Mapping soil properties using geostatistical methods for mid to high altitude temperate zone of Kashmir Himalayas Bangroo Shabir Ahmed*, Bhat Mohammad Iqbal, Wani Javaid Ahmad, Rasool Rihana, Madhi Syed Sheraz1, Bashir Owais, Shah Tajamul Islam Division of Soil Science, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Shalimar, 190025, Jammu and Kashmir, India 1Division of Agronomy, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Shalimar, 190025, Jammu and Kashmir, India *Corresponding author (Email: shalzsab@gmail.com)
Online published on 28 April, 2023. Abstract Mapping soil properties is crucial for the sustainable management of the soil resources especially in ecologically fragile regions like Kashmir Himalayas. This study was therefore conducted to evaluate and map the geographical distribution and variability of the soil in the agricultural landscape of the district of Kulgam. Ninety-nine soil samples (0 to 30 cm depth) from 51 villages of district Kulgam were collected and soil properties viz., pH, electrical conductivity (EC), organic carbon (OC), available nitrogen (N), available phosphorus (P), and available potassium (K) were determined. The soil parameters were transformed using Box-Cox transformation for data normalization except for N and K. Geostatistical models like ordinary kriging (OK) and universal kriging (UK) were used to interpolate analyzed values of these soil parameters, and different soil maps were prepared. Spatial autocorrelation was determined by geostatistical analyses of experimental variograms and model fitting. The nugget/sill ratio varied from 9.84 to 48.94%, demonstrating moderate to strong spatial dependency. The semivariograms of the soil properties were fitted with spherical and exponential models. The cross-validation results suggest that the UK is a better predictor than OK, except for pH. Farmers can use these maps to examine existing farm soils, making management decisions that are easier, and more efficient, and ensuring productivity and sustainability. Top Keywords Ordinary kriging, Universal kriging, Semivariogram, Spatial prediction. Top |