Soil variables and the SOC and TSN

Soil organic carbon (SOC) and total
soil nitrogen (TSN) are the important indicators of soil health and
biogeochemical cycle. Accurate estimation of spatial distribution and variation
of SOC and TSN is critical to climate change studies and sustainable soil
management. There has been little research on inclusion of secondary variables
(sampling location) and auxiliary information (topographic factors and
vegetation type) in prediction of spatial distribution of SOC and TSN based on
geostatistical techniques. To attempt this ninety-six soils samples were taken
at a depth of 0-20 cm from small forest area of North Kashmir Himalayas. The
effect of topographic parameters-elevation, slope, compound topographic index
(CTI), stream power index (SPI), sediment transport index (STI), normalized
difference vegetation index (NDVI) and vegetation type on spatial distribution
of SOC and TSN spatial distribution were examined using regression kriging. Results
indicated that spatial distribution of SOC and TSN were better predicted by
regression kriging than ordinary kriging with residuals moderately
autocorrelated. Semi-variogram test revealed topographic parameters- elevation
and slope and vegetation type as major factors of SOC and TSN spatial
variation. The negative correlation of elevation and slope with spatial
distribution of SOC and TSN suggest their better stabilization at lower degrees
of slope and lower altitudes.  Our study
suggest regression kriging can provide better estimations at larger scale,
provided there is a strong correlation between environmental variables and the SOC
and TSN concentrations, and residuals are spatially autocorrelated.


Soil organic carbon, total soil nitrogen, spatial distribution, regression
kriging, Kashmir Himalayas

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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).

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.

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).

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.

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.


Materials and Methods

Study area

Mawer forest range lies between 34° 17? to 34° 22? N and 73° 19? to 74° 59? E
in Kupwara District of Jammu & Kashmir, India (Fig. 1). The mountain range
has lacustrine origin with well drainage system to the Mawar river. It has an
area of 26.1 sq. km with slope ranging from 15–30% to 30–50%. Being pleistocene
and post-pleistocene in nature the area has good fertility levels. The forests
of the Mawer range is dominated by the coniferous species (Table 1). The
principal coniferous species are Deodar (Cedrus
deodara), Himalayan Pine (Pinus
wallichiana) and Fir (Abies pindrow).
The distribution pattern of the principal species is influenced mainly by the
factors such as altitude, aspect, and soil. The Deodar and Himalayan Pine on
lower belts occur both in mixtures and in pure stands. The Deodar and Himalayan
Pine covers about 44% and 19% of the total area of the commercial forest area
of the division. The broad-leaved species are irregularly distributed
throughout the division and are generally restricted to natural drains, moist
depressions, and damp localities.

Soil sampling and soil

total of ninety-six soil samples were selected from Mawer forest range. The
sampling design was based on a 10m x 10m gird generated from digital
topographic map of forest range at a scale of 1:10,000. The locations of the
sampling sites were recorded using a global positioning system (GPS) receiver
(Garmin3790T). Three soil samples were collected at depth 0–20 cm over a circle
of radius 10m surrounding the specified sampling location and mixed thoroughly.
The samples were air-dried and ground to pass a 2-mm sieve. Nitrogen was
determined by Kjeldahl method (Bremner, 1996) and OC by Walkley and Black
method (Nelson and Sommers, 1982).


Acquisition of auxiliary information

normalized difference vegetation index (NDVI) was acquired from Landsat 8 OLI
and topographic factors like elevation, slope, compound topographic index
(CTI), stream power index (SPI) and sediment power index (SPI) were computed
using cartosat DEM.

is the classical indication of plant health and is used to monitor the changes
in vegetation. The NDVI is closely related to vegetation cover, biomass and the
leaf area index (LAI). The NDVI is given as



the spectral reflectance of the band near-infrared and red, respectively in the
LANDSAT 8 satellite data. The NDVI ranges from -1 to +1 (NOAA Coastal Service
Centre, 2007). The NDVI derived from the Landsat data along with the terrain
attributes are utilized in the prediction of the soil organic carbon.

Slope and elevation are
derived from the DEM. Both the factors have a strong correlation with the SOC
stabilization (Perruchound et al., 2000; Bangroo et al., 2017).

is an important aspect of hydrologic system model and provides an indirect
information on land cover and agriculture potential. It is a function of both
slope and upstream contributing area per unit width orthogonal to the flow
direction. CTI is defined as

? represents the catchment area per unit width orthogonal to the direction of
the flow direction and ? refers to the slope.

SPI is
a measure of the erosive power of water flow based on the assumption that
discharge is proportional to the specific catchment area (Moore et al., 1991).
It takes into account a local slope geometry and site location in the landscape
and measures the erosive power of flowing water at a given point of the
topographic surface. SPI is defined as

represents the upstream drainage area (m2/m) and ? refers to the
slope gradient.

STI combines upslope contributing area under the assumption that
contributing area is directly related to discharge, and slope. STI is defined
as (Moore et al., 1993)


? is the specific catchment area (m2/m), and ? is the slope


Regression Kriging Methodology

points of SOC and TSN were interpolated in spatial domain by the regression
kriging method (Fig. 2). This method can consider the auxiliary variables at
those location points for interpolation of the outputs, which is restricted in
the simple kriging method (Hengl et al., 2007). Remote sensing images,
vegetation type, and elevation were considered as common auxiliary predictors
and topographic parameters elevation and slope, normalized difference
vegetation index (NDVI), compound topographic index (CTI), stream power index
(SPI) and sediment power index (SPI) have been used as the predictor variables
here. Regression kriging combines the two approaches of regression and kriging
where regression is applied to fit the explanatory variation and the simple
kriging with an expected value of 0 is applied to fit the residuals, i.e.
unexplained variation (Hengl et al., 2004; Mukherjee et al., 2015):





 denotes the interpolated value of the


the fitted drift,

 denotes the interpolated residual,

 stands for the estimated drift model
coefficients (

the estimated intercept),

 denotes the kriging weights that is determined
by the spatial dependence structure of the residual and

the residual at location


Model validation

of total 96 soil samples, 29 samples were randomly extracted from the data for
the model validation. The model efficiency was estimated by comparing observed
and predicted values of SOC and TSN from validation point location using mean
error (ME) and root mean square error (RMSE). R’ was used to analyze
improvement of the prediction accuracy by comparing RK with OK.




ME is the mean error; RMSE is the root mean square error; n represents sampling
validation points;


 are observed and predicted values of the
sampling points, respectively; and R’ is the improvement of prediction accuracy
from comparing RK with OK. If R’ is positive, it means that prediction accuracy
of RK was higher than that of OK, and vice-versa for a negative R’ value.

the root mean square error of OK, and

that of RK.

20.0 software was used for classical and regression analysis of SOC and TSN.
ArcGIS 10.2 was used delineate small forest based watershed DEM and extract its
topographic factors. Semi-variogram analysis and spatial operation between the
trend item of regression prediction and the residual value of OK and then
spatial prediction distribution map of SOC and TSN was produced.




SOC and TSN Descriptive Statistics

coefficient of variation (CV), standard deviation, and basic statistical
parameters of mean, range, minimum and maximum are shown in Table 1. The
average SOC and TSN concentration in the study area were 17.74 g kg-1 and
2.31g kg-1 respectively. Both the moderate CV 26.21% and 23.32 % could
be linked to uniform land use pattern, and/or soil erosion.


Correlation between SOC and TSN with
the environmental variables

SOC and TSN showed a negative correlation with the elevation (Table 2). This
indicates that the concentration of both SOC and TSN deceases with the
elevation. Similar, correlation was observed with the slope which is an
important soil erosion factor. This reveals that greater the slope more intense
is the soil erosion which results in decrease in SOC and TSN concentrations.

or no correlation of SOC and TSN was observed with CTI, SPI or STI. Correlation
of average SOC and TSN content along the elevation with NDVI was also analyzed
and found to be significant (r2 = 0.673, p Cedrus
deodara > Abies pindrow. This suggests that vegetation
type had a significant impact on spatial SOC and TSN patterns. Similar, trend
was observed by Peng et al., 2013 and Garcia et al 2016.

differences in SOC and TSN in varying vegetation types were observed

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