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Geostatistics tools like ordinary kriging was utilized to investigate the spatial variation in soil reaction (pH), electrical conductivity (EC), soil organic carbon (OC), calcium carbonate (CaCO3), available nitrogen (N), available phosphorus (P), available potassium (K), DTPA-extractable copper (Cu), zinc (Zn), manganese (Mn), and iron (Fe) in hot arid region of Western India. For this, total 132 surface soil samples (0–20 cm depth) were obtained with GPS coordinates from the study area. The study's finding showed that EC showed the highest variability (390%) whereas soil pH showed the least variability (3.72%). The best variogram fit model in ordinary kriging was selected on the basis of largest R2 values. For pH, EC CaCO3, N, K, Zn, and Fe, an exponential model imparted the best variogram fit, while a Gaussian model imparted the best variogram fit for OC, P, and Mn. Semi-variogram analysis (nugget/sill ratio) revealed that EC (0.25), CaCO3 (0.072) and N (0.027) were weakly spatial dependent, whereas pH (0.37), OC (0.456), P (0.598), K (0.70), Mn (0.57) and Fe (0.59) were moderately spatial dependent. However, Cu (1.0) and Zn (0.78) were strongly spatial dependent. The largest goodness-of-prediction criterion (G) values were found for the exponential model for soil pH, EC, CaCO3, N, K, Zn, and Fe while the largest G values were found for the Gaussian model for OC, P, and Mn. The exponential and Gaussian models of ordinary kriging, were able to map the spatial variations in investigated soil-variables. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) and its interaction with normalized difference vegetation index (NDVI) are crucial for better understanding of environmental changes in current scenario. Therefore, the purpose of conducting this study was to examine, how LST and NDVI change as a function of elevation in Rajasthan. In present study, MODIS derived NDVI and LST and digital elevation model (DEM) from shuttle radar topography mission (SRTM) have been used. Results revealed that the LST and NDVI both were significantly influenced by elevation. Elevation, NDVI and LST varied from -6 to 1698 m, -0.09 to 0.65 and 24 to 45°C throughout the study region. In contrast to LST, which has a decreasing gradient from western to eastern portions, the spatial variability of NDVI has decreasing gradients from southern and eastern to western regions. The highest mean LST value (39.76 ± 0.2.9 0C) was obtained in elevation range of -6 to 168 m, whereas NDVI value (0.38 ± 0.06) in elevation ranges of 589 - 1698 m. The elevation has strong positive correlation with NDVI (R² = 0.26) and negative correlation with LST (R² = 0.28). Findings from this research can be utilized as a platform for environmental and land use planning for sustainable ecosystem management. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

This study aims to identify soil quality indicators, assess soil quality index (SQI), and validate them with crop yield for India's two contrasting Agro-ecological Sub-Regions (AESRs). For this, 340 and 85 soil samples (0–15 cm) from AESRs 6.1 (semi-arid dry) and 18.4 (sub-humid dry) were analysed for 26 soil physical, chemical, and biological properties. Principal component analysis (PCA) was used to identify soil quality indicators, and the SQI and relative SQI (RSQI) were calculated using the weighted index method. We identified soil pH, active carbon pool (ACP), and available Zn as common indicators for both the AESRs apart from specific indicators. The SQI of AESR 6.1 soils under cotton and sugarcane were higher (0.48–0.75) than the AESR 18.4 soils under rice (0.26–0.70). Among the indicators, EC (35%), CEC (24%), pH (23%), clay (17%), and ACP (14%) significantly contributed to the SQI. The strong relationship of RSQI with the yield of cotton (R2 = 0.52), sugarcane (R2 = 0.68), and rice (R2 = 0.64) indicated that the selected indicators are representative to measure soil quality. We conclude that AESR specific soil quality indicators are appropriate for the evaluation and monitoring of agroecosystem functions. [ABSTRACT FROM AUTHOR]

The ability to define site‐specific variability affects the acceptability of precision farming. A study was conducted to determine the soil nutrient spatial variability in the Nagpur Rural block of Nagpur district, Maharashtra, India. A total of 1707 surface soil samples (0‐to‐15‐cm soil depth) were collected from a 65,386‐ha region. Soil samples were analyzed for pH, electrical conductivity (EC), soil organic carbon (SOC), and available macronutrients (N, P, K, and S) and micronutrients (Fe, Mn, Cu, and Zn). Geostatistical modeling (kriging) was run on soil geodatabase to obtain the best‐fit experimental semivariogram having the lowest RMSE. Exponential (for pH, SOC, and N) and spherical (for P, K, and S) variogram model of ordinary kriging type as well as exponential variogram of simple kriging (for EC) were found to be best fitted to the soil parameters studied. The spatial distribution of maps showed deficiency of available N, P, and Zn, whereas soil test K, Mn, and Cu 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 soil test P exhibited strong spatial dependency (N/S ratio 0.19). The soil fertility maps developed through geostatistical techniques can be successfully used for managing site‐specific nutrients, enhancing fertilizer use efficiency, minimizing cost of cultivation, improving socioeconomic status of farmers, and increasing farm income without adverse effects on soil environment. Core Ideas: High‐density sampling produces better soil fertility maps.GIS and remote sensing supports cadastral‐level planning for plant nutrient management.Precision soil fertility maps can reduce cultivation costs and improve socioeconomic returns. [ABSTRACT FROM AUTHOR]

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