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.

Keywords:

Soil organic carbon, total soil nitrogen, spatial distribution, regression

kriging, Kashmir Himalayas

Introduction

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.

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.

Materials and Methods

Study area

The

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

analysis

A

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

The

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.

NDVI

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

where

and

are

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).

CTI

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

Where

? 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

Where

?

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)

Where

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

gradient.

Regression Kriging Methodology

Sampling

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):

where,

denotes the interpolated value of the

location,

,

gives

the fitted drift,

denotes the interpolated residual,

stands for the estimated drift model

coefficients (

is

the estimated intercept),

denotes the kriging weights that is determined

by the spatial dependence structure of the residual and

gives

the residual at location

.

Model validation

Out

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.

where

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

validation points;

and

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.

is

the root mean square error of OK, and

is

that of RK.

SPSS

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.

Results

SOC and TSN Descriptive Statistics

The

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

The

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.

Little

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.

Significant

differences in SOC and TSN in varying vegetation types were observed

(P