Big data and digital technologies can help farmers better adapt to threats — both present and future — from a changing climate, according to a study done by Michigan State University.
The study, the first to precisely quantify soil and landscape features and spatial and temporal yield variations in response to climate variability, is also the first to use big data to identify areas within individual fields where yield is unstable.
Bruno Basso, MSU Foundation professor of earth and environmental sciences, and his postdoctoral research fellow, Rafael Martinez-Feria, have done the study. “First, we wanted to know why — and where — crop yields varied from year to year in the corn and soybean belt of the U.S.,” Basso said. “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.”
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, Basso and Martinez-Feria 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 to 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 to 33% below field average during wet years.