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United States Department of Agriculture

Agricultural Research Service


item Stott, Diane

Submitted to: International Symposium on Soil Erosion and Dryland Farming
Publication Type: Abstract Only
Publication Acceptance Date: 9/17/1997
Publication Date: N/A
Citation: N/A

Interpretive Summary:

Technical Abstract: One cost-efficient way to protect soil against erosion is to surface manage the crop residues. The adoption of surface residue management systems will depend on how rapidly surface managed crop residues are broken down and lost from the field. Such information is needed to determine the amount of residue cover present on a field during critical erosion periods. The plant residue decomposition by soil microbes is dependent on several factors. These fall into 2 broad categories, plant residue quality and soil environmental factors. The soil environmental factors used in the model are harvest time, surface residue mass at harvest, climate, and degree of tillage. Among the plant characteristics influencing decay rates are the initial amounts of hemicellulose, sugar, lignin, and nitrogen, as well as the surface area-to-mass ratio. The decomposition and surface cover equations used in several USDA erosion models were originally developed for RESMAN (Residue Management Decision Support System). The algorithms were parameterized using field and laboratory data. Field data collected for model validation showed that the algorithms used to predict over-winter residue mass loss due to decay accounted for 97 percent ((=0.05) of the variation. The algorithm converting residue mass to surface coverage explained 52, 88 and 49 percent of the variation in field data for winter wheat, corn and soybeans, respectively. In 1992, due to user acceptance of RESMAN, the algorithms for predicting residue mass loss were incorporated into RUSLE (Revised Universal Soil Loss Equation) and WEPP (Water Erosion Prediction Project). Later, the technology was adapted and used in RWEQ (Revised Wind Erosion eQuation) and WEPS (Wind Erosion Prediction System).

Last Modified: 10/16/2017
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