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Soil erosion is one of the major environmental concerns, causing a reduction in soil carbon stock and soil fertility. It is generally difficult to estimate soil erosion in hilly terrains. To overcome this, different types of erodibility indices are developed and used to describe the intensity of soil erosion. The present study was conducted in Nagaland state of northeast India, where clay ratio (CR), modified clay ratio (MCR), critical level of organic matter (CLOM) and erodibility factor (K) were used to assess the severity of soil erosion under different land uses. To study these, soil samples from forest, jhum, fallow jhum and tea gardens were collected from 0 to 30 cm soil depth. The results showed that sand and clay content were significantly different under different land uses, while soil organic carbon (SOC) and stocks followed the same trend, with maximum and minimum values under tea (60.55 t C ha–1 ) and jhum lands (40.74 t C ha–1 ) respectively. Among the different erodibility indices, only CR and MCR values were found to be significantly different from each other. Higher values of CR and MCR under jhum and fallow jhum lands indicate that these land uses are more prone to erosion. The present study provides a picture of severity of soil erosion under different land uses in Nagaland and recommends that CR and MCR can be used to evaluate soil erosion in hilly terrain areas. [ABSTRACT FROM AUTHOR]

Cation exchange capacity (CEC) is a soil property that significantly determines nutrient availability and effectiveness of fertilizer applied in lands under different managements. CEC's accurate and high-resolution spatial information is needed for the sustainability of agricultural management on farms in the Nagaland state (northeast India) which are fragmented and intertwined with the forest ecosystem. The current study applied the digital soil mapping (DSM) methodology, based on the CEC values determined in soil samples obtained from 305 points in the region, which is mountainous and difficult to access. Firstly, digital auxiliary data were obtained from three open-access sources, including indices generated from the time series Landsat 8 OLI satellite, topographic variables derived from a digital elevation model (DEM), and the WorldClim dataset. Furthermore, the CEC values and the auxiliary were used data to model Lasso regression (LR), stochastic gradient boosting (GBM), support vector regression (SVR), random forest (RF), and K-nearest neighbors (KNN) machine learning (ML) algorithms were systematically compared in the R-Core Environment Program. Model performance were evaluated with the square root mean error (RMSE), determination coefficient (R2), and mean absolute error (MAE) of 10-fold cross-validation (CV). The lowest RMSE was obtained by the RF algorithm with 4.12 cmolc kg−1, while the others were in the following order: SVR (4.27 cmolc kg−1) c kg−1) c kg−1) c kg−1). In particular, WorldClim-based climate covariates such as annual mean temperature (BIO-1), annual precipitation (BIO-12), elevation, and solar radiation were the most important variables in all algorithms. High uncertainty (SD) values have been found in areas with low soil sampling density and this finding is to be considered in future soil surveys. [ABSTRACT FROM AUTHOR]

Northeast (NE) India is a typical tropical ecosystem with a luxuriant forest vegetation cover, but nowadays forests are under stress due to exploitation and land use changes, which are known to affect soil health and productivity. However, due to a scarcity of data, the influence of land uses and altitude on soil properties of this peculiar ecosystem is poorly quantified. This study presents the changes in soil properties in two districts of Nagaland (Mon and Zunheboto) in relation to land uses (forest, plantation, jhum and fallow jhum), altitude (<500 m, 500–1000 m, >1000 m) and soil texture (coarse, medium, fine). For this, a random soil sampling was performed in both the districts. Results indicated that soil organic carbon (SOC) stocks and available potassium (K) were significantly influenced by land uses in the Mon district, while in Zunheboto a significant difference was observed in available phosphorus (P) content. SOC stocks showed an increasing trend with elevation in both districts. The influence of altitude on P was significant and the maximum concentration was at lower elevations (<500 m). In Mon, soil texture significantly affected SOC stocks and the available N and P content. The variability in soil properties due to land uses, altitudinal gradients and textural classes can be better managed with the help of management options, which are still needed for this ecosystem. [ABSTRACT FROM AUTHOR]