Indian Regions Soil Image Database (IRSID) : A dataset for classification of Indian soil types

Soil erosion has been spotted as one of the major global problems, and soil erodibility is the property of soil which refers to the erosive nature of the soil. India is also struggling with the erosion problem in most parts of the country. A national-scale soil erosion study is needed to assess such types of problems. ISED (Indian Soil Erodibility Dataset) is a step toward building datasets in mapping soil erosion at a national scale after IRED (Indian Rainfall Erosivity Dataset). This dataset consists of K-factor (t-ha-h/ha/MJ/mm), soil erodibility indices like CR (Clay Ratio), Modified Clay Ratio (MCR), and CLOM (Critical Level of Organic Matter) map over India at high resolution (250 m). It also includes a Susceptibility to Erosion map of India due to Organic Matter availability. Distribution maps of soil erodibility corresponding to the districts and soil types of India are also added to this dataset. This dataset is an additional dataset at a national scale for soil erosion modeling which will be handy data for experts and researchers.

The dataset contains state-wise distribution of land by different types of soil such as Alluvial, Coastal Alluvial, Black, Red, Rock, Desert, Mountain Medow, Glacier, Sub-montane, Brown, Salf, Hill, Water Bodies, Terai, Peat, Mangrove, Swamps, Beach, Creeks, Lagoons, Gullied, etc.

IWED (Indian Water Erosion Dataset) consists of long term annual Potential Soil Loss (PSL), Sediment Delivery Ratio (SDR), Specific Sediment Yield (SSY) and all the five factors (R, K, LS, C and P) of the RUSLE Model at the national scale having a composite spatial resolution of 250 m. This dataset will be valuable for strategic planning at the national level to apply soil mitigation and conservation approaches for sustainable soil management in light of the impact of soil losses on soil and agricultural productivity.

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India Soil Testing Equipment Market was valued at USD 114.43 million in 2024 and is anticipated to project impressive growth in the forecast period with a CAGR of 4.08% through 2030.

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information. This data set consists of georeferenced digital map data and computerized attribute data. The map data are in a soil survey area extent format and include a detailed, field verified inventory of soils and miscellaneous areas that normally occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. A special soil features layer (point and line features) is optional. This layer displays the location of features too small to delineate at the mapping scale, but they are large enough and contrasting enough to significantly influence use and management. The soil map units are linked to attributes in the National Soil Information System relational database, which gives the proportionate extent of the component soils and their properties.

India Soil Moisture Sensor Market was valued at USD 58 Million in 2024 and is expected to reach at USD 118 Million in 2030 and project robust growth in the forecast period with a CAGR of 12.4% through 2030.

India Exports of agricultural for soil preparation to Australia was US$3.48 Million during 2024, according to the United Nations COMTRADE database on international trade. India Exports of agricultural for soil preparation to Australia - data, historical chart and statistics - was last updated on June of 2025.

Research Data for Estimating and Modelling the Vertical Distribution of Soil Organic Carbon Stock Density in India on the basis of SoilGrids V2.0 Estimates

Fluazinam a promising fungicide, is not yet registered in India. Consequently it is important to study the dissipation of its specific formulation in Indian soil and water. This study focuses on the degradation and residue dynamics of Fluazinam (40% SC) in different soil types (alluvial, lateritic, coastal saline and black) and water pH (4.0, 7.0, 9.2). Adsorption kinetic models suggested that the half-life period (days) varies among soils following the order lateritic (Jhargram), 54.07 > alluvial (Mohanpur), 45.10 > coastal saline (Canning), 28.33 > black (Pune) 26.18. These differences are attributed to soil pH and organic carbon (OC) content, where higher pH levels reduce pesticide adsorption, leading to quicker dissipation, while higher organic carbon content provides more binding sites, slowing down the process. The first order kinetics explained the dissipation better compared to second order model across all soil types. The study also found that the half-life of was lowest at pH 9.2, as compared to pH 7.0, and very high stability at pH 4.0. Additionally, the study introduces an interactive R-based tool for analysing dissipation kinetics and half-life of different pesticides offering a valuable resource for researchers and stakeholders.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Soil erosion induced by water has been identified as one of the major environmental problems worldwide. The erosive force of rainfall, also known as rainfall erosivity (R-factor), is the potential of rain to cause soil degradation and one of the factors in the widely adopted RUSLE (Revised Universal Soil Loss Equation) empirical soil erosion estimation model. About 68.4% of total eroded soil in India is eroded due to erosion by water, and rainfall erosivity is one of the major factors. The past assessments of rainfall erosivity in India were however largely based on rain-gauge recordings and surveys which hinders its understanding and estimation over large areas. Growing availability of gridded precipitation datasets presents an unprecedented opportunity to study long-term rainfall erosivity over varied terrains and address some of the limitations of point data-based studies. IRED (Indian Rainfall Erosivity Dataset) is the first such national-scale assessment of rainfall erosivity over India using gridded precipitation datasets, which will be helpful for agricultural experts, watershed managers, agronomists, and soil-conservational experts in order to understand and mitigate rainfall-induced erosion. In this dataset, long term yearly average R-factor, Fourier Index (FI), and Modified Fourier Index (MFI) maps have been included with a distributional analysis over IMD (India Metrological Department) defined regions, states and districts of India.

description: The objective of this investigation is to determine whether the soil and/or water in the Indian Lakes area exceeds the EPA's hazardous waste level criterion for mercury, whether there is the possibility of mercury bioaccumulation to organisms, whether there is a health hazard to employees working at Indian Lakes and to the public visiting Indian Lakes and whether there are any other trace elements of concern in the Indian Lakes area.; abstract: The objective of this investigation is to determine whether the soil and/or water in the Indian Lakes area exceeds the EPA's hazardous waste level criterion for mercury, whether there is the possibility of mercury bioaccumulation to organisms, whether there is a health hazard to employees working at Indian Lakes and to the public visiting Indian Lakes and whether there are any other trace elements of concern in the Indian Lakes area.

Abstract

This dataset created by the Native Lands Advocacy Project contains spatial and tabular data derived from the NRCS's 2020 national SSURGO soils database and has been formatted to include only data within current American Indian areas for the conterminous United States. Boundary data was acquired from the US Census Bureau's Tiger Database (2010) reported by the federally recognized tribal governments through the Census Bureau's Boundary and Annexation Survey (BAS) and includes a total of 549 areas associated with 398 unique American Indian groups. The boundary data includes all Indian Lands defined by CFR 25 CFR § 502.12 which includes both lands held in trust by the United States Government and fee lands within the boundaries of American Indian Lands encompassing a total of 109,465,623 acres of land. The spatial data includes each soil unit and its associated MUKEY making it possible to join this data with other GSSURGO soil characteristics tables.According to the NRCS4, "The SSURGO database provides the most detailed level of information and was designed primarily for farm and ranch, landowner/user, township, county, or parish natural resource planning and management. Using the soil attributes, this database serves as an excellent source for determining erodible areas and developing erosion control practices; reviewing site development proposals and land use potential; making land use assessments and chemical fate assessments; and identifying potential wetlands and sand and gravel aquifer areas." The NRCS's SSURGO data is used in numerous land valuation, carbon and hydrologic assessment models including the proprietary AcreValue™ valuation estimation tool5, NRCS's Rapid Carbon Assessment RaCa6, the EPA's Automated Geospatial Watershed Assessment (AGWA), and the Soil and Water Assessment Tool (SWAT), to name a few.

*This dataset is authored by the Washington Department of Natural Resources and is being shared as a direct link by Pend Oreille County. This is a primary reference soils dataset used by our organization for building, planning, and permitting purposes.This data can be used for determining a variety of soils information that was derived from the Private Forest Land Grading system and subsequent soil surveys. See Description and Metadata for more information.For large areas, like Washington State, download as a file geodatabase. Large data sets like this one, for the State of Washington, may exceed the limits for downloading as shape files, excel files, or KML files. For areas less than a county, you may use the map to zoom to your area and download as shape file, excel or KML, if that format is desired.Information for SOILS data layer was derived from the Private Forest Land Grading system (PFLG) and subsequent soil surveys. PFLG was a five-year mapping program completed in 1980 for the purpose of forestland taxation. It was funded by the Washington State Department of Revenue. The Department of Natural Resources, Soil Conservation Service (now known as the Natural Resources Conservation Service or NRCS), USDA Forest Service and Washington State University conducted soil mapping cooperatively following national soil survey standards. Private lands having the potential of supporting commercial forests were surveyed along with interspersed small areas of State lands, Indian tribal lands, and federal lands. Because this was a cooperative soil survey project, agricultural and non-commercial forestlands were included within some survey areas. After the Department of Natural Resources originally developed its geographic information system, digitized soil map unit delineations and a few soil attributes were transferred to the system. Remaining PFLG soil attributes were later added and are now available through associated lookup tables. SCS (NRCS) soils data on agricultural lands also have been subsequently added to this data layer. The SOILS data layer includes approximately 1,100 townships with wholly or partially digitized soils data. State and private lands which have the potential of supporting commercial forest stands were surveyed. Some Indian tribal and federal lands were surveyed. Because this was a cooperative soils survey project, agricultural and non-commercial forestlands were also included within some survey areas. After the Department of Natural Resources originally developed its geographic information system, digitized soils delineations and a few soil attributes were transferred to the system. Remaining PFLG soil attributes were added at a later time and are now available through associated lookup tables. SCS soils data on agricultural lands also have subsequently been added to this data layer. This layer includes approximately 1, 100 townships with wholly or partially digitized soils data (2,101 townships would provide complete coverage of the state of Washington).- The soils_sv resolves one to many relationships and as such is one of those special "DNR" spatial views ( ie. is implemented similar to a feature class). Column names may not match between SOILS_SV and the originating datasets. Use limitations This Spatial View is available to Washingotn DNR users and those with access to the Washington State Uplands IMS site. The following cautions only apply to one-to-many and many-to-many spatial views! Use these in the metadata only if the SV is one-to-many or many-to-many. CAUTIONS: Area and Length Calculations: Use care when summarizing or totaling area or length calculations from spatial views with one-to-many or many-to-many relationships. One-to-many or many-to-many relationships between tabular and spatial data create multiple features in the same geometry. In other words, if there are two or more records in the table that correspond to the same feature (a single polygon, line or point), the spatial view will contain an identical copy of that feature's geometry for every corresponding record in the table. Area and length calculations should be performed carefully, to ensure they are not being exaggerated by including copies of the same feature's geometry. Symbolizing Spatial Features: Use care when symbolizing data in one-to-many or many-to-many spatial views. If there are multiple attributes tied to the same feature, symbolizing with a solid fill may mask other important features within the spatial view. This can be most commonly seen when symbolizing features based on a field with multiple table records. Labeling Spatial Features: Spatial views with one-to-many or many-to-many relationships may present duplicate labels for those features with multiple table records. This is because there are multiple features in the same geometry, and each one receives a label.Soils Metadata

This data set is a digital soil survey and generally is the most detailed level of soil geographic data developed by the National Cooperative Soil Survey. The information was prepared by digitizing maps, by compiling information onto a planimetric correct base and digitizing, or by revising digitized maps using remotely sensed and other information.

The size of the India Agrochemicals Market was valued at USD 33.16 billion in 2023 and is projected to reach USD 51.53 billion by 2032, with an expected CAGR of 6.5 % during the forecast period, is poised for sustained growth, driven by favorable agricultural practices, government initiatives, and advancements in technology. Hybrid seeds, a blend of genetically different parent plants, offer enhanced crop yield, disease resistance, and adaptability to varying climatic conditions. Agrochemicals refer to a broad range of chemical products used in agriculture to enhance crop production, protect crops from pests and diseases, and improve overall agricultural efficiency. These chemicals include fertilizers, pesticides, herbicides, fungicides, insecticides, and plant growth regulators, each serving a specific purpose in the agricultural ecosystem. Fertilizers are used to supply essential nutrients like nitrogen, phosphorus, and potassium to the soil, ensuring healthy plant growth and increased yield. Pesticides protect crops from harmful insects, while herbicides are used to control weeds that compete with crops for nutrients, water, and sunlight. Fungicides prevent and manage fungal infections, ensuring the health of crops, and insecticides target harmful insects that damage plants. The growing demand for hybrid seeds is positively influencing the agrochemicals market, as they require specialized crop protection chemicals to maximize their potential. Rising food security concerns, coupled with government support for agricultural innovation, further contribute to the market's growth. Technological advancements, such as the adoption of drones for crop monitoring and precision agriculture, are also revolutionizing the industry. Recent developments include: In March 2023, Bayer CropScience Limited and Superplum formed a strategic partnership focusing on sustainable crop protection. Bayer's expertise in crop science and innovative technologies combined with Superplum's digital farming platforms aimed to optimize pest management and reduce chemical usage. The collaboration leveraged data analytics and AI-driven insights to enhance crop health, productivity, and environmental sustainability. The partnership emphasized farmer education and the adoption of integrated pest management practices for long-term resilience in agriculture. Overall, the alliance aimed to create a holistic approach to crop protection, promoting environmentally friendly and efficient farming practices. , In January 2021, UPL partnered with TeleSense, a provider of IoT-based grain monitoring solutions, to address post-harvest challenges in the agricultural supply chain. The collaboration aimed to leverage TeleSense's advanced sensors, data analytics, and AI-powered predictive insights to monitor grain quality, storage conditions, and pest infestations in real-time. By integrating TeleSense's technology with UPL's expertise in crop protection and post-harvest management, the partnership sought to optimize storage practices, reduce spoilage, and improve grain quality throughout the storage and transportation phases. , In April 2024, ADAMA introduced Forapro, a cereal fungicide for revolutionizing disease control and crop yields. Forapro offers robust protection against a broad range of fungal diseases that commonly affect cereals like wheat, barley, and oats. Its innovative formula incorporates multiple modes of action, ensuring unparalleled effectiveness in combating evolving pathogens. Additionally, Forapro is designed to enhance plant health and resilience, ultimately resulting in increased yields and superior crop quality. This launch signifies a significant leap forward in sustainable agriculture, equipping farmers with a potent solution to safeguard their investments and bolster global food security. , In December 2023, Sumitomo Chemical, announced plans to establish a new agrochemical plant in India, marking a significant investment in the country's agricultural sector. This strategic move by Sumitomo Chemical underscores its commitment to serving the growing demand for agricultural solutions in India and the broader Asia-Pacific region. The new plant is expected to leverage advanced technologies and sustainable practices to manufacture a wide range of agrochemical products aimed at enhancing crop productivity, quality, and sustainability. By setting up a local manufacturing facility, Sumitomo Chemical aims to strengthen its market presence, improve supply chain efficiency, and cater to the specific needs of Indian farmers while contributing to the country's agricultural growth and food security initiatives. This investment also reflects confidence in India's favorable business environment, skilled workforce, and potential for long-term growth in the agrochemical sector. , In November 2023, India Pesticides Limited (IIL) expanded its product portfolio by launching four new crop protection solutions: "GreenGuard Pro" for effective pest control, "BioShield Max" for disease management, "YieldBoost Pro" for enhancing crop yields, and "QualityGuard Plus" for improving crop quality. This launch marked the company's growth trajectory and commitment to agricultural innovation. These new solutions are designed to address key challenges faced by farmers, ranging from pest and disease management to enhancing crop yields and quality.IIL's strategic focus on research and development has enabled the development of cutting-edge formulations and technologies, ensuring that the new crop protection solutions are effective, sustainable, and environmentally friendly. , In April 2021, Azelis India expanded their business by acquiring Spectrum Chemicals and Nortons Exim, increasing their presence in the agrochemicals, home care, and specialty chemicals segments for industrial applications. This strategic acquisition not only expanded Azelis India's product portfolio but also strengthened their market position, allowing the company to offer a wider range of innovative solutions to its customers in India and beyond. With Spectrum Chemicals' expertise in agrochemicals and Nortons Exim's specialization in specialty chemicals for industrial applications, Azelis India gained access to new technologies, capabilities, and customer networks, enabling it to deliver enhanced value and customized solutions across various sectors. .

This report contains data from the physical and chemical analysis of native soils and retort soils from the Geokinetics Oil Shale Site, Uintah County, Utah. Four native soil types were sampled: loam, JR loam, Havre silt loam, and Luhon loam. Samples from retort soils were collected at 15 cm depths before and after the completion of the retorting process. There are no apparent differences between native soil types and retort soils. (GMC)

For large areas, like Washington State, download as a file geodatabase. Large data sets like this one, for the State of Washington, may exceed the limits for downloading as shape files, excel files, or KML files. For areas less than a county, you may use the map to zoom to your area and download as shape file, excel or KML, if that format is desired.Information for SOILS data layer was derived from the Private Forest Land Grading system (PFLG) and subsequent soil surveys. PFLG was a five-year mapping program completed in 1980 for the purpose of forestland taxation. It was funded by the Washington State Department of Revenue. The Department of Natural Resources, Soil Conservation Service (now known as the Natural Resources Conservation Service or NRCS), USDA Forest Service and Washington State University conducted soil mapping cooperatively following national soil survey standards. Private lands having the potential of supporting commercial forests were surveyed along with interspersed small areas of State lands, Indian tribal lands, and federal lands. Because this was a cooperative soil survey project, agricultural and non-commercial forestlands were included within some survey areas. After the Department of Natural Resources originally developed its geographic information system, digitized soil map unit delineations and a few soil attributes were transferred to the system. Remaining PFLG soil attributes were later added and are now available through associated lookup tables. SCS (NRCS) soils data on agricultural lands also have been subsequently added to this data layer. The SOILS data layer includes approximately 1,100 townships with wholly or partially digitized soils data. State and private lands which have the potential of supporting commercial forest stands were surveyed. Some Indian tribal and federal lands were surveyed. Because this was a cooperative soils survey project, agricultural and non-commercial forestlands were also included within some survey areas. After the Department of Natural Resources originally developed its geographic information system, digitized soils delineations and a few soil attributes were transferred to the system. Remaining PFLG soil attributes were added at a later time and are now available through associated lookup tables. SCS soils data on agricultural lands also have subsequently been added to this data layer. This layer includes approximately 1, 100 townships with wholly or partially digitized soils data (2,101 townships would provide complete coverage of the state of Washington).-

The soils_sv resolves one to many relationships and as such is one of those special "DNR" spatial views ( ie. is implemented similar to a feature class). Column names may not match between SOILS_SV and the originating datasets. Use limitations

This Spatial View is available to Washingotn DNR users and those with access to the Washington State Uplands IMS site.

The following cautions only apply to one-to-many and many-to-many spatial views! Use these in the metadata only if the SV is one-to-many or many-to-many.

CAUTIONS: Area and Length Calculations: Use care when summarizing or totaling area or length calculations from spatial views with one-to-many or many-to-many relationships. One-to-many or many-to-many relationships between tabular and spatial data create multiple features in the same geometry. In other words, if there are two or more records in the table that correspond to the same feature (a single polygon, line or point), the spatial view will contain an identical copy of that feature's geometry for every corresponding record in the table. Area and length calculations should be performed carefully, to ensure they are not being exaggerated by including copies of the same feature's geometry.

Symbolizing Spatial Features:
Use care when symbolizing data in one-to-many or many-to-many spatial views. If there are multiple attributes tied to the same feature, symbolizing with a solid fill may mask other important features within the spatial view. This can be most commonly seen when symbolizing features based on a field with multiple table records.

Labeling Spatial Features: Spatial views with one-to-many or many-to-many relationships may present duplicate labels for those features with multiple table records. This is because there are multiple features in the same geometry, and each one receives a label.Soils Metadata