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Please use this identifier to cite or link to this item:
http://krishi.icar.gov.in/jspui/handle/123456789/37794
Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Nitin G. Patil and Arun Chaturvedi | en_US |
dc.date.accessioned | 2020-06-30T16:08:11Z | - |
dc.date.available | 2020-06-30T16:08:11Z | - |
dc.date.issued | 2011-01-20 | - |
dc.identifier.citation | Nitin G. Patil & Arun Chaturvedi (2012) Estimation of bulk density of waterlogged soils from basic properties, Archives of Agronomy and Soil Science, 58:5, 499-509, DOI: 10.1080/03650340.2010.530254 | en_US |
dc.identifier.issn | Not Available | - |
dc.identifier.uri | http://krishi.icar.gov.in/jspui/handle/123456789/37794 | - |
dc.description | Not Available | en_US |
dc.description.abstract | 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. | en_US |
dc.language.iso | English | en_US |
dc.publisher | Taylor & Francis | en_US |
dc.relation.ispartofseries | Not Available; | - |
dc.subject | bulk density; neural networks; pedotransfer function; waterlogged soils | en_US |
dc.title | Estimation of bulk density of waterlogged soils from basic properties | en_US |
dc.type | Research Paper | en_US |
dc.publication.projectcode | Not Available | en_US |
dc.publication.journalname | Archives of Agronomy and Soil Science | en_US |
dc.publication.volumeno | 58:5 | en_US |
dc.publication.pagenumber | 499-509 | en_US |
dc.publication.divisionUnit | Division of Land Use Planning | en_US |
dc.publication.sourceUrl | 10.1080/03650340.2010.530254 | en_US |
dc.publication.authorAffiliation | ICAR::National Bureau of Soil Survey and Land Use Planning | en_US |
dc.ICARdataUseLicence | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf | en_US |
dc.publication.naasrating | 8.14 | en_US |
Appears in Collections: | NRM-NBSSLUP-Publication |
Files in This Item:
File | Description | Size | Format | |
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6 GAGS Bulk Density 2012.pdf | 653.48 kB | Adobe PDF | View/Open |
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