Abbreviations in this text NPP: Net Primary Production, average of stand ages 36-40 GCM: Global Climate Model HT: Historical Trend, average of five years 1996-2000 FP2050: Future Prediction, average of five years 2046-2050 FP2100: Future Prediction, average of five years 2096-2100 (except for a GCM HadGEM2-ES of 2095-2099) # Description The data is prepared as four files in tab-delimited text format or ten netcdf files in a zip file. The annual NPP of cedar plantations under current and future climates are calculated in 196928 meshes in Japan. The climate scenarios used are owned and distributed by the third party (National Agriculture and Food Research Organization, Japan). For the methodology on modeling, parameterization and nation-wide calculation, please check the paper below. # Reference Toriyama J, Hashimoto S, Osone Y, Yamashita N, Tsurita T, Shimizu T, Saitoh TM, Sawano S, Lehtonen A, Ishizuka S (2021) Estimating spatial variation in the effects of climate change on the net primary production of Japanese cedar plantations based on modeled carbon dynamics. PLoS ONE 16(2): e0247165. https://doi.org/10.1371/journal.pone.0247165 # List of variables in text files # site.txt (6.8 MB) mesh: Number of third-mesh order in the Japanese grid square system lat: Latitude (degree) of center point of third-mesh order long: Longitude (degree) of center point of third-mesh order block: Block number in Toriyama et al. (2021), 1, 2 and 3 for SW, CT and NW, respectively pref: Prefecture number in the Japanese administrative system # npp_2000.txt (8.7 MB) mesh: Number of third-mesh order in the Japanese grid square system npp_2000_cgcm: Annual NPP (kgC m-2 year-1) in HT, MRI-CGCM3 npp_2000_csiro: Annual NPP (kgC m-2 year-1) in HT, CSIRO-Mk3-6-0 npp_2000_gfdl: Annual NPP (kgC m-2 year-1) in HT, GFDL-CM3 npp_2000_hadgem: Annual NPP (kgC m-2 year-1) in HT, HadGEM2-ES npp_2000_miroc: Annual NPP (kgC m-2 year-1) in HT, MIROC5 # npp_2050.txt (15.4 MB) mesh: Number of third-mesh order in the Japanese grid square system npp_rcp26_2050_cgcm: Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, MRI-CGCM3 npp_rcp26_2050_csiro: Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, CSIRO-Mk3-6-0 npp_rcp26_2050_gfdl: Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, GFDL-CM3 npp_rcp26_2050_hadgem: Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, HadGEM2-ES npp_rcp26_2050_miroc: Annual NPP (kgC m-2 year-1) in FP2050, RCP2.6, MIROC5 npp_rcp85_2050_cgcm: Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, MRI-CGCM3 npp_rcp85_2050_csiro: Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, CSIRO-Mk3-6-0 npp_rcp85_2050_gfdl: Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, GFDL-CM3 npp_rcp85_2050_hadgem: Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, HadGEM2-ES npp_rcp85_2050_miroc: Annual NPP (kgC m-2 year-1) in FP2050, RCP8.5, MIROC5 # npp_2100.txt (15.4 MB) mesh: Number of third-mesh order in the Japanese grid square system npp_rcp26_2100_cgcm: Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, MRI-CGCM3 npp_rcp26_2100_csiro: Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, CSIRO-Mk3-6-0 npp_rcp26_2100_gfdl: Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, GFDL-CM3 npp_rcp26_2100_hadgem: Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, HadGEM2-ES npp_rcp26_2100_miroc: Annual NPP (kgC m-2 year-1) in FP2100, RCP2.6, MIROC5 npp_rcp85_2100_cgcm: Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, MRI-CGCM3 npp_rcp85_2100_csiro: Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, CSIRO-Mk3-6-0 npp_rcp85_2100_gfdl: Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, GFDL-CM3 npp_rcp85_2100_hadgem: Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, HadGEM2-ES npp_rcp85_2100_miroc: Annual NPP (kgC m-2 year-1) in FP2100, RCP8.5, MIROC5 # npp_netcdf.zip (594 MB) The 10 files in netcdf format are compiled in a zip file for NPP data of different RCP scenarios and GCMs. Please check the content of each file by following command. ncdump -h filename # summary_map.zip (5.8 MB) The 20 files in png format are compiled in a zip file for maps of NPP and its change. The maps were created using the Generic Mapping Tools version 5 (http://gmt.soest.hawaii.edu/). The average values of five GCMs are used for mapping. # Acknowledgement This dataset was funded by the Agriculture, Forestry and Fisheries Research Council in Japan, under the project “Research on adaptation to climate change for agriculture, forestry and fisheries”.

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Precision Agriculture Market Size 2024-2028

The precision agriculture market size is forecast to increase by USD 6.09 billion at a CAGR of 13.63% between 2023 and 2028.

Precision agriculture, fueled by the integration of smart technological innovations, is revolutionizing crop yields and production in the agricultural sector. Technological advances, including GPS, drones, and IoT devices, enable smart agriculture monitoring and farm machinery management. These tools provide real-time data analysis, enhancing decision-making capabilities and optimizing resource utilization. Additionally, digital agriculture platforms and remote sensing technologies offer accurate and timely information, ensuring efficient farming practices. Unmanned aerial vehicles, equipped with AI capabilities, offer extensive coverage and detailed analysis. However, the high initial investment required for implementing these advanced technologies poses a challenge for small-scale farmers.
Data security and privacy concerns are also critical issues that need to be addressed as the agricultural sector transitions to digital platforms. The market is witnessing significant growth due to the rise in investments in agricultural technologies and the need for improved crop yields and production. The integration of smart agricultural technology, such as GPS, drones, IoT devices, and AI capabilities, is transforming farming practices, offering numerous benefits while presenting challenges related to initial investment and data security.

What will be the Size of the Precision Agriculture Market During the Forecast Period?

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The market encompasses a range of technologies and services that enhance farming operations through data-driven decision-making and real-time monitoring. Key components include farm machinery equipped with sensors, GPS, and IoT devices, drones for aerial imaging, and monitoring systems for soil conditions, crop health, and weather patterns. These technologies enable navigation, guidance, and variable rate application for optimized crop management. Precision agriculture also incorporates AI and data analytics for site-specific crop management, field mapping, and satellite agriculture. The market's growth is driven by the increasing demand for environmental sustainability, improved crop yields, and operational efficiency.
Specialized systems and software, as well as IT services, play crucial roles in processing and interpreting real-time data for farmers. Overall, precision agriculture represents a significant shift towards on-demand, data-driven agricultural management theory.

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 2024-2028, as well as historical data from 2018-2022 for the following segments.

Product

  Hardware
  Software
  Services


Technology

  Guidance Technology
  Remote Sensing Technology
  Variable Rate Technology


Application

  Yield Monitoring
  Weather Tracking and Forecasting
  Field Mapping
  Crop Scouting Irrigation Management


Geography

  North America

    US
    Canada


  Europe

    Germany
    Netherlands
    UK


  APAC

    Australia
    China
    India
    Japan


  South America



  Middle East and Africa

By Product Insights

The hardware segment is estimated to witness significant growth during the forecast period.

The market is experiencing significant growth due to the introduction of advanced hardware and software solutions. Farm machinery, such as tractors and harvesters, are being equipped with sensors, GPS, and data analytics capabilities, enabling farmers to optimize resource usage, improve crop yields, and practice sustainable farming. Drones, equipped with high-resolution cameras and sensors, provide valuable data on crop health, soil conditions, and weather patterns, enabling farmers to make data-driven decisions in real-time. Monitoring systems allow for remote monitoring of irrigation, fertilization, and pest control, reducing labor costs and increasing agricultural productivity. Navigation and guidance technologies, such as GPS receivers and yield monitoring applications, enable variable rate application of inputs, optimizing resource allocation and improving environmental sustainability.

The integration of climate service initiatives and satellite imagery further enhances precision techniques, allowing farmers to make informed decisions based on real-time data. IoT devices, AI, and data-driven decision-making are transforming modern agricultural practices, enabling farmers to conserve water, optimize planting times, and manage inventory and labor more effectively. The precision farming industry continues to evolve, with a focus on automation, resource optimization, and e

1476 Global export shipment records of Agriculture Sprayer with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Forecast studies show an increasing demand for greenhouses worldwide, as governments encourage local, safe and sustainable food production. Climate change, scarcity of water and other key resources are adding to the trend towards greenhouses. This project shows a world map of the highest suitability for greenhouses, broken down by mid-tech and high-tech greenhouses. This is done by performing a quick scan, which means synthesis and application of existing knowledge and data. Of the countries with the highest potential, more detailed maps are shown. This paper is commissioned by the Netherlands Enterprise Agency (Rijksdienst voor Ondernemend Nederland (RVO)) and funded by the European Community. Dutch Green Delta (DGD) and some of their partners contributed with their expertise and experiential knowledge. The total area of covered crops is very difficult to indicate because there are no clear definitions and hence no uniform data. This study estimates approximately 700,000 hectares of protected horticulture worldwide, of which approximately 53,000 hectares are high-tech greenhouses. This is in line with other literature sources. China provides the greatest uncertainty in data. Based on the analysis of area suitability, USA is the country with the highest relative score for high-tech, followed by France, Germany, UK and Ukraine. For mid-tech, the USA and France are also the countries with the highest relative score, followed by India, Libya, and Brazil.Based on the highest market opportunities for greenhouses explored for the production and sales of tomato, the top 5 countries are Germany, the Netherlands, France, the USA, and Spain.Based on the presence of existing greenhouses, Mexico is the country with the highest surface for high-tech, followed by the Netherlands, Turkey, Belgium and Germany. For mid-tech surfaces the top 5 countries are China, Turkey, Spain, Republic of Korea, and Egypt. Based on the combination of the 3 analyses above, the top 10 countries with the strongest expected growth in high-tech greenhouses are: the USA, France, Spain, Germany, Poland, the Netherlands, Italy, Japan, Turkey, and China. When we differentiate these countries in 3 categories we identify:• Emerging countries: the USA, Poland, Italy, Saudi Arabia, and the UK.• Conversion countries from mid-tech to high-tech greenhouses: Spain, France, China, Japan, India, and South Korea.• Countries that already have areas of high-tech greenhouses: Germany, the Netherlands, Turkey, Belgium, and Mexico. The whitepaper, PowerPoint, all maps, and PowerBI datafiles generated by this project can be downloaded below.

GCOM-W/AMSR2 L3 Soil Moisture Content (1-Month, 0.1 deg) dataset is obtained from the AMSR2 sensor onboard GCOM-W and produced by the Japan Aerospace Exploration Agency (JAXA). GCOM-W was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on May 18th, 2012 (Japan Standard Time, JST) and inserted into a planned position on the "A-Train" orbit. GCOM-W equipped with AMSR2 takes measurements at multiple microwave frequencies and multiple polarizations of weak electromagnetic waves in the microwave band radiated from the Earth’s surface and the atmosphere. AMSR2 has swath of 1450 km and 7 microwave bands. The observation data will enable the creation of long-term trustworthy data sets of global physical amount. The Level 3 process uses as its inputs one day's worth of Level 1B data and Level 2 data and calculates, by taking a simple arithmetic mean, the daily statistical mean value at each grid point in the specified mapping projection method (either equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one month's worth of each geophysical parameter's Level 3 daily statistical mean values and calculates the monthly statistical mean value at each grid point using a simple arithmetic mean in the same way as the daily statistical mean calculation. The statistical means are calculated separately for observations along the satellite's ascending and descending tracks. This dataset includes Month Mean Soil Moisture Content (SMC), Standard Deviation (Standard_Deviation), Average Number (Average_Number) and Total Number (Total_Number). SMC is amount of soil wetness near the ground surface as volume water content. Coverage of the product is over land only, and unit is [%]. Soil moisture cannot be estimated near the coast, around big lakes and marshes, or areas with wide spread dense forests. Since microwave radiometer can get data constantly and frequently, this product is used in monitoring of large-scale cultivation areas in the continents. Standard_Deviation is standard deviation value for each pixel. This item is only stored in monthly product. Average_Number is the number of valid physical quantity data (except error and missing) which was used to determine "Geophysical Data". Total_Number is the number of physical quantity data included in the grid (include valid and invalid). The provided format is HDF5. The Sampling resolution is 0.1degree grid. The statistical period is 1 month. The current version of the product is Version 3. The Version 2 is also available. The projection method is PS. The generation unit is global.

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GIS Market Size 2025-2029

The GIS market size is forecast to increase by USD 24.07 billion, at a CAGR of 20.3% between 2024 and 2029.

The Global Geographic Information System (GIS) market is experiencing significant growth, driven by the increasing integration of Building Information Modeling (BIM) and GIS technologies. This convergence enables more effective spatial analysis and decision-making in various industries, particularly in soil and water management. However, the market faces challenges, including the lack of comprehensive planning and preparation leading to implementation failures of GIS solutions. Companies must address these challenges by investing in thorough project planning and collaboration between GIS and BIM teams to ensure successful implementation and maximize the potential benefits of these advanced technologies.
By focusing on strategic planning and effective implementation, organizations can capitalize on the opportunities presented by the growing adoption of GIS and BIM technologies, ultimately driving operational efficiency and innovation.

What will be the Size of the GIS 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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The global Geographic Information Systems (GIS) market continues to evolve, driven by the increasing demand for advanced spatial data analysis and management solutions. GIS technology is finding applications across various sectors, including natural resource management, urban planning, and infrastructure management. The integration of Bing Maps, terrain analysis, vector data, Lidar data, and Geographic Information Systems enables precise spatial data analysis and modeling. Hydrological modeling, spatial statistics, spatial indexing, and route optimization are essential components of GIS, providing valuable insights for sectors such as public safety, transportation planning, and precision agriculture. Location-based services and data visualization further enhance the utility of GIS, enabling real-time mapping and spatial analysis.

The ongoing development of OGC standards, spatial data infrastructure, and mapping APIs continues to expand the capabilities of GIS, making it an indispensable tool for managing and analyzing geospatial data. The continuous unfolding of market activities and evolving patterns in the market reflect the dynamic nature of this technology and its applications.

How is this GIS Industry segmented?

The GIS 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

  Software
  Data
  Services


Type

  Telematics and navigation
  Mapping
  Surveying
  Location-based services


Device

  Desktop
  Mobile


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.

The Global Geographic Information System (GIS) market encompasses a range of applications and technologies, including raster data, urban planning, geospatial data, geocoding APIs, GIS services, routing APIs, aerial photography, satellite imagery, GIS software, geospatial analytics, public safety, field data collection, transportation planning, precision agriculture, OGC standards, location intelligence, remote sensing, asset management, network analysis, spatial analysis, infrastructure management, spatial data standards, disaster management, environmental monitoring, spatial modeling, coordinate systems, spatial overlay, real-time mapping, mapping APIs, spatial join, mapping applications, smart cities, spatial data infrastructure, map projections, spatial databases, natural resource management, Bing Maps, terrain analysis, vector data, Lidar data, and geographic information systems.

The software segment includes desktop, mobile, cloud, and server solutions. Open-source GIS software, with its industry-specific offerings, poses a challenge to the market, while the adoption of cloud-based GIS software represents an emerging trend. However, the lack of standardization and interoperability issues hinder the widespread adoption of cloud-based solutions. Applications in sectors like public safety, transportation planning, and precision agriculture are driving market growth. Additionally, advancements in technologies like remote sensing, spatial modeling, and real-time mapping are expanding the market's scope.

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The Software segment was valued at USD 5.06 billion in 2019

地図(マップ)上に耕地放棄面積の統計データを市区町村別で色分け表示しています。過去から現在までの耕地放棄面積の推移も階級区分図(コロプレスマップ)で変化が見えるよう高速読込で可視化し、どの市区町村が広いかが視覚で理解できます。GeoJsonの無料ダウンロードも可能です。研究や分析レポートにお役立て下さい。

地図(マップ)上に農林水産業費の統計データを市区町村別で色分け表示しています。過去から現在までの農林水産業費の推移も階級区分図(コロプレスマップ)で変化が見えるよう高速読込で可視化し、どの市区町村が高いかが視覚で理解できます。GeoJsonの無料ダウンロードも可能です。研究や分析レポートにお役立て下さい。

地図(マップ)上に農林水産業の県内総生産額の統計データを都道府県別で色分け表示しています。過去から現在までの農林水産業の県内総生産額の推移も階級区分図(コロプレスマップ)で変化が見えるよう高速読込で可視化し、どの都道府県が高いかが視覚で理解できます。GeoJsonの無料ダウンロードも可能です。研究や分析レポートにお役立て下さい。

地図(マップ)上に農業・林業の売上金額（民営）の統計データを都道府県別で色分け表示しています。過去から現在までの農業・林業の売上金額（民営）の推移も階級区分図(コロプレスマップ)で変化が見えるよう高速読込で可視化し、どの都道府県が高いかが視覚で理解できます。GeoJsonの無料ダウンロードも可能です。研究や分析レポートにお役立て下さい。

地図(マップ)上に農林漁業の売上金額（民営）の統計データを市区町村別で色分け表示しています。過去から現在までの農林漁業の売上金額（民営）の推移も階級区分図(コロプレスマップ)で変化が見えるよう高速読込で可視化し、どの市区町村が高いかが視覚で理解できます。GeoJsonの無料ダウンロードも可能です。研究や分析レポートにお役立て下さい。