dc.contributor.author |
Salehi, Mohammad Hassan |
|
dc.contributor.author |
Mohajer, Reza |
|
dc.contributor.author |
Beigie, Habib |
|
dc.date.accessioned |
2016-03-21T11:24:20Z |
|
dc.date.available |
2016-03-21T11:24:20Z |
|
dc.date.issued |
2008 |
|
dc.identifier.citation |
Salehi, H. M., Mohajer, R., Beigie, H. (2008). Developing soil cation exchange capacity pedotransfer functions using regression and neural networks and the effect of soil partitioning on the accuracy and precision of estimation. International Meeting on Soil Fertility Land Management and Agroclimatology, Special Issue, 345-356. |
tr_TR |
dc.identifier.uri |
http://hdl.handle.net/11607/2673 |
|
dc.description.abstract |
Soil fertility measures such as cation exchange capacity (CEC) may be used in upgrading soil maps and
improving their quality. Direct measurement of CEC is costly and laborious. Therefore, indirect estimation of
CEC via pedotransfer functions may be appropriate and effective. Several delineations of two consociation map
units consisting of two soil families (Shahrak series and Chaharmahal series), located in Shahrekord plain, Iran
were identified. Soil samples were taken from two depths of 0-20 and 30-50 cm and were analyzed in lab for
several physico-chemical properties. Clay and organic matter percentages as well as moisture content at -1500
kpa best correlated with CEC. Pedotransfer functions were successfully developed using regression and neural
networks. Soil partitioning increased the accuracy and precision of functions. Compared to regression, neural
network technique resulted in pedotransfer functions with higher R2 and lower RMSE. |
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dc.language.iso |
eng |
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dc.publisher |
Adnan Menderes Üniversitesi Ziraat Fakültesi Dergisi |
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dc.rights |
info:eu-repo/semantics/openAccess |
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dc.subject |
Cation Exchange Capacity (Cec) |
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dc.subject |
Pedotransfer |
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dc.subject |
Regression |
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dc.subject |
Neural Network |
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dc.subject |
Soil Partitioning |
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dc.title |
Developing soil cation exchange capacity pedotransfer functions using regression and neural networks and the effect of soil partitioning on the accuracy and precision of estimation |
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dc.type |
article |
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dc.relation.journal |
International Meeting on Soil Fertility Land Management and Agroclimatology |
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dc.contributor.department |
Assistant Professors, Soil Science Dept., College of Agriculture, Shahrekord University |
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dc.identifier.issue |
Special Issue |
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dc.identifier.startpage |
345 |
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dc.identifier.endpage |
356 |
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