You are here
Field Maps Can Help You Better Understand Soils
For decades, NRCS soil maps have been a great way to detect soil types in a field. There’s a new generation of technologies and maps, though, that better decipher those differences and the way soils function. This enables you to more efficiently plant seed and apply inputs.
“We are lucky to have had NRCS maps and to understand them as we have,” says Bruce Erickson, Purdue University education distance and outreach director. “But in precision farming, we are ready to go to the next step.”
Topography keys differences
NRCS soil maps delineate soil types via boundary lines. In reality, changes between soil types in a field are gradual and don’t stop and start on a line, says Erickson.
Changes can be due to differences among soil formation factors that include:
- Parent materials (such as a lake bed or glacier)
- Organisms (trees vs. grass)
If just one of these factors changes (such as topography), a soil type can differ. In central Iowa, for example, swell-top soils are lighter in color and most likely a Clarion soil, says Erickson. As topography changes in a valley, soils become darker and morph into a gray color (most likely a Webster soil).
Soil-mapping every part of the field where this occurs would be impossibly slow, says Erickson. Still, soil scientists can use slope position to help predict a soil’s characteristics.
Soil topography can key changes in soil nutrients over a small distance.
Combine sensors have been even able to pick up moderate change across the width of a combine head. “Crops can be lower yielding on the left part of the head compared with the right side,” says Erickson.
Elevation differences within a field impact how crops interact with seed and chemical and fertilizer applications.
“There is a strong correlation,” says Austin Bontrager, a Servi-Tech agronomy technology support specialist. “Within a field, you might see a general trend between soil type boundaries and actual field topography, but it will not line up perfectly.”
He notes that in-season images captured by drone or aerial photographs are better able to show soil-moisture variability compared with those of bare soil.
That also applies to maps that tip you off to in-season nutrient needs that vary in a field.
“You may see a nitrogen or sulfur deficiency on eroded slopes that have little soil organic matter,” Bontrager says.
The new generation of high-resolution imagery shot from UAVs can be used for other agronomic tests, such as weed management. Maps showing contrasting colors for weeds and crops can pinpoint weed problems just as they are starting, he says.
Soil type repeatability
Knowing where these areas begin and end on an accurate spatial basis can reveal in-depth information about soil functionality and productivity.
Phillip Owens, a former Purdue University soil scientist now with USDA-ARS in Booneville, Arkansas, worked with Jenette Ashtekar, a Purdue postdoctoral research assistant, to create functional soil maps.
This process used algorithms to link soil properties to the landscape. The algorithms also determined the best sample locations in a field in order to make the best map possible using a limited number of samples.
Following are the soil characteristics the scientists examined:
- Organic carbon content
- Soil clay content
- Water table location
- Cation exchange capacity
- High- and low-yielding crop areas
- Soil water storage and runoff following rainfall
“You can extract lots of information from maps like these,” says Erickson. “Let’s say you get a 2-inch rain in an hour on a field that is dish-shape. There will be tops of the hills that get an inch and lower parts that get 3 inches of rain. This might make a difference in the hybrid you plant or the field’s nitrogen management. It can go into a prediction model to predict how to better target inputs the next time.”
GIS technology is currently used to make maps. Eventually, Owens says it could be made accessible on iPhones or tablets. The functional maps may be downloaded in-field where farmers could make on-the-go adjustments to seeding or fertilizer applications.
Purdue has filed a patent application on the technology, which was developed with funding from USDA and Purdue. It is also looking for venture capitalists or agriculture firms to license and scale up the technology for the marketplace.
For more tips on turning data into decisions, download our Precision Decisions e-book.