The Van Trump Report

Stanford Researchers Develop New Tool to Help Optimize Irrigation

Researchers at Stanford have designed an irrigation optimization tool that could help farmers reduce water use. The tool rapidly estimates water loss from soils due to “evapotranspiration,” a process that involves the evaporation of water into the atmosphere and the uptake of water by plants. The tool is designed to work with drip irrigation systems and could help farmers save even more water by providing them with real-time data on how much water their crops are using and how much they need.

“Evapotranspiration is a critical piece of information for designing efficient irrigation systems,” explains Weiyu Li, a PhD candidate in energy science and engineering and lead author of a study. Conventional accounting for evapotranspiration rely on what’s called the “vertical-flow assumption,” where water is treated as only moving straight down into the soil. Horizontal flow, including evapotranspiration, is ignored. Li says this is inadequate for truly “smart” agriculture, particularly drip irrigation system.

For those not familiar, drip irrigation emits small drops of water directly to the root zone of the plant. These systems are most common in arid regions. Systems that are informed by “smart” technology optimize the timing so the plants are watered only when needed.

Daniel Tartakovsky, a professor of energy science and engineering who is also Li’s advisor, says part of the challenge of making these systems “smart” is knowing where to best position sensors and drippers. Existing designs are reliant on approximations and assumptions, partially because of the time involved in calculating real-time data. The new tool aims to provide guidance based on real-world and nearly real-time conditions such as weather and the plant’s stage of growth.

In their test plot, the new modeling tool was able to calculate a precise estimate of the evapotranspiration rate in about 10 minutes. This was done by bringing together two separate algorithms that make predictions based on available measured data like soil moisture and root water uptake, then reduce uncertainties based on subsequent measurements.

The researchers say that if the typical single algorithm had been used, the computational time would have been 100 times longer, or nearly 17 hours, which is a key reason most systems completely ignore horizontal flow. This is obviously not timely or very actionable in the field, especially for larger operations. SO in theory, the new tool would dramatically reduce the time needed to devise strategic, efficient irrigation schedules and could even crunch data fast enough to adjust irrigation on the fly, in near real-time.

Li and his team plan to test their modeling tool in real fields next. “We look forward to honing our approach further with all the variables presented by real sensors, real drippers, real crops, and real weather.” Learn more at Stanford News.

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