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The National Agricultural Library is one of four national libraries of the United States, with locations in Beltsville, Maryland and Washington, D.C. It houses one of the world's largest and most accessible agricultural information collections and serves as the nexus for a national network of state land-grant and U.S. Department of Agriculture field libraries. In fiscal year 2011 (Oct 2010 through Sept 2011) NAL delivered more than 100 million direct customer service transactions.

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Journal Article

Journal Article

Use of the nonparametric nearest neighbor approach to estimate soil hydraulic properties  [2006]

Nemes, A.; Rawls, W.J.; Pachepsky, Y.A.;

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Nonparametric approaches are being used in various fields to address classification type problems, as well as to estimate continuous variables. One type of the nonparametric lazy learning algorithms, a k-nearest neighbor (k-NN) algorithm has been applied to estimate water retention at -33- and -1500-kPa matric potentials. Performance of the algorithm has subsequently been tested against estimations made by a neural network (NNet) model, developed using the same data and input soil attributes. We used a hierarchical set of inputs using soil texture, bulk density (D(b)), and organic matter (OM) content to avoid possible bias toward one set of inputs, and varied the size of the data set used to develop the NNet models and to run the k-NN estimation algorithms. Different 'design-parameter' settings, analogous to model parameters have been optimized. The k-NN technique showed little sensitivity to potential suboptimal settings in terms of how many nearest soils were selected and how those were weighed while formulating the output of the algorithm, as long as extremes were avoided. The optimal settings were, however, dependent on the size of the development/reference data set. The nonparametric k-NN technique performed mostly equally well with the NNet models, in terms of root-mean-squared residuals (RMSRs) and mean residuals (MRs). Gradual reduction of the data set size from 1600 to 100 resulted in only a slight loss of accuracy for both the k-NN and NNet appro
aches. The k-NN technique is a competitive alternative to other techniques to develop pedotransfer functions (PTFs), especially since redevelopment of PTFs is not necessarily needed as new data become available.
From the journal
Soil Science Society of America journal
ISSN : 0361-5995

Bibliographic information

Language:
English
Type:
Journal Article
In AGRIS since:
2013
Volume:
70
Issue:
2
Start Page:
327
End Page:
336
All titles:
"Use of the nonparametric nearest neighbor approach to estimate soil hydraulic properties"@eng
Other:
"Includes references"
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Bibliographic information

Language:
English
Type:
Journal Article
In AGRIS since:
2013
Volume:
70
Issue:
2
Start Page:
327
End Page:
336
All titles:
"Use of the nonparametric nearest neighbor approach to estimate soil hydraulic properties"@eng
Other:
"Includes references"