Development and validation of a model for soil wetting geometry under Moistube Irrigation

We developed an empirical soil wetting geometry model for silty clay loam and coarse sand soils under a semi-permeable porous wall line source Moistube Irrigation (MTI) lateral irrigation. The model was developed to simulate vertical and lateral soil water movement using the Buckingham pi (pi) theorem. This study was premised on a hypothesis that soil hydraulic properties influence soil water movement under MTI. Two independent, but similar experiments, were conducted to calibrate and validate the model using MTI lateral placed at a depth of 0.2 m below the soil surface in a soil bin with a continuous water supply (150 kPa). Soil water content was measured every 5 min for 100 h using MPS-2 sensors. Model calibration showed that soil texture influenced water movement (p < 0.05) and showed a good fit for wetted widths and depths for both soils (nRMSE = 0.5-10%; NSE > 0.50; and d-index >= 0.50. The percentage bias (PBIAS) statistic revealed that the models' under-estimated wetted depth after 24 h by 21.9% and 3.9% for silty clay loam and sandy soil, respectively. Sensitivity analysis revealed agreeable models' performance values. This implies the model's applicability for estimating wetted distances for an MTI lateral placed at 0.2 m and MTI operating pressure of 150 kPa. We concluded that the models are prescriptive and should be used to estimate wetting geometries for conditions under which they were developed. Further experimentation under varying scenarios for which MTI would be used, including field conditions, is needed to further validate the model and establish robustness. MTI wetting geometry informs placement depth for optimal irrigation water usage.

Automated summary

This study developed an empirical soil wetting geometry model for Moistube Irrigation (MTI) lateral irrigation, showing that soil texture influences water movement and the model's performance is satisfactory for different soil types. Further experimentation under varying scenarios is needed for validation and robustness.