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In-field sensing of soil organic matter content would be desirable to provide a control input for the variation of soil-applied herbicide application rates. Control based on organic matter content could eliminate overapplication on less productive areas within a field, thereby improving profitability and reducing the potential for groundwater contamination.
Color coordinate data and spectral reflectance data in the visible and near infrared (NIR) regions were correlated with organic carbon contents of 30 Illinois mineral soils, using multiple linear regression, stepwise multiple linear regression, principal components regression, and partial least squares regression. The combination of NIR spectral reflectance and partial least squares regression was the most predictive, and was chosen for implementation.
The prototype NIR soil organic matter sensor, designed and fabricated to withstand field use, was tested both in the laboratory and in the field. Laboratory calibration of the sensor was accomplished with a test set of thirty Illinois mineral soils prepared at soil moisture tensions ranging from 1.5 MPa to 0.033 MPa. Laboratory predictions yielded an r$\sp2$ of 0.89 and a standard error of prediction of 0.23 percent organic carbon (0.40 percent organic matter). The sensor was also able to predict soil moisture and cation exchanges capacity. Limited in-furrow field operation of the sensor produced a much higher standard error (0.53 percent carbon), due to the movement of the soil past the sensor as scanning was accomplished.
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