Soil Water Content Sensor Response to Organic Matter Content under Laboratory Conditions

Abstract

Studies show that the performance of soil water content monitoring (SWCM) sensors is affected by soil physical and chemical properties. However, the effect of organic matter on SWCM sensor responses remains less understood. Therefore, the objectives of this study are to (i) assess the effect of organic matter on the accuracy and precision of SWCM sensors using a commercially available soil water content monitoring sensor; and (ii) account for the organic matter effect on the sensor’s accuracy. Sand columns with seven rates of oven-dried sawdust (2%, 4%, 6%, 8%, 10%, 12% and 18% v/v, used as an organic matter amendment), thoroughly mixed with quartz sand, and a control without sawdust were prepared by packing quartz sand in two-liter glass containers. Sand was purposely chosen because of the absence of any organic matter or salinity, and also because sand has a relatively low cation exchange capacity that will not interfere with the treatment effect of the current work. Sensor readings (raw counts) were monitored at seven water content levels (0, 0.02, 0.04, 0.08, 0.12, 0.18, 0.24, and 0.30 cm3 cm−3) by uniformly adding the corresponding volumes of deionized water in addition to the oven-dry one. Sensor readings were significantly (p < 0.05) affected by the organic matter level and water content. Sensor readings were strongly correlated with the organic matter level (R2 = 0.92). In addition, the default calibration equation underestimated the water content readings at the lower water content range (<0.05 cm3 cm−3), while it overestimated the water content at the higher water content range (>0.05 cm3 cm−3). A new polynomial calibration equation that uses raw count and organic matter content as covariates improved the accuracy of the sensor (RMSE = 0.01 cm3 cm−3). Overall, findings of this study highlight the need to account for the effect of soil organic matter content to improve the accuracy and precision of the tested sensor under different soils and environmental conditions.

Carbon dioxide emission in relation with irrigation and organic amendments from a sweet corn field

“…Our findings concur with previous finding of Fares et al [36] who found that soil moisture readings using capacitance sensor were negatively affected by organic matter content. Contrary to previous findings, e.g., Fares et al, [37] who observed a significant effect of organic amendments on bulk density, we found that bulk density was not affected by any of the treatments (data not shown).…”

Calibration and Validation of a Low-Cost Capacitive Moisture Sensor to Integrate the Automated Soil Moisture Monitoring System

“…Many researchers have focused on the calibration of low-cost sensors which are used in different sensing techniques, such as capacitance-based sensors [9,10], resistivity-based granular matrix sensors [9,11], and sensors based on a tensiometer technique [9], to measure the soil water content in fields [2,[12][13][14] and to develop low-cost automated irrigation systems [15]. Some studies have considered the effects of the physical and chemical properties of soil on the performances of soil moisture sensors [4,13,14,[16][17][18]. These studies provide valuable insight into the necessity of soil-specific calibration.…”

Quantification of water balance using a lysimeter in Mexico

“…On the other hand, in days with rainfall and saturated soil, the correlation coefficient between and diminished to values of 0.5. As pointed out by Mittelbach et al (2011) and Fares et al (2016), this result may be related to the sensor sensitivity that decreases in saturated soil conditions. Taking advantage of the strong correlation between and , was fitted to using the following regression equation: result of this procedure, N-S coefficients were equal to 0.973 and 0.931 for 2015 and 2016, respectively.…”