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CROP RESIDUES

Scaling Effects of Standing Crop Residues on the Wind Profile

^a Northwest Res. Ext. Center, 105 Experiment Farm Road, Kansas State Univ., Colby, KS 67701
^b USDA-ARS, Central Great Plains Research Station
^c USDA-ARS, Great Plains Systems Research

^* Corresponding author (raiken{at}ksu.edu)

Received for publication June 3, 2002. Standing senescent stems increase the aerodynamic roughness of the surface, reducing wind energy available for momentum transfer at the soil surface, such as for wind erosion, and also the soil–atmosphere convective exchanges of heat, water vapor, and trace gases. We conducted studies to determine the predictive accuracy of an algorithm derived for plant canopies to scale effects of standing crop residues on the wind profile. We used this algorithm to calculate aerodynamic properties (displacement height and roughness length) of standing crop residues related to the log wind profile equation. We also calculated apparent roughness length from wind profiles measured under neutral stability conditions over stems of wheat (Triticum aestivum L.), corn (Zea mays L.), millet (Panicum miliaceum L.), and sunflower (Helianthus annuus L.) using calibrated single-needle and cup anemometers. A least-squares fit of roughness length calculated by an algorithm derived for crop canopies indicated a systematic, positive bias when it was applied to standing stems. After adjusting for bias, calculated windspeeds generally were contained in 80% confidence intervals for observations above and within the crop stubble. Predictive root mean square errors (RMSE) within profiles ranged from 0.6 to 4.6% of reference wind speed. The nonlinear forms of the scaling algorithms are consistent with theory and wind tunnel observations, representing an advance over parameterization schemes assuming a linear relation with residue height. This advance warrants evaluation of the adjusted algorithm for simulation of microclimate in the soil–residue–crop canopy regime. Application to momentum transfer problems requires further investigation of drag partitioning.

Abbreviations: LAI, leaf area index • RMSE, root mean square errors • SAI, silhouette area index

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