Spatial prediction of soil properties in a watershed scale through maximum likelihood approach
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Title |
Spatial prediction of soil properties in a watershed scale through maximum likelihood approach
Not Available |
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Creator |
Santra, P.
Das, B.S. Chakravarty, D. |
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Subject |
Residual maximum likelihood
Best linear unbiased prediction Kriging Semivariogram Soil hydraulic properties Elevation |
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Description |
Not Available
Surface map of soil properties plays an important role in various applications in a watershed. Ordinary kriging (OK) and regression kriging (RK) are conventionally used to prepare these surface maps but generally need large number of regularly girded soil samples. In this context, REML-EBLUP (REsidual Maximum Likelihood estimation of semivariogram parameters followed by Empirical Best Linear Unbiased Prediction) shown capable but not fully tested in a watershed scale. In this study, REML-EBLUP approach was applied to prepare surface maps of several soil properties in a hilly watershed of Eastern India and the performance was compared with conventionally used spatial interpolation methods: OK and RK. Evaluation of these three spatial interpolation methods through root-mean-squared residuals (RMSR) and mean squared deviation ratio (MSDR) showed better performance of REML-EBLUP over the other methods. Reduction in sample size through random selection of sampling points from full dataset also resulted in better performance of REML-EBLUP over OK and RK approach. The detailed investigation on effect of sample number on performance of spatial interpolation methods concluded that a minimum sampling density of 4/km2 may successfully be adopted for spatial prediction of soil properties in a watershed scale using the REML-EBLUP approach Not Available |
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Date |
2019-05-06T11:15:10Z
2019-05-06T11:15:10Z 2011-07-26 |
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Type |
Research Paper
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Identifier |
Not Available
Not Available http://krishi.icar.gov.in/jspui/handle/123456789/19259 |
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Language |
English
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Relation |
Not Available;
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Publisher |
Elsevier
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