A new Michigan State University (MSU) study published in Scientific Reports shines a light on how big data and digital technologies can help farmers better adapt to threats—both present and future—from a changing climate. Between 2007 and 2016, the U.S. economy took an estimated $536 million economic hit because of yield variation in unstable farmland caused by climate variability across the Midwest. More than one-quarter of corn and soybean cropland in the region is unstable. Yields fluctuate between over-performing and underperforming on an annual basis.
“First, we wanted to know why—and where—crop yields varied from year to year in the corn and soybean belt of the United States,” said Bruno Basso, MSU Foundation professor of earth and environmental sciences, in a university press release. “Next, we wanted to find out if it was possible to use big data to develop and deploy climate-smart agriculture solutions to help farmers reduce cost, increase yields, and limit environmental impact.”
The researchers first examined soil and discovered that alone, it could not sufficiently explain such drastic yield variations. Using an enormous amount of data obtained from satellites, research aircraft, drones, and remote sensors, and from farmers via advanced geospatial sensor suites present in many modern combine harvesters, the researchers wove big data and digital expertise together.
What they found is that the interaction between topography, weather, and soil has an immense impact on how crop fields respond to extreme weather in unstable areas. Terrain variations, such as depressions, summits, and slopes, create localized areas where water stands or runs off. Roughly two-thirds of unstable zones occur in these summits and depressions, and the terrain controls water stress experienced by crops.
With comprehensive data and the technology, the team quantified the percentage of every single corn or soybean field in the Midwest that is prone to water excess or water deficit. Yields in water-deficient areas can be 23%–33% below the field average for seasons with low rainfall but are comparable to the average in very wet years. Areas prone to water excess experienced yields 26%–33% below field average during wet years.
Basso believes their work, which was funded by AgBioResearch and the U.S. Department of Agriculture National Institute of Food and Agriculture (USDA NIFA), will help determine the future of climate-smart agriculture technologies.