This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years since 1992. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2019Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: AnnualWhat can you do with this layer?This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro.In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend.To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Different Classifications Available to MapFive processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display.Using TimeBy default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year.In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change.Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009.This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover.Land Cover ProcessingTo provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015.Source dataThe datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.phpCitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

83 Global export shipment records of Agricultural Products with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The mapping population comprises of 180 RILs developed from a cross between GW322 and KAUZ. Phenotypic dataPhenotypic evaluation was conducted across two consecutive crop seasons (2021-22 and 2022-23) under late sown irrigation (LSIR) and late sown restricted irrigation (LSRI) conditions at ICAR-IARI, New Delhi. Various physiological and agronomic traits of importance were measured. The component traits of yield including days to heading (DH), normalized difference vegetation index (NDVI), SPAD chlorophyll content (SPAD), plant height (PH), spike length (SL), thousand-grain weight (TGW), grain weight per spike (GWPS), biomass (BM) and grain yield per plot (PY) were measured under heat and combined stress conditions. Genotypic dataDNA was isolated from 21-day-old seedlings using the CTAB method (Murray and Thompson, 1980). DNA quality check was done using 0.8% agarose gel electrophoresis. The Axiom Breeders' array containing 35K Single Nucleotide Polymorphism (SNP) was employed for the g..., The experiment was carried out at ICAR-IARI, New Delhi (28.6550° N, 77.1888° E, MSL 228.61 m) over two consecutive crop seasons (2021-22 and 2022-23). The study involved the evaluation of RILs developed from GW322/KAUZ along with their parents under two distinct conditions: heat stress (late sown irrigation; LSIR) and combined drought and heat stress (late sown restricted irrigation; LSRI) in augmented design (Federer, 1956; Federer, 1961; Searle, 1965). The experimental field was divided into four blocks, with four checks utilized in the study. Each check was replicated three times. Phenotyping:The instrument GreenSeeker™ was used to record NDVI. NDVI was measured at four stages: at anthesis, 10 days after anthesis, 20 days after anthesis and 30 days after anthesis. To record TGW, manual counting of grains was followed and the weight of the grains was recorded in grams with an electronic balance. Days to heading is the number of days from sowing to 50% heading is recorded for each geno..., , # QTL mapping: insights into genomic regions governing component traits of yield under combined heat and drought stress in wheat

Author/Principal Investigator Information Name: Dr Harikrishna ORCID: Institution: ICAR-Indian Agricultural Research Institute, New Delhi, India Address: ICAR-Indian Agricultural Research Institute, New Delhi, India-110012 Email: Date of data collection: 2021-12-01 TO 2023-07-31 Geographic location of data collection: ICAR-Indian Agricultural Research Institute, New Delhi (28°38′30.5″N, 77°09′58.2″E, 228 m AMSL)

Information about funding sources that supported the collection of the data: Part of the research was supported by a grant from Bill & Melinda Gates Foundation (Grant number # OPP1215722) sub-grant to India for Zn mainstreaming project and National Innovations on Climate Resilient Agriculture (NICRA) a network project of the Indian Council of Agricultural Research (ICAR).

Description of the data and file structure

File List:

Phenoty...

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.

This dataset contains remote sensing data for every village in the state of Bihar, India. For most of these villages, the data contains the corresponding electrification rate as reported by the Garv data platform from the Indian government as of July 2017. This dataset contains satellite imagery, political boundaries, lights at night imagery, rainfall measurements, and vegetation indices data for 45,220 villages and the electrification rate data for 32,817 of those villages. This dataset may be of particular interest to those investigating how electricity access maps to infrastructure and agricultural production.This dataset was compiled as part of the Summer 2017 Duke University Data+ team, titled "Electricity Access in Developing Countries from Aerial Imagery."

The Census of Agriculture, produced by the USDA National Agricultural Statistics Service (USDA), provides a complete count of America's farms, ranches and the people who grow our food. The census is conducted every five years, most recently in 2017, and provides an in-depth look at the agricultural industry.This layer summarizes corn production from the 2017 Census of Agriculture at the county level.This layer was produced from data downloaded using the USDA's QuickStats Application. The data was transformed using the Pivot Table tool in ArcGIS Pro and joined to the county boundary file provided by the USDA. The layer was published as feature layer in ArcGIS Online. Dataset SummaryPhenomenon Mapped: 2017 Corn ProductionCoordinate System: Web Mercator Auxiliary SphereExtent: 48 Contiguous United States and HawaiiVisible Scale: All ScalesSource: USDA National Agricultural Statistics Service QuickStats ApplicationPublication Date: 2017AttributesThis layer provides values for the following attributes. Note that some values are not disclosed (coded as -1 in the layer) to protect the privacy of producers in areas with limited production.Operations with SalesSales in US DollarsGrain - Area Harvested in AcresGrain - Operations with Area HarvestedGrain - Production in BushelsGrain - Irrigated Area Harvested in AcresGrain - Operations with Irrigated Area HarvestedSilage - Area Harvested in AcresSilage - Operations with Area HarvestedSilage - Production in TonsSilage - Irrigated Area Harvested in AcresSilage - Operations with Area HarvestedTraditional or Indian - Area Harvested in AcresTraditional or Indian - Operations with Area HarvestedTraditional or Indian - Production in PoundsTraditional or Indian - Irrigated Area Harvested in AcresTraditional or Indian - Operations with Area HarvestedAdditionally attributes of State Name, State Code, County Name and County Code are included to facilitate cartography and use with other layers.What can you do with this layer?This layer can be used throughout the ArcGIS system. Feature layers can be used just like any other vector layer. You can use feature layers as an input to geoprocessing tools in ArcGIS Pro or in Analysis in ArcGIS Online. Combine the layer with others in a map and set custom symbology or create a pop-up tailored for your users. For the details of working with feature layers the help documentation for ArcGIS Pro or the help documentation for ArcGIS Online are great places to start. The ArcGIS Blog is a great source of ideas for things you can do with feature layers. This layer is part of ArcGIS Living Atlas of the World that provides an easy way to find and explore many other beautiful and authoritative layers, maps, and applications on hundreds of topics.

7571 Global export shipment records of Agriculture with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

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Precision Agriculture Market Size 2025-2029

The precision agriculture market size is forecast to increase by USD 8.02 billion at a CAGR of 15.4% between 2024 and 2029.

The market is experiencing significant growth, driven by escalating investments in agricultural technologies and advancements in precision farming techniques. These technological innovations enable farmers to optimize crop yields, reduce input costs, and improve overall farm efficiency. However, the market faces a notable challenge: the high initial investment required for implementing precision agriculture solutions. Additionally, collaborations and partnerships among industry players could help reduce costs and expand market reach.
Overall, the market holds immense potential for growth, with technological advancements and increasing demand for sustainable farming practices fueling its expansion. Companies that navigate the challenge of high initial investment and offer innovative, cost-effective solutions will be well-positioned to succeed. This barrier may limit adoption, particularly among small-scale farmers. To capitalize on market opportunities, companies must offer affordable and scalable precision agriculture solutions. Agricultural management theory is being redefined with the integration of IT services, sensors, and real-time monitoring.

What will be the Size of the Precision Agriculture Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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In the agricultural sector, Precision Agriculture is revolutionizing farming practices through advanced monitoring systems and variable rate application. Harvesters and farm machinery are now equipped with specialized systems for data-driven decision-making, enabling on-demand agriculture. Real-time data from soil conditions, weather patterns, and crop health is analyzed using AI and data analytics to optimize site-specific crop management. Navigation systems and GPS technology are crucial for precision farming, while drones offer a bird's-eye view for field mapping. Precision Ag's focus on real-time monitoring and data analytics transforms crop management into a more efficient, effective, and eco-friendly practice.

The Precision Agriculture Market is evolving with cutting-edge technologies that improve sustainability and productivity. Advanced irrigation methods like drip irrigation, sprinkler irrigation, and subsurface irrigation are enhancing wateruse efficiency, ensuring optimal resource allocation. Farmers are implementing nutrient management strategies alongside fertilizer application technologies and pesticide application technologies to reduce waste and environmental impact. These are driven by precision application technologies and targeted application systems that deliver inputs only where needed. Leveraging prescriptive analytics, growers can make data-informed decisions that fine-tune input usage, increase yields, and boost profitability.

How is this Precision Agriculture Industry segmented?

The precision agriculture industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Product

  Hardware
  Software and services


Application

  Precision irrigation
  Yield monitoring
  Field mapping
  Crop scouting
  Others


Technology

  Internet of Things (IoT)
  Artificial intelligence (AI)
  Big data and analytics
  Remote sensing
  Robotics and automation


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    Spain
    UK


  APAC

    Australia
    China
    India


  Rest of World (ROW)

By Product Insights

The Hardware segment is estimated to witness significant growth during the forecast period. In the dynamic market, companies are innovating to enhance productivity and mitigate challenges such as pests, weather patterns, and labor costs. Hardware systems, including ground-based sensing and handheld sensing devices, enable farmers to analyze soil conditions, monitor crop health, and optimize irrigation and fertilization. Real-time data from these sensors, combined with weather forecasting and satellite imagery, informs data-driven decision-making for crop yield enhancement and resource optimization. Variable-rate technology, such as sensor-based VRT and precision spraying, targets specific areas of a field, reducing input application rates and promoting eco-friendly agriculture. Fleet management and optimal planting times streamline farm operations, while precision farming equipment, from tractors to harvesters, increases efficiency.

Assisted professional services, inventory management, and farm labor management further support far

This harmonized set of soil parameter estimates for the Indo-Gangetic Plains (IGP) of India, at scale 1:1 000 000, has been derived from soil and terrain data collated in SOTER format by staff of the National Bureau of Soil Survey and Land Use Planning (NBSS and LUP) at Nagpur, India. The data set has been prepared for use in the project on "Assessment of soil organic carbon stocks and change at ... national scale" (GEFSOC), which has IGP-India as one of its four case study areas (see http://www.nrel.colostate.edu/projects/gefsoc-uk/).

The land surface of IGP-India has been characterized using 36 unique SOTER units, corresponding with 497 polygons. The major soils of these units have been described using 36 profiles, selected by national soil experts as being representative for these units. The associated soil analytical data have been derived from soil survey reports.

Gaps in the measured soil profile data have been filled using a scheme of taxotransfer rules. Parameter estimates are presented by soil unit for fixed depth intervals of 0.2 m to 1 m depth for: organic carbon, total nitrogen, pH(H2O), CECsoil, CECclay, base saturation, effective CEC, aluminum saturation, CaCO3 content, gypsum content, exchangeable sodium percentage (ESP), electrical conductivity of saturated paste (ECe), bulk density, content of sand, silt and clay, content of coarse fragments, and available water capacity(-33 to-1500 kPa). These attributes have been identified as being useful for agro-ecological zoning, land evaluation, crop growth simulation, modelling of soil carbon stocks and change, and analyses of global environmental change.

The current parameter estimates should be seen as best estimates based on the current selection of soil profiles and data clustering procedure; taxotransfer rules have been flagged to provide an indication of the confidence in the derived data.

Results are presented as summary files and can be linked to the 1:1M scale SOTER map in a GIS, through the unique SOTER-unit code.

The secondary SOTER data set for IGP-India is considered appropriate for exploratory studies at regional scale (greater than1:1M); correlation of soil analytical data should be done more rigorously when more detailed scientific work is considered.

2924 Global export shipment records of Agriculture Seeds with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The India satellite-based Earth observation market is experiencing robust growth, driven by increasing government investments in space technology, rising demand for precise geospatial data across various sectors, and the expanding adoption of advanced analytics. The market, valued at approximately ₹1500 Crore (approximately $180 Million USD) in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 15.34% from 2025 to 2033, reaching an estimated ₹6000 Crore (approximately $720 Million USD) by 2033. This growth is fueled by the increasing need for efficient resource management, improved infrastructure planning, and climate change monitoring. Key application areas like agriculture (precision farming, crop monitoring), urban development (mapping, planning), and climate services (disaster management, weather forecasting) are significant contributors to market expansion. The strong presence of both government agencies like ISRO and Antrix Corporation and private companies like Pixxel Space Technologies and MapmyIndia indicates a dynamic and competitive market landscape. Further growth is anticipated from the development and deployment of higher-resolution satellites and the integration of artificial intelligence and machine learning for advanced data analysis. The segmentation of the market reveals the dominance of Earth observation data as a key service offering. Low Earth Orbit (LEO) satellites are likely to hold a significant share in the satellite orbit segment due to their superior image resolution and frequent data acquisition capabilities. While urban development and cultural heritage are currently strong end-use segments, the agriculture sector is poised for substantial growth due to the increasing adoption of precision farming techniques. Government initiatives promoting digitalization and the availability of supportive policies will further accelerate market expansion. However, challenges remain, including high initial investment costs for satellite technology and the need for robust data infrastructure to effectively process and utilize the large volumes of data generated. Despite these challenges, the long-term outlook for the India satellite-based Earth observation market remains exceptionally positive. Recent developments include: Jun 2024 - The Indian Space Research Organisation (Isro) has finalized the plan for building its own space station, Bharatiya Antariksha Station (BAS), and will soon submit it to the government for approval,The final plans for Chandrayaan-4, India’s next lunar mission, which includes a crucial space docking station, and the Next Generation Launch Vehicle (NGLV), is also awaiting approval. The NGLV will replace the current heavy space launcher, Launch Vehicle Mark III (LVM3)., March 2024 - The Indian Space Research Organisation (ISRO) will conduct the Space science and Technology Awareness Training (START) 2024 programme during April and May. In this connection, ISRO solicits Expression of Interest (EOI) to host START-2024 in educational institutes, universities, colleges within India who are offering UG and PG courses in physical sciences and technology.. Key drivers for this market are: Government Initiatives and Investments, Increasing Demand for Geospatial Information. Potential restraints include: Government Initiatives and Investments, Increasing Demand for Geospatial Information. Notable trends are: Government Initiatives and Investments to Drive the Market Growth.

India Precision Agriculture Market was valued at USD 102.31 Million in 2024 and is anticipated to reach USD 145.49 million with a CAGR of 6.12% through the forecast period.

Smart Agriculture Solution Market Outlook

The global smart agriculture solution market size was valued at approximately USD 13.6 billion in 2023 and is projected to reach USD 27.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.2% from 2024 to 2032. This impressive growth can be attributed to increasing demand for sustainable farming practices, advancements in technology, and the growing need for efficient crop management techniques. Additionally, the integration of IoT, AI, and big data analytics is revolutionizing the agricultural sector by enabling real-time monitoring and data-driven decision-making.

One of the key growth factors driving the smart agriculture solution market is the rising global population, which necessitates an increase in food production. With traditional farming methods proving inadequate to meet the growing demand, smart agricultural solutions provide efficient alternatives. Precision farming, for instance, allows for optimal use of resources such as water and fertilizers, thereby enhancing crop yields and reducing waste. Moreover, the adoption of smart technologies can help farmers monitor crop health, weather conditions, and soil quality, ensuring timely interventions and better productivity.

Technological advancements are another significant driver of market growth. Innovations in IoT, AI, and robotics are transforming the way farming operations are conducted. IoT devices and sensors enable continuous monitoring of crops and livestock, offering real-time data that can be used to make informed decisions. AI algorithms can analyze this data to predict crop yields, identify pest infestations, and recommend the best farming practices. Robotics, on the other hand, automate labor-intensive tasks such as planting, watering, and harvesting, thereby increasing efficiency and reducing labor costs.

Government initiatives and supportive policies are also playing a crucial role in the expansion of the smart agriculture solution market. Many governments around the world are promoting the adoption of smart farming technologies through subsidies, grants, and training programs. These initiatives aim to modernize the agricultural sector, improve food security, and promote sustainable farming practices. For example, the European Union's Common Agricultural Policy (CAP) has allocated significant funds to digitize farming operations across member states, thereby boosting the adoption of smart agriculture solutions.

From a regional perspective, North America and Europe are leading the way in the adoption of smart agriculture solutions, driven by high levels of technological adoption and substantial investments in R&D. However, Asia Pacific is expected to witness the fastest growth during the forecast period, owing to the increasing demand for food production and the rising awareness about the benefits of smart farming practices. Countries like China, India, and Japan are investing heavily in agricultural technology to enhance productivity and ensure food security.

Component Analysis

In the smart agriculture solution market, the component segment is categorized into hardware, software, and services. The hardware segment includes sensors, drones, GPS systems, and other physical devices that are crucial for collecting and transmitting data. This segment is expected to hold a significant market share due to the growing adoption of advanced farming equipment. Sensors, for instance, are widely used for soil monitoring, weather tracking, and crop health assessment. They provide real-time data that helps farmers make informed decisions, thereby improving crop yields and reducing resource wastage.

The software segment comprises various applications and platforms that analyze the data collected by hardware devices. This includes farm management software, yield mapping, and data analytics tools. The software segment is anticipated to grow rapidly due to the increasing need for data-driven decision-making in farming operations. Advanced software solutions offer predictive analytics and real-time monitoring capabilities, enabling farmers to optimize resource use and enhance productivity. These solutions also facilitate better planning and forecasting, which are essential for efficient farm management.

Services in the smart agriculture solution market include consulting, system integration, and maintenance services. This segment is expected to witness substantial growth as farmers increasingly seek expert advice to implement and manage smart farming technologies. Consulting services help farmers choo

Cadastral Mapping Market Outlook

The global cadastral mapping market size was valued at approximately USD 4.2 billion in 2023 and is projected to reach around USD 7.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.2% during the forecast period. This market growth can be attributed to increasing urbanization, rapid advancements in geospatial technologies, and the growing need for efficient land management systems across various regions.

The expansion of urban areas and the corresponding increase in the need for effective land management infrastructure are significant growth factors driving the cadastral mapping market. As urbanization accelerates globally, local governments and planning agencies require sophisticated tools to manage and record land ownership, boundaries, and property information. Enhanced geospatial technologies, including Geographic Information Systems (GIS) and remote sensing, are pivotal in facilitating accurate and efficient cadastral mapping, thus contributing to market growth.

Another key growth factor is the rising demand for infrastructure development. As nations invest in large-scale infrastructure projects such as roads, railways, and smart cities, there is an increased need for precise land data to ensure the proper allocation of resources and to avoid legal disputes. Cadastral mapping provides the critical data needed for these projects, hence its demand is surging. Additionally, governments worldwide are increasingly adopting digital platforms to streamline land administration processes, further propelling the market.

Furthermore, the agricultural sector is also significantly contributing to the growth of the cadastral mapping market. Modern agriculture relies heavily on accurate land parcel information for planning and optimizing crop production. By integrating cadastral maps with other geospatial data, farmers can improve land use efficiency, monitor crop health, and enhance yield predictions. This integration is particularly valuable in precision farming, which is becoming more prevalent as the world's population grows and the demand for food increases.

Regionally, Asia Pacific is expected to witness the highest growth in the cadastral mapping market. Factors such as rapid urbanization, extensive infrastructure development projects, and the need for improved land management are driving the demand in this region. Moreover, governments in countries like India and China are investing heavily in creating digital land records and implementing smart city initiatives, which further boosts the market. The North American and European markets are also substantial, driven by the advanced technological infrastructure and well-established land administration systems.

Component Analysis

The cadastral mapping market can be segmented by component into software, hardware, and services. The software segment holds a significant share in this market, driven by the increasing adoption of advanced GIS and mapping software solutions. These software solutions enable accurate land parcel mapping, data analysis, and integration with other geospatial data systems, making them indispensable tools for cadastral mapping. Companies are continuously innovating to provide more intuitive and comprehensive software solutions, which is expected to fuel growth in this segment.

Hardware components, including GPS devices, drones, and other surveying equipment, are also critical to the cadastral mapping market. The hardware segment is expected to grow steadily as technological advancements improve the accuracy and efficiency of these devices. Innovations such as high-resolution aerial imaging and LIDAR technology are enhancing the capabilities of cadastral mapping hardware, allowing for more detailed and precise data collection. This segment is particularly essential for field surveying and data acquisition, forming the backbone of cadastral mapping projects.

The services segment encompasses a wide range of offerings, including consulting, implementation, and maintenance services. Professional services are vital for the successful deployment and operation of cadastral mapping systems. Governments and private sector organizations often rely on specialized service providers to implement these systems, train personnel, and ensure ongoing support. As the complexity of cadastral mapping projects increases, the demand for expert services is also expected to rise, contributing to the growth of this segment.

Integration services are another critical component within the

The India Annual Winter Cropped Area, 2001 - 2016 consists of annual winter cropped areas for most of India (except the Northeastern states) from 2000-2001 to 2015-2016. This data set utilizes the NASA Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI; spatial resolution: 250m) for the winter growing season (October-March). The methodology uses an automated algorithm identifying the EVI peak in each pixel for each year and linearly scales the EVI value between 0% and 100% cropped area within that particular pixel. Maps were then resampled to 1 km and were validated using high-resolution QuickBird, RapidEye, SkySat, and WorldView-2 images spanning 2008 to 2016 across 11 different agricultural regions of India. The spatial resolution of the data set is 1 km, resampled from 250m. The data are distributed as GeoTIFF and NetCDF files and are in WGS 84 projection.

This dataset contains India Economic Survey Agriculture Production Index Numbers. Follow datasource.kapsarc.org for timely data to advance energy economics research.

This web map contains reference data points with specific site information on vegetation dominance type and tree size for the Tongass National Forest, Ketchikan Misty Fjords Project Area to provide up-to-date and more complete information about vegetative communities, structure, and patterns across the project area.Reference data for this project came from numerous sources including: 1) Forest Service field crews collecting vegetation information specific to this project in 2019-2021 (582 total); 2) Young Growth Inventory data (1,444 total); 3) legacy data from previous Forest Service survey plots (556 total) and the Forest Inventory and Analysis (FIA) program; and 4) field data from Annette Islands supplied by the Bureau of Indian Affairs (94 total). These data posted here do not contain the FIA data nor the Bureau of Indian Affairs field data.Tongass National Forest personnel collected most of the ground data for this mapping effort using a variety of access means—such as, by helicopter, floatplane, boat, or by foot from existing trail and road infrastructure. The Young Growth Inventory information was leveraged for forests that are currently, or have been, actively managed in the past. The legacy, FIA, and Annette Islands data were all cross-referenced with the classification key to label each plot with a vegetation type class. Reference data was consolidated into a single database and reviewed within the context of their corresponding mapping segment using high-resolution imagery.For more detailed information on mapping methodology please see the Ketchikan Misty Fjords Existing Vegetation Project Report.

This land suitability for Indian Sandalwood raster data (in GeoTIFF format) represents areas of potential suitability for this crop and its specific irrigation management systems in the Flinders and Gilbert catchments of North Queensland. The data is coded 1-5: 1 - Suitable with no limitations; 2 - Suitable with minor limitations; 3 - Suitable with moderate limitations; 4 - Marginal; 5 - Unsuitable. The land suitability evaluation methods used to produce this data are a modification of methods of the Food and Agriculture Organisation of the UN (FAO). This data is part of the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project and is designed to support sustainable regional development in Australia being of importance to Australian Governments and agricultural industries. The project identifies new opportunities for irrigation development in these remote areas by providing improved soil and land evaluation data to identify opportunities and promote detailed investigation. A companion dataset exists, “Confidence of suitability data for the FGARA project”. A link to this dataset can be found in the “related materials” section of this metadata record. Lineage: These suitability raster data for Indian Sandalwood and its individual irrigation management systems have been created from a range of inputs and processing steps. Below is an overview. For more information refer to the CSIRO FGARA published reports and in particular: Bartley R, Thomas MF, Clifford D, Phillip S, Brough D, Harms D, Willis R, Gregory L, Glover M, Moodie K, Sugars M, Eyre L, Smith DJ, Hicks W and Petheram C (2013) Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project, CSIRO. Broadly, the steps were to: 1. Collate existing data (data related to: climate, topography, soils, natural resources, remotely sensed etc of various formats; reports, spatial vector, spatial raster etc). 2. Select additional soil and attribute site data by Latin hypercube statistical sampling method applied across the covariate space. 3. Carry out fieldwork to collect additional soil and attribute data and understand geomorphology and landscapes. 4. Build models from selected input data and covariate data using predictive learning via rule ensembles in the RuleFit3 software. 5. Create Digital Soil Mapping (DSM) key attributes output data. DSM is the creation and population of a geo-referenced database, generated using field and laboratory observations, coupled with environmental data through quantitative relationships. It applies pedometrics - the use of mathematical and statistical models that combine information from soil observations with information contained in correlated environmental variables, remote sensing images and some geophysical measurements. 6. Choose land management options and create suitability rules for DSM attributes. 7. Run suitability rules to produce limitation datasets using a modification on the FAO methods. 8. Create final suitability data for all land management options. Two companion datasets exist for this dataset. The first is linked to in the “related materials” section of this metadata record, entitled “Confidence of suitability data for the FGARA project”. The second (held by CSIRO Land and Water) includes expert opinion and knowledge about landscape processes or conditions that will influence agricultural development potential in these catchments, but were not captured sufficiently in the modelling process (and areas of expert opinion where the Mahanabolis method underestimates confidence). The two landscape features that require special attention are the basalt rock outcrops in the Upper Flinders catchment that were not well captured by the covariate data, and the secondary salinisation hazard in the central Flinders catchment. For more information refer to the report “Land suitability: technical methods. A technical report to the Australian Government for the Flinders and Gilbert Agricultural Resource Assessment (FGARA) project”.

This map provides Agriculture Data at district Level for Jharkhand, India which includes Rain fall report of Jharkhand for the year-2008 to 2011 , Area and production (2010 and 2007), Total Rainfall for the Year 2008- 2011 unit (mm), Average Rainfall for the Year 2008-2011 unit (mm), Area_Rabi_Arhar/Tur_2010(Hctr.), Production_Rabi_Arhar/Tur_2010(Intonns.), Area_Potato_2007(Hctr.), Production_Potato_2007(Intonns.), etc.Source link for the Agriculture MIS data is given below:Data Gov.in, Data Portal for IndiaDirectorate of Economics and statisticsThis web layer is offered by Esri India, for ArcGIS Online subscribers. If you have any questions or comments, please let us know via content@esri.in.

A Geographic Information System (GIS) shapefile and summary tables of the extent of irrigated agricultural land-use are provided for eleven counties fully or partially within the St. Johns River Water Management District (full-county extents of: Brevard, Clay, Duval, Flagler, Indian River, Nassau, Osceola, Putnam, Seminole, St. Johns, and Volusia counties). These files were compiled through a cooperative project between the U.S. Geological Survey and the Florida Department of Agriculture and Consumer Services, Office of Agricultural Water Policy. Information provided in the shapefile includes the location of irrigated lands that were verified during field surveying that started in November 2022 and concluded in August 2023. Field data collected were crop type, irrigation system type, and primary water source used. A map image of the shapefile is also provided. Previously published estimates of irrigation acreage for years since 1987 are included in summary tables.