The current study was conducted at the different villages of the Arajiline block of Varanasi district, Uttar Pradesh, India during November, 2022–June, 2023 to evaluate the variability of soil properties and nutrient indexof soils. The random sampling technique was used to collect 40 soil samples at different locations with the help of GPS at 0–15 cm depth. The collected soil samples were air-dried in shade at room temperature, passed through a 2 mm filter paper, and analyzed for different physico-chemical properties. The result revealed that the TS (range 8.2–81.3%), VFS (range 4.2–35.5%), Silt (range 10.1–54.2%), Clay (range 7.7–38.8%), BD (range 1.08–1.45 g cm^-3), PD (range 2.04–2.33), Porosity (range 30.24–51.56%) pH (range 6.4-7.8), EC (range 0.1-0.3 dS m^-1), OC (0.2-0.6%), and available N, P, K, S, Ca, and Mg varied from 130.12–260.30 kg ha^-1, 14.2-23.2 kg ha^-1, 145.0–240.0 kg ha^-1, 7.5–17.6 kg ha^-1, 3.8–8.2 cmol(P+)/kg, and 2.1-4.1 cmol(P+)/kg, respectively. The findings indicated that the studied soils were slightly acidic to moderately acidic and free from salinity hazards; 70% of the soil samples were low in organic carbon, 30% were medium in organic carbon, and 100% of the soil samples were low in available N and medium in available P, K, and S. 100% of soil samples were found to be sufficient in exchangeable Ca and Mg. The findings of this research could help in crop nutrient management, fertilizer recommendation, and decision-making for increasing agricultural production and farmer profitability.

The loss of soil due to erosion is one of the most critical land degradation issues globally, representing a vital asset for both the economy and the environment. To effectively manage and regulate such a global issue, it is imperative to estimate the loss. With technological advancements, methodologies such as Geographic Information Systems (GIS) and Remote Sensing (RS) are crucial in addressing these difficulties. The primary objective of this study was to employ the Revised Universal Soil Loss Equation (RUSLE) model inside a GIS framework to quantify soil loss in the Ranganadi river basin of Assam, providing a more rapid and accurate estimate. Three distinct physiographic units, i.e., Piedmont Plain, Alluvial Plain, and Flood Plain, were delineated. Collected 60 GPS-based soil samples from distinct physiographic units were collected and analyzed for different soil physico-chemical properties, in addition to taking into account a variety of criteria, such as rainfall erosivity factor (R), soil erodibility factor (K), topography factor (LS), cover and management factor (C), and conservation practices factor (P), the RUSLE approach is based on the evaluation of soil loss per unit area. Five basic RUSLE factors, viz., R factor, K factor, LS factor, C factor, and P factor, were used to determine soil erosion. Further, erosion ratio, dispersion ratio, and erosion index are the basic examples of erodibility indicators that were taken into consideration while used to evaluating the erodibility of the soil. The anticipated soil erosion in the above-said area varied from minimal to severe, with values between 0.01 and 27.38 t ha-1 yr-1. Among the physiographic units, alluvial plain soils had the greatest mean soil erosion value of 8.52 t ha-1 yr-1, whereas floodplain landscapes indicated the lowest average value of 3.39 t ha-1 yr-1. The dispersion ratio varied between 0.08 and 0.33, with soils exhibiting a dispersion ratio exceeding 0.15, signifying their vulnerability to erosion. The erosion ratio varied between 0.04 and 0.61, whereas the erosion index fluctuated from 0.06 to 0.84. As a result, this model is particularly useful in anticipating soil loss in an area, allowing community members, legislatures, and other linked agencies to plan ahead of time for future efforts to mitigate the degradation.