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Estimation of bulk density of waterlogged soils from basic properties

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Title Estimation of bulk density of waterlogged soils from basic properties
Not Available
 
Creator Nitin G. Patil and Arun Chaturvedi
 
Subject bulk density; neural networks; pedotransfer function; waterlogged soils
 
Description Not Available
Pedotransfer functions (PTFs) to predict bulk density (BD) from basic soil data
are presented. Available data pertaining to seasonally impounded shrink–swell
soils of Jabalpur district in the Madhya Pradesh state of India were used for the
study. The data included horizon-wise information of 41 soil profiles in the study
area covering nearly 5 million ha. Six independent variables, namely textural data
(sand, silt and clay), field capacity (FC), permanent wilting point (PWP) and
organic carbon content (OC) were used as input in hierarchical steps to establish
dependencies, with bulk density as the dependent variable, using statistical
regression and artificial neural networks. The PTFs derived using neural networks
[average root mean square error (RMSE) 0.05] were relatively better than
statistical regression PTFs (average RMSE 4 0.1). The best-performing PTFs
required input data on sand, silt content, FC and PWP, with lowest prediction
errors (RMSE 0.01, maximum absolute error (MAE) 0.01) and highest values of
index of agreement (d, 0.95) and R2 (0.65). Use of measures of structure, as well as
information on pore structure, was found to be essential to derive acceptable
PTFs. Inclusion of OC as an input variable showed relatively better fitting to the
training data set, implying an underlying relationship between OC and BD, but
the neural networks could not mimic the relationship when tested against subset
Not Available
 
Date 2024-07-01T10:51:46Z
2024-07-01T10:51:46Z
2010-10-03
 
Type Research Paper
 
Identifier Nitin G. Patil and Arun Chaturvedi
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/83813
 
Language English
 
Relation Not Available;
 
Publisher Not Available