Yield Mapping in Saline Soils

Project Objective

Step 1: Perform EMI surveys and map ECa in study region

We used an EMI device called an EM38-MK2 to perform non-invasive surveys of bulk average soil apparent conductivity from 0 - 1.5 m deep (ECa).

ECa is influenced by soil texture, moisture, and salinity, so we have to take soil samples and derived soil saturated paste extract electrical conductivity (ECe) to calibrate our measured ECa values to ECe with a statistical model (e.g., linear regression). This let's us create a map of just salinity (i.e., Step 2).

Step 2: Mapping salts via EMI methods

After performing our ECa to ECe calibration, here is the resulting map that shows the levels of soil salt in the fairmont district region. Soil and water salinity is often measured by electrical conductivity (EC), based on the principle that salt is an electrolyte. The most commonly used EC units are deciSiemens per meter (dS/m). The higher the conductivity observed, the more likely that there is high amounts of salt there. The map also shows the locations of the tile drains that are placed about 1.2 meters below the ground surface.

Based on this map we can see if yield loss is expected based on published research provided by the Food and Agriculture Organization of the United Nations.

Step 3: Identify sub fields for corn yield sampling

We sampled corn by hand to measure yield in the following two fields with a large range of observed salinity.

Step 4: Plot yield related to salinity

Results and Conclusion

ECa is directly measured, while ECe is likely going to be derived through a calibration process (like we did in this study). This means additional uncertainty is incurred through the yield prediction process as shown in the graphs via error bars.

This means that in certain cases, ECa may be a better predictor of yield loss than ECe, which could be incredibly useful since it is non-invasive, and relatively much less labor-intensive than creating ECa to ECe calibrations and maps.

Note: this is only true when salt is the dominant contributor to ECa readings. In low-salt settings, this is likely not to be the case, so do not expect ECa to always be a good proxy for estimating yield loss. Always test this prior to making models by taking a small sample set to test for plausibility.

For more details on this methodology, please feel free to contact me!

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