The TSN with determination coefficients (r2) of 0.426 (at

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

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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<0.001). This indicates that SOC and TSN increases with an increase in vegetation NDVI.   Spatial variability and distribution of SOC and TSN Topographic factors (elevation, slope, SPI, STI and CTI) and NDVI were used to predict the spatial variability of SOC and TSN through multiple linear regression method. Among these, elevation and slope proved to be the optimal factors for the prediction of SOC and TSN with determination coefficients (r2) of 0.426 (at P<0.05) and 0.406 (at P<0.001) respectively. The regression kriging provided better results for spatial autocorrelation of SOC and TSN than that of ordinary kriging (Fig. 3). The Nugget/Sill ratio for regression kriging and ordinary kriging for SOC were 0.28 and 9.81 and for TSN were 0.24 and 4.59 respectively (Table 4). The semi-variogram analysis showed that environmental factors such as topography and vegetation were the primary causes of SOC and TSN spatial variance. SOC and TSN were also found to be strongly correlated, with a correlation coefficient of 0.7121 (P<0.05) and have highly significant linear relationship.   Prediction accuracy of OK and RK Location points of SOC and TSN samples were interpolated in spatial domain by the regression kriging method and using topographic factors (elevation, slope, SPI, STI and CTI) and NDVI as predictor variables. Regression was applied to fit the explanatory variation and simple kriging with an expected value of 0 was applied to fit the residuals, i.e., unexplained variation in regression kriging method. Sixty-seven samples were randomly selected to conduct ordinary kriging interpolation for regression residual error of SOC and TSN in the study area. In the meantime, ordinary kriging interpolation was also conducted on these samples as a control. From the results of prediction errors, regression-kriging was found better than that of ordinary kriging (Fig. 4). The 29 training samples were used for model validation and comparison of the two prediction methods (Table 5).  Satisfactory results were obtained with regression kriging with predicted values close to observed ones and much more detailed concerning the partly variation and topographical relationships than that of ordinary kriging. The improvements of prediction accuracy (R') of SOC and TSN were 17.82% and 19.44%, respectively (Table 5).   Discussion Effect of vegetation type on SOC and TSN The type of vegetation has a significant effect on corresponding changes in micro-climate in an ecologically fragile environment like of Kashmir Himalayas which subsequently alter soil nutrient dynamics (Bangroo et al., 2017). The SOC and TSN concentration in existing dominant vegetation types of the study area ranked as Pinus wallichina > 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
(P<0.05), this may be attributed to species composition, stand structure, and management history (Dar and Sundarapandian, 2015). The thicker forest litter and well flourished soil plant root system of P. wallichina and C. deodara fix and more SOC and TSN which cause high accumulation. The shrub biomass was also found highest under C. deodara in Western Himalayas (Wani et al., 2016). The study area being a protected forest had less human intervention and less soil erosion in P. wallichina and C. deodara belt which favored SOC and TSN accumulation.   Effect of topographic parameters on SOC and TSN Topographic parameters have a significant effect on the spatial distribution of SOC and TSN (Mondal et al., 2017). Research indicate that SOC is primarily controlled by the variation in temperature, and soil moisture which vary with elevation gradients (Griffiths et al., 2009), aspect (Måren et al., 2015; Garcia et al., 2016) and slope (Perruchoud et al., 2000). While, the N stock variation with altitude are partly influenced by vegetation type and partly by altitude (Bangroo et al., 2017). The correlation analysis revealed negative correlation of SOC and TSN in our study area with the elevation (Table 3). This may be attributed to the 1) lower mineralization rate and net nitrification rate at the higher altitude, 2) decline in total tree density, and species richness with increasing altitude, and 3) better stabilization of SOC at lower altitudes. A characteristic decline in vegetation was observed across altitudinal strata. The decrease in species richness in high elevation strata significant in Himalayan forests could be due to eco-physiological constraints, low temperature, and productivity (Gairola et al., 2008; Hardy et al., 2001). The characteristic decline in vegetation with increasing altitude results in less accumulation of litter and low input of organic carbon in soils. We observed negative correlation of SOC and TSN spatial distribution with the slope (Table 3). This is attributed to 1) higher rates of erosion with the slope which increases with increasing rainfall, 2) poor soil development which results in poor retention of SOC and 3) soil temperature gradients along the slope under different aspects which affect the rate of SOC decomposition. These results concur with other findings, Bookhagen et al., 2005, observed lowest rates of soil erosion at less than 2% of slope and highest at more than 20% of slope resulting in highest SOC loss. High erosion rate in steep slopes along with low carbon stock causes further depletion of SOC whereas lower areas have better retention of the SOC stock.     Conclusion The spatial distribution of SOC and TSN across the complex topography of small forest area of Kashmir Himalaya is better predicted by regression kriging as compared to ordinary kriging with a prediction accuracy of 17.82 % and 19.44 % respectively. The spatial autocorrelation of SOC and TSN is better explained by regression kriging with Nugget/Sill ratio of 0.28 and 0.24 respectively. It is important to select appropriate environmental variables for interpolation techniques and semi-variogram analyses showed topographic parameters of elevation, slope and vegetation type/ land use as major factors influencing the spatial distribution of SOC and TSN. Both elevation and slope has significant influence on spatial distribution of SOC and TSN concentrations. Negative correlation of SOC and TSN with elevation indicate better stabilization at lower altitudes. High degree of slope has low vegetation and high soil erosion rate which leads to low SOC and TSN concentrations. In conclusion, 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.