Assessing in-field soil moisture variability in the active root zone using granular matrix sensors

Site-specific irrigation decisions require information about variations in soil moisture within the active rooting depth of the crop. Producers have been using soil moisture sensors to make irrigation decisions, and soil moisture sensors have been shown to help reduce water usage without reducing yields. There are still unanswered questions on improving efficiency with soil moisture sensors based on density and location of sensors within a field. This three-year study used sensors to evaluate the spatio-temporal variability of soil moisture across an 18 -ha production field in a corn/soybean rotation. A 55 m by 55 m grid was laid on the field, resulting in 44 sampling points that fell either underneath the center-pivot irrigation or the end gun. At each point location, two Watermark granular matrix sensors were installed at depths of 31 and 61 cm for 2018 and 2020 and an addi-tional 76 - cm sensor in 2019. Analysis of soil samples collected in year one revealed fairly homogeneous soils across the field with silty clay loam as the major soil type and only eight percent silt loam. Plant height and leaf area index (LAI) were measured weekly at each of the 44 sampling points. Inverse distance weighted (IDW) interpolation methods were used to predict soil water tension (SWT) values for locations between known points and aid in sensor density and placement within the field. Linear regression was used to model the relationship of LAI and plant height with soil matric potential to find surrogate methods for predicting SWT. The IDW results show that when uniform irrigation applications are made to the field, fewer sensors that are placed in better locations throughout the field can be as useful as a densely gridded array of sensors. Results showed that, while not strong, plant height had a better relationship to SWT than LAI.