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ARS Home » Pacific West Area » Maricopa, Arizona » U.S. Arid Land Agricultural Research Center » Plant Physiology and Genetics Research » Research » Publications at this Location » Publication #418157
Research Project: Analysis and Quantification of G x E x M Interactions for Sustainable Crop Production

Location: Plant Physiology and Genetics Research

Title: A standardized approach to map spatially explicit crop rotations at regional scale

Author
item NANDAN, ROHIT - Oak Ridge Institute For Science And Education (ORISE)
item Bandaru, Varaprasad

Submitted to: International Journal of Applied Earth Observation and Geoinformation
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 2/22/2026
Publication Date: 3/4/2026
Citation: Nandan, R., Bandaru, V. 2026. A standardized approach to map spatially explicit crop rotations at regional scale. International Journal of Applied Earth Observation and Geoinformation. 148:105208. https://doi.org/10.1016/j.jag.2026.105208.
DOI: https://doi.org/10.1016/j.jag.2026.105208

Interpretive Summary: Current crop rotation data in the U.S. is not detailed enough for effective agricultural planning. This study addresses this limitation by developing a framework using Markov chains and USDA-NASS satellite based crop type maps to map crop rotations. Applied to Kansas, the model identified soybeans, winter wheat, and corn as dominant crops, with corn and soybean rotations covering 40% of the area. Winter wheat is prevalent in central Kansas, while corn is the main irrigated crop in the west. The model achieved 71% accuracy for high-probability predictions. This approach can map current rotations and support large-scale assessments of agricultural management practices.

Technical Abstract: Current U.S. crop rotation data lacks adequate spatial coverage, resolution, and explicitness, all crucial for effective and sustainable agricultural planning. This study presents a standardized framework combining data-driven techniques with expert insights and satellite-based crop maps to generate detailed maps of regional crop rotations. Using Markov chains, the model predicts likely crop sequences from historical data, which are classified into distinct rotations via a knowledge-based lookup table. This framework was applied to USDA Economic Research Service’s field boundaries in Kansas. Further, the accuracy was assessed using surveyed reference data from 280 field sites. Findings show soybeans, winter wheat, and corn dominate one-third of Kansas farmland. Winter wheat, prevalent in central and western Kansas, features in 13 of 18 dominant rotations, either as continuous crop or with summer crops and fallow. Corn is the primary irrigated crop in western Kansas, typically rotated with non-irrigated crops. Corn and soybeans-based rotations cover 40% of the area. These rotations, along with continuous winter wheat, show high consistency, unlike more variable sorghum rotations. The framework achieved 71% accuracy for high-probability sites. Considering the high performance of crop rotation predictive model, it can be expanded to map current and historical rotations in the other U.S. regions and resulting maps can be used for analyzing the environmental consequences.