• A crop rotation mapping framework was developed using Markov chains. • Framework uses crop type data at field scale resolution across Kansas. • Results show detailed spatial patterns and highlight dominant winter wheat rotations. • Study shows framework’s potential for analyzing crop sequences at a regional scale. 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.
Nandan et al. (Wed,) studied this question.