Analysis Of Spatial Variability Research Articles

Simple model to incorporate statistical noise based on a modified hodgkin-huxley approach for external electrical field driven neural responses

Noise activity is known to affect neural networks, enhance the system response to weak external signals, and lead to stochastic resonance phenomenon that can effectively amplify signals in nonlinear systems. In most treatments, channel noise has been modeled based on multi-state Markov descriptions or the use stochastic differential equation models. Here we probe a computationally simple approach based on a minor modification of the traditional Hodgkin-Huxley approach to embed noise in neural response. Results obtained from numerous simulations with different excitation frequencies and noise amplitudes for the action potential firing show very good agreement with output obtained from well-established models. Furthermore, results from the Mann–Whitney U Test reveal a statistically insignificant difference. The distribution of the time interval between successive potential spikes obtained from this simple approach compared very well with the results of complicated Fox and Lu type methods at much reduced computational cost. This present method could also possibly be applied to the analysis of spatial variations and/or differences in characteristics of random incident electromagnetic signals.

Analysis of spatial and temporal variations of NDVI and its driving factors in the corridor of Lan-Xin railway

Introduction: Vegetation is the main factor of ecological stability, but the construction of largescale corridor projects will bring some damage to vegetation.Methods: Based on the NDVI, temperature and precipitation data of the growing season from 1990–2021 (June to September), this paper uses the maximum synthesis method, trend analysis method, correlation analysis method and coefficient of variation method to study the spatial-temporal changes and driving factors of NDVI in the past 32 years, taking the 30 km belt buffer zone along the Lan-Xin Railway as the research area. The influence range of railway construction on NDVI in different climatic conditions is analyzed.Results: The results showed that: (1) From 1990–2021, the average annual temperature of the growing season in the study area showed an increasing trend, the average annual precipitation showed a decreasing trend, and the average annual NDVI showed an increasing trend; NDVI was positively correlated with temperature in 54.04%, and positively correlated with precipitation in 62.88%. Precipitation had a greater effect on NDVI than temperature. (2) Under the same climatic conditions, the NDVI variability coefficient in the study area fluctuates significantly within 3,000–21,000 m along the railway line, stabilizing beyond 21,000 m; the impact range of railway construction on NDVI gradually increases with increasing precipitation within the same temperature range, and increases gradually with rising temperatures within the same precipitation range. (3) During the growing season (June to September) in the study area, when the temperature is below 25°C, NDVI increases initially with increasing precipitation before decreasing, showing an overall upward trend; when the temperature is above 25°C, NDVI initially increases with increasing precipitation before decreasing. High temperature and high precipitation will inhibit vegetation growth and reduce NDVI. The NDVI of the core area before the railway construction is larger than that of the background area, while the NDVI of the core area after the railway construction is smaller than that of the background area.Discussion: In short, the construction of Lan-Xin Railway has a certain influence on NDVI along the line. When studying the impact of large-scale engineering projects on vegetation in the future, higher resolution imagery can be used along with the incorporation of human activities' influence. These factors will enable more accurate and comprehensive research.

Multi-modal domain adaptation for revealing spatial functional landscape from spatially resolved transcriptomics.

Spatially resolved transcriptomics (SRT) has emerged as a powerful tool for investigating gene expression in spatial contexts, providing insights into the molecular mechanisms underlying organ development and disease pathology. However, the expression sparsity poses a computational challenge to integrate other modalities (e.g. histological images and spatial locations) that are simultaneously captured in SRT datasets for spatial clustering and variation analyses. In this study, to meet such a challenge, we propose multi-modal domain adaption for spatial transcriptomics (stMDA), a novel multi-modal unsupervised domain adaptation method, which integrates gene expression and other modalities to reveal the spatial functional landscape. Specifically, stMDA first learns the modality-specific representations from spatial multi-modal data using multiple neural network architectures and then aligns the spatial distributions across modal representations to integrate these multi-modal representations, thus facilitating the integration of global and spatially local information and improving the consistency of clustering assignments. Our results demonstrate that stMDA outperforms existing methods in identifying spatial domains across diverse platforms and species. Furthermore, stMDA excels in identifying spatially variable genes with high prognostic potential in cancer tissues. In conclusion, stMDA as a new tool of multi-modal data integration provides a powerful and flexible framework for analyzing SRT datasets, thereby advancing our understanding of intricate biological systems.

Spatial variability and source analysis of soil heavy metals: A case study of the key planting area of special agricultural products in Cangxi County, China.

Heavy metal pollution in farmland soil represents a considerable risk to ecosystems and human health, constituting a global concern. Focusing on a key area for the cultivation of special agricultural products in Cangxi County, we collected 228 surface soil samples. We analyzed the concentration, spatial distribution, and pollution levels of six heavy metals (Cr, Cu, Pb, Ni, Zn, and Hg) in the soil. Moreover, we investigated the sources and contribution rates of these heavy metals using Principal Component Analysis/Absolute Principal Component Scores (PCA/APCS) and Positive Matrix Factorization (PMF) models. Our findings indicate that none of the six metals exceeded the pollution thresholds for farmland soils. However, the mean concentrations of Cr and Ni surpassed the background levels of Sichuan Province. A moderate spatial correlation existed between Pb and Ni, attributable to both natural and anthropogenic factors, whereas Zn, Cu, Hg, and Cr displayed a strong spatial correlation, mainly due to natural factors. The spatial patterns of Cr, Cu, Zn, Pb, and Ni were similar, with higher concentrations in the northern and eastern regions and lower concentrations centrally. Hg's spatial distribution differed, exhibiting a broader range of lower values. The single pollution index evaluation showed that Cr and Ni were low pollution, and the other elements were no pollution. The average value of comprehensive pollution index is 0.994, and the degree of pollution is close to light pollution. Predominantly, higher pollution levels in the northern and eastern regions, lower around reservoirs. The PCA/APCS model identified two main pollution sources: agricultural traffic mixed source (65.2%) and natural parent source (17.2%). The PMF model delineated three sources: agricultural activities (32.59%), transportation (30.64%), and natural parent sources (36.77%). Comparatively, the PMF model proved more accurate and reliable, yielding findings more aligned with the study area's actual conditions.

Climate of the Pyrenees: Extremes indices and long-term trends

We utilized an extensive, multisource, cross-border dataset of daily meteorological observations from over 1500 stations in the Pyrenees, spanning from the mid-20th century to 2020, to examine the spatial and temporal climate patterns. Our focus was on 17 indices related to extreme precipitation and temperature events across the mountain range. The original data underwent rigorous quality control and homogenization processes, employing a comprehensive workflow that included spatial modeling based on environmental predictors. This process yielded two main outcomes: 1) a high-resolution gridded dataset (1 km2) of daily precipitation, maximum and minimum temperature from 1981 to 2020, allowing for a detailed analysis of spatial variations; and 2) an evaluation of long-term annual and seasonal trends from 1959 to 2020, using selection of high-quality data series that were homogenized to preserve their temporal structure and coherence. The findings revealed a clear elevation-related pattern in temperature indices (with the exception of tropical nights, which were predominantly observed on the Mediterranean side) and a distinct north-south latitudinal disparity in precipitation, turning longitudinal when focusing on extreme precipitation events. Overall, there was a notable and significant warming trend of 0.2 to 0.4 °C per decade, and a non-significant change of precipitation, with the exception of the southern and Mediterranean regions, where there was a notable decrease, approximately −3 % per decade, observed on an annual basis.

Analysis of spatial and seasonal variations of Haemaphysalis longicornis population based on field survey collected under different habitats and years

AbstractTicks are vectors of disease‐causing pathogens and are found on domestic and wild animals; thus, they are also vectors of significant human diseases. For this reason, pre‐emptive measures to prevent tick‐borne diseases are necessary in the form of exploring their major habitats, population increase period, and factors affecting their population growth, all of which indicate the purpose of this study. In the study, a variation of Haemaphysalis longicornis, a major vector of fever‐causing conditions, was statistically analyzed to identify the spatial and climatic factors affecting the time‐dependent variations of its population. The survey occurred in different habitats (grassland, mixed forest, deciduous forest, and coniferous forest) in South Korea. In addition, we fitted a phenology model by using a probability function to find the peak occurrence time annually. As a result, the numbers of adults and nymphs were found to be related to temperature and relative humidity and their population peaked at the end of May in all habitats except deciduous forests. This study is expected to provide information on habitat types, times, and climate patterns that require attention to help control H. longicornis populations.

The standardized precipitation evapotranspiration index based on cumulative effect attenuation

The drought index is an important indicator that reflects the degree of regional drought and plays an important role in drought disaster monitoring, prediction and evaluation. According to the process definition of the standard precipitation evapotranspiration index (SPEI), the influence of cumulative effect attenuation was considered and aggregated at different time scales k, the linear attenuation coefficient was defined, and attenuation parameters were used to adjust the degree of attenuation. A value closer to the current month was assigned a larger weight, and a value further from the current month was assigned a smaller weight. Thus, the cumulative water surplus or deficit was recalculated, and the standard precipitation evapotranspiration index based on the cumulative effect attenuation (CASPEI) was defined and applied to the drought analysis of Heilongjiang Province, which has the highest grain output in China. The comparative analysis showed that the CASPEI dataset was more consistent with the log-logistic distribution, and its sensitivity in identifying regional drought events was greater than that of the SPEI, which effectively corrected the original index's errors in identifying the drought levels of some stations and was more consistent with regional historical drought events. The temporal and spatial variation analysis revealed that drought occurrence is seasonal: summer is the season with the highest frequency of severe drought and extreme drought, and the area experiencing extreme drought is greater than that during other seasons. The areas with moderate drought in spring and autumn were relatively large. With the increase in time scale, the area of extreme drought decreased, and the area of severe drought increased. The area of moderate drought increased, while the area of light drought decreased. The CASPEI can accurately judge the extent to which drought occurs to respond to drought and effectively reduce or prevent large losses in people and the economy caused by drought.

Microplastics, their abundance, and distribution in water and sediments in North Chennai, India: An assessment of pollution risk and human health impacts

Plastic particles, measuring <5 mm in size, mainly originate from larger plastic debris undergoing degradation, fragmenting into even smaller fragments. The goal was to analyze the spatial diversity and polymer composition of microplastics (MPs) in North Chennai, South India, aiming to evaluate their prevalence and features like composition, dimensions, color, and shape. In 60 sediment samples, a combined count of 1589 particles were detected, averaging 26 particles per 5 g−1 of dry sediment. The water samples from the North Chennai vicinity encompassed a sum of 1588 particles across 71 samples, with an average of 22 items/L. The majority of MPs ranged in size from 1 mm to 500 μm. The ATR-FTIR results identified the predominant types of MPs as polystyrene, polyvinyl chloride, polyethylene, polyethylene terephthalate, and polypropylene in sediment and water. The spatial variation analysis revealed high MPs concentration in landfill sites, areas with dense populations, and popular tourist destinations. The pollution load index in water demonstrated that MPs had contaminated all stations. Upon evaluating the polymeric and pollution risks, it was evident that they ranged from 5.13 to 430.15 and 2.83 to 15,963.2, which is relatively low to exceedingly high levels. As the quantity of MPs and hazardous polymers increased, the level of pollution and corresponding risks also escalated significantly. The existence of MPs in lake water, as opposed to open well water, could potentially pose a cancer risk for both children and adults who consume it. Detecting MPs in water samples highlights the significance of implementing precautionary actions to alleviate the potential health hazards they create.

Spatial patterns in the diet of Gyps vultures in India and their implications for conservation

Dietary analyses are particularly useful for developing conservation programmes for species threatened by resource depletion, poisoning and environmental pollution. Gyps vultures in South Asia represent one such case, having undergone a population collapse caused by feeding on carcasses of cattle treated with the non-steroidal anti-inflammatory drug diclofenac. Following a ban on the veterinary use of diclofenac, populations of vultures remain low and mostly concentrated near protected areas. Understanding the role of protected areas in the recovery of these critically endangered species requires analyses of spatial variation in their diet. We used faecal metabarcoding to investigate the spatial variation in the diet of Gyps vultures across four landscapes from sites located inside and outside protected areas. We collected faecal 642 samples, of which 419 yielded adequate molecular data to identify the vulture species and 30 molecular operational taxonomic units corresponding to at least 28 diet species. The diet was dominated by large ungulates and varied across landscapes, protection status and vulture species, with the observed variation largely explained by differential intake of wild and domestic species. Domestic livestock was present in >95 % of the samples from Central, West and North India, but 77 % of the samples had only wild species in South India. This variation was explained by livestock density within 100 km radius of the sampling site. Our results imply that protected areas may not offer a respite from possibility of diclofenac-poisoning across most parts of the country and efforts should continue unabated to remove nephrotoxic drugs from veterinary use.

Mode decomposition and spatial propagation regularity analysis of cavitation variation on a twisted hydrofoil

The cavitation phenomenon can induce non-uniformity in the fluid, impacting fluid dynamic performance. This paper focuses on the cavitation shedding of the Delft Twist 11 hydrofoil. First, the reliability of numerical simulations is verified by computational fluid dynamics results. Utilizing the variational mode decomposition method, the cavitation signals on two cavitation paths are decomposed. Finally, the cavitation pulsation tracking network method is proposed to extensively investigate the spatial propagation patterns of cavitation signals at various sections above the twisted hydrofoil. The results reveal that typical frequencies at different monitoring planes are 30, 58, and 88 Hz. The corresponding amplitude analysis at these frequencies provides insight into the spatial propagation and attenuation process of cavitation vortices shedding. This study offers a novel perspective for a deeper understanding of cavitation mechanisms. Simultaneously, this provides references for enhancing the performance of mechanical engineering systems, reducing energy consumption, and improving structural reliability.

Simplified Transient Model of DFIG Wind Turbine for COI Frequency Dynamics and Frequency Spatial Variation Analysis

Simplified Transient Model of DFIG Wind Turbine for COI Frequency Dynamics and Frequency Spatial Variation Analysis

Temporal and Spatial Variation Analysis of Groundwater Stocks in Xinjiang Based on GRACE Data

Situated in China’s arid and semi-arid zones, the Xinjiang region heavily relies on groundwater for its freshwater supply. This study utilizes data from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, covering the years 2003 to 2021, to quantitatively evaluate the temporal and spatial changes in groundwater storage anomalies (GWSA) in the Xinjiang region. Furthermore, we incorporate the HydroSHEDS dataset to examine the spatial variations in groundwater storage anomalies across watersheds of varying scales. Based on our findings, the GWSA decreased during the study period at a mean rate of −0.381 mm/month, marked by a consistent trend and notable interannual variability. In addition, significant regional disparities are observed; while groundwater storage in the southeastern watersheds is on an upward trend, a general decline is noted in the northern and central regions. The most pronounced depletion is detected in the northwest, especially in the Ili River basin and along the western slopes of the Tianshan Mountains. These changes are intricately linked to anthropogenic factors, including population growth and escalating water demands. In response, the study advocates for the development and enforcement of more rigorous and scientifically informed groundwater management strategies to promote sustainable water use in Xinjiang.

Seasonal Variability of Rainfall and Thunderstorm Patterns in Kenya

This paper presents an analysis of spatial and temporal variation of rainfall and thunderstorm occurrence over Kenya from January 1987 to December 2017. The meteorological data used were obtained from the Kenya Meteorological Department (KMD) for the same period. This included the monthly thunderstorm occurrences and rainfall amounts of 26 synoptic stations across the country. The characteristics of monthly, seasonal and annual frequency results were presented on spatial maps while Time series graphs were used to display the pattern for annual cycle, seasonal variations and the inter-annual variability of rainfall amounts and thunderstorm occurrences. A well-known non-parametric statistical method Mann Kendall (MK) trend test was used to determine and compare the statistical significance of the trends. Thunderstorm frequencies over the Eastern, Central and Coast regions of the country showed a bimodal pattern with high frequencies coinciding with March-April-May (MAM) and October-November-December (OND) rainy seasons. Very few thunderstorm days were detected over June-July-August (JJA) season. The areas to the western part of the country, near Lake Victoria, had the highest thunderstorm frequencies in the country over the three seasons: MAM, JJAS and OND. The annual frequency showed a quasi-unimodal pattern. These places near Lake Victoria showed significantly increasing thunderstorm trends during the MAM and OND seasons irrespective of the rainfall trends. This shows the effects of Lake Victoria over these areas, and it acts as a continuous source of moisture for thunderstorm formation. However, most stations across the country showed a reducing trend of thunderstorm frequency during MAM and JJA seasons. The importance of these findings is that they could support various policy makers, and users of climate information, especially in the agriculture and aviation industries.

Synergistic impacts of Landscape, Soil, and environmental factors on the spatial distribution of soil aggregates stability in the Danjiangkou reservoir area

Soil aggregate stability (SAS) plays a crucial role in determining the soil's resistance to erosion. Conducting a spatial variability analysis of SAS in the reservoir area, which is highly susceptible to soil degradation and erosion, can yield valuable insights for agricultural practices, landscape planning, and environmental management in this region. Nevertheless, existing research remains constrained in its capacity to accurately map and forecast soil aggregates at a spatial level. This study aims to investigate the spatial distribution of SAS in the Danjiangkou reservoir area, as well as to explore the intrinsic linkage mechanisms between changes in soil properties, environmental factors and landscape patterns and SAS. Results revealed significant spatial variations in SAS within the vicinity of the Danjiangkou reservoir area. Notably, soil properties (including clay, available potassium, soil organic matter, and available phosphorus), environmental factors (such as digital elevation model, slope, land surface temperature difference between seasons (ΔLST)), and landscape pattern (largest patch index (LPI), shannon’s diversity index (SHDI), edge density (ED)) significantly affected the spatial variability of SAS, explaining 18.91%, 8.22%, and 8.17% of the total variability of soil SAS, respectively. Structural equation modeling results indicated that altering soil properties serves as the primary approach to enhance SAS. Furthermore, interventions targeting landscape factors such as LPI, SHDI, and ED, alongside environmental factors like ΔLST and slope, exert direct influences on SAS, while also indirectly impacting it through their effects on soil properties. These findings contribute to the design of land planning and soil and water conservation measures in the reservoir watershed, promoting its protection of soil structure and its ecological service function.

Spatial and temporal variability analysis of snow cover parameters according to the urbanized area profile system

The purpose of the study is to analyze the spatial and temporal variability of snow cover parameters in the urbanized area of the city of Irkutsk and its adjacent areas according to the system of profiles reflecting the features of the area microrelief. Field surveys were conducted in accordance with standard recommendations for snow and geochemical surveys. Snow sampling was carried out at the entire snow depth, with the exception of the snow depth of 0.5-1 cm. Sampling points located at least 25 meters away from the roads. Snow sampling in the residential areas of the city was carried out in the places with undisturbed snow cover and free from extraneous snow banks and landfills. Global Mapper, Golden Software Surfer, Statistica software were used to carry out statistical analysis, build the models of profile sections and obtain the diagrams of snow cover parameter distribution. A 3D relief model was obtained on the basis of radar topographic survey data, which revealed a multiple formation relief of the studied area featuring mountains, high plains with undulating flat gently sloping watersheds, valleys, hollows and depressions. The height difference is up to 230 m. The diagrams of snow depth distribution built with regard to the profiles made it possible to identify the territories with the deepest snow cover. A geochemical analysis of the melt water filtrate was carried out over a three-year period. In 2021 a high correlation with electrical conductivity was found for the contents of tungsten, sodium, bromine, calcium, molybdenum, sulfur, barium, magnesium, antimony, tantalum, cesium, titanium, chromium, and silicon. The average correlation level with electrical conductivity was recorded for arsenic, copper, and lead. The analysis data obtained indicate a different contamination level of the area as a result of atmospheric precipitation in those years due to the activity of industrial facilities. The distribution patterns of the pH snow index have been revealed. It is shown that zones with pH&lt;6 are mostly confined to the Irkutsk aluminum smelter. The zones with pH &gt;6 are caused by the influence of the power plants using hydrocarbon fuels: gasoline, kerosene, fuel oil, diesel fuel, coal. The snow cover was subjected to field and laboratory studies using GIS technologies and physico-chemical methods. The distribution and migration of pollutants in various spatial and temporal aspects have been revealed taking into account the terrain relief. The conducted study opens up opportunities for modeling the landscape structure, taking into account meteorological parameters, phenological processes and snow cover state for the purposes of the national economy and the location of construction of various facilities.

Climatology of Synoptic Non-Gaussian Meteorological Anomalies in the Northern Hemisphere during 1979–2018

The analysis of spatial and temporal variability in the number of non-Gaussian extreme anomalies of climatic parameters was carried out for both the initial time series and synoptic variability in the troposphere of the Northern Hemisphere over the period 1979–2018, based on ERA-Interim reanalysis data. There are predominantly three types of empirical distribution densities at 850 hPa, each characterizing the processes of advective and convective heat transfer. At the beginning of the 21st century, compared to the end of the 20th century, there was an increase in the number of anomalies in vertical wind speed and specific humidity for the Northern Hemisphere. Additionally, there is an increase in the number of zonal wind speed anomalies in the low and middle latitudes. Regions with the maximum number of anomalies are primarily located over the continents, while for vertical wind speed anomalies, they are predominantly over the oceans. The application of R/S analysis and multifractal analysis has established that the identified tendencies (which are persistent processes) will continue in the identified regions. The time series of non-Gaussian anomalies (both initial and synoptic scales) exhibit a long-term memory of approximately four years, and synoptic extreme anomalies were found to be more predictable.

A sustainable mechanism for digitization to promote green technological innovation in enterprises

Abstract This study delves into the spatial-temporal dynamics of urban digital advancement and its green economic efficacy through methods like kernel density analysis, exploratory spatial data analysis, Gini coefficient, and coefficient of variation. Utilizing entropy weighting and regression analysis, it investigates the influence of digital development on green economic efficiency. Sustainable development performance (EWS), the volume of green patent applications (Lngrd), and other pivotal control variables are incorporated into a regression framework to elucidate the mechanisms by which digital evolution impacts green economic efficiency. Empirical findings reveal a consistent annual growth rate exceeding 5% across six regions, showcasing the steady progress and substantial achievements in digitalization. However, a wide disparity exists within enterprise digitalization levels, with the maximum digital transformation index at 4.996, a minimum of 0.002, and a median of 0.696, indicating an ongoing uneven development. Digital transformation significantly enhances corporate green technological innovation, evidenced by a coefficient of 0.014. Its effect on sustainability performance is notably more substantial in enterprises with higher equity concentrations (0.004) and greater pollution levels (0.005).

Geostatistical Insights into Trace Metal Dynamics: Techniques for Spatial Mapping, Variability Analysis, and Source Characterization in the Environment

Geostatistical Insights into Trace Metal Dynamics: Techniques for Spatial Mapping, Variability Analysis, and Source Characterization in the Environment

Adolescent marriage a violation of sexual and reproductive health rights: Trend, spatial variation and covariate analysis from national family health survey

IntroductionTen-to-Nineteen-year adolescent period is a very sensitive time for both boys and girls. Especially adolescent girls suffer more due to early marriage and its consequences of unintended pregnancy, unsafe abortion, high risk for sexually transmitted disease etc. ObjectiveThis study tried to analyse trend and spatial variation of adolescent girls' marriage practice in India. Further, this study is also tried to identify major socio-economic factors responsible for adolescent girl's marriage. MethodsPresent study utilized different rounds of National Family Health Survey (NFHS) data. Trend analysis carried out with the help of 3rd, 4th and 5th round of NFHS data. Bivariate distribution and logistic regression are used for identifying determining factors for adolescent girls’ marriage. ResultsTrend analysis result reveals the declining trend of early adolescent girls' marriage in India. However, the data reveals early girl's marriage practice is still very high. This practice is more in the states of Tripura (31%) followed by West Bengal (28%). Place of residence, wealth status, education status and mass media exposure are the main determining factors in early marriage. ConclusionMost of the states in Eastern and North-Eastern states are having high prevalence of adolescent marriage. Thus, there is an urgent need to focus on these states along with addressing socio-economic disparities. Further, there is a need of effective interventions of girl specific programmes like Beti Bachao Beti Padhao, Balika Samridhi yojana etc. working for empowerment and wellbeing of adolescent girls.

Spatial Variability and Statistical Analysis of Soil Properties in the Rice Wheat-Based Systems of North-West India

ABSTRACT There are limited reports about the impacts of long-term rice wheat cropping system (RWCS) on soil properties and nutrient stocks under smallholder farmer’s conditions in developing counties. The study was carried out in RWCS in Haryana (10 districts) of North-West India in order to map out some soil properties and assess their variability within the area. From the study area, a total of 150 surface soil samples (15 samples each district) were collected using Global Positioning System (GPS) after harvest of wheat crop. Then, soil properties, that is, pH, electrical conductivity (EC), soil organic carbon (SOC), carbonate content, cation exchange capacity (CEC), salinity content (Na+, K+, Ca2+ + Mg2+) soil available nutrients (NPK, Fe & Zn), and micronutrient fractions were measured in the laboratory. All soil properties significantly changed under RWCS, except for bulk density, SOC, and SAR variables. The SOC stock varied significantly among the majority of RWCS in north-western India but was not significantly different in total nitrogen (TN) stocks. After log-transformation of data, classical statistics were used to describe the soil properties, and one-way analysis of variance. The PCA chosen soil properties explained 81.37% of the soil quality variability among the RWCS. Such soil properties and nutrient stock variability among the RWCS suggested the introduction of suitable management practices that sustain the soil system of the RWCS with poor properties and nutrients. On the basis of the linear function, soil chemical properties (44.65%) contributed more to soil quality index (SQI) than physical ones.