Spring 2020

Living Science

Jingyi Huang stands in his lab with two rows of soil samples in dishes and soil-sensing equipment.
Photo by Michael P. King

For Jingyi Huang, it all started with a pair of orange hula hoops. At least, that’s what they looked like to him when he was searching online for potential graduate programs and stumbled on a photo. It depicted researchers from the University of New South Wales in Sydney, Australia, using the hoops — part of an instrument called Geonics EM34 — to survey an agricultural field. Huang started reading about how the device uses electromagnetic (EM) induction to assess soil. He was fascinated.

“Using the instrument, you can actually see through the soil, see what is underneath your foot without digging any holes,” says Huang. “I find this really interesting.”

Huang’s fascination propelled him to Sydney, where he earned a master’s degree and Ph.D. During his graduate training, and later his postdoctoral position, he honed his skills with the EM34 and other soil-sensing technologies.

Now, as an assistant professor of soil science in CALS, Huang continues to focus his research on the use of soil-sensing technologies — including ground- based proximal sensing and remote sensing — to study soil processes at various spatial and temporal scales. His goal is to develop models that make sense of the data and help with efforts to monitor, map, manage, and conserve soil and water resources in Wisconsin and across the country.

At the national level, Huang has created a model that predicts soil moisture levels throughout the entire U.S. and can inform drought monitoring efforts. At the state level, one of his models estimates soil carbon stocks across Wisconsin at various points from 1850 to 2002. For some of his projects, Huang works directly with the state’s farmers. This includes helping a farmer in Rock County assess a field with drainage problems and a “smart irrigation” management project in the Central Sands region, the state’s so-called vegetable basket.

How does the Geonics EM34 instrument work?

It’s like if someone gets sick and went to the hospital to get some image scanning — for example, an MRI scan. They use different instruments to visualize what is going on inside a human’s brain and body. And similarly, this kind of instrument can be used to understand what is inside of soil.

It sounds like you’re taking medical technologies and applying them to soil.

It’s not exactly like MRI, but it has similar principles. They work in different frequency domains of EM waves, but they can both penetrate into objects.

So, from this instrument, you get an image, just like the imaging scan of your body. But you need to do a lot of complex numeric models to interpret this data. Properties that can be measured from this instrument include soil moisture, the salts in the soil, and different [types and quantities] of soil minerals, like clay particles.

Most of the time I say I’m a soil scientist. But sometimes, when I’m trying to explain what I do, I call myself a soil doctor.

Can you tell us about a project where you’re using this kind of instrument?

We have a new project in Janesville, Wisconsin. They have a roughly 80-hectare [just under 200 acres] field where they use irrigation, but the problem is the high variability of the soil. Of course, you have to manage each soil type differently.

The farmers gave me their corn yield map for last year. A lot of areas have really low yield, and we’re trying to help them better understand the yield variation using soil science knowledge.

We use some of the proximal sensing instruments, like what I used for my Ph.D. We just drive the tractor with the instrument behind it, and we do many transects of the whole field to survey the soil. Now, as I said before, this sensor can capture the soil clay particles, soil water, and soil salts. We also go to the field and collect some soil samples. As a doctor, you have to do diagnostics of the farm, the soils, to know what is happening.

With the help of the sensor, we’re trying to understand the variation of the soil and to make maps for the different soil properties. Eventually, we will try to use these maps to understand the yield variation. The aim is coming up with better management zones for the farmers so they can apply different management practices based on the soil types.

What are you working on in the Central Sands?

In central Wisconsin, if you are having a dry year, there is water stress for the region. So that’s why there has been a lot of investment in the irrigation infrastructure in the region in recent decades, and some of the farmers are taking the initiative to use some newer soil-sensing technologies to help schedule the irrigation.

At the farms I visited they use irrigation systems, center pivot systems, but they also install a lot of soil moisture sensors. They want to maximize water use efficiency and only irrigate when the soil and crops need the water.

But it’s cost-prohibitive for farmers to [install soil moisture sensors] at farm-scale. The farmers can only install, in this case, about 10 sensors for 10 of their fields. For the remaining 40 or so fields, they have to rely on their own experience to say, “If I think the soil is too dry, I start irrigating. If I think it is OK, then I stop irrigating.” So that’s a limitation for the data-driven application of irrigation.

How does your project help?

I used the three years of soil moisture data they collected and coupled it with remote sensing data. Then I built a model to predict soil moisture at a resolution where every pixel on the map is 100 square feet. So, with the model, instead of one point of data [from a soil moisture sensor] for a given field, you’re going to have a lot of 100-square-feet pixels showing soil moisture variations across the entire field.

Basically, now you have these high-resolution maps every six days during the growing seasons that tell you the soil moisture status across the whole Central Sands area. All the farmers can share this information and potentially use these maps to help guide irrigation. Now we are improving this model.

What kind of improvements are you making?

We are applying for some grants to improve this model because it has limitations. It only tells you moisture every six days during the growing season, but in the hot summer, farmers have to irrigate every second day. We’re trying to shorten the model’s interval. Hopefully, in the next few years, we’ll have a better version, and we can work with farmers to test it in the field.

What has it been like working closely with farmers?

It’s really amazing when I talk with these farmers. Some are really proactive about innovation, and they are taking the latest technology from the industry to improve their agriculture management.

I feel like they really pay a lot of attention to the local community, that they want to protect their environment, protect their water resources, reduce leaching, reduce contamination, and of course, at the same time, maintain their yield, maintain their profitability. I hope the research conducted from my lab and other researchers’ labs at UW can help them achieve these goals.

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