Abstract

Abstract Crop rotation is a commonly used management practice in the central U.S. whereby different agricultural crops are grown on a farm field in successive years to help maintain the productive capacity of the soil. Understanding hydrologic responses to crop rotation is critical for developing appropriate crop rotation strategies for sustainable water resources management. These responses are not well understood due to limited availability of multi-year, crop-specific land use-land cover data. The study focuses on the Smoky Hill River watershed in west-central Kansas, a typical agro-ecosystem watershed in the Central Great Plains of the U.S. A multi-year land use dataset developed for the state of Kansas was used to identify 3-year crop rotation patterns in the watershed for three different periods (2006–08, 2008–10, 2010–12). Out of 276 unique rotations, 21 rotations were found to be dominant, with rotation patterns of grain sorghum-fallow-winter wheat (G-F-W), winter wheat-winter wheat-fallow (W-W-F), and continuous winter wheat (W-W-W) occupying more than 81% of the cropland in each rotation period. From 2006 to 2012, the coverage of grain sorghum increased by 26% and corn by 305%, while winter wheat decreased by 20%. Three 11-year simulation scenarios based on three 3-year crop rotation patterns were implemented in the SWAT model and separately calibrated for streamflow at two gauge sites. The SWAT model produced good calibration statistics for the periods corresponding to the applied crop rotations, while the statistics tended to decrease for other periods, with Nash-Sutcliffe Efficiency coefficient becoming unsatisfactory for periods of six to eight years outside of the calibration period. Applying a different number of dominant crop rotations to the SWAT model, it was found that the number can be reduced down to the five most dominant without significant loss in model performance. While the optimum number of crop rotations must be evaluated for each agricultural watershed, this finding provides valuable information for watershed model development and calibration, which can help to avoid issues of over-parameterization and equifinality.

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