Evaluating Spatial Soil Parameters of an Apple Orchard in ‘R’ Software: A Study from Kashmir Valley

Abstract

Maintaining the ecological balance of the physical and biological environment depends greatly on the spatial variation of soil parameters across a given region and its elucidation using remote sensing and GIS applications. R software was used to apply the geostatistical methods of Ordinary Kriging and Inverse Distance Weightage to the study of the spatial distribution and variability of the soil parameters of an apple orchard in the Valley of Kashmir. Average values for available phosphorus (P), potassium (K), calcium (Ca), available nitrogen (N), and soil organic carbon (OC) were 1.15 percent, 315 percent, 22.5 kg, 221.4 kg, and 250 mg, respectively. Ordinary kriging (OK) and IDW were used to plot the spatial distribution of soil parameters using spherical (pH, OC, and Ca), steradian (EC, N, P, and K), and mean square error (MSE) values. According to the semi-variogram analysis, there was a strong to moderate spatial dependence. The interpolated maps showed various soil distribution patterns for pH (5.4-5.7), EC (0.46-0.56 DSM-1), OC (0.9-1.4 percent), N (296-335) kg ha-1, P (20-25 kg ha-1), K (221-221.8 kg ha-1), and Ca (100-400 mg kg-1) at the regional scale. This study represented a wide range of spatial soil variability. The estimation of MSE for Ordinary Kriging and IDW was used to predict the best model after performing a validation accuracy assessment. The best fit model for interpolation was the standard kriging model with low MSE. The maps of soil spatial distribution created to serve as an effective tool for policymakers and those planning farms.