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Artificial Neural Network estimation of soil erosion and nutrient concentrations in runoff from land application areas

Permanent URL:
http://handle.nal.usda.gov/10113/32586
Abstract:
The transport of sediment and nutrients from land application areas is an environmental concern. New methods are needed for estimating soil and nutrient concentrations of runoff from cropland areas on which manure is applied. Artificial Neural Networks (ANNs) trained with a backpropagation (BP) algorithm were used to estimate soil erosion, dissolved P (DP) and NH₄-N concentrations of runoff from a land application site near Lincoln, Nebraska, USA. Simulation results from ANN-derived models showed that the amount of soil eroded is positively correlated with rainfall and runoff. In addition, concentrations of DP and NH₄-N in overland flow were related to measurements of runoff, EC and pH. Coefficient of determination values (R ²) relating predicted versus measured estimates of soil erosion, DP, and NH₄-N were 0.62, 0.72 and 0.92, respectively. The ANN models derived from measurements of runoff, electrical conductivity (EC) and pH provided reliable estimates of DP and NH₄-N concentrations in runoff.
Author(s):
Kim, Minyoung , Gilley, John E.
Subject(s):
neural networks , estimation , soil erosion , soil nutrients , agricultural runoff , sediment transport , simulation models , remediation , land application , environmental degradation
Format:
p. 268-275.
Note:
Includes references
Source:
Computers and electronics in agriculture 2008 Dec., v. 64, no. 2
Language:
English
Publisher:
[Amsterdam]: Elsevier Science
Year:
2008
Collection:
Journal Articles, USDA Authors, Peer-Reviewed
File:
Download [PDF File]
Rights:
Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted.