Plant Pathologist Forrest Nutter leads a research team that is developing predictions of the soybean fields' oil and protein content, fiber content and soybean cyst nematode (SCN) density by ground and aerial readings of reflected light. The group is using satellites to take some of the readings in the near infrared part of the light spectrum.

"This is the first application and mapping of oil and protein and then comparing the findings with the harvested crop," Nutter says.

The readings from the remote sensing can predict yield within 80 to 90 percent accuracy. They are working on improving oil and protein content accuracy, which is about 50 percent.

Detecting SCN was about 60 percent accurate. Greg Tylka, Iowa State nematologist, says 90 to 95 percent accuracy would be needed to make it useful for predicting SCN densities.

"There's potential to use this technique to identify fields that have SCN so producers could target them for resistant varieties," he says.

Using remote sensing would be one way to make producers aware that their fields have the yield-robbing pest, Tylka adds. By using "smart sampling," scientists would use images to determine where SCN is likely to be and then confirm it with soil samples.

The remote sensing detects stresses on the soybean plants by looking for sunlight reflected off the foliage in the near-infrared part of the spectrum. "The higher the reflection of light in the near-infrared band means the healthier the crop canopy and that relates to the stresses on the plants," Nutter says.

Geographic information systems (GIS) technologies, associated with precision farming, make it possible to direct the sensing equipment taking the readings, he added.

Two Central Iowa soybean fields have been studied for three years. Detectors on the ground, in airplanes and on satellites check the fields every couple weeks during the growing season. Each field is divided into six-by-10-foot plots; one soybean field contains 995 and the other 613 of these plots, called quadrants.

After the technology is developed, potential users could include growers and seed companies.

"Knowing ahead of time (harvest) the oil and protein content while the soybeans are still in the field would probably be important for marketing and market projections," Nutter says.

In the case of crop damage from hail or plant diseases, the technology could be used by crop insurers to compensate growers. In the future, the technology could be used in mapping pharmaceutical content and yield in crops genetically modified to produce drug components.

Tylka adds that this type of sampling could be adapted to detect other diseases, such as soybean rust, which is spreading through South American soybean fields.

The research is supported by soybean checkoff funds from the North Central Soybean Research Program and the Iowa Soybean Promotion Board and by the Iowa Space Grant Consortium. John Basart in aerospace engineering at Iowa State has provided the aerial images from airplane sensors. Research associate Jie Guan and graduate student Antonio Moreira are part of the research team.