Wind erosion is a major natural disaster worldwide, and it is a key problem in western Rajasthan in India. The Analytical Hierarchy Process (AHP), the Geographic Information System (GIS), and remote sensing satellite images are effective tools for modeling and risk assessment of land degradation. The present study aimed to assess and model the land degradation vulnerable (LDV) zones based on the AHP and geospatial techniques in the Luni River basin in Rajasthan, India. This study was carried out by examining important thematic layers, such as vegetation parameters (normalized difference vegetation index and land use/land cover), a terrain parameter (slope), climatic parameters (mean annual rainfall and land surface temperature), and soil parameters (soil organic carbon, soil erosion, soil texture, and soil depth), using the Analytical Hierarchical Process (AHP) and geospatial techniques in the Luni River basin in Rajasthan, India. The weights derived for the thematic layers using AHP were as follows: NDVI (0.27) > MAR (0.22) > LST (0.15) > soil erosion (0.12) > slope (0.08) > LULC (0.06) > SOC (0.04) > soil texture (0.03) > soil depth (0.02). The result indicates that nearly 21.4 % of the total area is prone to very high degradation risks; 12.3% is prone to high risks; and 16%, 24.3%, and 26% are prone to moderate, low, and very low risks, respectively. The validation of LDV was carried out using high-resolution Google Earth images and field photographs. Additionally, the Receiver Operating Characteristic (ROC) curve found an area under the curve (AUC) value of 82%, approving the prediction accuracy of the AHP technique in the study area. This study contributes by providing a better understanding of land degradation neutrality and sustainable soil and water management practices in the river basin.

Capturing site-specific variability in soil attributes is crucial for precision agriculture. A study was conducted in Kelapur block of Yavatmal district, Maharashtra, covering 83,000 ha area in the basaltic region of central India for investigating the spatial distribution of soil fertility parameters viz., pH, EC, organic matter, available macro (AvN, AvP, AvK and AvS) and cationic micronutrients (Fe, Mn, Zn and Cu). Total 4627 surface samples (0–15 cm depth) were collected using grid sampling method at 325 m interval and analysed for the soil properties. The geo-database was subjected to kriging through best-fit experimental semivariogram based on lowest root mean squared error. Spherical model was found best fit for AvP, AvK and AvS whereas exponential model was best fit for remaining soil parameters. The spatial distribution of maps showed deficiency of AvN, AvP, Zn and Fe whereas AvK was high in most of the study area. Spatial dependence was moderate for all soil fertility parameters (N:S ratio 0.25–0.75) whereas AvP exhibited strong spatial dependency (N:S ratio 0.19). Strong spatial dependence of AvP is mainly regulated by pH and smectitic clay minerals. This study can support site-specific plant nutrient management at cadastral level for precision farming.

Three representative alluvial soils were studied from Kothari river basin of Bhilwara district in southeast Rajasthan to assess degree of chemical weathering and pedogenesis. Morphological, geochemical, mineralogical and other analytical investigations were carried out. Soils were classified as Entisols and Inceptisols. These soils are mostly sandy with more than 50% of fine and medium sand fractions, silt to clay ratio more than 0.45 and little textural variation suggesting more uniform weathering. These soils are slight to strongly alkaline with high exchangeable sodium (>15%) and cation exchange capacity less than 10 cmol(+)kg⁻¹. Mineralogical investigations showed the dominance of micas and smectites in Pedon 1 (P1) and Pedon 2 (P2) and increase of smectites and micas in Bw3 horizon of P3 under strong alkalinity and high silica activity with limited lessivage. The low chemical index of alteration (CIA) in soils further indicated an incipient pedogenesis with a relative proportion of mica-smectite composition. The A-CNK-FM diagram shows abundance of CaO + Na₂O + K₂O as against Fe₂O₃+ MgO components under limited leaching environment and chemical weathering. The results of bivariate plot of SiO₂ to (Al₂O₃ + K₂O + Na₂O) indicated the past weathering which influenced by prevailing arid climate in the region.

Goa, a biodiversity hotspot, is also a major iron ore producing state in India. This study investigated total heavy metal (HM) contamination in 65 georeferenced soil samples through inductively coupled plasma atomic emission spectrophotometer (ICP-AES) for assessing risk of mining activity on agricultural soils. The mean concentrations of copper (Cu), zinc (Zn), lead (Pb), nickel (Ni), chromium (Cr), cadmium (Cd), manganese (Mn), iron (Fe) and cobalt (Co) were 125.60, 75.60, 96.40, 51.20, 69.30, 25.77, 2091, 162638 and 21.20 mg kg^−1, respectively. Lower pH, higher organic carbon (OC) and fine-textured soil augmented metal accumulation processes. Boxplot of geoaccumulation index (I_geo) values indicated that lead (Pb) metal had more variation in the study area. Mean I_geo values of metals in descending order are Fe (4.24) > Mn (3.08) > Cu (2.84) > Co (2.47) > Pb (1.85) > Cd (0.7) > Zn (0.58) > Ni (0.45) > Cr (0.16). Mean values of modified degree of contamination (mC_deg) (10.4) and pollution load index (PLI) (6.19) revealed high contamination with deteriorating soil quality. Combined approach of correlation, principal component (PC) and cluster analysis indicated the source of origin by describing association of metals with Fe. PC biplot showed that impact of mining was more on lowland agro-ecosystem rather than natural due to slope factor which led to clogging of pores by mining overburden mixed soil deposition from uphill as evidenced from the presence of waterlogged fallow lands. Fuzzy k-mean clustering yielded three contamination zones (CZs) of low (CZ2), moderate (CZ1) and high (CZ3) degree of contamination. The CZs-based site-specific land management plan might lead to a sustainable ecosystem through coexistence of both mining and agricultural activity by arresting contamination and restoring fallow lands.

Understanding the dynamics of vegetative greenness and how it interacts with various hydroclimatic factors is crucial for comprehending the implications of global climate change. The present study utilized the MODIS-derived normalized difference vegetation index (NDVI) to understand the vegetation patterns over 21 years (2001–2021) in Rajasthan, India. The rainfall, land surface temperature (LST), and evapotranspiration (ET) were also analyzed. The changes, at a 30 m pixel resolution, were evaluated using Mann–Kendall’s trend test. The results reveal that the NDVI, ET, and rainfall had increasing trends, whereas the LST had a decreasing trend in Rajasthan. The NDVI increased for 96.5% of the total pixels, while it decreased for 3.4% of the pixels, of theh indicates vegetation improvement rather than degradation. The findings of this study provide direct proof of a significant reduction in degraded lands throughout Rajasthan, particularly in the vicinity of the Indira Gandhi Canal command area. Concurrently, there has been a noticeable expansion in the cultivated land area. The trend of vegetation decline, particularly in the metro cities, has occurred as a result of urbanization and industrialization. In contrast to the LST, which has a decreasing gradient from the western to eastern portions, the spatial variability in the NDVI, ET, and rainfall have decreasing gradients from the southern and eastern to western regions. The results of correlations between the vegetative indices and hydroclimatic variables indicate that the NDVI has a strong positive correlation with ET (r² = 0.86), and a negative correlation with LST (r² = −0.55). This research provides scientific insights into vegetation change across Rajasthan, and may help the state to monitor vegetation changes, conserve ecosystems, and implement sustainable ecosystem management.