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Serving: IA
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NEED BETTER DATA: The use of precision farming practices and digital data to make crop management decisions requires more precise soil information.

The science of soil

ISU agronomy professor is working to update Iowa’s soil maps.

Ask any farmer and they will explain the importance of soil. While seasonal weather can be the difference from a good harvest to a worrisome one, the soil moderates the long-term productivity of that harvest. The inherent properties of soil types are vital to know when it comes to management practices on any agricultural landscape. 

“We rely on soil for so many different things; the list can be overwhelming at times,” says Bradley Miller, assistant professor of agronomy at Iowa State University. “You think about why Iowa has the agricultural economy that it does, and it’s largely because of the soil.” 

Miller is working to update Iowa’s soil survey (soil maps), a project which was completed about 30 years ago, with small tweaks since then. Those original maps were produced by the Cooperative Soil Survey, a partnership between ISU and USDA’s Natural Resources Conservation Service. Funding for that project came from a combination of federal, state and county sources. The maps were compiled county by county, and each took about four years to complete. 

Soil survey needs updating 

A lot of the mapping was done with aerial photography, and while many of the maps may have been completed in the 1980s, the technology used to produce the maps was from the World War I era. “We have essentially been mapping soils in Iowa the same way for almost 100 years now,” Miller says. “The concept has been that we use aerial photography as a base map. And based on what we see in that photography, we delineate different areas of the landscape with what we believe have similar soil properties.” 

With any soil survey comes a certain element of prediction, because a hole can’t be dug everywhere. Miller says maps were made by surveyors building mental models from the experience of taking representative soil samples around each county. They identify all the different environmental factors, like geological and topography associated with that sample, and matched areas with the same factors to predict similar soil samples. This information was combined with aerial photography to produce the maps, which were hand-drawn onto the photos.  

Soils are placed in a series, and a series is a collection of soils having similar properties. Each soil series gets a name, and the names are usually tied to the area, like Clarion, Webster or Nicollet in north-central Iowa. 

More precision needed 

Miller says the maps in use today were never meant to be used at the sub-field level when deciding management practices for crops, but that is how they are used. “The existing soil maps were supposed to give a general idea of the soil resources, but as we get into precision agriculture, a lot of farmers — because they don’t have a better resource — are using this map to decide their management zones within the field,” he notes. “Even though the creators of this map never intended it for such use, it’s still the best available for that purpose.” 

When considering the accuracy of Iowa’s soil maps, the question is really about what can be done with the information it provides. “The reality is that we want higher spatial detail and a more statistically based approach in how the uncertainty in predicting soil properties is described,” he says. 

This is what Miller is trying to accomplish with research. He and his team are working on an algorithm they developed to automatically classify a landscape with different soil properties. “We have several algorithms as part of our toolbox, and we could do a better map than the current map right now,” he says. “But every landscape has some special characteristics that we need to customize to, and we do that through machine learning.” 

Statistically based approach 

This means that the algorithm “learns” about these characteristics in the landscape without having to specifically be programed to do so. Different technologies Miller is using are being input into the algorithm to be consistent and accurate. 

He uses aerial photography, and stacks together a large number of potential covariates or predictors. This means taking remote sensors from an airplane or satellite platform that could predict, or covary, different soil properties. The difference from the traditional soil survey methods is the large quantity of covariates and the complex, quantitative models built by machine learning. 

ISUIowa Soil Survey maps


NOW ONLINE: Iowa Soil Survey maps and information were originally published as printed books; now they can be found online. 

There are two different ways Miller is collecting the covariates. The first is terrain analysis. This starts with detailed elevation data coming from an airplane equipped with LIDAR, which stands for Light Detection and Ranging. This measures the rise and fall of the landscape and records the elevation information by shooting a laser from the plane to the ground and recording the amount of time it takes to bounce back. Then Miller’s team analyzes the elevation data for the different landscape aspects that influence environmental conditions, such as water flow. 

Using satellites shows promise 

The second method is collecting spectral information from satellites. This means the whole electromagnetic spectrum, both light seen by the human eye and light that can’t be seen. Miller says healthy vegetation on a landscape is directly related to the plant’s root system in the soil, thus being able to infer the soil based on what is aboveground. He says that strategy, classifying soil by the vegetation growing from it, dates back to the ancient Greeks. 

“In some ways, we aren’t inventing the wheel; we are using concepts that we have known for a while,” Miller says. “The big difference is we now have this big data source with this satellite information, LIDAR information, plus we have the machine learning that helps us find much more complex patterns.” 

The key questions Miller is tackling are: What is the appropriate machine learning algorithm to find those complex patterns, what is the best sampling design that can capture the variation in the landscape, and what are the best covariates to use?  

The inventory of Iowa’s soil is important, not only for people in the ag industry, but also for tax assessment, real estate valuation and the environment. Miller adds, “We are always working to improve the accuracy of our predictions.” 

Clemens is a communications specialist with the ISU department of agronomy. 

Source: ISU, which is solely responsible for the information provided and is wholly owned by the source. Informa Business Media and all its subsidiaries are not responsible for any of the content contained in this information asset.

 

 

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