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

Current U.S. crop rotation data lacks adequate spatial coverage, resolution, and explicitness, all crucial for effective and sustainable agricultural planning. The study aims to develop a standardized framework that integrates data-driven techniques with expert insights and satellite based historical crop maps to generate detailed maps of regional crop rotations. The developed predictive model utilizes Markov chains to produce the most likely crop rotations using historical cropping information. The predicted sequences are then defined as unique crop rotations using a look-up table developed through a knowledge-based approach. This framework was applied to USDA Economic Research Service’s field boundaries dataset for Kansas. Further, the accuracy of estimated crop rotations was assessed using surveyed reference data from 280 field sites.The estimated crop rotations for Kansas revealed that soybeans, winter wheat, and corn dominate one-third of the agricultural land. Winter wheat, prevalent in the central and western Kansas, appears in 13 of 18 dominant rotations, either as continuous crop or integrated with summer crops and fallow periods. 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. High consistency is observed for corn and soybean rotations and continuous winter wheat, while sorghum rotations show variability. The proposed framework achieved 71% accuracy for high-probability sites. Considering the high performance of our crop rotation predictive model, it can be expanded to map current and historical rotations in the other U.S regions and resulted maps can be used for analyzing the environmental consequences.