CHARACTERIZATION OF VARIABILITY OF SOIL PROPERTIES USING GEOSTATISTICAL APPROACH: A CASE STUDY OF MUZAFFARPUR DISTRICT IN BIHAR

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dc.contributor.advisorNidhi, Dr.
dc.contributor.authorKUMAR, ABHINEET
dc.date.accessioned2023-02-06T07:13:39Z
dc.date.available2023-02-06T07:13:39Z
dc.date.issued2022
dc.description.abstractIn this study an attempt is made to assess spatial variability of soil properties Muzaffarpur district in Bihar. The geostatistical approach has been applied to study the spatial variability of soil properties like pH, electrical conductivity (EC), organic carbon (OC), phosphorous(P), potassium(K), sulphur(S), zinc (Zn), copper (Cu), iron (Fe), manganese (Mn) and boron(B). Data on soil properties were obtained from AICRP on micronutrients from department of soil science, RPCAU, Pusa. Variogram has been used to express the spatial variability where variogram is a plot of the variances of subsequent point in the space vs. distance. Variogram models are used to create the spatial dependence of all the soil parameters. The four variogram models are linear, spherical, exponential, gaussian. Ordinary kriging has been used to interpolate for unsampled locations. Kriging is a spatial interpolation technique that create spatial distribution map as well as map for prediction of variance of soil parameters. To check the accuracy of soil nutrient maps cross validation approach has been used where mean absolute error (MAE), Mean square error (MSE), Goodness of prediction(G) values are computed for accuracy check. It is found that pH has very lowest CV (6.37%) indicating homogeneous soil for pH across the study region while highest CV (78.44%) is found for zinc indicating high variation across the study region. EC, OC, P, K, S, Zn, Cu, Fe, Mn, and B with respect to their CV are under moderate variation across the study region. In different classes, almost 40% of soil sample have soil pH between 8.0 to 8.5 which mean moderate alkaline. Almost 85% of soil sample have OC less than 0.5%. 98% of soil sample have EC less than 1ds/m. 60% of soil sample have phosphorous between 25-50ppm. 63% of soil sample have potassium between 125 -300ppm. 82% of soil sample have Sulphur less than 22.4ppm. 54% of soil samples have Zn above 1.20ppm. 96% of soil samples have greater than1.20ppm. almost 80% of soil samples have greater than 12.0ppm. 88% of soil samples have Mn greater than 5.0ppm. 52% of soil samples have boron between 0.5-1.0ppm. Spherical and exponential model has been used to fit the experimental variogram of the soil parameters. Range of spatial dependence for each parameter has been established. values are computed to analyse the goodness of the variogram model. Based on nugget-sill ratio, the spatial dependence for each parameter has been classified as weak, moderate and strong. Mn, B, EC, OC and Zn values influenced their neighbouring values over greater distances than pH, P, K, S, Cu and Fe, all of which have range of below 10 km. The highest range of 72 km is observed for Manganese means the concentration is highly correlated spatial up to a large distance. The nugget sill ratio also proved its moderate spatial dependence in area under study. Range of Boron (B) concentration is 66 km while the largest nugget effect is observed for K and moderate dependence is observed for its concentration under study area due to large nugget effect. The goodness of fit statistic indicates a moderate fit for the variogram model. The soil pH is also observed to be spatially correlated to a distance of 10.6 km in the study area and the degree of spatial continuity is moderate. All the other parameters are observed to have low values of range varying below 10 km. It might be due to cumulative effect of climate, parent material and adopting of different land management, the range values of soil parameters are different. This observed spatial dependency can be used to support spatial sampling for detailed soil mapping in site specific soil management. Predictive spatial maps have been generated for each soil parameter by interpolating the values using ordinary kriging method using variogram model. The different distribution pattern is exhibited by the kriged surface maps developed for soil parameters. These maps could help for site specific nutrient management and also in designing future soil sampling strategies in the intensively cultivated alluvial soil of Muzaffarpur. The predictive maps produced by interpolating the values using ordinary kriging show distinct patchy distribution of pH, K, P, S, B, Zn, EC, OC and Mn across different parts of Muzaffarpur. These types of maps can help in identifying the pockets of soil available nutrients according to their concentration and may in turn help for region specific management.en_US
dc.identifier.otherM/STAT /404/2020-21
dc.identifier.urihttps://krishikosh.egranth.ac.in/handle/1/5810193303
dc.keywordsGeostatistics, spatial variability, Variogram, Kriging, Spatial Interpolation, cross-validationen_US
dc.language.isoEnglishen_US
dc.pages73 + viii (Bibliography)en_US
dc.publisherDRPCAU, PUSAen_US
dc.subAgricultural Statistics and Informaticsen_US
dc.themeCharacterization of Variability of Soil Properties Using Geostatistical Approach: A Case Study of Muzaffarpur District in Biharen_US
dc.these.typeM.Scen_US
dc.titleCHARACTERIZATION OF VARIABILITY OF SOIL PROPERTIES USING GEOSTATISTICAL APPROACH: A CASE STUDY OF MUZAFFARPUR DISTRICT IN BIHARen_US
dc.typeThesisen_US
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