New Study Shows Corn-Soybean Crop Rotation Benefits Sensitive to Climate

A new study led by the University of Minnesota highlights the importance of corn-soybean crop rotation in increasing agricultural yields and adapting to climate change. The research underscores how this practice can help farmers respond to rising temperatures and weather extremes.

A recent study led by researchers at the University of Minnesota Twin Cities provides valuable insights into the benefits of alternating corn and soybean crops, especially in the face of climate change. The findings are published in the peer-reviewed journal Global Change Biology.

Rising temperatures and increasing weather extremes pose a significant threat to global food security, emphasizing the need for sustainable agricultural practices. Crop rotation, the practice of alternating crops in the same field, has been proven beneficial. However, this new study reveals just how sensitive these benefits are to climatic conditions.

The research highlights that corn benefits from rotation in colder regions, while soybeans see more advantages in warmer areas. Additionally, non-growing season warming diminishes corn yields’ benefits, while growing season warming enhances soybeans’ yields.

“Corn and soybeans may experience different crop rotation benefit changes in the future, which can help U.S. farmers make more informed decisions when facing climate warming,” first author Junxiong Zhou, a doctoral candidate in the University of Minnesota’s Department of Bioproducts and Biosystems Engineering (BBE), said in a news release.

The study used satellite data and a “causal forest model,” a sophisticated machine learning method, to estimate crop rotation benefits under various climate scenarios in the U.S. Midwest. This model allowed the researchers to understand cause-and-effect relationships in data at a granular level.

“Millions of satellite observations and advanced machine learning models enable us to quantify the climate impacts on crop rotation benefits at the subfield level over the Midwest,” added senior author Zhenong Jin, an associate professor in BBE.

Agricultural experts believe this study underscores the power of machine learning in estimating the large-scale effects of farming practices.

“This study demonstrates the great potential of interpretable machine learning for estimating large-scale effects of agricultural management practices,” added David Mulla, a professor and Larson Endowed Chair in soil and water resources at the U of M’s College of Food, Agricultural and Natural Resource Sciences, and a senior researcher at the AI Institute for Climate-Land Interactions, Mitigation, Adaptation, Tradeoffs and Economy (AI-CLIMATE).

In the future, researchers aim to broaden their study to include long-term diverse crop rotations and their interactions with all-season climates. Further studies will explore crop management at the field level, focusing on nutrient cycling and pest dynamics amidst changing climate scenarios.

The research was a collaborative effort, including contributions from Peng Zhu at The University of Hong Kong and Dan M. Kluger and David B. Lobell at Stanford University.