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

• Published in: PloS one 2018-10, Vol.13 (10), p.e0206350-e0206350
• Main Authors: Panday, Dinesh, Maharjan, Bijesh, Chalise, Devraj, Shrestha, Ram Kumar, Twanabasu, Bikesh
• Format: Article
• Language: English
• Subjects: Agricultural land; Agricultural management; Agricultural productivity; Agriculture; Agrochemicals; Biology and Life Sciences; Boron; Carbon; Chemical properties; Digital mapping; Ecology and Environmental Sciences; Environmental Monitoring - methods; Farms; Floodplains; Gene expression; Geostatistics; Horticulture; Interpolation; Kriging interpolation; Laboratories; Mapping; Mathematical models; Methods; Nepal; Nitrogen; Nitrogen - analysis; Nutrients; Organic chemistry; Organic matter; Organic phosphorus; People and Places; pH effects; Phosphorus; Phosphorus - analysis; Phosphorus pentoxide; Physical Sciences; Potassium; Soil - chemistry; Soil fertility; Soil mapping; Soil properties; Soil sciences; Soil surfaces; Spatial Analysis; Spatial distribution; Spatial variability; Statistical analysis; Statistical methods; Statistical models; Statistics as Topic; Studies; Sustainability; Tillage; Transformation; Variability; Zinc
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0206350

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 non-normally 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.