Geoscience Reference
In-Depth Information
To use spatial measurements of EC a in a site-specific crop management context, it is not only
necessary to understand those soil-related factors that influence within-field variation in crop yield
(or crop quality), but also to pinpoint the dominant soil-related factors influencing within-field
crop variation. Corwin et al. (2003b) used sensitivity analysis simulations to arrive at the domi-
nant edaphic and anthropogenic factors influencing within-field cotton yield variations. Sensitivity
analysis involves increasing a single independent variable (i.e., edaphic factors) and observing the
resultant effect on the dependent variable (i.e., crop yield or crop quality). This is done for each
independent variable. The relative effect of each independent variable on the dependent variable
determines the independent variable that most significantly influences the dependent variable.
4.3.4 c a s e s t u d i e s
Table 4.4 is a compilation of correlation data for six field study sites where EC a surveys using EMI
were performed for the purpose of salinity appraisal. Table 4.4 shows the variation in the influ-
ence of various soil properties upon EC a for different field locations. In all cases, the surveys were
performed as outlined in Table 4.2. An intensive EC a survey was performed, followed by soil core
sample site selection where from six to twenty sites were selected for sampling. An analysis was
performed on the soil cores for various physical and chemical properties (e.g., saturation percentage,
salinity, and water content). The correlations in Table 4.2 were determined using the EC a survey and
soil sample data. The correlations in Table 4.2 indicate the soil properties influencing the EC a read-
ing most. Following is a discussion about the six EC a surveys presented in Table 4.2.
4.3.4.1
Coachella valley Wheat ( triticum aestvum l.) field
This is an example of a survey where the salinity represented by ln (EC e ), the soil texture reflected
by the saturation percentage (SP), and the volumetric water content (θ w ) correlate with the EMI
data, which are represented as ln(EMI ave ), where EMI ave is the geometric mean of the vertical and
horizontal EMI readings (i.e., sqrt[EMI h · EMI v ]).
4.3.4.2
Coachella valley Sorghum field
This field is an example of where only salinity correlates well with the EMI data. Note from Table 4.5
that neither the soil texture nor volumetric water content correlate with salinity with r = −0.10 and
r = 0.28, respectively. Because of this lack of correlation with salinity and because the texture and
water content exhibit minimal sample variation (i.e., sample range for SP is 51.0 to 61.1 percent;
sample range for θ w is 0.33 to 0.41 cm 3 cm −3 ), they correlate poorly with the EMI data with r = −0.20
and r = 0.25, respectively (Table 4.2).
4.3.4.3
broadview Water district (Quarter Sections 16-2 and 16-3)
These combined quarter sections display large variability in soil texture, as indicated by SPs rang-
ing from 33.2 to 85 percent, and in water content (θ w ranges from 0.21 to 0.39 cm 3 cm −3 ) with rela-
tively minimal salinity variation (80 percent of the samples fell below the mean value of 3.65 dS
m −1 ). Salinity, SP, and water content correlate with the EMI data. Saturation percentage and water
content are highly correlated with r = 0.84 and r = 0.86, respectively (Table 4.2).
4.3.4.4
fresno Cotton ( Gossypium hirsutum l.) field
The Fresno cotton field is of particular interest because of the high positive correlation of EMI data
with salinity ( r = 0.87) and moderate positive correlation with SP ( r = 0.71) but a negative correlation
with water content ( r = −0.65). The negative correlation between SP and water content (see Table 4.5;
r = −0.78) suggests an unexpected inverse relationship between texture and water content.
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