By Marianne Stein
Conservation tillage practices, such as no-till and reduced till, are critical for sustainable agriculture, and they are gradually becoming popular with farmers across the Midwest. Monitoring tillage usage can provide insights into soil health, water levels, and nutrient loss, as well as guide management and policy decisions.
A University of Illinois Urbana-Champaign research team has developed a dynamic framework that uses satellite imagery and machine learning to detect tillage practices over large areas and long time periods. The team discusses their methodology and findings in a new paper.
“Conservation tillage helps reduce soil erosion, and it affects soil nutrients and moisture retention. Mapping tillage practices across large areas is also important to quantify soil carbon change. But current data are mainly obtained from farmer surveys, which lack timely and detailed spatial information,” said lead author Xiaocui Wu, a research scientist affiliated with the Agroecosystem Sustainability Center, the Institute for Sustainability, Energy, and Environment, the Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), and the Department of Natural Resources and Environmental Sciences in the College of Agricultural, Consumer and Environmental Sciences at Illinois.
Other studies have applied remote sensing with hyperspectral or multispectral imagery to monitor tillage practices by estimating crop residue. But these approaches are typically limited to smaller regions, and the images are sensitive to soil and weather variations, which can lead to inaccuracies.
“We found that satellite signals could vary across regions, as they are affected by soil type, moisture levels, and weather variables. The model needs to account for those elements,” Wu said.
To address these challenges, the researchers developed a new framework that combines crop residue indices from remote sensing data with environmental factors and machine learning to create a dynamic model. They used the approach to estimate tillage percentage across the U.S. Midwest from 2000 to 2022.
Source : illinois.edu