial variation in the measured soil physical and chemical properties to crop yield variations.
To a varying extent from one field to the next, crop patterns are influenced by the spatial vari-
ability of edaphic properties. Conventional farming does not address these variations because it
manages a field uniformly; as a result, within-field variations in soil properties cause less than
optimal crop yields. Site-specific crop management (SSCM) seeks to address variations in crop
yield by managing edaphic, anthropogenic, biological, meteorological, and topographic factors to
optimize yield and economic return. Bullock and Bullock (2000) point out the importance to SSCM
of developing efficient methods for accurately measuring within-field variations in soil physical and
chemical properties that influence spatial variation in crop yield. The geospatial measurement of
EC
a
is a technology that has become an invaluable tool for identifying the soil physical and chemi-
cal properties influencing crop yield patterns and for establishing the spatial variation of these soil
properties (Corwin et al., 2003a).
