stract provided there is a strong correlation between

stractSoil organic carbon (SOC) and totalsoil nitrogen (TSN) are the significant indicators of soil fertility andbiogeochemical cycle. Spatial distribution and variation of SOC and TSN estimationis central to climate change and sustainable soil management studies.

Littleresearch on spatial prediction of SOC and TSN based on geostatisticaltechniques employing secondary variables (sampling location) and auxiliaryinformation (topographic factors and type of vegetation) has been conductedglobally and under Himalayas in particular. To attempt this ninety-six soilssamples of 0-20 cm depths were taken from small forest area of North KashmirHimalayas. The effect of topographic factors-elevation, slope, compoundtopographic index (CTI), stream power index (SPI), sediment transport index(STI), normalized difference vegetation index (NDVI) and vegetation type on SOCand TSN spatial distribution were studied using regression kriging. Resultsindicated regression kriging as better predictor of SOC and TSN spatialdistribution than ordinary kriging with residuals moderately auto-correlated.

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Semi-variogram test indicated topographic factors- elevation and slope andvegetation type as major factors of SOC and TSN spatial variation. The negativecorrelation of topographic elevation and slope with spatial distribution of SOCand TSN reveal better stabilization of SOC and TSN at lower degrees of slopeand lower altitudes.  Our study suggestregression kriging can provide better estimations at larger scale, providedthere is a strong correlation between environmental variables and the SOC andTSN contents, and residuals are spatially auto-correlated. Keywords:Soil organic carbon, total soil nitrogen, spatial distribution, regressionkriging, Kashmir Himalayas  IntroductionTheworld climate change studies are centric to carbon-nitrogen cycling.

Soilorganic carbon (SOC) and total soil nitrogen (TSN) play an important role inecosystem functioning (Gregorich et al., 1994). They act as an important factorin food and fuel security, reclamation of degraded lands and mitigation ofclimate 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).Thesoil biodiversity and soil physical stability is controlled by the spatialvariability of SOC and TSN (Stevenson and Cole, 1999). Therefore, their preciseestimation and spatial distribution is important to comprehend thecarbon-nitrogen dynamics and assist in the decision support system for theecosystem recuperation.

Thespatial and temporal SOC and TSN variation with soil and atmosphere is affectedby topographic factors (altitude, aspect and slope), land use/ management,temperature and soil moisture (Bangroo et al., 2017). Appreciable research isavailable on factors affecting SOC and TSN under different physiographic, landuse/management and climatic conditions (Zhang et al., 2012; Peng et al., 2013;Mondal et al., 2017).

A non-uniform SOC and TSN spatial distribution andcorrelation with auxiliary information (topography, land use/management,vegetation and parent material) show a changing continuum on different scales (Tanand Lal, 2005; Su et al., 2006; Liu et al., 2006). Attemptsin recent past have been made to assess the SOCand TSN spatial distribution in relation to these factors byemploying the geostatistical techniques (Kerry and Oliver, 2007; Chai et al.

,2008; Marchetti et al., 2012). Many geospatial prediction models have been developedto interpolate soil variables into spatially distributed continuum surface fromtarget sampling points (Harries et al., 2010; Kumar et al., 2012).

Not all takeinto account the large uncertainty inherent soil spatial heterogeneity such asordinary kriging. More recently regression kriging has been extensively usedthat combines multiple linear regression using auxiliary information withkriging and thus incorporates the topography, vegetation and other factors forhigher prediction accuracy. Inthis study, we selected small forest area of Kashmir Himalayan region as aresearch site. We used the regression kriging capability to accomplish these objectivesi) to estimate the SOC and TSN spatialdistribution; ii) to evaluate the impact of topographic attributesand vegetation indices on spatial interpolation accuracy; and iii) to analyzethe spatial prediction accuracy for SOC and TSN using regression and ordinarykriging methods.

 Materials and MethodsStudy areaTheMawer forest range lies between 34° 17? to 34° 22? N and 73° 19? to 74° 59? Ein Kupwara District of Jammu & Kashmir, India (Fig. 1). The area is lacustrinein origin crossed by the Mawar river. It is spread to an area of 26.1 sq. kmwith slope ranging from 15–30% to 30–50%. Being pleistocene andpost-pleistocene in nature the area has good fertility levels. The forests ofthe Mawer range is dominated by the coniferous species (Table 1) like Deodar (Cedrus deodara), Himalayan Pine (Pinus wallichiana) and Fir (Abies pindrow).

The distribution patternof 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 inmixtures and in pure stands. The Deodar and Himalayan Pine covers about 44% and19% of the total area of the commercial forest area of the division. Thebroad-leaved species are irregularly distributed throughout the division andare mainly confined to natural drains, moist depressions, and damp localities.Soil sampling and soilanalysisAtotal of ninety-six soil samples were selected from Mawer forest range. A 10m x10m gird sampling design was generated from digital topographic map of forestrange at 1:10,000 scale.

The locations of the sampling sites were recordedusing a global positioning system (GPS) receiver (Garmin3790T). Three soil sampleswere collected at depth 0–20 cm over a circle of radius 10m surrounding thespecified sampling location and mixed thoroughly. The samples were air-driedand 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 Thenormalized difference vegetation index (NDVI) was procured from Landsat 8 OLIand topographic factors like elevation, slope, compound topographic index(CTI), stream power index (SPI) and sediment power index (SPI) were calculatedfrom cartosat DEM.NDVIis the classical indication of plant health and is used to monitor the changesin vegetation. The NDVI is closely related to vegetation cover, biomass and theleaf area index (LAI).

The NDVI is given as where  and  arethe near-infrared and red band spectral reflectance, respectively in theLANDSAT 8 satellite data. The NDVI ranges from -1 to +1 (NOAA Coastal ServiceCentre, 2007). The NDVI derived from the Landsat data along with the terrainattributes are utilized in the prediction of the soil organic carbon.

Slope and elevation arederived from the DEM. Both the factors have a strong correlation with the SOCstabilization (Perruchound et al., 2000; Bangroo et al.

, 2017). CTIis an important aspect of hydrologic system model and provides an indirectinformation on land cover and agriculture potential. It is a function of bothslope and upstream contributing area per unit width extraneous to the flowdirection. CTI is defined as Where? represents the catchment area per unit width extraneous to the direction ofthe flow direction and ? refers to the slope.SPI isa measure of the potential flow erosion of water flow based on the assumptionthat flow is proportional to the specific topographic surface (Moore et al.

,1991). It takes into account a local slope geometry and site location in thelandscape and measures the erosive power of flowing water at a given point ofthe topographic surface. SPI is defined as Where?represents the upstream drainage area (m2/m) and ? refers to theslope gradient.STI takes into account the upslope contributing area assuming it tobe 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 slopegradient. Regression Kriging MethodologySamplingpoints of SOC and TSN were interpolated in spatial domain by the regressionkriging method (Fig. 2). Regression kriging method can perceive the auxiliary variablesfor interpolation of the output at those locations points which is restrainedin the simple kriging method (Hengl et al., 2007). Remote sensing images,vegetation type, and elevation were considered as common auxiliary predictorsand topographic parameters elevation and slope, normalized differencevegetation index (NDVI), compound topographic index (CTI), stream power index(SPI) and sediment power index (SPI) have been used as the predictor variableshere. Regression kriging combines the two approaches of regression and krigingwhere regression is applied to fit the explanatory variation and the simplekriging 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 thelocation, ,    givesthe fitted drift,  denotes the interpolated residual,  stands for the estimated drift modelcoefficients (  isthe estimated intercept),  denotes the kriging weights that is determinedby the spatial dependence structure of the residual and  givesthe residual at location .Model validationForthe model validation 29 soil samples out of total 96 samples were randomlyextracted from the data.

The mean error (ME) and root mean square error (RMSE)were used for the model efficiency estimated by comparing SOC and TSN observedand predicted values from the validation point location. Prediction accuracyimprovement was computed by comparing RK with OK.      whereME is the mean error; RMSE is the root mean square error; n represents samplingvalidation points;  and  are observed and predicted values of thesampling points, respectively; and R’ is the prediction accuracy improvement fromcomparing RK with OK. For positive value of R’, RK has higher predictionaccuracy than that of OK and vice-versa for the negative value of R’.

 isthe root mean square error of OK, and  isthat of RK.Descriptiveand regression analysis of SOC and TSN data was performed in SPSS 20.0 software.Study area was delineated and topographic factors were extracted from watershedDEM using ArcGIS 10.2. Spatial and semi-variogram analysis between regressionprediction and residual values of OK were computed and lastly spatialprediction SOC and TSN distribution maps were produced.

  ResultsSOC and TSN Descriptive StatisticsThecoefficient of variation (CV), standard deviation, and basic statisticalparameters of mean, range, minimum and maximum are shown in Table 2. Theaverage SOC and TSN concentration in the study area were 17.74 g kg-1 and2.31g kg-1 respectively. Both the moderate CV 26.21% and 23.

32 % couldbe linked to uniform land use pattern, and/or soil erosion.  Correlation between SOC and TSN withthe environmental variablesTheSOC and TSN showed a negative correlation with the elevation (Table 3). Thisindicates that the concentration of both SOC and TSN deceases with theelevation. Similar, correlation was observed with the slope which is animportant soil erosion factor. This reveals that greater the slope more intenseis the soil erosion which results in decrease in SOC and TSN concentrations. Littleor no correlation of SOC and TSN was observed with CTI, SPI or STI. Correlationof average SOC and TSN content along the elevation with NDVI was also analyzedand found to be significant (r2 = 0.

673, p<0.001). This indicatesthat SOC and TSN increases with an increase in vegetation NDVI.  Spatial variability and distributionof SOC and TSNTopographicfactors (elevation, slope, SPI, STI and CTI) and NDVI were used to predict thespatial variability of SOC and TSN through multiple linear regression method. Amongthese, elevation and slope proved to be the optimal factors for the predictionof SOC and TSN with determination coefficients (r2) of 0.426 (atP<0.

05) and 0.406 (at P<0.001) respectively. Theregression kriging provided better results for spatial autocorrelation of SOCand TSN than that of ordinary kriging (Fig. 3). The Nugget/Sill ratio forregression kriging and ordinary kriging for SOC were 0.28 and 9.81 and for TSNwere 0.

24 and 4.59 respectively (Table 4). The semi-variogram analysis showedthat environmental factors such as topography and vegetation were the primarycauses of SOC and TSN spatial variance.SOCand TSN were also found to be strongly correlated, with a correlationcoefficient of 0.7121 (P<0.05) and have highly significant linear relationship.

 Prediction accuracy of OK and RKLocationpoints of SOC and TSN samples were interpolated in spatial domain by theregression kriging method and using topographic factors (elevation, slope, SPI,STI and CTI) and NDVI as predictor variables. Regression was applied to fit theexplanatory variation and simple kriging with an expected value of 0 wasapplied to fit the residuals, i.e., unexplained variation in regression krigingmethod.Sixty-sevensamples were randomly selected to conduct ordinary kriging interpolation forregression residual error of SOC and TSN in the study area.

In the meantime,ordinary kriging interpolation was also conducted on these samples as acontrol. From the results of prediction errors, regression-kriging was found betterthan that of ordinary kriging (Fig. 4). The 29 training samples were used formodel validation and comparison of the two prediction methods (Table 5).  Satisfactory results were obtained withregression kriging with predicted values close to observed ones and much moredetailed concerning the partly variation and topographical relationships thanthat of ordinary kriging. The improvements of prediction accuracy (R’) of SOCand TSN were 17.82% and 19.

44%, respectively (Table 5). DiscussionEffect of vegetation type on SOC andTSN Thetype of vegetation has a significant effect on corresponding changes inmicro-climate in an ecologically fragile environment like of Kashmir Himalayaswhich subsequently alter soil nutrient dynamics (Bangroo et al., 2017). The SOCand TSN concentration in existing dominant vegetation types of the study arearanked as Pinus wallichina > Cedrusdeodara > Abies pindrow. This suggests that vegetationtype had a significant impact on spatial SOC and TSN patterns.

Similar, trendwas observed by Peng et al., 2013 and Garcia et al 2016.Significantdifferences in SOC and TSN in varying vegetation types were observed(P<0.

05), this may be attributed to species composition, stand structure,and management history (Dar and Sundarapandian, 2015). The thicker forestlitter and well flourished soil plant root system of P. wallichina and C. deodarafix and more SOC and TSN which cause high accumulation.

The shrub biomass was alsofound highest under C. deodara in WesternHimalayas (Wani et al., 2016). The study area being a protected forest had lesshuman intervention and less soil erosion in P.

wallichina and C. deodara beltwhich favored SOC and TSN accumulation. Effect of topographic parameters onSOC and TSNTopographicparameters have a significant effect on the spatial distribution of SOC and TSN(Mondal et al., 2017).

Research indicate that SOC is primarily controlled bythe variation in temperature, and soil moisture which vary with elevationgradients (Griffiths et al., 2009), aspect (Måren et al., 2015; Garcia et al.,2016) and slope (Perruchoud et al.

, 2000). While, the N stock variation withaltitude are partly influenced by vegetation type and partly by altitude(Bangroo et al., 2017). The correlation analysis revealed negative correlationof SOC and TSN in our study area with the elevation (Table 3). This may beattributed to the 1) lower mineralization rate and net nitrification rate atthe higher altitude, 2) decline in total tree density, and species richnesswith increasing altitude, and 3) better stabilization of SOC at loweraltitudes. A characteristic decline in vegetation was observed acrossaltitudinal strata.

The decrease in species richness in high elevation strata significantin Himalayan forests could be due to eco-physiological constraints, lowtemperature, and productivity (Gairola et al., 2008; Hardy et al., 2001). Thecharacteristic decline in vegetation with increasing altitude results in lessaccumulation of litter and low input of organic carbon in soils. Weobserved negative correlation of SOC and TSN spatial distribution with theslope (Table 3). This is attributed to 1) higher rates of erosion with theslope which increases with increasing rainfall, 2) poor soil development whichresults in poor retention of SOC and 3) soil temperature gradients along theslope under different aspects which affect the rate of SOC decomposition.

Theseresults concur with other findings, Bookhagen et al., 2005, observed lowestrates of soil erosion at less than 2% of slope and highest at more than 20% ofslope resulting in highest SOC loss. High erosion rate in steep slopes alongwith low carbon stock causes further depletion of SOC whereas lower areas havebetter retention of the SOC stock.   ConclusionThespatial distribution of SOC and TSN across the complex topography of smallforest area of Kashmir Himalaya is better predicted by regression kriging ascompared to ordinary kriging with a prediction accuracy of 17.

82 % and 19.44 %respectively. The spatial autocorrelation of SOC and TSN is better explained byregression kriging with Nugget/Sill ratio of 0.

28 and 0.24 respectively. It isimportant to select appropriate environmental variables for interpolationtechniques and semi-variogram analyses showed topographic parameters ofelevation, slope and vegetation type/ land use as major factors influencing thespatial distribution of SOC and TSN. Bothelevation and slope has significant influence on spatial distribution of SOCand TSN concentrations. Negative correlation of SOC and TSN with elevationindicate better stabilization at lower altitudes.

High degree of slope has lowvegetation and high soil erosion rate which leads to low SOC and TSNconcentrations. Inconclusion, regression kriging can provide better estimations at larger scale,provided there is a strong correlation between environmental variables and theSOC and TSN concentrations, and residuals are spatially autocorrelated.

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