Predicting Soil Organic Carbon and Total Nitrogen at the Farm Scale Using Quantitative Color Sensor Measurements
2018
Roxanne Y. Stiglitz | Elena A. Mikhailova | Julia L. Sharp | Christopher J. Post | Mark A. Schlautman | Patrick D. Gerard | Michael P. Cope
Sensor technology can be a reliable and inexpensive means of gathering soils data for soil health assessment at the farm scale. This study demonstrates the use of color system readings from the Nix ProTM color sensor (Nix Sensor Ltd., Hamilton, ON, Canada) to predict soil organic carbon (SOC) as well as total nitrogen (TN) in variable, glacial till soils at the 147 ha Cornell University Willsboro Research Farm, located in Upstate New York, USA. Regression analysis was conducted using the natural log of SOC (lnSOC) and the natural log of TN (lnTN) as dependent variables, and sample depth and color data were used as predictors for 155 air dried soil samples. Analysis was conducted for combined samples, Alfisols, and Entisols as separate sample sets and separate models were developed using depth and color variables, and color variables only. Depth and L* were significant predictors of lnSOC and lnTN for all sample sets. The color variable b* was not a significant predictor of lnSOC for any soil sample set, but it was for lnTN for all sample sets. The lnSOC prediction model for Alfisols, which included depth, had the highest R2 value (0.81, p-value < 0.001). The lnSOC model for Entisols, which contained only color variables, had the lowest R2 (0.62, p-value < 0.001). The results suggest that the Nix ProTM color sensor is an effective tool for the rapid assessment of SOC and TN content for these soils. With the accuracy and low cost of this sensor technology, it will be possible to greatly increase the spatial and temporal density of SOC and TN estimates, which is critical for soil management.
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