Identifying Representative Corn Rotation Patterns in the U.S. Western Corn Belt


To accurately assess the impacts of biofuel crop production on regional ecosystem services such as crop yields, carbon and nutrient cycling, soil erosion, water quality, and pest and disease control, it is necessary to have an accurate picture of which crop rotation systems are utilized by growers. Despite the availability of databases such as the Cropland Data Layer (CDL), which provide remotely sensed data on U.S. crop types on a yearly basis, crop rotation patterns remain poorly mapped due to the lack of tools that allow for efficient and consistent analysis of multiyear CDLs. Researchers at the Department of Energy’s Great Lakes Bioenergy Research Center created an algorithm that can select representative crop rotation systems by combining and analyzing multiyear CDLs. Among the findings using this algorithm is that only 82 representative crop rotations accounted for over 90% of the spatiotemporal variability of the more than 13,000 rotations in the Western Corn Belt; it also can detect pronounced shifts from grassland to monoculture corn and soybean cultivation. Furthermore, the area estimates of the rotation systems are comparable to those obtained from agricultural census data. Given this algorithm’s novel capability to flexibly and efficiently derive representative crop rotation patterns in a spatially and temporally explicit manner, it is expected to be a useful tool for providing input data to drive agro-ecosystem models and for detecting shifts in cropping patterns in response to environmental and socio-economic changes.


Sahajpal, R., X. Zhang, R. C. Izaurralde, I. Gelfand, and G. C. Hurtt. 2014.   “Identifying Representative Crop Rotation Patterns and Grassland Loss in the U.S. Western Corn Belt,” Computers and Electronics in Agriculture 108, 173–82. DOI: 10.1016/j.compag.2014.08.005.