Digital soil mapping in the Bara district of Nepal using kriging tool in ArcGIS

Authors

Dinesh Panday, University of Nebraska-LincolnFollow
Bijesh Maharjan, University of Nebraska-LincolnFollow
Devraj Chalise, Nepal Agricultural Research Council
Ram Kumar Shrestha, Institute of Agriculture and Animal Science, Lamjung, Nepal
Bikesh Twanabasu, Westfalische Wilhelms Universitat, Munster

ORCID IDs

Dinesh Panday http://orcid.org/0000-0001-8452-3797

Document Type

Article

Date of this Version

10-26-2018

Citation

Panday D, Maharjan B, Chalise D, Shrestha RK, Twanabasu B (2018) Digital soil mapping in the Bara district of Nepal using kriging tool in ArcGIS. PLoS ONE 13(10): e0206350.

doi 10.1371/journal.pone.0206350

Comments

Copyright: © 2018 Panday et al. This is an open access article distributed under the terms of the Creative Commons Attribution License

Abstract

Digital soil mapping has been widely used to develop statistical models of the relationships between environmental variables and soil attributes. This study aimed at determining and mapping the spatial distribution of the variability in soil chemical properties of the agricultural floodplain lands of the Bara district in Nepal. The study was carried out in 23 Village Development Committees with 12,516 ha total area, in the southern part of the Bara district. A total of 109 surface soil samples (0 to 15 cm depth) were collected and analyzed for pH, organic matter (OM), nitrogen (N), phosphorus (P, expressed as P₂O₅), potassium (K, expressed as K₂O), zinc (Zn), and boron (B) status. Descriptive statistics showed that most of the measured soil chemical variables (other than pH and P₂O₅) were skewed and nonnormally distributed and logarithmic transformation was then applied. A geostatistical tool, kriging, was used in ArcGIS to interpolate measured values for those variables and several digital map layers were developed based on each soil chemical property. Geostatistical interpolation identified a moderate spatial variability for pH, OM, N, P₂O₅, and a weak spatial variability for K₂O, Zn, and B, depending upon the use of amendments, fertilizing methods, and tillage, along with the inherent characteristics of each variable. Exponential (pH, OM, N, and Zn), Spherical (K₂O and B), and Gaussian (P₂O₅) models were fitted to the semivariograms of the soil variables. These maps allow farmers to assess existing farm soils, thus allowing them to make easier and more efficient management decisions and maintain the sustainability of productivity.