The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to improve the water usage in agricultural operations. With growing water scarcity from the lack of natural rainfall and depleted groundwater tables, the need to maintain agricultural wells and agricultural production is dramatic. The proposed subsurface irrigation technology will reduce irrigated water by 50% over drip irrigation. This could enable significant reduction in water use and allow the groundwater tables to recover and perform as water reservoirs during drought years. It potentially can raise farm yields, improve weed control for organic farmers, and promote better uptake of fertilizers - thereby minimizing the pollution of underground aquifers. The proposed technology also can lead to energy cost reductions and increased flexibility in scheduling power usage. At a systems level, it offers a new paradigm for a resilient and sustainable irrigation. The proposed research is focused on the development of an easy-to-use moisture plume simulation calculator that enables farmers to self-analyze how their soil type will react when installing a virtual water table irrigation system. The latter is designed to eliminate water lost by surface evaporation and deep percolation. The research approach taken is an interdisciplinary collaboration to mathematically model the physics of how liquid transports from the subsurface feed pipes through the porous soil to the roots of the plants in an efficient manner with adequate accuracy. The predictions will be compared with more comprehensive industry-standard hydrological model predictions and also with the experimentally collected moisture data from the field. The technical risks lie in determining whether the basic soil properties collected by the non-scientist farmers would be adequate as inputs to generate an accurate prediction of moisture plume profile across many soil types.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
