The water-holding capacity and other soil quality parameters (Kosmas

The
global climate change research revolves around the nucleus of 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 are the driving force of
agro-ecosystem functions- regulating soil fertility, water-holding capacity and
other soil quality parameters (Kosmas et al., 2000; Bangroo et al., 2013).

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

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The
three dimensional SOC and TSN variation with soil and atmosphere is affected by
physiographic factors (altitude, aspect and slope), land use type and
management, temperature and soil moisture (Bangroo et al., 2017). There is a
considerable research 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., 2006).

Attempts
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). A number of 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 variables with kriging and thus incorporates the topography,
vegetation and other factors for higher prediction accuracy.

In
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.

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