Correlation Matrix of Soil Properties for the Six Field-Scale ECa Surveys

Coachella Valley Wheat Field ln(ECe) 1.00 0.69 0.66

Coachella Valley Sorghum Field ln(ECe) 1.00 -0.10 0.28

Broadview Water District (Quarter Sections 16-2 and 16-3)

Fresno Cotton Field ln(ECe) 1.00 0.38 -0.37

0W 1.00

Coachella Valley—Kohl Ranch Field ln(ECe) 1.00 -0.39 0.72

0W 1.00

Broadview Water District (Quarter Section 10-2)

Source: Modified from Corwin, D.L., and Lesch, S.M., Agron. J., 95,

455-471, 2003. With permission. Note: ECe is the electrical conductivity of the saturation extract (dS m-1), SP is the saturation percentage. 0w is the volumetric water content (cm3 cm-3). Coachella Valley—Kohl Ranch Field

This field displays a range of correlations between EMI and soil properties (Table 4.2). Salinity correlates very well, water content fairly well, and soil texture exhibits weak negative correlation indicating that the dominant soil properties influencing the EMI reading are salinity and water content. In addition, two secondary properties, SAR and boron, were measured. The fact that these correlated quite well with the EMI data suggests the close association of these properties with salinity in this particular field because the EMI reading does not directly measure SAR or boron but is rather an artifact of solute flow. Broadview Water District (Quarter Section 10-2)

The dominant soil property influencing the EMI reading is salinity, with a correlation between ln(EMIave) and ln(ECe) of 0.80. No strong correlation was found between EMI data and a variety of soil properties, including SP, water content, bulk density, and separates of sand, silt, and clay.

From Table 4.4 and Table 4.5, what is known about the interrelationship of soil properties influencing the ECa measurement for agricultural soils in the arid southwest? First, it is clear that the inner-correlation structure of the various primary soil properties (ECe, SP, 0w) determines how well each property ultimately correlates with the ECa signal data. However, the variability of each soil property also influences the final correlation estimates, because increased variability in any given soil property directly translates into increased variation in the ECa data. Obviously, one may encounter many diverse types of inner-correlation structures and different degrees of specific soil property variation as shown in Table 4.4 and Table 4.5. Thus, the ultimate correlation between the ECa signal data and any specific soil property may be quite different from field to field. For example, this effect is clearly evident in the ln(EMIave) and SP correlation estimates shown in Table 4.4, where the observed estimates range from -0.33 to 0.84. Second, with respect to ECe data, the best scenario for the prediction of salinity from ECa signal data occurs when the ECe, SP, and 8w cross-correlation estimates are all positive and high (i.e., near 1), and the SP and 8w variation is minimal.

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