Evaluating the Effect of Soil Texture on the Response of Three Types of Sensors Used to Monitor Soil Water Status

  • Payero J
  • Qiao X
  • Khalilian A
  • et al.
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Abstract

Accurate monitoring of soil water status can be an important component of precision irrigation water management. A variety of commercial sensors measure soil water status by relating sensor electrical output to soil water content or soil water potential. However, sensor electrical output can also be affected by soil characteristics other than water content, such as soil texture, salinity, and temperature. This makes it difficult to accurately measure and interpret soil water status without prior on-site calibration. In this study, we investigated the impact of soil texture on the response of three types of sen-sors commonly used to monitor soil water status, including the Decagon EC-5, the Vegetronix VH400, and the Watermark 200ss granular matrix sen-sor. A replicated laboratory experiment was conducted to evaluate the re-sponse of these types of sensors using four major soil textural classes com-monly found in South Carolina. We found that the three types of sensors had a significant response to changes in soil water content, but while the EC-5 and VH400 sensors had a linear response, the Watermark 200ss had a curvilinear response that was explained by an exponential decay function. The response of the three sensor types, however, was significantly affected by soil texture, which will significantly affect the trigger point used to initiate irrigation based on the output from these sensors. Therefore, it is suggested that guidelines on how to use these sensors for local soils need to be developed and made availa-ble to farmers, so that they can make better irrigation scheduling decisions.

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APA

Payero, J. O., Qiao, X., Khalilian, A., Mirzakhani-Nafchi, A., & Davis, R. (2017). Evaluating the Effect of Soil Texture on the Response of Three Types of Sensors Used to Monitor Soil Water Status. Journal of Water Resource and Protection, 09(06), 566–577. https://doi.org/10.4236/jwarp.2017.96037

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