The al., 2000; Bangroo et al., 2013). The soil

world climate change studies are centric to carbon-nitrogen cycling. Soil
organic carbon (SOC) and total soil nitrogen (TSN) play an important role in
ecosystem functioning (Gregorich et al., 1994). They act as an important factor
in food and fuel security, reclamation of degraded lands and mitigation of
climate change (Lal, 2004). They act as driving force of agro-ecosystem functions-
controlling soil fertility, water holding capacity and other soil quality factors
(Kosmas et al., 2000; Bangroo et al., 2013).

soil biodiversity and soil physical stability is controlled by the spatial
variability of SOC and TSN (Stevenson and Cole, 1999). Therefore, their precise
estimation and spatial distribution is important to comprehend the
carbon-nitrogen dynamics and assist in the decision support system for the
ecosystem recuperation.

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spatial and temporal SOC and TSN variation with soil and atmosphere is affected
by topographic factors (altitude, aspect and slope), land use/ management,
temperature and soil moisture (Bangroo et al., 2017). Appreciable research is
available on factors affecting SOC and TSN under different physiographic, land
use/management and climatic conditions (Zhang et al., 2012; Peng et al., 2013;
Mondal et al., 2017). Studies on the spatial distribution of SOC and TSN on
different scales show that both have a changing continuum with a non-uniform
spatial distribution and correlation with the topography, land use/management,
vegetation and parent material (Tan and Lal, 2005; Su et al., 2006; Liu et al.,

in recent past have been made to assess the spatial distribution of SOC and TSN
in relation to these factors by employing the geostatistical techniques (Kerry
and Oliver, 2007; Chai et al., 2008; Marchetti et al., 2012). Many prediction
methods have been developed to interpolate soil variables at discrete soil
sampling points into continuous spatially-distributed surfaces (Harries et al.,
2010; Kumar et al., 2012). Not all take into account the large uncertainty
inherent soil spatial heterogeneity such as ordinary kriging. More recently
regression kriging has been extensively used that combines multiple linear
regression using auxiliary information with kriging and thus incorporates the topography,
vegetation and other factors for higher prediction accuracy.

this study, we selected small forest area of Kashmir Himalayan region as a
research site. We used the regression kriging capability to achieve the
following objectives i) to estimate the spatial distribution of SOC and TSN;
ii) to evaluate the impact of topographic attributes and vegetation indices on
spatial interpolation accuracy; and iii) to analyze the spatial prediction
accuracy for SOC and TSN using regression and ordinary kriging methods.