Integrating Remote Sensing and GIS for Sustainable Soil Management: A Case Study of Jamtara District, Jharkhand

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Soil analysis of 15 sites revealed moderate nitrogen (mean: 305.01 kg/ha) and soil organic carbon (0.71%), but widespread phosphorus (mean: 11.67 kg/ha) and potassium (mean: 188.15 kg/ha) deficiencies, especially in northern, central, and southeastern zones. Predominantly acidic soils (mean pH 5.53) highlight the need for liming to improve nutrient availability. Using Inverse Distance Weighting (IDW) interpolation and weighted summation, soils were classified into five quality grades; Grade III (moderate fertility) was most extensive (~ 37%), followed by Grades II and IV, with limited areas of high (Grade I) and low (Grade V) quality. Block-wise analysis revealed significant variability, with high-quality soils concentrated in Jamtara, Narayanpur, and Kundahit blocks, while Fatehpur showed predominantly moderate to low quality soils. Integration with Land Use/Land Cover (LULC) data demonstrated that higher-grade soils correlate with agricultural and fallow lands, whereas lower-grade soils align with barren and degraded lands. The soil quality map achieved 85.71% validation accuracy, confirming its reliability for land management. These findings highlight how parent material, topography, and land use affect soil health and show that integrating geospatial tools with traditional soil testing supports precise management and sustainable farming in the area. Soil Quality Assessment Spatial Variability Jamtara District Geospatial Analysis Soil Management Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction The fertility of soil is contingent upon the presence of vital nutrients, specifically nitrogen (N), phosphorus (P) and potassium (K). Microbial mineralization plays a vital role in the assimilation of nitrogen in plants, given that nitrogen primarily exists in organic forms and is essential for plant growth (Kuzyakov & Xu, 2013 ). A comprehensive approach is essential for an in-depth assessment of soil quality, taking into account physical, chemical, and biological attributes, along with enzyme activity (Maurya et al., 2020 ). The challenges in evaluating the comprehensive spectrum of soil characteristics have resulted in the development of a Minimum Data Set (MDS), comprising essential indicators that reflect soil fertility, health, and quality (Maurya et al., 2020 ). Indicators are chosen according to agro-ecological conditions (Govaerts et al., 2006 ; Yao et al., 2013 ), and contemporary analytical methods, such as microbiome analysis, improve the accuracy of soil quality evaluations (Jansson & Baker, 2016 ). Geospatial technologies, particularly Geographic Information Systems (GIS), provide a solid basis for integrating soil data and developing tailored interpretative maps (Quan et al., 2001 ). These maps facilitate informed strategic planning and decision-making regarding soil conservation, pH regulation, and nutrient enhancement. The characteristics of soil are not static; they are shaped by the interplay of physical, chemical, and biological processes. The physicochemical properties of soil are shaped by factors such as temperature, topography, vegetation, and biological activity across various regions and time periods. Land use changes over time, particularly those related to agricultural operations and deforestation may have a negative influence on soil characteristics and lower ecosystem productivity. Soil health is evaluated using important markers such as bulk density (BD), potential of hydrogen (pH), electrical conductivity (EC), moisture content, and nutrient concentration. Effective planning for sustainable land management requires current knowledge of soil and land resource availability. Soil, climate, water, nutrients, and biota all collaborate in ecosystems to provide critical services that sustain life while preserving the environment's capacity to recover. Integrated land resource planning is crucial for settling conflicts and supporting sustainability in areas with limited land supply, which is exacerbated by rising competition for land use as the global population expands. Soil resource evaluations are essential for determining soil fertility, acidity/alkalinity, and other factors that affect sustainable land use (Huang et al., 2020 ). The fertility of soil, a crucial element in the interactions between soil and plants, is affected by the presence of both macro- and micronutrients (Srivastava et al., 2019 ). The ongoing extraction of nutrients by crops, combined with insufficient replenishment, results in nutrient stress and a reduction in productivity. The interplay of plant cover, climate, terrain, soil texture, and organic matter decomposition plays a crucial role in determining soil fertility. Soil quality, in conjunction with soil productivity, reflects the ability of soil to support plant growth and uphold ecological processes (Bünemann et al., 2018 ). Defining and standardizing soil quality assessment presents a challenge due to the varied functions of soils, which include agronomic and forestry crop production, waste filtration, and groundwater protection (Lal, 2020 ). Maintenance or enhancement of soil quality is a more important criterion for analysis and sustainability of soil ecosystems (Thakur et al., 2024 ). Healthy soils exhibit resilience and rapid recovery from natural and anthropogenic disturbances (Schimel & Schaeffer, 2012 ). Soil moisture is a vital determinant of plant development, since it regulates water balance, runoff, and energy exchange activities within ecosystems (Rowe, 2018 ). It is important to assess soil quality in forest ecosystems, since forest degradation threatens biodiversity and community livelihoods (Konyak et al., 2025 ). The main aim of this study is to use remote sensing and GIS techniques to examine key physical and chemical properties of soil and assess the quality across the Jamtara district of Jharkhand state. This study also focuses on sustainable soil management due to the improvement of soil quality. Materials and Methods Study area The study area is Jamtara district of Jharkhand state (Fig. 1 ). Geologically the area is comprised with basaltic trap and sedimentary beds. Quartz and gneiss are found at some places. The Ajay is the main river flowing through the district (National Bureau of Soil Survey and Land Use Planning, 2020). Jamtara district in southeastern Jharkhand has a humid subtropical climate with hot summers (34–40°C), mild winters (10–16°C), and most rainfall during the June–September monsoon. Annual precipitation averages about 1,500 mm, peaking in July (~ 307 mm) and August (~ 270 mm) (Jamtara District Administration, 2025 .; NearWeather, 2025; NomadSeason, 2025 ). Sampling Methods The soil samples were collected after clearing the litters on the topsoil. The area was considered as a single sampling unit and randomly selected points throughout the district were chosen for analysis (15 study points) as well as for the validation (7 study points) (Table 1 ). The selected points cover all the directions and corners of the district as well as both agriculture and forest lands, making the data evenly distributed. Each location bears samples of single depth i.e. 0 to 30cm as that is the range for most soil activity and maximum productivity. Data Analysis The collected soil samples were air-dried and sieved using a 2 mm sieve following grinding by mortar and pestle before being analyzed for key physico-chemical parameters including BD, SOC, pH, electrical conductivity, moisture content, and macronutrients available nitrogen, phosphorus and potassium (Table 2 ). The analysed values of each parameters were noted and a table was prepared for the further analysis (Table 1 ) Table 1 Chemical and Physical properties of the different soil samples in different location of Jamtara district Location Latitude Longitude Chemical properties Physical properties 1 2 3 4 5 6 7 8 Run Data Dokidih 24.0862 86.5589 217.23 8.75 158.25 0.45 4.6 118 6.68 1.45 Kalajhariya 24.1463 86.7013 226.36 9.25 106.35 0.48 6.2 168 7.84 1.43 Chitakuri 24.087 87.0689 297.35 7.72 288.48 0.72 4.7 147 6.54 1.26 Ranchapar 24.0136 87.1443 317.54 8.76 116.25 0.63 5.1 174 7.84 1.34 Khajuri 23.9389 87.2317 257.14 17.72 97.41 0.49 5.3 178 7.14 1.35 Sudrakshipur 23.9004 87.2693 326.98 16.52 236.14 0.66 4.9 121 6.54 1.48 Muraberia 23.8478 87.2392 397.45 19.29 314.25 0.98 6.3 165 7.45 1.22 Chandrabad 23.8692 87.1675 271.58 13.91 260.35 0.52 5.3 152 6.14 1.37 Mihijam 23.8666 86.8689 306.35 11.27 96.98 0.91 7.5 201 8.84 1.21 Dudh Kaura 23.9555 86.8574 253.69 7.21 89.97 0.42 4.7 124 6.54 1.44 Madnadi 23.9703 86.7364 356.14 8.97 101.59 0.89 4.1 119 6.58 1.23 Thekbahiar 24.039 86.6187 381.88 9.11 269.54 0.82 4.7 128 6.95 1.25 Rupaidi 23.9364 86.7905 414.14 15.52 402.31 1.29 6.7 176 7.01 1.2 Hathiya Pathar 23.9296 87.0965 236.59 11.16 117.18 0.29 5.4 149 6.93 1.49 Pagla 23.9335 86.9383 314.78 9.91 167.25 1.03 7.4 196 8.48 1.26 Mean 305.01 11.67 188.15 0.71 5.53 154.4 7.17 1.33 Test Data Kandi 24.0160 86.5411 299.50 9.227 200.30 0.66 4.87 130 6.914 1.34 Kathbarari 24.0962 86.6529 296.12 9.314 183.22 0.65 5.25 141 7.17 1.34 Bodma 23.9100 86.8337 326.61 11.12 198.32 0.87 6.14 164 7.39 1.28 Maurbasa 23.8823 87.0930 271.67 12.42 170.2 0.50 5.50 155 6.93 1.40 Lahat 23.9562 87.1544 285.93 12.48 156.42 0.54 5.34 161 7.14 1.37 Kalojora 23.9703 87.2193 278.80 15.81 138.08 0.55 5.30 168 7.10 1.35 Mohanpur 23.9443 87.0433 276.02 11.31 154.37 0.54 5.64 159 7.19 1.39 Mean 290.15 11.49 170.15 0.61 5.42 153.4 7.12 1.35 *1 = Nitrogen (Kg/ha), 2 = Phosphorus (Kg/ha), 3 = Potassium (Kg/ha), 4 = SOC (%), 5 = pH, 6 = EC(S/m), 7 = Moisture(%), 8 = Bulk Density (g/cm3) Table 2 Methodology adopted for the analysis of soil physical and chemical parameters Name of Parameter Name of Method Used Reference Bulk Density Core Method Blake & Hartge, ( 1986 ) Organic Carbon Modified Walkley and Black Method Chan et al., ( 2001 ) Soil pH Soil/Water Extract pH Smith & Doran, ( 1997 ) Electrical Conductivity Soil/Water Extract EC Smith & Doran, ( 1997 ) Available Nitrogen Kjeldahl Digestion-Distillation Method Zhao et al., ( 2018a ) Available Phosphorus Molybdenum Blue Colorimetric Method Zhao et al., ( 2018b ) Soil Moisture Content Oven-Drying Technique Schmugge et al., ( 1980 ) Available Potassium Flame Photometry Stanford & English, ( 1949 ) GIS-Based Soil Quality Assessment Spatial Analysis Using QGIS The tabulated data analyzed through GIS-based techniques, including interpolation and weighted summation, following model-based analysis with QGIS version 3.36, open-source software. This method enabled the creation of soil quality maps by employing physical and chemical soil parameters to classify the land into High, Moderate, and Low soil quality zones in Jamtara District, Jharkhand (Abdulmanov et al., 2021 ). Tabulated excel data converted into the CSV format to remove the runtime error. The Inverse Distance Weighted (IDW) method was employed for the spatial interpolation of each parameter (Morgan et al., 2017 ). IDW indicates that values nearer to a location have a greater impact than those situated further away, leading to continuous surfaces for each soil property. After preparing individual IDW maps for each parameter, the soil parameters were categorized into distinct classes according to established thresholds (Table 3 ). Then the data layers underwent reclassification and were processed using a weighted summation technique, resulting in the derivation of a cumulative soil quality score. All the layers were clipped using the Survey of India (SOI) district boundary of the district, further block/tehsil boundary also used for the analysis ( https://onlinemaps.surveyofindia.gov.in/ ). Table 3 Soil parameters categorization into distinct classes according to established thresholds and scoring Parameter Description Threshold Score Reference pH Neutral 6.5–7.5 10 Diztler et. al.,2017 Slightly Acidic 6.0–6.5 8 Moderately Acidic 5.5–6.0 6 Strongly Acidic 5.0–5.5 4 Very Strongly Acidic 4.5–5.0 3 Extremely Strongly Acidic 4.0–4.5 2 EC Slightly Saline 360 10 Yao et al. ( 2013 ) Moderately High 320–360 8 Moderate 280–320 6 Moderately Low 260–280 4 Low 0–260 2 Available P High > 18 10 Soltanpour (1991); Moderately High 15–18 8 Moderate 12–15 6 Moderately Low 09–12 4 Low 0–9 2 Available K High > 380 10 Soltanpour (1991); Moderately High 280–380 7 Moderate 180–280 5 Moderately Low 108–180 3 Low 0–108 2 SOC High 1.25–1.5 10 Amacher et al., 2007 Moderately High 1.0–1.25 5 Moderate 0.75–1.0 3 Moderately Low 0.5–0.75 2 Low 0–0.5 1 Moisture High > 8.0 10 Tale & Ingole, 2015 Moderately High 7.5–8.0 8 Slightly High 7.0–7.5 6 Moderate 6.5 -7.0 4 Moderately Low 0–6.5 2 BD Low 1.45 2 Soil Quality Mapping & Grading Subsequent to interpolation and classification, the raster layers were amalgamated using the weighted summation method. Weights were assigned to each indication according to their significance in assessing soil quality (Table 4 ). The weighted summation results provide a comprehensive score reflecting the region's total soil quality. Table 4 Soil parameter weights as per the significance Weight Parameters Significance 20 Nitrogen (Kg/ha) Nitrogen is critical for vegetative growth; it is often the most limiting nutrient. (Singh et al., 2015 ) 15 Phosphorus (Kg/ha) Essential for root development and energy transfer. (FAO, 2006 ; Sharma et al., 2012) 15 Potassium (Kg/ha) Important for water regulation and disease resistance. (Das, 2009 ) 20 SOC (%) Key indicator of soil fertility and structure. (NRSA, 2008 ) 10 pH Affects nutrient availability and microbial activity. (ICAR, 2010 ) 5 EC(s/m) Indicator of salinity; excess levels harmful to plant growth. (FAO, 2006 ) 10 Moisture (%) Influences plant growth and microbial processes. (Lal, 2001 ) 5 Bulk Density (g/cm 3 ) Affects root penetration and soil aeration. (FAO, 2006 ) *Based on the cumulative scores, soil across the district was graded into five soil quality classes: Grade I – High quality, Grade II – Moderately high, Grade III – Moderate, Grade IV – Moderately low, and Grade V– Low quality. These grades were then visualized in the form of soil quality maps to facilitate spatial interpretation. Preparation of LULC The land use land cover map of the Jamtara district was prepared using cloud-free Landsat 8 OLI satellite imagery using the Google Earth Engine (GEE) cloud platform (Gorelick et al., 2017 ; U.S. Geological Survey, 2013 ). The data was captured by the satellite on 1st May 2024 (Path-140 and Row-43, 44) and 2nd May 2024 (Path-139 and Row-43, 44). The random forest supervised classification technique was used to prepare the LULC map for the study area. Validation To ensure the reliability and accuracy of the soil analysis results, a validation process was conducted using an independent set of soil samples collected from 7 additional randomly selected points within the district. These validation points were chosen to cover similar land types and spatial distribution as the main sampling locations. The validation samples were analyzed using the same procedures and compared with the initial dataset to assess the consistency and robustness of the sampling and analytical methods. This step helped to confirm that the findings are representative of the district’s soil conditions. The overall methodology of this study is presented in Fig. 2 as a flowchart for better understanding. Results and Discussion Remote sensing and GIS techniques were effectively applied to map the physical and chemical properties of soils and to develop a Soil Quality (SQ) map for Jamtara district, Jharkhand. These geospatial tools proved highly effective for assessing spatial variability, integrating field and laboratory data, and producing detailed soil quality maps that support precision land management (Zhang et al., 2018 ; Lal, 2020 ; Melesse et al., 2020 ). The spatial distribution of nutrients revealed distinct geographic patterns. Nitrogen levels were mostly moderate, with higher concentrations in the western and southern zones, while phosphorus was generally deficient outside the central and south-eastern areas, potentially constraining crop productivity (Fageria & Baligar, 2005 ; Malhotra et al., 2018 ). Potassium levels were low to moderate in central and south-eastern regions, with isolated high pockets linked to soil parent material and historical land use (Vidyavathi et al., 2012 ). SOC ranged from low to moderate, with higher concentrations in the south-central zone, indicating localized organic matter build-up and scope for enhancement through organic matter management (Lal, 2020 ). These patterns (Fig. 3 ) underscore the need for site-specific nutrient management to sustain productivity and soil health. Soil pH variability indicates a predominantly acidic landscape, with extremely to very strongly acidic soils in the western and central zones and slightly acidic to near-neutral areas in the south-central section that are more favourable for agriculture. Liming of acidic soils can enhance nutrient availability and microbial activity (Goulding, 2016 ). Electrical conductivity values (118–201 µS cm⁻¹) classify most soils as very low to low salinity, suggesting minimal constraints, though isolated higher EC zones warrant monitoring (Hardie & Doyle, 2012 ; Sheldon et al., 2017 ). Soil moisture levels were generally moderate (15–20%), with higher values in the southwest and drier conditions in the north and east, influenced by topography, drainage, and texture, which in turn affect nutrient uptake and microbial processes (Franzluebbers, 2022 ). Bulk density ranged from 1.20 to 1.49 g cm⁻³, with most areas in the low range (1.25–1.35 g cm⁻³), indicating good porosity, while localized higher values suggest compaction that may restrict root growth (Kay, 2008; Hao et al., 2008 ). The variability in pH, moisture, EC and BD (Fig. 4 ) highlights the combined influence of inherent soil properties and land management, reinforcing the importance of targeted interventions. The spatial analysis revealed distinct geographic patterns in soil properties, indicating the need for site-specific management to optimize productivity (Table 5 ). Acidic soils dominate large portions of the study area, consistent with patterns reported in similar agroecological zones (Singh et al., 2021 ). Phosphorus deficiency is widespread, while potassium distribution is uneven, aligning with previous findings that nutrient imbalances often follow soil parent material and historical land use (Gupta & Sharma, 2018 ). Moderate organic carbon and nitrogen levels, along with localized compaction and variable moisture availability, further highlight the importance of targeted nutrient application, organic matter enhancement, and appropriate irrigation or drainage measures. These results reinforce evidence that uniform soil management is often inefficient, and that precision nutrient and structural interventions are essential for sustaining long-term soil health and agricultural output (FAO, 2020 ; Lal, 2020 ). Table 5 Spatial distribution of soil physicochemical properties and management implications Parameter Key areas Management Implications Soil pH Predominantly extremely to strongly acidic soils in western, central, and northern regions; slightly acidic patches in south-central parts. Liming recommended to ameliorate soil acidity and enhance nutrient availability. Electrical Conductivity (EC) Generally low EC values across the study area; slight increases in northwestern and southeastern pockets. Low salinity risk; monitor localized areas with slightly higher EC to prevent salinity buildup. Bulk Density (BD) Mostly slightly low (1.25–1.35 g/cm³) bulk density; pockets of low ( 1.45 g/cm³) values observed. Maintain organic matter to sustain low BD; address compacted zones through soil loosening practices. Soil Moisture Moderate moisture across most areas; high moisture content concentrated in northern and northeastern zones. Optimize irrigation scheduling; high moisture areas may require drainage management. Soil Organic Carbon (SOC) South-central and southeastern zones Enhance organic matter inputs (e.g., compost, cover crops) Nitrogen (N) Western and southern zones higher Sustain fertility; site-specific nutrient management Phosphorus (P) Northern and western regions Phosphorus fertilization needed to correct deficiencies Potassium (K) Central and southeastern zones; high pockets in south-central and northeast Targeted potassium supplementation in deficient areas The Soil Quality (SQ) map of Jamtara district was prepared with an accuracy of 85.71%, illustrating a systematic approach to assessing and comparing soil health across the region (Fig. 5 ). Six out of seven validation points matched the predicted soil quality classes, indicating reliable spatial representation suitable for land management (McBratney et al., 2003 ). The single mismatch may be due to local variability or sampling limitations, which could be addressed by increasing sampling density or incorporating remote sensing data (Grunwald, 2009 ; Kumar & Min, 2015 ). Spatially, Grade III soils dominate the district (37.13%), followed by Grade II (25.02%), Grade IV (24.91%), Grade I (7%), and Grade V (5.94%) (Fig. 6 ). The area is predominantly characterized by the Inceptisols soil order (Agarwal et al., 2010 ), which typically exhibits moderate nutrient levels (Krishnapriya et al., 2023 ). Grade I soils demonstrate optimal pH, electrical conductivity (EC) and nutrient availability, rendering them highly suitable for agriculture (Brady & Weil, 2016 ). Grade II soils retain adequate nutrient levels and acceptable pH and salinity, indicating moderately high suitability for crop production (Havlin et al., 2014 ). Grade III soils, although balanced in nutrients and within acceptable pH and salinity ranges, exhibit moderate suitability due to combined influences of parent material, climate, terrain, and biological activity (Lal, 2015 ). Grade IV soils are characterized by moderately low quality, with specific nutrient deficiencies and potential pH or salinity issues, requiring targeted management interventions to improve fertility (FAO, 2017 ). Grade V soils are considered least suitable for agriculture, marked by poor physical and chemical properties, low nutrient content, elevated salinity, and unsuitable pH levels (Smith et al., 2013 ). Soil quality, encompassing physical, chemical, and biological dimensions, reflects the soil’s ability to function as a sustainable ecosystem for plants, animals, and humans (Delgado & Gomez, 2017 ; Usharani et al., 2019 ). These findings are essential for region-specific soil management and sustainable land-use planning (Obade & Lal, 2013; Abdel Rahman & Tahoun, 2019; Mukhopadhyay & Mishra, 2024 ). The generated SQ maps support district-level land use planning and crop recommendations, contributing to climate-smart and sustainable agriculture (Bel-Labib et al., 2023). The spatial distribution of soil quality grades across the blocks of Jamtara district exhibits considerable variation (Fig. 7 ). Fatehpur block contains only two soil quality categories Grade IV and Grade III—indicating a predominance of moderately low to moderate soil quality in this region. Grade I soils, representing high-quality soil, are entirely absent in Vidyasagar and Nala blocks. The highest proportion of Grade I soils is concentrated in Jamtara block, followed by Narayanpur and Kundahit blocks, suggesting these areas have greater potential for high agricultural productivity (FAO, 2020 ; Singh et al., 2018 ). Grade III soils are most extensively distributed in Fatehpur block, with subsequent presence in Nala, Jamtara, Vidyasagar, Narayanpur, and Kundahit blocks, in descending order of coverage. These variations align with earlier studies that report soil quality heterogeneity as a function of both physiographic and land-use factors (Lal, 2015 ; Kumar & Sharma, 2019 ). Integration of soil grade maps with Land Use/Land Cover (LULC) data revealed a clear association between soil quality and prevailing land use patterns in Jamtara district (Fig. 8 ). High-grade soils (Grades I–II) were primarily concentrated in areas under agricultural and fallow land, reflecting their inherent suitability for crop cultivation and sustained productivity (FAO, 2020 ; Singh et al., 2018 ). In contrast, lower-grade soils (Grades IV–V) were predominantly distributed over barren lands, scrublands, and degraded forest patches, indicating limited agricultural potential and a heightened vulnerability to further degradation (Lal, 2015 ; Ghosh & Dey, 2021 ). These spatial correlations suggest that tailored management interventions—such as site-specific nutrient management, organic matter enrichment, and erosion control are vital for enhancing soil health and ensuring sustainable land use in the district (Kumar &Sharma, 2019 ). This study provides baseline soil health data for Jamtara, offering a framework for long-term monitoring. The results are valuable for policymakers, extension workers, and farmers, enabling informed decisions on crop selection, fertilizer application, and conservation measures. Remote sensing and GIS offer cost-effective tools for soil quality assessment, applicable across various districts and agroecological zones in India. Future work should integrate biological indicators such as microbial biomass and enzymatic activity, along with temporal monitoring to capture seasonal and interannual variations. Incorporating socioeconomic factors could further link soil health to farming practices, allowing for more tailored management strategies. Conclusion This study advances understanding of soil health in Jamtara district through remote sensing and GIS-based soil quality mapping with 85.71% accuracy. The Soil Quality (SQ) map reveals that Grade III soils dominate the district (37.13%), followed by Grades II, IV, I, and V, reflecting moderate to low soil fertility overall. Significant spatial variability and block-wise differences in soil quality were identified, with higher-quality soils concentrated in Jamtara, Narayanpur and Kundahit blocks, while Fatehpur exhibited predominantly moderate to low-quality soils. The district’s soils, mainly Inceptisols, showed moderate nitrogen and organic carbon levels but widespread phosphorus and potassium deficiencies. Predominantly acidic soils and modest electrical conductivity values highlight the need for liming and effective irrigation and drainage management to prevent salinity risks. Integration with land use/land cover data revealed strong correlations between soil quality and land use patterns, with higher-grade soils associated with agricultural lands and lower grades linked to degraded or barren areas. These findings emphasize the critical need for site-specific soil management interventions such as targeted fertilization, organic amendments, and soil acidity correction. The validated soil quality map provides a valuable decision-support tool for precision agriculture, sustainable land-use planning, and long-term monitoring, empowering stakeholders to improve productivity, maintain ecological balance, and foster environmental sustainability in Jamtara district and similar agroecological zones. Declarations Authors Contribution Kanti Hansda: Conceptualization, Methodology, Field Investigation. Debaaditya Mukhopadhyay: Writing– Original Draft, Writing– Review & Editing, Data Curation, Visualization (Map Preparation), Formal Analysis . Kambam Boxen Meetei: Writing– Original Draft, Formal Analysis, Statistical Interpretation. Ajujil Mir: Visualization (Map Preparation), Statistical Analysis, Writing– Review & Editing. Harshavardhan Kumar: Supervision, Writing– Review & Editing. Sk Mujibar Rahaman: Data Analysis, Writing– Review & Editing, Visualization (Map Preparation). Acknowledgement The authors express their sincere gratitude to the editorial team and reviewers of the journal for their valuable feedback and guidance, which have greatly improved the quality and clarity of this manuscript. Ethics approval and consent to participate Not Applicable Consent for publication Not Applicable Availability of data and material The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests Clinical trial number Not Applicable Funding Not Applicable References AbdelRahman M A and Tahoun S 2019. GIS model-builder based on comprehensive geostatistical approach to assess soil quality. Remote sensing Applications: society and Environment , 13 , 204-214. Abdulmanov R, Miftakhov I, Ishbulatov M, Galeev E and Shafeeva E 2021. 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Interpreting soil test results: What do all the numbers mean? . CSIRO publishing. Huang B, Liu S and Wang Y 2020. Soil quality assessment and sustainable land use management: A review. Sustainability , 12(15), 6198. https://doi.org/10.3390/su12156198 ICAR 2010. Handbook of Agriculture . Indian Council of Agricultural Research, New Delhi. Jamtara District Administration 2025. About district . Government of Jharkhand. https://jamtara.nic.in/about-district/ Jansson J K and Baker E S 2016. A multi-omic future for microbiome studies. Nature microbiology , 1 (5), 1-3. Karlen D L 2004. Soil quality as an indicator of sustainable tillage practices. Soil and Tillage Research, 78(2), 129- 130. Kay B D 2018. Soil structure and organic carbon: a review. Soil processes and the carbon cycle , 169-197. Konyak P A, Puro N and Temjen W 2025. Enhancing soil quality monitoring for sustainable forest management in Northeast India: Role of soil quality index in Hongmong Conservation Area, Nagaland. Indian Journal of Ecology, 52 (1), 57–63. https://doi.org/10.55362/IJE/2025/4455 Krishnapriya M K, Patil G D, Leno N and Yadav G K 2023. Spatial Variability of Infiltration Rate in Inceptisol and Entisol Soils of Sahyadri Foothills of Western India. Int. J. Environ. Clim. Change , 13 (9), 1570-1578. Kumar S and Min N 2015. Accuracy assessment of soil quality maps using field validation data. International Journal of Environmental Science and Development, 6 (8), 615-619. https://doi.org/10.7763/IJESD.2015.V6.686 Kumar A and Sharma V 2019. Spatial characterization of soil quality using GIS and remote sensing. International Journal of Environmental Sciences , 9(4), 567–574. Kuzyakov Y and Xu X 2013. Competition between roots and microorganisms for nitrogen: Mechanisms and ecological relevance. New Phytologist , 198(3), 656-669. https://doi.org/10.1111/nph.12235 Lal R 2001. Soil degradation by erosion . Land Degradation & Development , 12(6), 519–539. Lal R 2015. Soil health and climate change. Soil Science Society of America Journal , 79(2), 461–470. Lal R 2020. Soil health and carbon management. Food and Energy Security , 9(1), e200. https://doi.org/10.1002/fes3.200 Malhotra H, Vandana S and Pandey R 2018. Phosphorus nutrition: plant growth in response to deficiency and excess. Plant nutrients and abiotic stress tolerance , 171-190. Maurya S, Abraham J S, Somasundaram S, Toteja R, Gupta R and Makhija S 2020. Indicators for assessment of soil quality: a mini-review. Environmental Monitoring and Assessment , 192 , 1-22. McBratney A B, Mendonça Santos M L and Minasny B 2003. On digital soil mapping. Geoderma, 117 (1-2), 3-52. https://doi.org/10.1016/S0016-7061(03)00223-4 Melesse A M, Abtew W and Senay G B 2020. Remote sensing for sustainability . CRC Press. Morgan R S, Abd El-Hady M, Rahim I S, Silva J and Ribeiro S 2017. Evaluation of various interpolation techniques for estimation of selected soil properties. GEOMATE Journal , 13 (38), 23-30. Mukhopadhyay D and Mishra G 2024. Monitoring land degradation and desertification using the state-of-the-art methods and remote sensing data. In Modern Cartography Series (Vol. 12, pp. 627-654). Academic Press. National Bureau of Soil Survey and Land Use Planning (ICAR), Regional Centre, Kolkata 2020. Assessment and mapping of some important soil parameters including soil acidity for the state of Jharkhand (1:50,000 scale) towards rational land use plan . Pages 1-17. NearWeather 2025. Weather in Jamtara . https://www.nearweather.com/location/1269298 NomadSeason. (2025). Climate in Jamtara, Jharkhand, India . https://nomadseason.com/climate/india/jharkhand/jamtara.html NRSA 2008. Soil resource mapping of India: Technical Manual . National Remote Sensing Agency, Dept. of Space, Govt. of India, Hyderabad. Quan J, Oudwater N, Pender J and Martin A 2001. GIS and participatory approaches in natural resources research. Rowe ROSIA 2018. Soil moisture. Biosystems Engineering. Auburn University, Auburn, Alabama, United States . Schimel J and Schaeffer S M 2012. Microbial control over carbon cycling in soil. Frontiers in Microbiology , 3, 348. https://doi.org/10.3389/fmicb.2012.00348 Schmugge T J, Kustas W P and Ritchie J C 1980. Soil moisture content measurement by oven drying. Remote Sensing of Environment , 11(1-3), 263-273. Sharma P K and Yadav R K 2012. Soil quality indicators and their use in sustainable land management . Indian Journal of Fertilisers , 8(2), 14–22. Sheldon A R, Dalal R C, Kirchhof G, Kopittke P M and Menzies N W 2017. The effect of salinity on plant-available water. Plant and Soil , 418 , 477-491. Singh A, Kumar S and Verma R 2021. Status and management of acidic soils in eastern India. Indian Journal of Soil Conservation , 49(2), 103–110. Singh RP, Mishra PK and Rai RB 2015. Soil fertility evaluation using GIS-based weighted overlay analysis: A case study of Gonda district, UP, India . Journal of the Indian Society of Remote Sensing , 43, 573–582. https://doi.org/10.1007/s12524-014-0417-9 Singh R, Mishra P and Verma S 2018. Spatial variability in soil properties and its impact on crop productivity. Journal of Soil Science and Plant Nutrition , 18(2), 523–535. Smith J L and Doran J W 1997. Measurement and Use of pH and Electrical Conductivity for Soil Quality Analysis. In J. W. Doran & A. J. Jones (Eds.), Methods for Assessing Soil Quality (pp. 169–185). Soil Science Society of America. Smith P, Ashmore M R, Black H I, Burgess P J, Evans C D, Quine T A and Orr H G 2013. The role of ecosystems and their management in regulating climate, and soil, water and air quality. Journal of Applied Ecology , 50 (4), 812-829. Srivastava A K, Jerai M C and Lal J K 2019. Nutrient status of Dhanbad district soils. Journal of Pharmacognosy and Phytochemistry , 8 (2S), 137-140. Stanford S and English L 1949. Use of flame photometer in rapid soil tests for K and Ca. Agronomy Journal , 41(4), 446-447. Tale K S and Ingole S 2015. A review on role of physico-chemical properties in soil quality. Chemical Science Review and Letters , 4 (13), 57-66. Thakur P, Thakur C L and Bhardwaj D R 2024. Dynamics of soil physical and chemical properties under fruit tree-based agroforestry systems in sub-humid agro-climatic zone. Indian Journal of Ecology, 51 (6), 1156–1159. https://doi.org/10.55362/IJE/2024/4370 U.S. Geological Survey 2013. Landsat 8 (L8) Data Users Handbook. https://www.usgs.gov/media/files/landsat-8-data-users-handbook Usharani K V, Roopashree K M and Naik D 2019. Role of soil physical, chemical and biological properties for soil health improvement and sustainable agriculture. Journal of Pharmacognosy and Phytochemistry , 8 (5), 1256-1267. Vidyavathi V, Dasog G S, Babalad H B, Hebsur N S, Gali S K, Patil S G and Alagawadi A R 2012. Nutrient status of soil under different nutrient and crop management practices. Karnataka Journal of Agricultural Sciences , 25 (2), 193-198. Yao R, Yang J, Gao P, Zhang J and Jin W 2013. Determining minimum data set for soil quality assessment of typical salt-affected farmland in the coastal reclamation area. Soil and Tillage Research , 128 , 137-148. Zhang X, Davidson E A, Mauzerall D L, Searchinger T D, Dumas P and Shen Y 2018. Managing nitrogen for sustainable development. Nature , 564(7735), 51–59. https://doi.org/10.1038/s41586-018-0746-2 Zhao L, Zheng W and Wang Q 2018a. A comparative study of soil nitrogen and phosphorus determination methods. Soil Science and Plant Nutrition , 64(2), 140-150. Zhao Q, Tang J, Li Z, Yang W and Duan Y 2018b. The influence of soil physico-chemical properties and enzyme activities on soil quality of saline-alkali agroecosystems in western Jilin Province, China. Sustainability (Switzerland) , 10 (5). https://doi.org/10.3390/su10051529. Additional Declarations No competing interests reported. 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Institute","correspondingAuthor":false,"prefix":"","firstName":"Debaaditya","middleName":"","lastName":"Mukhopadhyay","suffix":""},{"id":516846229,"identity":"845257dd-c77e-445d-a301-76d077bf1e07","order_by":2,"name":"Kambam Boxen Meetei","email":"","orcid":"","institution":"ICFRE-Rain Forest Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Kambam","middleName":"Boxen","lastName":"Meetei","suffix":""},{"id":516846230,"identity":"96d8b2af-aef0-4a62-9afd-1ada22a42004","order_by":3,"name":"Ajujil Mir","email":"","orcid":"","institution":"Pandit Raghunath Murmu Smriti Mahavidyalaya","correspondingAuthor":false,"prefix":"","firstName":"Ajujil","middleName":"","lastName":"Mir","suffix":""},{"id":516846231,"identity":"1c5d0352-c468-496b-947f-653deed564fc","order_by":4,"name":"Harshavardhan Kumar","email":"","orcid":"","institution":"St. Columba’s College","correspondingAuthor":false,"prefix":"","firstName":"Harshavardhan","middleName":"","lastName":"Kumar","suffix":""},{"id":516846232,"identity":"c5c42a17-5a2a-4511-b1dc-94020e0b97cd","order_by":5,"name":"Sk Mujibar Rahaman","email":"data:image/png;base64,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","orcid":"","institution":"National Atlas and Thematic Mapping 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marked\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7600492/v1/e44f507799810c5e34a57bf3.png"},{"id":93730601,"identity":"efeb0f00-c71c-486b-b046-94fdbdbd03da","added_by":"auto","created_at":"2025-10-17 02:21:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":552656,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological Framework for Soil Quality Mapping Using Remote Sensing and GIS\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7600492/v1/fc78e1004536ff37f17221b6.png"},{"id":93732272,"identity":"9c08dc36-7f13-4874-922a-708b232ac34d","added_by":"auto","created_at":"2025-10-17 02:29:17","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1609240,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution maps of (A) potassium, (B) phosphorus, (C) nitrogen, and (D) soil organic carbon using IDW interpolation technique\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7600492/v1/e85e0af9ba5b2864944522c7.png"},{"id":93730603,"identity":"82f3528d-85f4-4b82-bfaf-c7228a32a2a2","added_by":"auto","created_at":"2025-10-17 02:21:17","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1619313,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution maps of (A) pH, (B) moisture, (C) electrical conductivity and (D) soil bulk density using IDW interpolation technique\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7600492/v1/ff4902106fd5c202307d5094.png"},{"id":93730607,"identity":"b83fee18-3cc3-4503-a815-b98995e6a7ed","added_by":"auto","created_at":"2025-10-17 02:21:17","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":905572,"visible":true,"origin":"","legend":"\u003cp\u003eGraded soil divisions in Jamtara district; Soil Quality: Grade I – High; Grade II – Moderately High; Grade III – Moderate; Grade IV – Moderately Low; Grade V – Low\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7600492/v1/bb1004113bd8b7e53a9a9f48.png"},{"id":93730605,"identity":"53add737-ecdf-4395-9e75-4a3ca39993b3","added_by":"auto","created_at":"2025-10-17 02:21:17","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":186527,"visible":true,"origin":"","legend":"\u003cp\u003eBar graph showing total area (in hectares) under each soil quality grade.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7600492/v1/4931ea3e9d32ac6f060ccb22.png"},{"id":93730608,"identity":"d9df8cae-a141-43ad-8221-06823161b123","added_by":"auto","created_at":"2025-10-17 02:21:17","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":76102,"visible":true,"origin":"","legend":"\u003cp\u003eBar graph showing soil quality area distribution for each block.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-7600492/v1/fd57f32812f99af788f116b7.png"},{"id":93730610,"identity":"1919d75b-2613-4de7-ba42-bbab455a88fe","added_by":"auto","created_at":"2025-10-17 02:21:18","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":2342920,"visible":true,"origin":"","legend":"\u003cp\u003eLand Use/Land Cover map with soil grades overlaid.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-7600492/v1/3fdb2594b4340a6197e8c78a.png"},{"id":98776677,"identity":"86476b22-8be7-4f6a-950e-1483dc1e00a8","added_by":"auto","created_at":"2025-12-22 12:23:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12737764,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7600492/v1/48a135cd-fc2c-4a25-9ad3-cab8a5bb234d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eIntegrating Remote Sensing and GIS for Sustainable Soil Management: A Case Study of Jamtara District, Jharkhand\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe fertility of soil is contingent upon the presence of vital nutrients, specifically nitrogen (N), phosphorus (P) and potassium (K). Microbial mineralization plays a vital role in the assimilation of nitrogen in plants, given that nitrogen primarily exists in organic forms and is essential for plant growth (Kuzyakov \u0026amp; Xu, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). A comprehensive approach is essential for an in-depth assessment of soil quality, taking into account physical, chemical, and biological attributes, along with enzyme activity (Maurya et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The challenges in evaluating the comprehensive spectrum of soil characteristics have resulted in the development of a Minimum Data Set (MDS), comprising essential indicators that reflect soil fertility, health, and quality (Maurya et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Indicators are chosen according to agro-ecological conditions (Govaerts et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Yao et al., \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and contemporary analytical methods, such as microbiome analysis, improve the accuracy of soil quality evaluations (Jansson \u0026amp; Baker, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Geospatial technologies, particularly Geographic Information Systems (GIS), provide a solid basis for integrating soil data and developing tailored interpretative maps (Quan et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). These maps facilitate informed strategic planning and decision-making regarding soil conservation, pH regulation, and nutrient enhancement. The characteristics of soil are not static; they are shaped by the interplay of physical, chemical, and biological processes. The physicochemical properties of soil are shaped by factors such as temperature, topography, vegetation, and biological activity across various regions and time periods. Land use changes over time, particularly those related to agricultural operations and deforestation may have a negative influence on soil characteristics and lower ecosystem productivity. Soil health is evaluated using important markers such as bulk density (BD), potential of hydrogen (pH), electrical conductivity (EC), moisture content, and nutrient concentration. Effective planning for sustainable land management requires current knowledge of soil and land resource availability. Soil, climate, water, nutrients, and biota all collaborate in ecosystems to provide critical services that sustain life while preserving the environment's capacity to recover. Integrated land resource planning is crucial for settling conflicts and supporting sustainability in areas with limited land supply, which is exacerbated by rising competition for land use as the global population expands. Soil resource evaluations are essential for determining soil fertility, acidity/alkalinity, and other factors that affect sustainable land use (Huang et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe fertility of soil, a crucial element in the interactions between soil and plants, is affected by the presence of both macro- and micronutrients (Srivastava et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The ongoing extraction of nutrients by crops, combined with insufficient replenishment, results in nutrient stress and a reduction in productivity. The interplay of plant cover, climate, terrain, soil texture, and organic matter decomposition plays a crucial role in determining soil fertility. Soil quality, in conjunction with soil productivity, reflects the ability of soil to support plant growth and uphold ecological processes (B\u0026uuml;nemann et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Defining and standardizing soil quality assessment presents a challenge due to the varied functions of soils, which include agronomic and forestry crop production, waste filtration, and groundwater protection (Lal, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Maintenance or enhancement of soil quality is a more important criterion for analysis and sustainability of soil ecosystems (Thakur et al., \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Healthy soils exhibit resilience and rapid recovery from natural and anthropogenic disturbances (Schimel \u0026amp; Schaeffer, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Soil moisture is a vital determinant of plant development, since it regulates water balance, runoff, and energy exchange activities within ecosystems (Rowe, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). It is important to assess soil quality in forest ecosystems, since forest degradation threatens biodiversity and community livelihoods (Konyak et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe main aim of this study is to use remote sensing and GIS techniques to examine key physical and chemical properties of soil and assess the quality across the Jamtara district of Jharkhand state. This study also focuses on sustainable soil management due to the improvement of soil quality.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy area\u003c/h2\u003e\u003cp\u003eThe study area is Jamtara district of Jharkhand state (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Geologically the area is comprised with basaltic trap and sedimentary beds. Quartz and gneiss are found at some places. The Ajay is the main river flowing through the district (National Bureau of Soil Survey and Land Use Planning, 2020). Jamtara district in southeastern Jharkhand has a humid subtropical climate with hot summers (34\u0026ndash;40\u0026deg;C), mild winters (10\u0026ndash;16\u0026deg;C), and most rainfall during the June\u0026ndash;September monsoon. Annual precipitation averages about 1,500 mm, peaking in July (~\u0026thinsp;307 mm) and August (~\u0026thinsp;270 mm) (Jamtara District Administration, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e.; NearWeather, 2025; NomadSeason, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSampling Methods\u003c/h3\u003e\n\u003cp\u003eThe soil samples were collected after clearing the litters on the topsoil. The area was considered as a single sampling unit and randomly selected points throughout the district were chosen for analysis (15 study points) as well as for the validation (7 study points) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The selected points cover all the directions and corners of the district as well as both agriculture and forest lands, making the data evenly distributed. Each location bears samples of single depth i.e. 0 to 30cm as that is the range for most soil activity and maximum productivity.\u003c/p\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eThe collected soil samples were air-dried and sieved using a 2 mm sieve following grinding by mortar and pestle before being analyzed for key physico-chemical parameters including BD, SOC, pH, electrical conductivity, moisture content, and macronutrients available nitrogen, phosphorus and potassium (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The analysed values of each parameters were noted and a table was prepared for the further analysis (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eChemical and Physical properties of the different soil samples in different location of Jamtara district\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLocation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLatitude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eLongitude\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e\u003cp\u003eChemical properties\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c12\" namest=\"c9\"\u003e\u003cp\u003ePhysical properties\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"15\" rowspan=\"16\"\u003e\u003cp\u003e\u003cb\u003eRun Data\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDokidih\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.0862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.5589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e217.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e158.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e118\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKalajhariya\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.1463\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.7013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e226.36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e106.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChitakuri\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.0689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e297.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e288.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e147\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRanchapar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.0136\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.1443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e317.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e116.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKhajuri\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.9389\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.2317\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e257.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e97.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e178\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSudrakshipur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.9004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.2693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e326.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e16.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e236.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" 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colname=\"c11\"\u003e\u003cp\u003e7.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.22\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChandrabad\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.8692\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.1675\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e271.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e260.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e152\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMihijam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.8666\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.8689\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e306.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e96.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8.84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDudh Kaura\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.9555\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.8574\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e253.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.21\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e89.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e124\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.44\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMadnadi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.9703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.7364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e356.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e101.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e119\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eThekbahiar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.039\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.6187\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e381.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e269.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRupaidi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.9364\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.7905\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e414.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e402.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHathiya Pathar\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.9296\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.0965\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e236.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e117.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e149\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePagla\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.9335\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.9383\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e314.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.91\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e167.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e196\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.26\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e305.01\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e188.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e154.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e\u003cb\u003eTest Data\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKandi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.0160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.5411\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e299.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.227\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e200.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.66\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e4.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e130\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6.914\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKathbarari\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e24.0962\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.6529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e296.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.314\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e183.22\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.34\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBodma\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.9100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e86.8337\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e326.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e198.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.28\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMaurbasa\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.8823\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.0930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e271.67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e170.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e155\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.40\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLahat\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.9562\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.1544\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e285.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e156.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.14\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKalojora\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.9703\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.2193\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e278.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e138.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e168\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMohanpur\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23.9443\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e87.0433\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e276.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e154.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eMean\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e290.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e11.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e170.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e153.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"12\"\u003e*1\u0026thinsp;=\u0026thinsp;\u003cb\u003eNitrogen\u003c/b\u003e (Kg/ha), 2\u0026thinsp;=\u0026thinsp;Phosphorus (Kg/ha), 3\u0026thinsp;=\u0026thinsp;Potassium (Kg/ha), 4\u0026thinsp;=\u0026thinsp;SOC (%), 5\u0026thinsp;=\u0026thinsp;pH, 6\u0026thinsp;=\u0026thinsp;EC(S/m), 7\u0026thinsp;=\u0026thinsp;Moisture(%), 8\u0026thinsp;=\u0026thinsp;Bulk Density (g/cm3)\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMethodology adopted for the analysis of soil physical and chemical parameters\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eName of Parameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eName of Method Used\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBulk Density\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCore Method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBlake \u0026amp; Hartge, (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1986\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOrganic Carbon\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModified Walkley and Black Method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChan et al., (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil pH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSoil/Water Extract pH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSmith \u0026amp; Doran, (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1997\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eElectrical Conductivity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSoil/Water Extract EC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSmith \u0026amp; Doran, (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e1997\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAvailable Nitrogen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKjeldahl Digestion-Distillation Method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZhao et al., (\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAvailable Phosphorus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMolybdenum Blue Colorimetric Method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZhao et al., (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil Moisture Content\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOven-Drying Technique\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSchmugge et al., (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1980\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAvailable Potassium\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eFlame Photometry\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStanford \u0026amp; English, (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1949\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eGIS-Based Soil Quality Assessment\u003c/h3\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003eSpatial Analysis Using QGIS\u003c/h2\u003e\u003cp\u003eThe tabulated data analyzed through GIS-based techniques, including interpolation and weighted summation, following model-based analysis with QGIS version 3.36, open-source software. This method enabled the creation of soil quality maps by employing physical and chemical soil parameters to classify the land into High, Moderate, and Low soil quality zones in Jamtara District, Jharkhand (Abdulmanov et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Tabulated excel data converted into the CSV format to remove the runtime error. The Inverse Distance Weighted (IDW) method was employed for the spatial interpolation of each parameter (Morgan et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). IDW indicates that values nearer to a location have a greater impact than those situated further away, leading to continuous surfaces for each soil property. After preparing individual IDW maps for each parameter, the soil parameters were categorized into distinct classes according to established thresholds (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Then the data layers underwent reclassification and were processed using a weighted summation technique, resulting in the derivation of a cumulative soil quality score. All the layers were clipped using the Survey of India (SOI) district boundary of the district, further block/tehsil boundary also used for the analysis (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://onlinemaps.surveyofindia.gov.in/\u003c/span\u003e\u003cspan address=\"https://onlinemaps.surveyofindia.gov.in/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSoil parameters categorization into distinct classes according to established thresholds and scoring\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThreshold\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eScore\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003epH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeutral\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.5\u0026ndash;7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eDiztler et. al.,2017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlightly Acidic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.0\u0026ndash;6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerately Acidic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.5\u0026ndash;6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStrongly Acidic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.0\u0026ndash;5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery Strongly Acidic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.5\u0026ndash;5.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExtremely Strongly Acidic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.0\u0026ndash;4.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eEC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlightly Saline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eDiztler et. al., 2017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerately Saline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e120\u0026ndash;140\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlightly Strongly Saline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e140\u0026ndash;160\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStrongly Saline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e160\u0026ndash;180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eVery Strongly Saline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e180\u0026ndash;200\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eAvailable N\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eYao et al. (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2013\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerately High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e320\u0026ndash;360\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e280\u0026ndash;320\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerately Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e260\u0026ndash;280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;260\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eAvailable P\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eSoltanpour (1991);\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerately High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15\u0026ndash;18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12\u0026ndash;15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerately Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e09\u0026ndash;12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eAvailable K\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eSoltanpour (1991);\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerately High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e280\u0026ndash;380\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e180\u0026ndash;280\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerately Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e108\u0026ndash;180\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;108\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eSOC\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.25\u0026ndash;1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eAmacher et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2007\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerately High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.0\u0026ndash;1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.75\u0026ndash;1.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerately Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.5\u0026ndash;0.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eMoisture\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;8.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eTale \u0026amp; Ingole, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2015\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerately High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.5\u0026ndash;8.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlightly High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7.0\u0026ndash;7.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6.5 -7.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerately Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u0026ndash;6.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eBD\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;1.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eHazelton \u0026amp; Murphy (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlightly Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.25\u0026ndash;1.35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerately Low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.35\u0026ndash;1.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.40\u0026ndash;1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlightly High\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eSoil Quality Mapping \u0026amp; Grading\u003c/h2\u003e\u003cp\u003eSubsequent to interpolation and classification, the raster layers were amalgamated using the weighted summation method. Weights were assigned to each indication according to their significance in assessing soil quality (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The weighted summation results provide a comprehensive score reflecting the region's total soil quality.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSoil parameter weights as per the significance\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eParameters\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSignificance\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNitrogen (Kg/ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNitrogen is critical for vegetative growth; it is often the most limiting nutrient. (Singh et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2015\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePhosphorus (Kg/ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEssential for root development and energy transfer. (FAO, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Sharma et al., 2012)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePotassium (Kg/ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eImportant for water regulation and disease resistance. (Das, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSOC (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eKey indicator of soil fertility and structure. (NRSA, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003epH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAffects nutrient availability and microbial activity. (ICAR, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2010\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEC(s/m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndicator of salinity; excess levels harmful to plant growth. (FAO, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMoisture (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eInfluences plant growth and microbial processes. (Lal, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2001\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBulk Density (g/cm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAffects root penetration and soil aeration. (FAO, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e*Based on the cumulative scores, soil across the district was graded into five soil quality classes: Grade I \u0026ndash; High quality, Grade II \u0026ndash; Moderately high, Grade III \u0026ndash; Moderate, Grade IV \u0026ndash; Moderately low, and Grade V\u0026ndash; Low quality. These grades were then visualized in the form of soil quality maps to facilitate spatial interpretation.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePreparation of LULC\u003c/h3\u003e\n\u003cp\u003eThe land use land cover map of the Jamtara district was prepared using cloud-free Landsat 8 OLI satellite imagery using the Google Earth Engine (GEE) cloud platform (Gorelick et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; U.S. Geological Survey, \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The data was captured by the satellite on 1st May 2024 (Path-140 and Row-43, 44) and 2nd May 2024 (Path-139 and Row-43, 44). The random forest supervised classification technique was used to prepare the LULC map for the study area.\u003c/p\u003e\n\u003ch3\u003eValidation\u003c/h3\u003e\n\u003cp\u003eTo ensure the reliability and accuracy of the soil analysis results, a validation process was conducted using an independent set of soil samples collected from 7 additional randomly selected points within the district. These validation points were chosen to cover similar land types and spatial distribution as the main sampling locations. The validation samples were analyzed using the same procedures and compared with the initial dataset to assess the consistency and robustness of the sampling and analytical methods. This step helped to confirm that the findings are representative of the district\u0026rsquo;s soil conditions.\u003c/p\u003e\u003cp\u003eThe overall methodology of this study is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e as a flowchart for better understanding.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003eRemote sensing and GIS techniques were effectively applied to map the physical and chemical properties of soils and to develop a Soil Quality (SQ) map for Jamtara district, Jharkhand. These geospatial tools proved highly effective for assessing spatial variability, integrating field and laboratory data, and producing detailed soil quality maps that support precision land management (Zhang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Lal, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Melesse et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe spatial distribution of nutrients revealed distinct geographic patterns. Nitrogen levels were mostly moderate, with higher concentrations in the western and southern zones, while phosphorus was generally deficient outside the central and south-eastern areas, potentially constraining crop productivity (Fageria \u0026amp; Baligar, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Malhotra et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Potassium levels were low to moderate in central and south-eastern regions, with isolated high pockets linked to soil parent material and historical land use (Vidyavathi et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). SOC ranged from low to moderate, with higher concentrations in the south-central zone, indicating localized organic matter build-up and scope for enhancement through organic matter management (Lal, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These patterns (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) underscore the need for site-specific nutrient management to sustain productivity and soil health.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eSoil pH variability indicates a predominantly acidic landscape, with extremely to very strongly acidic soils in the western and central zones and slightly acidic to near-neutral areas in the south-central section that are more favourable for agriculture. Liming of acidic soils can enhance nutrient availability and microbial activity (Goulding, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Electrical conductivity values (118\u0026ndash;201 \u0026micro;S cm⁻\u0026sup1;) classify most soils as very low to low salinity, suggesting minimal constraints, though isolated higher EC zones warrant monitoring (Hardie \u0026amp; Doyle, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sheldon et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Soil moisture levels were generally moderate (15\u0026ndash;20%), with higher values in the southwest and drier conditions in the north and east, influenced by topography, drainage, and texture, which in turn affect nutrient uptake and microbial processes (Franzluebbers, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Bulk density ranged from 1.20 to 1.49 g cm⁻\u0026sup3;, with most areas in the low range (1.25\u0026ndash;1.35 g cm⁻\u0026sup3;), indicating good porosity, while localized higher values suggest compaction that may restrict root growth (Kay, 2008; Hao et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The variability in pH, moisture, EC and BD (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) highlights the combined influence of inherent soil properties and land management, reinforcing the importance of targeted interventions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe spatial analysis revealed distinct geographic patterns in soil properties, indicating the need for site-specific management to optimize productivity (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Acidic soils dominate large portions of the study area, consistent with patterns reported in similar agroecological zones (Singh et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Phosphorus deficiency is widespread, while potassium distribution is uneven, aligning with previous findings that nutrient imbalances often follow soil parent material and historical land use (Gupta \u0026amp; Sharma, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Moderate organic carbon and nitrogen levels, along with localized compaction and variable moisture availability, further highlight the importance of targeted nutrient application, organic matter enhancement, and appropriate irrigation or drainage measures. These results reinforce evidence that uniform soil management is often inefficient, and that precision nutrient and structural interventions are essential for sustaining long-term soil health and agricultural output (FAO, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Lal, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSpatial distribution of soil physicochemical properties and management implications\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eParameter\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eKey areas\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eManagement Implications\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSoil pH\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePredominantly extremely to strongly acidic soils in western, central, and northern regions; slightly acidic patches in south-central parts.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLiming recommended to ameliorate soil acidity and enhance nutrient availability.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eElectrical Conductivity (EC)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGenerally low EC values across the study area; slight increases in northwestern and southeastern pockets.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow salinity risk; monitor localized areas with slightly higher EC to prevent salinity buildup.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBulk Density (BD)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMostly slightly low (1.25\u0026ndash;1.35 g/cm\u0026sup3;) bulk density; pockets of low (\u0026lt;\u0026thinsp;1.25 g/cm\u0026sup3;) and slightly high (\u0026gt;\u0026thinsp;1.45 g/cm\u0026sup3;) values observed.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMaintain organic matter to sustain low BD; address compacted zones through soil loosening practices.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSoil Moisture\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate moisture across most areas; high moisture content concentrated in northern and northeastern zones.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOptimize irrigation scheduling; high moisture areas may require drainage management.\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSoil Organic Carbon (SOC)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSouth-central and southeastern zones\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEnhance organic matter inputs (e.g., compost, cover crops)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNitrogen (N)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWestern and southern zones higher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSustain fertility; site-specific nutrient management\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePhosphorus (P)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNorthern and western regions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePhosphorus fertilization needed to correct deficiencies\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePotassium (K)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCentral and southeastern zones; high pockets in south-central and northeast\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTargeted potassium supplementation in deficient areas\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe Soil Quality (SQ) map of Jamtara district was prepared with an accuracy of 85.71%, illustrating a systematic approach to assessing and comparing soil health across the region (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Six out of seven validation points matched the predicted soil quality classes, indicating reliable spatial representation suitable for land management (McBratney et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The single mismatch may be due to local variability or sampling limitations, which could be addressed by increasing sampling density or incorporating remote sensing data (Grunwald, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Kumar \u0026amp; Min, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Spatially, Grade III soils dominate the district (37.13%), followed by Grade II (25.02%), Grade IV (24.91%), Grade I (7%), and Grade V (5.94%) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The area is predominantly characterized by the Inceptisols soil order (Agarwal et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), which typically exhibits moderate nutrient levels (Krishnapriya et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGrade I soils demonstrate optimal pH, electrical conductivity (EC) and nutrient availability, rendering them highly suitable for agriculture (Brady \u0026amp; Weil, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Grade II soils retain adequate nutrient levels and acceptable pH and salinity, indicating moderately high suitability for crop production (Havlin et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Grade III soils, although balanced in nutrients and within acceptable pH and salinity ranges, exhibit moderate suitability due to combined influences of parent material, climate, terrain, and biological activity (Lal, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Grade IV soils are characterized by moderately low quality, with specific nutrient deficiencies and potential pH or salinity issues, requiring targeted management interventions to improve fertility (FAO, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Grade V soils are considered least suitable for agriculture, marked by poor physical and chemical properties, low nutrient content, elevated salinity, and unsuitable pH levels (Smith et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSoil quality, encompassing physical, chemical, and biological dimensions, reflects the soil\u0026rsquo;s ability to function as a sustainable ecosystem for plants, animals, and humans (Delgado \u0026amp; Gomez, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Usharani et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These findings are essential for region-specific soil management and sustainable land-use planning (Obade \u0026amp; Lal, 2013; Abdel Rahman \u0026amp; Tahoun, 2019; Mukhopadhyay \u0026amp; Mishra, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The generated SQ maps support district-level land use planning and crop recommendations, contributing to climate-smart and sustainable agriculture (Bel-Labib et al., 2023).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe spatial distribution of soil quality grades across the blocks of Jamtara district exhibits considerable variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Fatehpur block contains only two soil quality categories Grade IV and Grade III\u0026mdash;indicating a predominance of moderately low to moderate soil quality in this region. Grade I soils, representing high-quality soil, are entirely absent in Vidyasagar and Nala blocks. The highest proportion of Grade I soils is concentrated in Jamtara block, followed by Narayanpur and Kundahit blocks, suggesting these areas have greater potential for high agricultural productivity (FAO, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Grade III soils are most extensively distributed in Fatehpur block, with subsequent presence in Nala, Jamtara, Vidyasagar, Narayanpur, and Kundahit blocks, in descending order of coverage. These variations align with earlier studies that report soil quality heterogeneity as a function of both physiographic and land-use factors (Lal, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Kumar \u0026amp; Sharma, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIntegration of soil grade maps with Land Use/Land Cover (LULC) data revealed a clear association between soil quality and prevailing land use patterns in Jamtara district (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e). High-grade soils (Grades I\u0026ndash;II) were primarily concentrated in areas under agricultural and fallow land, reflecting their inherent suitability for crop cultivation and sustained productivity (FAO, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Singh et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In contrast, lower-grade soils (Grades IV\u0026ndash;V) were predominantly distributed over barren lands, scrublands, and degraded forest patches, indicating limited agricultural potential and a heightened vulnerability to further degradation (Lal, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Ghosh \u0026amp; Dey, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These spatial correlations suggest that tailored management interventions\u0026mdash;such as site-specific nutrient management, organic matter enrichment, and erosion control are vital for enhancing soil health and ensuring sustainable land use in the district (Kumar \u0026amp;Sharma, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis study provides baseline soil health data for Jamtara, offering a framework for long-term monitoring. The results are valuable for policymakers, extension workers, and farmers, enabling informed decisions on crop selection, fertilizer application, and conservation measures. Remote sensing and GIS offer cost-effective tools for soil quality assessment, applicable across various districts and agroecological zones in India. Future work should integrate biological indicators such as microbial biomass and enzymatic activity, along with temporal monitoring to capture seasonal and interannual variations. Incorporating socioeconomic factors could further link soil health to farming practices, allowing for more tailored management strategies.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study advances understanding of soil health in Jamtara district through remote sensing and GIS-based soil quality mapping with 85.71% accuracy. The Soil Quality (SQ) map reveals that Grade III soils dominate the district (37.13%), followed by Grades II, IV, I, and V, reflecting moderate to low soil fertility overall. Significant spatial variability and block-wise differences in soil quality were identified, with higher-quality soils concentrated in Jamtara, Narayanpur and Kundahit blocks, while Fatehpur exhibited predominantly moderate to low-quality soils. The district\u0026rsquo;s soils, mainly Inceptisols, showed moderate nitrogen and organic carbon levels but widespread phosphorus and potassium deficiencies. Predominantly acidic soils and modest electrical conductivity values highlight the need for liming and effective irrigation and drainage management to prevent salinity risks. Integration with land use/land cover data revealed strong correlations between soil quality and land use patterns, with higher-grade soils associated with agricultural lands and lower grades linked to degraded or barren areas. These findings emphasize the critical need for site-specific soil management interventions such as targeted fertilization, organic amendments, and soil acidity correction. The validated soil quality map provides a valuable decision-support tool for precision agriculture, sustainable land-use planning, and long-term monitoring, empowering stakeholders to improve productivity, maintain ecological balance, and foster environmental sustainability in Jamtara district and similar agroecological zones.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKanti Hansda:\u003c/strong\u003e Conceptualization, Methodology, Field Investigation. \u003cstrong\u003eDebaaditya Mukhopadhyay:\u003c/strong\u003e Writing\u0026ndash; Original Draft, Writing\u0026ndash; Review \u0026amp; Editing, Data Curation, Visualization (Map Preparation), Formal Analysis\u003cstrong\u003e. Kambam Boxen Meetei:\u003c/strong\u003e Writing\u0026ndash; Original Draft, Formal Analysis, Statistical Interpretation. \u003cstrong\u003eAjujil Mir:\u003c/strong\u003e Visualization (Map Preparation), Statistical Analysis, Writing\u0026ndash; Review \u0026amp; Editing. \u003cstrong\u003eHarshavardhan Kumar:\u003c/strong\u003e\u0026nbsp; Supervision, Writing\u0026ndash; Review \u0026amp; Editing. \u003cstrong\u003eSk Mujibar Rahaman:\u003c/strong\u003e Data Analysis, Writing\u0026ndash; Review \u0026amp; Editing, Visualization (Map Preparation).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors express their sincere gratitude to the editorial team and reviewers of the journal for their valuable feedback and guidance, which have greatly improved the quality and clarity of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdelRahman M A and Tahoun S 2019. 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The influence of soil physico-chemical properties and enzyme activities on soil quality of saline-alkali agroecosystems in western Jilin Province, China. \u003cem\u003eSustainability (Switzerland)\u003c/em\u003e, \u003cem\u003e10\u003c/em\u003e(5). https://doi.org/10.3390/su10051529.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Soil Quality Assessment, Spatial Variability, Jamtara District, Geospatial Analysis, Soil Management","lastPublishedDoi":"10.21203/rs.3.rs-7600492/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7600492/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study assessed the spatial variability of key soil physicochemical properties in Jamtara district, Jharkhand, using field sampling and geospatial techniques to support sustainable agricultural planning. Soil analysis of 15 sites revealed moderate nitrogen (mean: 305.01 kg/ha) and soil organic carbon (0.71%), but widespread phosphorus (mean: 11.67 kg/ha) and potassium (mean: 188.15 kg/ha) deficiencies, especially in northern, central, and southeastern zones. Predominantly acidic soils (mean pH 5.53) highlight the need for liming to improve nutrient availability. Using Inverse Distance Weighting (IDW) interpolation and weighted summation, soils were classified into five quality grades; Grade III (moderate fertility) was most extensive (~\u0026thinsp;37%), followed by Grades II and IV, with limited areas of high (Grade I) and low (Grade V) quality. Block-wise analysis revealed significant variability, with high-quality soils concentrated in Jamtara, Narayanpur, and Kundahit blocks, while Fatehpur showed predominantly moderate to low quality soils. Integration with Land Use/Land Cover (LULC) data demonstrated that higher-grade soils correlate with agricultural and fallow lands, whereas lower-grade soils align with barren and degraded lands. The soil quality map achieved 85.71% validation accuracy, confirming its reliability for land management. These findings highlight how parent material, topography, and land use affect soil health and show that integrating geospatial tools with traditional soil testing supports precise management and sustainable farming in the area.\u003c/p\u003e","manuscriptTitle":"Integrating Remote Sensing and GIS for Sustainable Soil Management: A Case Study of Jamtara District, Jharkhand","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-17 02:21:12","doi":"10.21203/rs.3.rs-7600492/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7a63df4f-786e-4658-b050-9e5386bbff88","owner":[],"postedDate":"October 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T03:39:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-17 02:21:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7600492","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7600492","identity":"rs-7600492","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}