Geoscience Reference
In-Depth Information
tAble 15.1
descriptive Statistics and Correlation Coefficients for Combined
Samples from All fields
Correlation Coefficients
K
d
property
Mean
Sd
a
fo
c
eptC
Metribuzin
Metolachlor
EC
a
0-0.3 m (mS/m)
22.1
8.8
0.75
b
0.57
b
0.54
b
0.63
b
K
d
(L/kg) EPTC
0.90
0.32
0.69
b
K
d
(L/kg) Metribuzin
0.25
0.20
0.63
K
d
(L/kg) Metolachlor
1.20
0.51
0.71
b
f
oc
0.006
0.002
a
Standard deviation.
b
p
< 0.0001.
However, the question is: Can these relationships be used to map herbicide-soil binding variability
across a field? Each field was mapped into high and low
f
oc
zones (Figure 15.1) based on results from
the regression tree classification of the EC
a
and
f
oc
relationship. The validation soil data were used
to test the resulting maps. The results were good, although not perfect (Table 15.2). As expected,
the
K
d
of the three herbicides followed the
f
oc
. On average, the herbicides bound less tightly to soil
samples taken from the low
f
oc
zones compared to soils from the high
f
oc
zones (Table 15.2). These
results indicate that if a relationship between
f
oc
and EC
a
exists, EC
a
maps could be used to map
herbicide soil binding variability across a field.
tAble 15.2
the fraction of organic Carbon (f
oc
) and herbicide-Soil
binding (k
d
) of eptC, Metribuzin, and Metolachlor in
validation Soil Samples from three fields in Colorado
yuma
K
d
zone
eC
a
0-0.3 m
eptC
Metribuzin
Metolachlor
f
oc
15.06
0.38
0.04
0.58
0.0058
22.49
0.52
0.12
0.83
0.0058
Low
f
oc
18.47
0.52
0.15
0.91
0.0047
18.19
0.77
0.21
1.19
0.0061
11.33
0.78
0.29
1.29
0.0055
Avg
17.11
0.59
0.16
0.96
0.0056
Std. dev.
4.17
0.17
0.10
0.29
0.0005
27.03
0.98
0.35
1.44
0.0085
High
f
oc
40.94
1.11
0.42
1.66
0.0065
35.89
1.18
0.45
1.86
0.0110
44.38
1.32
0.64
2.26
0.0113
Avg
30.48
1.15
0.47
1.81
0.0075
Std. Dev.
16.09
0.14
0.13
0.35
0.0044
