Agricultural Mapping Software Market Outlook

The global agricultural mapping software market size was valued at approximately USD 1.2 billion in 2023 and is projected to reach around USD 3.4 billion by 2032, growing at a Compound Annual Growth Rate (CAGR) of 12.5% during the forecast period. This promising growth is driven by increasing adoption of precision farming techniques and the need for efficient agricultural management practices. Advances in technology, coupled with rising demand for food production, are significant factors propelling the agricultural mapping software market.

One of the primary growth factors for the agricultural mapping software market is the increasing need for precision farming. Precision farming techniques rely on detailed data collection and analysis, which is facilitated by advanced agricultural mapping software. These tools help farmers make informed decisions about planting, watering, and harvesting, thereby maximizing crop yield and resource efficiency. The emphasis on data-driven farming is expected to drive significant adoption of mapping software across the globe.

Another crucial growth factor is the rising global population, which directly correlates with the increasing demand for food. As the world population continues to grow, the pressure on agricultural systems becomes more intense. Agricultural mapping software aids in optimizing land use, monitoring crop health, and predicting yields, thus playing a pivotal role in meeting the escalating food demands. The software's ability to enhance productivity and sustainability is highly appealing to stakeholders in the agricultural sector.

Technological advancements in GIS (Geographic Information Systems) and remote sensing are also propelling the market. The integration of satellite imagery, drones, and IoT (Internet of Things) devices with agricultural mapping software enables real-time data acquisition and analysis. These technologies provide farmers with detailed insights into their fields, enabling them to detect issues early and take corrective action promptly. The continuous innovation in these technologies is expected to further boost market growth.

From a regional perspective, North America is anticipated to hold the largest market share due to the high adoption rate of advanced farming technologies and substantial investments in agricultural research. Europe follows closely, driven by stringent agricultural policies and a strong focus on sustainable farming practices. The Asia Pacific region is expected to witness the fastest growth, attributed to increasing government initiatives to modernize agriculture and substantial investments in agritech startups. Latin America and the Middle East & Africa also present significant growth opportunities due to expanding agricultural activities and adoption of modern farming techniques.

Crop Monitoring Software plays a pivotal role in the agricultural mapping software market by providing farmers with the tools necessary to maintain and enhance crop health. This software allows for continuous observation and analysis of crops, ensuring that any potential issues such as diseases, pest infestations, or nutrient deficiencies are identified early. By leveraging real-time data, farmers can make informed decisions that lead to improved crop yields and quality. The integration of Crop Monitoring Software with other agricultural technologies further enhances its capabilities, making it an indispensable tool for modern farming practices. As the demand for efficient and sustainable agriculture grows, the adoption of such software is expected to rise, contributing significantly to the market's expansion.

Component Analysis

The agricultural mapping software market by component is divided into two primary segments: software and services. The software segment encompasses a range of solutions tailored to various agricultural needs, including GIS software, remote sensing software, and farm management software. These tools are designed to collect, analyze, and interpret data to support decision-making processes in farming operations. The sophistication and variety of available software solutions are continually expanding, driven by ongoing research and development efforts in agritech.

In contrast, the services segment includes consulting, training, maintenance, and support services that complement the software solutions. As more farmers and agricultural enterprises adopt mapp

[Metadata] Description: Agricultural Land Use Maps (ALUM) for islands of Kauai, Oahu, Maui, Molokai, Lanai and Hawaii as of 1978-1980. Sources: State Department of Agriculture; Hawaii Statewide GIS Program, Office of Planning. Note: August, 2018 - Corrected one incorrect record, removed coded value attribute domain.For more information on data sources and methodologies used, please refer to complete metadata at https://files.hawaii.gov/dbedt/op/gis/data/alum.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, HI 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

The Agricultural Mapping Services Market is projected to exceed USD 7.7 billion by 2035, rising from an estimated USD 5.8 billion in 2025. A compound annual growth rate (CAGR) of 2.9% has been forecast for the 2025 to 2035 period.

Semi-annual update

Country-Wise Insights

The Agriculture Capability mapping dataset is the digitized equivalent of the legacy Agriculture Capability Scanned Maps, which date from the 1960's to the 1990s. Agriculture Capability mapping is also known as 'Soil Capability for Agriculture' and 'Agricultural Capability' mapping. Agricultural Capability is an interpreted mapping product based on soil and climate information. In general, climate determines the range of crops possible in an area and the soils determine the type and relative level of management practices required. This is legacy data and changes in climate are not reflected. For more information about the classification system see: Land Capability Classification for Agriculture. Use caution utilizing these legacy maps as the classifications were based on common land management practices and typical crops of the 1960s-1990s era, and subsequent site specific land management practices (e.g. installation of drainage) may have modified the soil conditions since the mapping was completed. This Agriculture Capability legacy mapping is included in the Soil Information Finder Tool (SIFT) mapping application. The SIFT application provides more detailed climate data (e.g. Growing Degree Days, Frost Free Period (5 C), (1960-1990 climate normals). The SIFT 'Soil query tools' may be useful for identifying areas with specific 'growing conditions' of interest based on soils present (soil name), soil texture, drainage, coarse fragment content, slope, elevation, growing degree days and frost free period. Note: This Agriculture Capability Mapping dataset is based on soil mapping at 1:100,000, 1:50,000 or 1:20,000 scale, and is more detailed than the 1:250,000 scale Canada Land Inventory (CLI) Agricultural Capability mapping (available here).

The agricultural mapping software market is experiencing robust growth, driven by the increasing adoption of precision agriculture techniques and the need for efficient farm management. The market, estimated at $1.5 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $4.2 billion by 2033. This expansion is fueled by several key factors. Firstly, the rising demand for higher crop yields and improved resource utilization is compelling farmers to adopt technology-driven solutions. Agricultural mapping software provides crucial insights into field conditions, allowing for optimized planting, fertilization, and irrigation strategies, leading to significant cost savings and increased profitability. Secondly, advancements in sensor technology, GPS accuracy, and data analytics are enhancing the capabilities of agricultural mapping software, making it more accessible and user-friendly. Finally, government initiatives promoting precision agriculture and digital farming are further stimulating market growth. The market is segmented by software type (e.g., cloud-based, on-premise), application (e.g., yield mapping, soil analysis), and farm size. Key players like Trimble, CNH Industrial, and Geosys are actively shaping the market through continuous innovation and strategic partnerships. Despite the significant growth potential, certain challenges remain. High initial investment costs for software and hardware can act as a barrier to entry for small-scale farmers. Furthermore, the reliance on robust internet connectivity and technical expertise can hinder adoption in regions with limited infrastructure. However, ongoing technological advancements, coupled with the increasing availability of affordable solutions and training programs, are gradually addressing these limitations. The market will continue to witness consolidation through mergers and acquisitions, as larger players seek to expand their market share and offerings. Future growth will be particularly driven by the integration of artificial intelligence and machine learning into agricultural mapping software, enabling more predictive and insightful analytics for improved farm management.

The agricultural mapping software market is experiencing robust growth, driven by the increasing adoption of precision agriculture techniques and the rising need for efficient resource management. The market's expansion is fueled by several factors, including advancements in GPS technology, the proliferation of affordable drones and sensors, and the growing availability of cloud-based data analytics platforms. Farmers are increasingly recognizing the value of detailed field mapping for optimizing planting, fertilization, irrigation, and pest control, leading to improved crop yields and reduced input costs. The integration of AI and machine learning capabilities further enhances the sophistication of these software solutions, providing farmers with actionable insights to improve decision-making. This market is segmented based on software type (e.g., field mapping, yield monitoring, soil analysis), deployment mode (cloud-based, on-premise), and farm size. Key players like Trimble, CNH Industrial, and others are constantly innovating to provide comprehensive solutions that cater to the evolving needs of farmers. The market's growth is, however, subject to certain restraints, such as the initial investment costs associated with adopting new technologies and the need for reliable internet connectivity in rural areas. Despite these challenges, the long-term outlook for agricultural mapping software remains positive, with a projected steady Compound Annual Growth Rate (CAGR) leading to significant market expansion over the forecast period. The competitive landscape is characterized by a mix of established players and emerging technology providers. While large agricultural equipment manufacturers like Trimble and CNH Industrial offer integrated solutions, smaller companies focus on specialized software or niche applications. The market is witnessing increased mergers and acquisitions, strategic partnerships, and product innovation to maintain competitiveness. Geographical distribution varies, with North America and Europe currently dominating the market due to higher adoption rates and technological advancements. However, developing regions in Asia and Africa are showing promising growth potential driven by increasing agricultural production and government initiatives promoting precision farming techniques. The ongoing trend towards data-driven agriculture, coupled with improvements in sensor technology and affordability, will continue to propel the growth of this dynamic sector in the coming years. Future advancements are likely to include enhanced data integration capabilities, improved AI-powered analytics, and the incorporation of blockchain technology for ensuring data security and traceability.

Agricultural Mapping Services Market Outlook

The global Agricultural Mapping Services market size was valued at approximately USD 2.5 billion in 2023 and is anticipated to grow significantly to reach around USD 5.8 billion by 2032, reflecting a Compound Annual Growth Rate (CAGR) of approximately 9.8%. The primary growth driver for this market is the increasing demand for precision agriculture practices worldwide, which necessitate the use of detailed mapping services to maximize crop yield and optimize resource utilization. The convergence of technology with agriculture has catalyzed a significant transition in farming methodologies, empowering farmers to make data-driven decisions and thereby enhancing productivity and sustainability.

A major growth factor contributing to the expansion of the Agricultural Mapping Services market is the increasing awareness and adoption of precision farming techniques. Precision agriculture relies heavily on accurate and timely mapping services to monitor and manage field variability in crops. Factors such as climate change and unpredictable weather patterns have also intensified the need for sophisticated agricultural mapping to ensure food security and optimize crop production. Furthermore, government initiatives and subsidies promoting the adoption of advanced agricultural technologies are providing an additional impetus to this market, encouraging both small and large-scale farmers to invest in mapping services.

Another significant factor propelling market growth is the technological advancements in Geographic Information System (GIS), remote sensing, and drone technologies. These advanced tools facilitate the collection and analysis of critical agricultural data, enabling more precise crop monitoring and management. The integration of Artificial Intelligence (AI) and machine learning into mapping technologies further enhances the accuracy and efficiency of agricultural mapping services, providing actionable insights that help in predictive analysis and risk management. As a result, farmers and agronomists are increasingly turning to these technologies to gain a competitive edge and improve their agricultural outputs.

The rising global population and the consequent increase in food demand are also pivotal growth drivers for the Agricultural Mapping Services market. As the world population continues to grow, there is mounting pressure on the agricultural sector to enhance productivity to meet food supply needs. Agricultural mapping services play a crucial role in this context by optimizing land use and improving crop yields. Additionally, the trend towards sustainable agriculture and the need to manage resources more judiciously are fueling the demand for mapping services, which help minimize environmental impact while maximizing crop production.

The integration of GIS Software In Agriculture has revolutionized the way farmers approach precision agriculture. By utilizing GIS technology, farmers can create detailed maps that illustrate various aspects of their fields, such as soil types, crop health, and water availability. This spatial data is crucial for making informed decisions about planting, fertilization, and irrigation, ultimately leading to improved crop yields and resource efficiency. GIS software allows for the layering of different data sets, providing a comprehensive view of the agricultural landscape that helps in identifying patterns and trends. As a result, farmers can optimize their operations, reduce waste, and enhance sustainability, making GIS an indispensable tool in modern agriculture.

Regionally, North America is anticipated to dominate the Agricultural Mapping Services market, owing to the early adoption of advanced agricultural technologies and strong government support. Europe follows closely, with significant investments in agricultural innovation and a focus on sustainable farming practices. The Asia Pacific region, however, is projected to witness the fastest growth during the forecast period, driven by the increasing penetration of precision agriculture practices and the rapid development of the agricultural sector in countries like China and India. Latin America and the Middle East & Africa are also expected to experience substantial growth as these regions strive to enhance agricultural productivity and security.

Service Type Analysis

The Agricultural Mapping Services market is segmented by service type into Soil Mapping, Yield Mapping, Crop Health Monitoring, and Othe

The global agricultural mapping software market is experiencing robust growth, driven by increasing demand for precision agriculture techniques and the rising adoption of technology in farming practices. This market is projected to reach a substantial size, with a Compound Annual Growth Rate (CAGR) reflecting significant expansion. While the exact market size and CAGR figures are not provided, based on industry reports and observed trends in related sectors like agricultural technology and precision farming, a reasonable estimate would place the 2025 market value at approximately $2.5 billion, growing at a CAGR of 15% from 2025 to 2033. This growth is fueled by several factors, including the increasing need for efficient resource management (water, fertilizers, pesticides), improved crop yields, and enhanced farm profitability. Farmers are increasingly adopting cloud-based solutions for their ease of use and accessibility, leading to a significant segment of the market focused on cloud-based software. Furthermore, the integration of GPS, GIS, and remote sensing technologies into these platforms is boosting market expansion, allowing for precise field monitoring, data analysis, and informed decision-making. The market is segmented by deployment type (cloud-based and on-premise) and application (personal farms and animal husbandry companies). The cloud-based segment is expected to maintain a dominant share owing to its scalability and cost-effectiveness. The competitive landscape comprises established players like Trimble and CNH Industrial, alongside specialized agricultural technology companies such as Agrivi and Xfarm. These companies are constantly innovating and expanding their product offerings to cater to diverse farming needs and geographical locations. Regional market penetration varies, with North America and Europe currently holding significant shares due to advanced agricultural practices and higher technology adoption rates. However, rapidly developing economies in Asia-Pacific and other regions are showing promising growth potential, fuelled by increasing government initiatives promoting digital agriculture and the rising awareness of precision farming techniques. Challenges remain, such as the need for robust internet connectivity in remote areas and the digital literacy gap among some farmers, but overall market projections remain positive, indicating a strong future for agricultural mapping software.

Cropland Index The Cropland Index evaluates lands used to produce crops based on the following input datasets: Revised Storie Index, California Important Farmland data, Electrical Conductivity (EC), and Sodium Adsorption Ratio (SAR). Together, these input layers were used in a suitability model to generate this raster. High values are associated with better CroplandsCalifornia Important Farmland data – statistical data used for analyzing impacts on California’s agricultural resources from the Farmland Mapping and Monitoring Program. Agricultural land is rated according to soil quality and irrigation status. The maps are updated every two years (on even numbered years) with the use of a computer mapping system, aerial imagery, public review, and field reconnaissance. Cropland Index Mask - This is a constructed data set used to define the model domain. Its footprint is defined by combining the extent of the California Important Farmland data (2018) classifications listed above and the area defined by California Statewide Crop Mapping for the state of California.Prime Farmland – farmland with the best combination of physical and chemical features able to sustain long term agricultural production. This land has the soil quality, growing season, and moisture supply needed to produce sustained high yields. Land must have been used for irrigated agricultural production at some time during the four years prior to the mapping date.Farmland of Statewide Importance – farmland similar to Prime Farmland but with minor shortcomings, such as greater slopes or less ability to store soil moisture. Land must have been used for irrigated agricultural production at some time during the four years prior to the mapping date. Unique Farmland – farmland of lesser quality soils used for the production of the state’s leading agricultural crops. This land is usually irrigated but may include Non irrigated orchards or vineyards as found in some climatic zones in California. Land must have been cropped at some time during the four years prior to the mapping date. Gridded Soil Survey Geographic Database (gSSURGO) – a database containing information about soil as collected by the National Cooperative Soil Survey over the course of a century. The information can be displayed in tables or as maps and is available for most areas in the United States and the Territories, Commonwealths, and Island Nations served by the USDA-NRCS. The information was gathered by walking over the land and observing the soil. Many soil samples were analyzed in laboratories. California Revised Storie Index - is a soil rating based on soil properties that govern a soil’s potential for cultivated agriculture in California. The Revised Storie Index assesses the productivity of a soil from the following four characteristics: Factor A, degree of soil profile development; factor B, texture of the surface layer; factor C, slope; and factor X, manageable features, including drainage, microrelief, fertility, acidity, erosion, and salt content. A score ranging from 0 to 100 percent is determined for each factor, and the scores are then multiplied together to derive an index rating.Electrical Conductivity - is the electrolytic conductivity of an extract from saturated soil paste, expressed as Deci siemens per meter at 25 degrees C. Electrical conductivity is a measure of the concentration of water-soluble salts in soils. It is used to indicate saline soils. High concentrations of neutral salts, such as sodium chloride and sodium sulfate, may interfere with the adsorption of water by plants because the osmotic pressure in the soil solution is nearly as high as or higher than that in the plant cells. Sodium Adsorption Ratio - is a measure of the amount of sodium (Na) relative to calcium (Ca) and magnesium (Mg) in the water extract from saturated soil paste. It is the ratio of the Na concentration divided by the square root of one-half of the Ca + Mg concentration. Soils that have SAR values of 13 or more may be characterized by an increased dispersion of organic matter and clay particles, reduced saturated hydraulic conductivity (Ksat) and aeration, and a general degradation of soil structure.

Drone Mapping Software for Agriculture Market Outlook

The global drone mapping software for agriculture market size is projected to grow from USD 200 million in 2023 to USD 800 million by 2032, reflecting a robust CAGR of 16.5%. This significant growth is driven by several factors, including advancements in drone technology, the increasing need for precision agriculture, and the growing adoption of smart farming practices worldwide.

One of the primary growth factors for the drone mapping software for agriculture market is the continual advancement in drone technology. Modern drones are equipped with high-resolution cameras, GPS technology, and sensors that can capture detailed images and data from agricultural fields. These technological advancements have enhanced the efficiency and accuracy of drone mapping software, making it an indispensable tool for farmers. The integration of AI and machine learning algorithms into these platforms further enhances their analytical capabilities, providing actionable insights that can help optimize crop yields and reduce operational costs.

Another crucial factor contributing to market growth is the increasing adoption of precision agriculture techniques. Precision agriculture aims to optimize field-level management concerning crop farming. By utilizing drone mapping software, farmers can monitor and analyze various field conditions, such as soil health, crop health, and pest infestations, more effectively. This data-driven approach allows for more precise application of water, fertilizers, and pesticides, leading to improved crop yields and resource efficiency. As the global population continues to rise, the demand for food increases, thereby driving the need for efficient agricultural practices.

The growing awareness and adoption of smart farming practices are also fueling the market's growth. Smart farming involves the use of advanced technologies, including IoT, AI, and drones, to enhance agricultural practices. Governments and agricultural organizations worldwide are promoting smart farming practices to improve food security and sustainability. Subsidies, grants, and favorable policies for the adoption of drone technology in agriculture are expected to further boost the market. Training programs and workshops to educate farmers about the benefits and usage of drone mapping software are also playing a significant role in market expansion.

Regionally, North America is expected to dominate the drone mapping software for agriculture market owing to technological advancements and the early adoption of precision agriculture practices. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period. The rapid adoption of modern agricultural practices in countries like China and India, along with the increasing investments in drone technology, are key factors driving the market in this region. Europe and Latin America are also showing promising growth, driven by the rising awareness about sustainable farming practices and technological advancements.

Component Analysis

In the drone mapping software for agriculture market, the component segment is categorized into software and services. The software segment holds a significant share of the market and is expected to continue its dominance throughout the forecast period. This is primarily due to the rising demand for advanced software solutions that offer high-resolution mapping, data analytics, and real-time monitoring capabilities. The software solutions are increasingly integrated with AI and machine learning algorithms, which enhance their efficiency and accuracy in analyzing agricultural data. This integration is particularly beneficial for tasks like crop health monitoring and field mapping, which require precise data interpretation.

The services segment, though smaller than the software segment, is also witnessing substantial growth. Services in this market include consulting, training, and maintenance services. As drone mapping technology becomes more sophisticated, there is a growing need for specialized services to help farmers effectively utilize these tools. Consulting services assist farmers in selecting the appropriate software and hardware solutions tailored to their specific needs, while training services educate them on how to use these technologies effectively. Maintenance services ensure that the drone mapping systems operate smoothly and provide accurate data over time.

The growing trend of software-as-a-service (SaaS) models in the agriculture industry is also contributing

Agriculture Drone Mapping and Analytics Market Outlook

The global market size of the Agriculture Drone Mapping and Analytics industry was valued at USD 1.2 billion in 2023 and is projected to reach USD 5.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 19.6%. One of the primary growth factors driving this market is the increasing adoption of precision agriculture techniques that significantly enhance crop productivity and resource management.

The growth of the Agriculture Drone Mapping and Analytics market is fueled by several key factors. Firstly, technological advancements in drone technology, coupled with the integration of artificial intelligence (AI) and machine learning (ML), have immensely improved the efficiency and accuracy of data collection and analysis. This technological synergy enables farmers to make data-driven decisions, enhancing crop yield and reducing resource wastage. Another significant driver is the rising global food demand due to a growing population, which necessitates the adoption of advanced agricultural practices to ensure food security. The utilization of drones for precise field mapping, soil analysis, and crop health monitoring is becoming indispensable in meeting this demand.

Additionally, government initiatives and subsidies aimed at promoting the adoption of smart farming technologies are contributing to market growth. Several countries are offering financial incentives for farmers to adopt drone technology to enhance agricultural productivity and sustainability. Moreover, the growing awareness among farmers regarding the benefits of drone mapping and analytics, such as optimized resource usage, reduced labor costs, and timely decision-making, is accelerating adoption rates. The increasing availability of user-friendly drone solutions and comprehensive training programs is also encouraging small and medium-sized farmers to integrate this technology into their operations.

However, the initial high costs associated with purchasing and maintaining drone technology can be a barrier for smaller farms. Despite this, the return on investment (ROI) in terms of increased yield and reduced operational costs is prompting many farmers to invest in drone technology. Furthermore, advancements in drone affordability and the availability of financing options are gradually overcoming this challenge. The market is also witnessing collaborations between drone manufacturers and agricultural service providers to offer bundled solutions, making it more accessible for farmers to adopt advanced drone mapping and analytics.

Drone Analytic Software plays a pivotal role in transforming raw data into actionable insights for the agriculture sector. These software solutions are designed to process vast amounts of data collected by drones, including high-resolution images and multispectral data, to provide detailed analysis of crop health, soil conditions, and field variability. By leveraging advanced algorithms and machine learning, Drone Analytic Software can offer predictive analytics, helping farmers anticipate issues and make proactive decisions. The integration of these software solutions with existing farm management systems further enhances their utility, allowing for seamless data flow and comprehensive farm management. As the demand for precision agriculture continues to grow, the development of sophisticated Drone Analytic Software is becoming increasingly important to meet the evolving needs of the agricultural industry.

Component Analysis

The Agriculture Drone Mapping and Analytics market is segmented into hardware, software, and services. The hardware segment includes the drones themselves, along with various sensors and cameras essential for data collection. This segment holds a significant market share due to the increasing adoption of advanced drones equipped with high-resolution cameras and multispectral sensors. These sophisticated drones provide accurate and detailed aerial imagery, enabling precise analysis of crop health, soil conditions, and field mapping. The continuous innovation in drone hardware, such as longer flight times, improved durability, and enhanced data capture capabilities, is driving demand in this segment.

The software segment encompasses the analytical tools and platforms used to process and interpret the data collected by drones. This segment is experiencing rapid growth as advanced software solutions integrate AI and ML algorithms to offer actionable insights. These software p

The agricultural mapping services market is experiencing robust growth, driven by the increasing need for precise and timely information to optimize farming practices and enhance crop yields. The market, valued at approximately $2.5 billion in 2025, is projected to expand significantly over the forecast period (2025-2033), with a Compound Annual Growth Rate (CAGR) of around 15%. This expansion is fueled by several key factors. Firstly, technological advancements in remote sensing, such as satellite imagery and drone technology, are providing higher-resolution data and more efficient data processing capabilities. Secondly, the rising adoption of precision agriculture techniques, including variable rate fertilization and targeted pesticide application, is boosting demand for accurate mapping data. Finally, government initiatives promoting sustainable agriculture and climate-smart practices are creating favorable market conditions. The market is segmented by application (agricultural companies, academic institutions, households, and others) and by type of service (irrigation monitoring, soil and crop analysis, livestock monitoring, and others). Agricultural companies represent the largest segment, followed by academic institutions, reflecting the high value placed on data-driven decision-making within these sectors. The market's growth trajectory is, however, not without challenges. High initial investment costs associated with advanced mapping technologies and the need for specialized expertise can serve as restraints, particularly for smaller farms. Data security and privacy concerns, especially when dealing with sensitive agricultural data, also pose a significant challenge. Overcoming these obstacles requires a concerted effort from technology providers, research institutions, and policymakers. Strategic partnerships and the development of cost-effective solutions, including user-friendly software and accessible training programs, will be critical in unlocking the full potential of this market. The geographic distribution of this market is expected to be concentrated in regions with a high density of agricultural activity and substantial investment in technology. North America and Europe are projected to maintain a leading position, though growth in developing economies with expanding agricultural sectors is anticipated.

Introduction and Rationale: Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product. Contents: Spatial data Attribute table for merged rasters Technical validation data Number and proportion of mismatched pixels Number and proportion of unresolved pixels Producer's and User's accuracy values and coverage of reference data Resources in this dataset:Resource Title: Attribute table for merged rasters. File Name: CombinedRasterAttributeTable_CDLNVC.csvResource Description: Raster attribute table for merged raster product. Class names and recommended color map were taken from USDA-NASS Cropland Data Layer and LANDFIRE National Vegetation Classification. Class values are also identical to source data, except classes from the CDL are now negative values to avoid overlapping NVC values. Resource Title: Number and proportion of mismatched pixels. File Name: pixel_mismatch_byyear_bycounty.csvResource Description: Number and proportion of pixels that were mismatched between the Cropland Data Layer and National Vegetation Classification, per year from 2012-2021, per county in the conterminous United States.Resource Title: Number and proportion of unresolved pixels. File Name: unresolved_conflict_byyear_bycounty.csvResource Description: Number and proportion of unresolved pixels in the final merged rasters, per year from 2012-2021, per county in the conterminous United States. Unresolved pixels are a result of mismatched pixels that we could not resolve based on surrounding agricultural land (no agriculture with 90m radius).Resource Title: Producer's and User's accuracy values and coverage of reference data. File Name: accuracy_datacoverage_byyear_bycounty.csvResource Description: Producer's and User's accuracy values and coverage of reference data, per year from 2012-2021, per county in the conterminous United States. We defined coverage of reference data as the proportional area of land cover classes that were included in the reference data published by USDA-NASS and LANDFIRE for the Cropland Data Layer and National Vegetation Classification, respectively. CDL and NVC classes with reference data also had published accuracy statistics. Resource Title: Data Dictionary. File Name: Data_Dictionary_RasterMerge.csv

The Census of Agriculture provides a detailed picture every five years of U.S. farms and ranches and the people who operate them. Conducted by USDA’s National Agricultural Statistics Service, the 2012 Census of Agriculture collected more than six million data items directly from farmers. The Ag Census Web Maps application makes this information available at the county level through a few clicks. The maps and accompanying data help users visualize, download, and analyze Census of Agriculture data in a geospatial context.

Global Agricultural Mapping Services market size 2025 was XX Million. Agricultural Mapping Services Industry compound annual growth rate (CAGR) will be XX% from 2025 till 2033.

The global agricultural mapping services market is experiencing robust growth, driven by the increasing adoption of precision agriculture techniques and the rising demand for efficient resource management in farming. This market is projected to reach a significant size, with a Compound Annual Growth Rate (CAGR) fueling expansion. While precise figures for market size and CAGR aren't provided, considering the rapid technological advancements in areas like drone technology, remote sensing, and data analytics, a reasonable estimate for the 2025 market size would be in the range of $2.5 billion to $3 billion. This substantial market value reflects the growing awareness among farmers and agricultural businesses regarding the benefits of data-driven decision-making. The CAGR is likely within the range of 12-15% over the forecast period (2025-2033), indicating a sustained trajectory of growth. Factors such as government initiatives promoting precision agriculture, the rising adoption of IoT devices in farming, and the availability of advanced analytical tools are key drivers propelling this expansion. The market is segmented by type (Irrigation Monitoring, Soil and Crop Analysis, Livestock Monitoring, Other) and application (Agricultural Company, Academic Institution, Household, Other). The Soil and Crop Analysis segment is expected to dominate due to the critical role of precise soil and crop data in optimizing yields and resource utilization. Geographically, North America and Europe currently hold substantial market shares, attributed to the higher adoption rates of advanced technologies and the presence of key players. However, rapid growth is anticipated in Asia-Pacific regions like India and China, fueled by expanding agricultural activities and increasing government investments in agricultural modernization. Challenges remain, including the high initial investment costs associated with adopting these services and the need for robust internet connectivity in certain regions, particularly in developing economies. Despite these hurdles, the long-term outlook for the agricultural mapping services market remains positive, with significant potential for growth and innovation.

This remote sensing-based model characterizes land suitability for agriculture in Malawi using a collection of terrain and soil characteristics, including slope, precipitation/runoff/erosion, drainage, sand/silt/clay fraction, soil organic carbon, Ph in H2O, and cation exchange capacity. The raster structure data set depicts agricultural land suitability in Malawi. The resulting classifications are [1] Marginally not suitable (N1), [2] Marginally suitable (S3), [3] Moderately suitable (S2), [4] Highly suitable (S1), and [5] Permanently not suitable (N2) or Unavailable (protected, settlement, or water). The study was partially supported by the Zhejiang A & F University’s Research and Development Fund (2013FR052)and by the Project “Perennial Grain Crops for African Smallholder Farming Systems” (grant number OPP1076311), which was funded by Bill & Melinda Gates Foundation through the support of the United States Agency for International Development (AID-OAA-A-13-00006). The opinions expressed herein are those of the authors and do not necessarily reflect the views of the US Agency for International Development or the US Government.

This dataset contains shape files and supporting files for the most up-to-date (as of the published date) land use map at the UBC Farm. The best uses of these maps are: 1) to visualize locations of field codes in other UBC Farm datasets; 2) to visualize field codes for UBC Farm research projects, and 3) to understand the general layout of the Farm.

Global maps of agricultural expansion potential at a 300 m resolution

This repository contains data from “Global maps of agricultural expansion potential at a 300 m resolution” study.

Abstract:

The global expansion of agricultural land is a leading driver of climate change and biodiversity loss. However, the spatial resolution of current global land change models is relatively coarse, which limits environmental impact assessments. To address this issue, we developed global maps representing the potential for conversion into agricultural land at a resolution of 10 arc-seconds (approximately 300 m at the equator). We created the maps using Artificial Neural Network (ANN) models relating locations of recent past conversions (2007-2020) into one of three cropland categories (cropland only, mosaics with >50% crops, and mosaics with <50% crops) to various predictor variables reflecting topography, climate, soil and accessibility. Cross-validation of the models indicated good performance with Area Under the Curve (AUC) values of 0.88-0.93. Hindcasting of the models from 1992 to 2006 revealed a similar high performance (AUC of 0.83-0.91), indicating that our maps provide representative estimates of current agricultural conversion potential provided that the drivers underlying agricultural expansion patterns remain the same. Our maps can be used to downscale projections of global land change models to more fine-grained patterns of future agricultural expansion, which is an asset for global environmental assessments.

Data description:

We provide here raster maps of agricultural expansion potential for three categories of agriculture - (i) cropland only, (ii) mosaics with >50% crops, and (iii) mosaics with <50% crops. The source for delineating categories was the ESA CCI land cover data. ESA CCI land cover data recognizes additional categories of agricultural land, however some of them have limited spatial coverage. For that reason, we merged the rainfed cropland and irrigated cropland categories into a single category - cropland only, where a grid cell is largely dominated by crops. Rainfed croplands account for 87% of the this category, while irrigated croplands account for the remaining 13%. Mosaic categories were defined in the same way as in the ESA CCI land cover dataset. Numerical designations of these categories in the ESA CCI land cover dataset are 10, 20, 30, and 40 for rainfed, irrigated, mosaics with >50% crops, and mosaics with <50% crops, respectively.

Global maps are provided at the spatial resolution of 10 arc-seconds (~300 meters at the equator). These files are available for three categories in the main folder with the filename prefix "Agri_potential_mosaic_*". The numerical value in the file name refers to the agricultural category type (10 - cropland only, 30 - mosaics with >50% crops, and 40 - mosaics with <50% crops). In addition to the 10 arc-second layers, we provide aggregated layers with the spatial resolution of 30 arc-seconds, 5 and 10 arc-minutes, for coarse-grained applications and less computationally-intensive analyses. We provide the aggregated layer maps for the minimum, median, mean/average, and maximum values of the aggregated 10 arc-seconds values within the coarser cells. There are in total 9 files provided for each of the aggregated spatial resolutions.

Repository content:

Full resolution layers: - “Agri_potential_mosaic_10.tif” is the global raster map for cropland only category at the spatial resolution of 10 arc-seconds. - “Agri_potential_mosaic_30.tif” is the global raster map for mosaics with >50% crops category at the spatial resolution of 10 arc-seconds. - “Agri_potential_mosaic_40.tif” is the global raster map for mosaics with <50% crops category at the spatial resolution of 10 arc-seconds. - "readme.txt" is the text file with the basic description and the metadata for the repository.

Aggregated layers: This folder contains files with a different spatial resolution (30s, 5m, 10m; see argument "RESL" below).

File names for the aggregated maps contain the following information: “Agri_potential_aggregated_RESL_TYPE_CATG.tif”

• "RESL" is the spatial resolution of the layer. Value is either "30s", "5m", or "10m", corresponding to spatial resolution of 30 arc-second, 5 arc-minutes, and 10 arc-minutes.

• "TYPE" is the type of aggregated values. Value is either "min", "avg", "med", or "max", corresponding to the minimum, mean, median, and maximum values of the aggregated 10 arc-seconds values within the coarser cells.

• "CATG" is the category of agricultural land. Value is either "10", "30", or "40", where category 10 is cropland only, category 30 is mosaics with >50% crops, and category 40 is mosaics with <50% crops.

Raster metadata:

Driver: GTiff Projection proj4string: +proj=longlat +ellps=WGS84 +no_defs

Notes on use:

Our conversion potential maps are useful for researchers and practitioners interested in downscaling projections of global land change models to a more fine-grained patterns of future agricultural expansion, or interested in assessing the locations and effects of future agricultural expansion, for example in integrated assessment modelling or biodiversity impact modelling. When coupling outputs with integrated assessment modelling, our maps need to be combined with estimates of the expected future demands for agricultural land per socio-economic region. In such a coupled approach, our global conversion potential maps can be used to spatially allocate the additional agricultural land demands. In this context, it is important to note that the modelled relationships between the agricultural conversions and our set of predictors may result in non-zero probabilities also in areas that are highly unlikely to be converted into agriculture, such as urban areas or strictly protected nature reserves. This implies that users of our maps may need to implement an additional map layer that masks areas unavailable for agricultural expansion. We also stress that our maps represent agricultural conversion potential conditional on the predictor variables that we included, implying that our maps do not capture the possible influences of other potentially relevant predictors. For example, our conversion potential models and maps do not account for permafrost, which may pose significant challenges to possible agricultural expansion to higher latitudes in response to climate change.

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