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Decomposition of Surface Crop Residues in Long-Term Studies of Dryland Agroecosystems

Great Plains Systems Res. Unit, USDA-ARS, P.O. Box E, Fort Collins, CO 80522;

Gary A. Peterson

Dep. of Soil and Crop Sciences, Fort Collins, CO 80523

Lucretia Sherrod

USDA and Dep. of Soil and Crop Sciences, Colorado State Univ., Fort Collins, CO 80523.

Decomposition of surface crop residues is important for agricultural management, especially under conservation tillage. The objective of this study was to test several models for describing crop residue decomposition under three eastern Colorado dryland agroecosystems at Sterling, Stratton, and Walsh with a yearly mean air temperature of 9.7, 10.4, and 12.0°C, respectively. At each site, a soil toposequence common to its geographic region was chosen to include a summit, a sideslope, and a toeslope position, and several crop rotations were practiced under no-till conditions. Grab samples were taken at planting and before harvesting for surface residue measurement since 1985. Simulation results showed that the Douglas-Rickman model described surface crop residue decomposition better than the Gregory model during a 13-year period, based on a normalized objective function (NOF). Our fitted decomposition rate coefficients using the Douglas-Rickman model matched those originally published. The Douglas-Rickman model, which uses a first-order decay with respect to degree-days, was further evaluated against two other first-order decay models: one using a first-order decay equation with respect to decomposition-days and the other assuming a first-order decay with respect to time (d). Although the three approaches performed equally well in terms of NOF values (P = 0.354), fitted decomposition rate coefficients were significantly different (P < 0.012) among the three experiment sites when models based on decomposition-days or on time (in days) were used. Therefore, the Douglas-Rickman model may be more applicable for describing long-term crop residue decomposition because of its consistency in model parameters among experimental sites and simplicity in modeling approach.

Received for publication February 14, 1998.

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