Geoscience Reference
In-Depth Information
tAble 4.3
Correlation Coefficients (r) between Shallow (0 to 0.3 m) and deep
(0 to 0.9 m) eC
a
(mS m
−1
) and their Corresponding (Same depth)
Soil properties at three Colorado fields
Wiggins 1
Wiggins 2
yuma
Shallow
eC
a
deep
eC
a
Shallow
eC
a
deep
eC
a
Shallow
eC
a
deep
eC
a
Soil property
Sand (%)
-0.96
-0.95
-0.84
-0.90
-0.76
-0.90
Silt (%)
0.82
0.91
0.67
0.80
0.68
0.84
Clay (%)
0.96
0.94
0.89
0.94
0.82
0.93
Bulk density ((b, Mg m
-3
)
-0.13
-0.53
-0.34
-0.52
—
—
Organic matter (g g
-1
%)
0.92
0.92
0.75
0.79
0.80
0.89
Ca
+2
(meq L
-1
)
0.85
0.92
0.94
0.88
0.82
0.82
Mg
+2
(meq L
-1
)
0.91
0.94
0.93
0.87
0.58
0.75
K
+1
(meq L
-1
)
0.80
0.76
0.73
0.75
0.67
0.86
Na
+1
(meq L
-1
)
0.65
0.87
0.62
0.79
0.58
0.26
CEC (meq 100 g
-1
)
0.86
0.93
0.94
0.88
0.87
0.87
pH
-0.81
-0.76
-0.48
-0.48
0.50
0.23
Soluble salts (EC
1:1
, mS m
-1
)
0.86
0.95
0.24
0.66
0.86
0.78
a design-based sampling scheme such as stratified random sampling is probably needed to better
spatially characterize these soil properties.
An example of a simple statistical approach to infer EC
a
versus soil properties relations is given
by the correlation coefficients between EC
a
and soil properties by depth (the terms “shallow” and
“deep” refer to soil depths of 0 to 0.3 m and 0 to 0.9 m, respectively) given in Table 4.3 (Farahani
et al., 2005). As given, the EC
a
measurements from these sandy soils are very useful in inferring
texture variability with correlation coefficient values between EC
a
and clay (or sand) well over 0.8.
Crop yield monitoring data in conjunction with EC
a
survey data can be used from a site-specific
crop management perspective (1) to identify those soil properties influencing yield and (2) to delin-
eate site-specific management units (SSMU). For site-specific crop management, an understanding
of the influence of spatial variation in soil properties on within-field crop-yield (or crop quality)
variation is crucial. To accomplish this using EC
a
, crop yield (or crop quality) must correlate with
EC
a
within a field. If crop yield (or crop quality) and EC
a
are correlated, then basic statistical analy-
ses by depth increment (e.g., 0 to 0.3, 0.3 to 0.6, 0.6 to 0.9, and 0.9 to 1.2 m) and by composite depths
(e.g., 0 to 0.3, 0 to 0.6, 0 to 0.9, and 0 to 1.2 m) are performed. As before, the correlation between
EC
a
and mean values of each physical and chemical soil property for each depth increment and
each composite depth establishes those soil properties that are spatially characterized with the EC
a
-
directed sampling design. The correlations between crop yield (or crop quality) and soil properties
will also establish the depth of concern (i.e., the root zone of the crop), which will be the composite
depth that consistently has the highest correlation of each soil property (i.e., each soil property
determined to be significant to influencing yield) with crop yield (or crop quality). Exploratory
graphical analyses (i.e., scatter plots of crop yield or crop quality and each soil property) are then
conducted for the depth of concern to determine the linear or curvilinear relationship between the
significant physical and chemical properties and crop yield (or crop quality). A spatial linear regres-
sion is formulated that relates the significant soil properties as the independent variables to crop
yield (or crop quality) as the dependent variable. The functional form of the model is developed
from the exploratory graphic analysis. If necessary, the model is adjusted for spatial autocorrelation
using restricted maximum likelihood or some other technique. This entire spatial statistical analysis
process is clearly demonstrated by Corwin et al. (2003b) and Corwin and Lesch (2005c).
