Application of predictor variables in spatial quantification of soil organic carbon and total nitrogen using regression kriging in the North Kashmir forest Himalayas

Abstract

Soil organic carbon (SOC) and total soil nitrogen (TSN) are the critical indices of soil fertility and the biogeochemical cycle. Distribution and variation of SOC and TSN in space are central to climate change and sustainable soil management studies. Little research on spatial prediction of SOC and TSN based on geostatistical techniques employing secondary variables (sampling location) and auxiliary information (topographic factors and type of vegetation) has been conducted globally and under the Himalayas in particular. In this study, 96 soil samples of 0–20 cm depths were taken from the forest area of North Kashmir Himalayas. The effect of topographic factors-elevation, slope, compound topographic index (CTI), stream power index (SPI), sediment transport index (STI), and normalized difference vegetation index (NDVI) on SOC and TSN spatial distribution were studied using regression kriging. Results indicate that regression kriging is a better predictor of SOC and TSN spatial distribution by 97.3% and 96.4% respectively than ordinary kriging with residuals moderately auto-correlated. Semi-variogram test indicated that elevation, slope, and NDVI are major factors driving SOC and TSN spatial variation. The negative correlation of topographic elevation and slope with the spatial distribution of SOC and TSN reveals better stabilization of SOC and TSN at lower degrees of slope and lower altitudes. Our study suggests that regression kriging at greater scale can provide the best estimations in a relatively uniform ecosystem provided there is a significant correlation between auxiliary variables and the SOC and TSN contents with residuals spatially auto-correlated.