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A crop modeling approach to analyze in-field soil moisture variability
Proceedings of the 2020 Mississippi Water Resources Conference
Year: 2020 Authors: Hodges B., Paz J.O., Tagert M.L., Reginelli D.
Site-specific irrigation decisions require information about variations in soil moisture throughout the rooting depth actively being used by the crop. An increasing number of producers are using soil moisture sensors to make irrigation decisions, and it has been shown that soil moisture sensors can reduce water usage without reducing yields, which also conserves money. This three-year study uses sensors and crop modeling 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, which resulted 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 30.5 and 61cm. Analysis of soil samples collected in year one of the project 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, which resulted in eight measurement dates during the 2018 growing season of the soybean crop. A digital elevation model was also used to log the elevation at each point location. The crop variables were inserted into the CROPGRO crop model in the Decision Support System for Agrotechnology Transfer suite of models to calibrate and predict soybean growth and water use in the field. The soil moisture values will also be inserted into the model when they are converted from soil matric potential to volumetric water content. The model will be run for every grid in the field to predict whether there should be a different irrigation schedule for parts of the field. In this presentation, the results from four grids will be discussed.